Felix User Guide

1       Tasks

Task: Importing data

Felix also includes a number of built in data filters for common spectrometer data formats. These built in filters all convert data to the Felix new format. The following built in data filters are available:

• X32 New Reads Bruker X32 and SGI/DMX data with ASCII parameters

• X32 Old Reads Bruker X32 data with binary parameters

• Aspect Bruknet Reads Bruker Aspect data transferred via Bruknet

• Aspect Kermit Reads Bruker Aspect data transferred via Kermit

• Varian Reads Varian Unity data files

• JEOL Reads JEOL Alpha and generic data files

All of the data filters require that the spectrometer data files be kept in their native form. For example, the X32 data filter for Bruker data expects to see the same file and directory structure that exists on the spectrometer. The Bruker "data file" will actually be a directory containing subdirectories with experimental and processed data files. When the spectrometer data is transferred to the Felix workstation, this entire directory structure must remain unchanged. One way to ensure that the directory structure remains unchanged is to first make a tar file of the desired data directories on the spectrometer. This tar file can then be transferred to the Felix workstation using ftp in binary mode. Note that it is important to transfer the data in binary mode to avoid corrupting the data. Once the data has been transferred you can untar the data to recreate the same directory structure as that which existed on the spectrometer.

Step 2: Converting the data

To invoke the data conversion routines, you select the File/Convert/Data command. This command brings up a pullright menu of the different spectrometer data formats that Felix can convert. Once you select the specific type of spectrometer data from the pullright, you are presented with a menu which asks you for information about the location and name of the input and output data files.

For the input data you specify the name of the Input File to convert. This Input File can be an individual file (as in the case of Bruker Aspect data) or a directory (as in the case of Bruker X32 or Varian data). When the data consists of a directory of information (such as with Bruker X32 data or Varian data) then the Input File will be a directory name containing the spectrometer data to be converted. You specify the location of the Input File by entering the relative or absolute path in the Data Prefix parameter. Be sure to include a trailing slash in the path designation. The Data Prefix is prepended to the Input File name to specify the exact location of the data. If the Input File is located in the current directory, then you can set Data Prefix to "./".

Output data requires that you specify both the Output File and the Data Prefix. The Output file will be the name of the new data format Felix file produced. This file is often given a .dat extension to identify it as a Felix data file. As with the Input File, the Data Prefix specifies the location of the Output File to be written. Once again, be sure to include the trailing slash in the Data Prefix.

Note that Bruker X32/DMX data has a directory structure in which multiple experiments and processed data sets can be stored under the same file name. In order to indicate which set of experimental and processed data you wish to convert, you need to specify a number for both the Experimental and Process data subdirectories. This number will be an integer which indicates the name of the Experimental and Process data subdirectories you wish to use for the conversion.

The last thing to specify when converting data is whether or not you wish to byte swap the input data. If the spectrometer computer and the workstation computer use different byte orderings then set the Byte Swap Input Data parameter to on. This would be the case if, for example, the data was collected on an X32 computer and then transferred to an SGI computer for analysis. If the spectrometer computer and the workstation computer have the same byte ordering then set the Byte Swap Input Data parameter to off. This would be the case, for example, between an SGI/DMX computer and an SGI workstation. If you are not sure how to set this parameter try both settings. The incorrect setting will produce an error message or an FID which looks like noise. The correct setting should produce a valid-looking FID. Once you have determined the correct setting, you can use it for all subsequent transfers between the same spectrometer/computer combination.

The procedure for converting data is the same for 1D, 2D and ND data. In each case, a single new format Felix data file is produced. In the case of 2D or ND data, the file produced contains multiple 1D data files.

Step 3: Checking the data

You can check the data by reading it using the File/Open command. If you set the file Dimension parameter to 1D, then each time you read the data, Felix loads the first data file from the multi-dimensional data set. If you set the file Dimension parameter to ND, each time you read the data, Felix loads the subsequent data file in the multi-dimensional data set. For 2D and ND data, it is a good idea to check at least the first few data blocks to make sure the data has been converted properly.

Converting processed data

Converting Varian spectra

When you specify that you want to import a Varian spectrum and provide the filename for that spectrum, Felix automatically searches for a corresponding .par file, and converts the data in that file into Felix matrix format. If Felix does not find a .par file with the same root name as the spectrum filename that you specified, the application displays a dialog box and prompts you to enter spectral parameters manually.

If you are importing a 2D spectrum, you must rename the phase file to a name that corresponds to the root name of the .par file. For example, if you use the sv2dpf macro to create a file called test.par, you must rename the phase file to test.

The sv2dpf macro

"sv2dpf - save 2d phasefile"
"usage: sv2dpf (basename)"


" $# is the number of input arguments. It must be greater than zero. "

if ($# < 1) then
write ('error','usage:sv2d(filename)')
return
endif


" If the file already exists, delete it. "

exists ($1,'file') : $e
if $e then
rm($1)
endif


"Flush the phasefile completely from memory"

write ('line3', 'saving raw data to disk')
trace='f1' dcon flush
trace='f2' dcon flush


" Create the text file with suffix .par containing parameters of the data"

$parfile=$1+'.par'
write ('line3', 'saving parameters to disk')
write ('reset', $parfile)
write ('file', $parfile, '%d %d %d',ni,np,0)
write ('file', $parfile, '%10.1f %10.1f %10.1f',sw1,sw,0)
if (tn=dn) then
$frq1=sfrq
else
$frq1=dfrq
endif
write ('file', $parfile, '%10.1f %10.1f %10.1f', $frq1, sfrq, 0)


" We are finished writing the parameters"

write ('line3', 'Data written to %s and %s', $1, $parfile)

"sv3dpf - save 3d phasefile"
" usage: sv3dpf(basefilename,orientation)"
"  where basefilename is the basic filename; to that will be appended"
"         '.orientation' to indicate the orientation and"
"         '.xxx' to indicate the index"
"        orientation is 'f1f2','f1f3', or 'f2f3'"


if ($#<2) then write('error','improper arguments') return endif
if ($2='f1f2') or ($2='all') then "save f1f2 data"
$i=1
repeat
	select('f1f2',$i)
	trace='f1' dcon flush
	trace='f2' dcon flush
	format($i,0,0):n1 length(n1):$len
	if ($len=2) then n1='0'+n1 endif
	if ($len=1) then n1='00'+n1 endif
	copy(curexp+'/datdir/phasefile',$1+'.f1f2.'+n1)
		$i=$i+1
until $i>(fn/2)
endif


if ($2='f1f3') or ($2='all') then "save f1f3 data"
	$i=1
	repeat
		select('f1f3',$i)
		trace='f1' dcon flush
		trace='f3' dcon flush
		format($i,0,0):n1 length(n1):$len
		if ($len=2) then n1='0'+n1 endif
		if ($len=1) then n1='00'+n1 endif
		copy(curexp+'/datdir/phasefile',$1+'.f1f3.'+n1)
		$i=$i+1
	until $i>(fn2/2)
endif
if ($2='f2f3') or ($2='all') then "save f2f3 data"
	$i=1
	repeat
		select('f2f3',$i)
		trace='f2' dcon flush
		trace='f3' dcon flush
		format($i,0,0):n1 length(n1):$len
		if ($len=2) then n1='0'+n1 endif
		if ($len=1) then n1='00'+n1 endif
		copy(curexp+'/datdir/phasefile',$1+'.f2f3.'+n1)
		$i=$i+1
	until $i>(fn1/2)
endif

Line number	Description
1	Size of data rows, columns, and tiers
2	Sweep width along rows, columns, and tiers
3	Spectrometer frequency along rows, columns, and tiers
4	Flag to define spectral zooming and the range of PPM along the tier dimension for a 3D data set

You need not have a fourth line in the .par file to import a Varian spectrum. However, you can add this line if either flags to define spectral zooming or the range of PPM along the tier dimension for 3D data appears.

According to VNMR convention, the format of the .par file is as follows. Keep in mind that the order of the values in the first three lines of the .par file varies depending on which set of planes is being read. That is

1.   For sv3d generated f1/f3 planes (where f2 is the tier dimension) :

	f3	f1	f2
1st line:	fn/2	fn1/2	fn2/2
2nd line:	sw	sw1	sw2
3rd line:	sfrq	(dfrq)	(dfrq2)

2.   For sv3d generated f2/f3 planes (where f1 is the tier dimension) :

	f3	f2	f1
1st line:	fn/2	fn2/2	fn1/2
2nd line:	sw	sw2	sw1
3rd line:	sfrq	(dfrq2)	(dfrq)

3.   For sv3d generated f1/f2 planes (where f3 is the tier dimension) :

	f2	f1	f3
1st line:	fn2/2	fn1/2	fn/2
2nd line:	sw2	sw1	sw
3rd line:	(dfrq2)	(dfrq)	sfrq

Other conversion options

Task: Modifying the quick access menu

root.mot


...


mouser NULL    NULL NULL NULL NULL NULL mouser.mot

mymouser.mot 

Task: Working with the Felix and Insight programs together

The Felix-Insight II interface capabilities encompass three general categories: starting Felix automatically from within the Insight II/NMRchitect program, sharing data between the two programs, and using the Insight II environment to display molecular structures. Each of these capabilities is discussed more fully below.

When both Felix and Insight II are running on your computer, each has a display window open on your screen. To switch from one program to the other, simply mouse click on a window's header to pop that window to the front.

Communication between Felix and Insight II

At program start-up, both Felix and Insight II automatically attempt to establish a socket connection, which can be thought of as a channel for communication between the two programs. A limitation of this UNIX facility is that the connection is always made for the first instance of each program currently running on that computer. This means that if another user in your group is already running Felix on the same machine which you start up Insight II and Felix on, then Insight II will make the socket connection to the other user, because his Felix socket request was made before yours. To assure an appropriate connection between Felix and Insight II, first make sure no one else is running either program on your computer when you initiate your session. Once the socket connection is made, other users may run additional copies of the Felix and/or Insight II software without disrupting your connection.

Starting Felix from Insight II/NMRchitect

The socket connection between the two programs works differently in some cases, depending on whether Felix has been started separately or from within Insight II/NMRchitect. The Insight II/NMRchitect program can only connect to Felix if Felix is started from within Insight II/NMRchitect. Felix, on the other hand, can connect to Insight II/NMRchitect whether Felix is started separately or from within Insight II, as long as the NMR_Refine module is currently running.

Sharing data between Felix and Insight II

For the Felix and Insight II programs, there are three categories of data that must be shared between the two programs: cross peak data, molecular coordinate data, and NMR-based molecular refinement restraint data. Each of these data classes is stored and used differently by the two programs, yet all still need to be communicated back and forth as seamlessly as possible. Each of these data types and the manner of sharing them between Felix and Insight II is discussed more fully below.

Exporting data from Felix to Insight II

After Felix has generated cross peak information, you can export this data to the Insight II program, for use by NMRchitect. Within Insight II, cross peak volume, assignment, and chemical shift information is stored within four separate ASCII files:

• .ppm files containing one-dimensional chemical shift information

• .pks files containing position, width, and intensity information for peaks

• .asn files containing one-dimensional peak assignment information

• .rstrnt files containing restraints for use in structure generation and refinement within the NMR_Refine module

After you have read the Felix database into Insight II, the NMR_Refine module posts a Read_From_Felix icon in the upper left corner of the Insight II display. Clicking this icon performs subsequent Felix/Read_From_Felix commands. It uses the same entity, file, and project names that you previously specified. This icon is useful for passing cross peak data back and forth between the two programs, as it automatically performs both halves of the data transfer; first exporting Felix database files, then reading those files into NMRchitect, all in a single step (with no dialog boxes to fill in).

Importing data from Insight II to Felix

To create cross peak files with NMRchitect, select the Felix/ Write_To_Felix command from within the NMR_Refine module. To read these ASCII files into Felix, you next select the File/Import/Restraints command from the Felix display. This generates a dialog box that prompts you for file and directory names for the files to be read in, and for a matrix name and entity names for storing these peaks into the Felix database.

After you have written the Insight II database to Felix, the NMR_Refine module posts a Write_To_Felix icon in the upper left corner of the Insight II display. Clicking this icon performs subsequent Felix/Write_To_Felix commands. This command uses the same entity, file, and project names as previously specified. The icon is useful for passing cross peak data back and forth between the two programs, as it automatically performs both halves of the data transfer in a single step: exporting NMRchitect database files, and reading those files into Felix (with no dialog boxes to fill in).

Importing coordinates from Insight II to Felix

For more information about Model, please see Chapter 9, Model Interface.

Reading IRMA files into Felix

After loading the Felix database with the appropriate entities, that data must be scaled to match the experimental cross peaks for direct comparison - the program will prompt you for a scalar peak or you can use later the Model/Set Scale command. This prompts you for an unique assignment name or you are given a cursor to graphically select a cross peak footprint, which must be in both your experimental database and your back-calculated database.

For a more detailed description of the Model module, please see Chapter 9, Model Interface.

Exporting restraints to the Insight II environment

Felix reads and writes .rstrnt files as part of the commands that import and export data between the two programs. Every time cross peak information is shared between Felix and Insight II, all restraint information is also shared.

While Insight II uses an ASCII file to store restraints, Felix uses a set of database entities. When Felix imports restraints from Insight II, it creates entities to store them. If Felix is used to generate NMR-based restraints, these are also stored in database entities. When Felix exports restraints to Insight II, it writes all the restraint entities to a new .rstrnt file, which is subsequently read into Insight II. Note that all restraint categories are preserved during this sharing of data between the two programs.

You use the commands in the Measure/DISCOVER Restraints or Measure/X-PLOR Restraints in Felix to calculate NMR restraints, and to create entities of volumes (NOE-intensities), distances, and ³J dihedrals. If you then export the Felix database to Insight II, you are ready to do structure generation or refinement against those NMR restraints using NMRchitect.

Using Insight II for molecular display

As Felix is an NMR processing software package, it is understandable that its molecular display and analysis capabilities are rather primitive. For this reason, Felix is set up to perform all molecular display and analysis in the Insight II program, while keeping the NMR display and analysis within Felix.

The Model/Setup/Connect command can be used to toggle the Insight II window for molecule display. The Model commands work as described in Chapter 5, Model Interface, but all commands that display or analyze molecular coordinates are performed by Insight II, and appear in the Insight II window instead of the Felix window. This gives you seamless access to the more advanced molecule rendering features of Insight II, using Felix for NMR display and analysis and NMRchitect for NMR-based structure determination. Working together, these software tools provide more functionality than either program by itself.

Task: Working with 1D data

Reading data files

Felix can also read native spectrometer data formats via a set of new commands: rf for reading Felix for Windows FID files, rb for reading Bruker FID or SER files, rv for reading Varian FED files and rj for reading JEOL ALPHA .nmf files and LAMBDA .nmfid files.

To read a data file, use the File/Open command. You have the option of specifying the data as either one-dimensional or N-dimensional, and as either Bruker, Varian, Felix For Windows, JEOL or Felix old or new format. If you define the data type as one-dimensional, the data file is explicitly closed before it is read, to reset the record pointer to the first record. When you are first working with N-dimensional data, the program reads the first record from the serial data file. Each subsequent read (during the same Felix session) increments the record pointer by one and reads the next data record.

Sample task:

Saving data

Displaying 1D spectra

By default, when Felix initially reads and displays a new file, it draws all the data points (the entire spectral width). To display or work with an expanded region of the spectrum, you must select the expansion limits as given below.

Changing 1D limits

Alternatively, you can use the icons--particularly the Zoom icon--to execute this task.

• To draw all the points in the spectrum, select the View/Limits/Full Limits command. All subsequent draws of your data will then display the entire spectrum.

• To redisplay the last plot drawn, select the View/Limits/Last Limits command.

• You can use the keypad to navigate in a zoomed region--the Keypad Help icon shows all the options.

• The View/Limits pullright displays a menu with numerous additional choices for expanding your spectrum.

• Access the PLOT PARAMETERS - BASIC dialog box by selecting the Preference/Plot Parameters command or the Plot Parameters icon; this displays the settings that control the appearance of the plotted data points. You can choose the plot color, the data scaling, the Plot Type (Lines or Points), and the Plot Mode (Real, Imaginary, or Real/Imag). Also, there is a set of buttons to access successive dialog boxes for further parameters - Scale, Tick, Place and Stack

• The dialog box (PLOT PARAMETERS - SCALE) that appears when selecting the Scale button in the PLOT PARAMETERS - BASIC dialog box controls the appearance of the non-data portions of the plot; that is, the axis units, the box around the plot, the grid spacing, the annotations, the 2D contour level display, and the picked peaks.

• Selecting the Place button in either dialog boxes presents you with a new dialog box (PLOT PARAMETERS - PLACE) and allows you to control the position and size of the plot within the Felix window

Buffers are accessed with the Tools/Buffers command. For example, to store the current information to a buffer, select the Tools/Buffers/Store Work to Buffer command, and enter the buffer number in the dialog box. To visualize this information, you must change the stack depth to include the buffers that you want to visualize. The Tools/Buffers command contains many additional options that allows you to manipulate the contents of the buffers.

Adjusting stack and buffer display

The Absolute Intensity parameter also affects the appearance of the stack display. This parameter is used to set the scale factor for all data in the window to the same value so that data intensities can be directly compared. When this parameter is set to No, all data in the stack is scaled to fill the available space. If this parameter is set to Yes, all data is scaled to the last spectrum drawn in the workspace. Enabling this switch is useful for displaying difference spectra, where the full spectrum and the difference can be compared on the same scale.

Axes and referencing

To reference a one-dimensional spectrum, select the Preference/Reference command, which prompts you for the referencing information using the REFERENCE 1D DATA dialog box. The Spectral Frequency and Spectral Width parameters must be set to the values of the spectrometer frequency in MHz, and the spectral width in Hz. You can enter the Reference Point value either by typing it into the dialog box or by clicking the Cursor button of the dialog box, causing Felix to display a vertical cursor, moving the cursor to the desired reference point on the plot, and clicking the left mouse button. At this time the REFERENCE 1D DATA dialog box reappears. Now enter the reference value in the appropriate parameter field--for axis units of Hertz, enter a reference value in the Reference Hertz field, for ppm units, enter a reference value in the Reference PPM field. When you are finished, click the OK button to close the dialog box and redisplay the data with the selected axis units.

Finding spectral data values

Correlated cursors

Spectral separation

Zero-filling and removing DC offset

Linear prediction

A second application of linear prediction to NMR data is to extend an FID. This is useful for experiments in which the data collection ceased before the signals completely decayed; that is, the FID is truncated. In this case, the data values of the FID are used to estimate new data values that are appended to the end of the FID.

The Process/Transform/Linear Predict First command uses linear prediction to replace data values at the beginning of the FID. Selecting this command displays a dialog box. In the dialog box you indicate the Points to use in the predication (the points that are used to calculate the coefficients) the Number of Coefficients and the Number of Peaks. The Points to use parameter defines the number of points used to calculate the LP coefficients. The Number of Coefficients parameter specifies the number of LP coefficients that are calculated. The Number of Peaks parameter is left in for compatibility with existing macros, but is not used in the current LP implementation.

The length of time taken by an LP calculation is determined in large part by the number of data points that are used to determine the LP coefficients. In general the quality of the LP calculation also increases with the number of data points used in the calculation. It therefore necessary to carefully pick a value for the Points to use parameter that is large enough to produce accurate predicted data values but that does not unduly lengthen the time taken by the LP calculation. The value for the Number of Coefficients parameter is generally set to be one-quarter to one-third of the Points to use parameter.

The Process/Transform/Linear Predict Last command uses linear prediction to replace data values at the end of the FID, and can also be used to extend the FID. Conventional linear prediction works well when the data is being extended by a small fraction of the number of points. However, when the number of data points is being extended by a large fraction (e.g., doubling the number of points), the predicted data can contain signals with increasing amplitude due to the effect of noise on the predicted LP coefficients. The net effect can be an FID with increasing (as opposed to decreasing) amplitude as a function of time. To prevent this, Felix allows the option of using Root Reflection when predicting data. Root Reflection ensures that the calculated frequency components will decay as a function of time, and thus more accurately reflect the correct physical nature of an FID. Using Root Reflection increases the time needed to perform the LP calculation, but the predicted points are more representative of a true FID. Root Reflection is essentially required when predicting large numbers of data points, to avoid having noise components with increasing amplitude dominate the predicted FID at longer time values.

Felix allows great flexibility in how the LP calculation is performed. By default the Process/Transform/Linear Predict Last command is used for extending the data but it also allows you to specify exactly which points you would like to use in the LP calculation and which points you would like to predict. The method used to perform the LP calculation is specified by the user and the available choices are Forward, Backward, Forward-Backward and Mirror Image. The Forward method of linear prediction is what was used in previous versions of Felix. The Forward-Backward technique (Zhu and Bax 1992) is a new method which performs both a Forward and a Backward calculation on the data. The resulting LP coefficients are then averaged to produce a more accurate set of LP coefficients than either the Forward or Backward method could produce when used alone.

The Forward-Backward method is a very robust and generally applicable technique that does not require any prior knowledge of the FID. Mirror Image Linear Prediction is a method by which predicted data at negative time values are used in the calculation. This technique allows a larger number of LP coefficients to be calculated and hence is very useful for severely truncated data. Utilizing the Mirror Image technique the Number of Coefficients parameter can be increased to between one-half and two-thirds of the Points to use parameter. The Mirror Image technique requires prior knowledge of the phase of the data and non decaying signals. Because of these restrictions the Mirror Image technique is used primarily for severely truncated indirect dimensions of N-dimensional data sets when there is a need to calculate a larger number of LP coefficients than would be possible with the Forward-Backward method. The Mirror Image method includes options for data collected with no sampling delay and data collected with a one-half dwell time sampling delay.

Solvent signal suppression

Linear-Prediction-Based solvent reduction is accessed by choosing the Linear Prediction as the Method. In this command the LP algorithm is used to estimate and remove contributions from the most intense components in the spectrum. This command works well when the intensity of the solvent signal to be removed is much larger than the other signals that are present.

Convolution-Based solvent reduction is accessed by selecting the Time-Domain Convolution from the Method popup in the Process/Solvent Suppression command. In this command a convolution is done to first identity the lowest frequency components that are present and then these components are subtracted from the data. This command is very useful as long as the solvent signals to remove are present at the carrier frequency.

Polynomial-Based solvent reduction is accessed by selecting the Polynomial item from the Method popup in the Process/Solvent Suppression command. In this command a polynomial is fitted to the data and the resulting function is subtracted from the time domain data. This command works best when the solvent resonance is close to zero frequency.

Viewing and applying window functions

Felix offers two methods to selecting window function parameters--you can enter the parameter values directly or you can adjust them interactively. The Process/Window Function command allows you to select a window function and adjust its parameters interactively while Felix displays plots of both the window function and the product of the FID and window function. Felix can also display the spectrum rather than the FID and window function product, while you adjust the window function parameters. This command is extremely useful in determining which window function is appropriate for your data.

You also can specify explicitly a window function and its parameters in this command. When you know exactly the window function that you want to use, including its parameters, this command lets you apply it quickly and precisely.

Window function descriptions

Sinebell

wsize	(slider) adjusts the number of data points for the window function.
wshift	(slider) adjust the phase shift of the window function.

Gaussian linebroadening

Kaiser

Trapezoidal

Fourier transforms

Accordingly, Felix provides a battery of Fourier integral transforms that are accessed by selecting the Process/Transform command. Selecting this command displays a dialog box with a list of the integral transforms that Felix can perform on the data in the work space. There are five Fourier transforms and a Hilbert transform on this list. The most commonly used transform commands are listed below.

The Complex FFT command applies a complex Fourier transform to the data in the work space. For this transform, the data must be true complex data, characterized by simultaneous sample and conversion of the real and imaginary signals. This is the most common of the Fourier Transform commands and is used for most data.

The Bruker FFT command performs a complex Fourier transform on complex data that is unique to some Bruker spectrometers. These spectrometers cannot sample and convert the real and imaginary signals simultaneously; instead, they collect the real and imaginary signals alternately. This is reflected in the acqus file when the AQ_mod parameter is set to 2. If your data was collected in this mode, you must use the Bruker FFT command.

The Oversampled FFT command performs a complex Fourier transform on digitally oversampled data collected on Bruker DMX series and newer spectrometers. If your data was collected using digital oversampling you should use this command to do the transform.

Phasing

Felix has three different ways to apply phase correction to a spectrum that are accessed by selecting the Process/Phase Correction command. The automatic phase correction algorithm works quickly without user input. However this procedure is prone to problems when data has even a minor baseline roll, a low signal to noise ratio, or a large first order phase error. In addition to the automatic phase correction routine, Felix provides an interactive real-time phase routine that is easy to use. Felix also allows you to enter the zero and first order phase parameters explicitly.

Correcting baseline distortions

In the Process/Baseline Correction command you can either add and delete baseline points or you can use different options to correct the baseline.

Baseline point entities and files

To view the contents of the baseline point entity, select the name of the entity in the Edit/Table command. This creates a table display of the baseline point entity contents in a separate window. While displaying the spreadsheet, you may add baseline points or delete baseline points on the spreadsheet.

At times you may want to save a baseline point entity in an ASCII file. This is accomplished by selecting the File/Export command. You are then prompted for an output file name, as well as the name of the baseline entity to save. The ASCII file is then written to the current directory or the Felix text directory depending on how you have your directory structure configured.

A baseline point ASCII file may be read into a baseline point entity by selecting the File/Import command. You are then prompted for an input file name as well as an entity name.

Spectral display for baseline correction

Subsequently, if you are not satisfied with the results of any given baseline correction function and its application to your data, select the File/Open command to re-read your spectral data, assuming that it was saved previously.

Adding and deleting baseline points

Conversely, you may also add baseline points singly using the Pick Points Via Cursor option. These points are added by moving the crosshair with the mouse to baseline points, then clicking the left mouse button. To exit this mode, you must click outside of the spectrum. If you wish you may add baseline points explicitly using point numbers with the Manual Pick Points command.

If you make a mistake while selecting individual baseline points, or if you want to modify the current list of baseline points, you may delete a region of points using the graphical interface. First, select the Delete Points in Region option in Process/Baseline Correction command to create a small crosshair cursor. Then, using the push-drag-release sequence with the left mouse button, you may drag out a region of baseline points to delete.

To delete all the baseline points, select the Delete All Points option. This deletes the current baseline points entity from the database; it requires confirmation via a dialog box.

Applying baseline correction functions

The second baseline correction algorithm, which generates smoother baseline correction functions from baseline points, is the polynomial baseline correction algorithm. This baseline correction is applied by choosing for Baseline Correction the Polynomial option in the Process/Baseline Correction command. The polynomial correction differs from the cubic spline correction algorithm in that the baseline does not necessarily pass exactly through each baseline point, but a best fit is calculated.

Real-time baseline correction

Automatic baseline flattening

One of the baseline correction functions supported by Felix, that does not require explicit baseline points, is applied by selecting for the Baseline Correction the Automatic w/abl option in the Process/Baseline Correction command. This automatically selects noise points, and performs a baseline correction for each point. The method involves the DC-convolution of baseline points with a moving average, while applying a straight-line correction to non-baseline intervals. This algorithm was reported by Dietrich et al. (1991).

Felix also provides the FLATT baseline correction algorithm, a technique introduced by Güntert and Wüthrich (1992). The FLATT algorithm automatically finds baseline segments in the spectrum and uses a linear least-squares solution to fit a truncated Fourier series to these points. To use FLATT, select for the Baseline Correction the Automatic w/FLATT option in the Process/Baseline Correction command.

Miscellaneous work tools

Task: 1D peak picking and integration

1D peak entities and files

To view the contents of the 1D peaks entity, select the Edit/Peaks command. This command creates a spreadsheet display of the 1D peaks entity contents. While displaying the spreadsheet, you may add 1D peaks or delete 1D peaks on the spreadsheet which also changes the information in the database.

At times you may wish to save a 1D peaks entity in an ASCII file. This is accomplished by selecting the File/Export/Peaks command. You are prompted for an output file name, as well as the name of the entity to save. The ASCII file is then written to the current directory or the text subdirectory depending on how you have the Felix directory structure configured.

A 1D peaks ASCII file may be read into a 1D peaks entity by selecting the File/Import/Peaks command. You are prompted for an input file name as well as an entity name.

Working with picked peaks

To automatically select peaks in the current display, select the Peaks/Pick All command.

To select peaks in a sub region of your display, select the Peaks/Pick Region command. This creates a small crosshair cursor that you may use with the push-drag-release sequence of the mouse to select a picking region.

You may also select one peak at a time with the Peaks/Pick One command.

There are three options for deleting picked peaks. The Peaks/Remove All command actually deletes the 1D pick entity and the peak table, and therefore removes all picked resonance information. The Peaks/Remove Region command is used to delete peaks in a selected region with the small crosshair cursor, using the push-drag-release sequence to select a picking region. Also, you can delete peaks one-by-one by pointing with the cursor using the Peaks/Remove One command.

The Preference/Pick Parameters command displays a dialog box listing those parameters that affect 1D peak picking. The threshold value can be inspected and set here. You can control the displayed names for picked peaks with the Peak Pick Units parameter. All peaks are displayed in one of the following units: Points, PPM, Hertz or None. Also, you can display peak names on the peaks selecting Assignment for the Peak Pick Units parameter. Three different peak selection modes are provided. They are: positive peaks only, negative peaks only, and positive and negative peaks together. You can also vary the style of the peak markers. For the peak markers you can specify arrowheads only, lines only, or lines with arrowheads.

1D line fitting

The interface, and display utilities, are accessed by selecting the Peaks/Optimize command. Selecting this command gives you access to the 1D line fitting command and other commands that display the actual data, the synthetic data or residual data--either separately or all together as an overlay.

To fit a spectrum, select the Optimize option in the Peaks/Optimize command.

Integration

Felix gives you the option of integrating either the entire spectrum as a single integral or as shorter segments. To integrate the entire spectrum, simply select the View/Draw Integrals command. However, if current segments are defined, you must first select the Preference/Integral command, and set the Integral Display Type parameter to Entire Spectrum.

Segment entities and integral files

To view the contents of the integral segment entity, select the Edit/Table command and choose the segment entity. This creates a spreadsheet display of the integral segment entity contents. While displaying the spreadsheet, you may add integral segments or delete integral segments on the spreadsheet.

At times you may want to save an integral segment entity in an ASCII file. This is accomplished by selecting the File/Export/Table command. You will be prompted for the name of the entity (table) to save, as well as for an output file name. The ASCII file will be written to the directory selected in the file selection dialog box.

An integral segment ASCII file may be read into an integral segment entity by selecting the File/Import/Table command. You are prompted for an input file name as well as an entity name.

Spectral dIsplay for integrals

The Preference/Integral command has the ability to affect integral displays. Selecting Entire Spectrum as the Integral Type shows only the full spectrum. Selecting Segments, no values shows integrals only, with no integral values. Segments, values above displays integral values above the integral, while Segments, values below displays the integral values below the spectrum, and selecting None turns off the display of integrals entirely.

Defining and deleting integral segments

If you make a mistake while selecting individual segments, or if you want to modify the current list of segments, you may delete a small subregion of segments graphically. First, select the Remove Segment option in the Measure/Integral/Volume command to create a small crosshair cursor. Then, using the click-drag-release sequence of the mouse, you may drag out a region of segments to delete.

To delete all the segments, select the Remove All Segments option in the Measure/Integral/Volume command. This action deletes the current integral segments entity from the database, and requires confirmation via a dialog box.

Adjusting integral slope and bias

The Slope slider is used to adjust the integral slope parameters between minus one and one.

The Bias slider is used to adjust the integral bias parameters between minus one and one.

Alternatively, you may also adjust the integral slope and bias parameters manually using the Preference/Integral command.

Integral normalization

• By Item Number of Segment creates a list box where you must graphically select the segment to normalize based on its beginning and ending point. You must also give a normalization value for this segment.

• By Data Point Limits prompts you within a dialog box for a low and high point to define a normalization range, as well as the normalization value.

• Select Segment via Cursor generates a small crosshair cursor that lets you select the segment to normalize, by clicking and dragging the mouse to enclose the segment.

• Raw Absolute Integrals stores and displays each segment's integral as its raw intensity with no normalization at all.

General processing steps

Once a matrix file has been created the individual FIDs that make up the Felix data file are read in one by one, processed and saved to the matrix file. Subsequent dimensions are then processed by loading each vector in the given dimension one by one from the matrix into the workspace, processing each vector in turn, then storing the processed vectors back to the matrix file. This process is repeated for each dimension of the matrix.

In practice the above processing steps are generally carried out using macros. Macros are files which contain the series of instructions used to process the data. The Felix program contains a very flexible macro language (FCL) which allows you to control all aspects of how the data is processed. You have the option of use their own macros to process the data or to use the EZ macros. The EZ macros are a series of predefined processing macros which are intended to be used for the most common types multidimensional data. In general using your own custom macros is preferred, but the EZ macros will often do an adequate job of processing your data.

Processing the D1 dimension using macros

1   c**simpled1.mac
2   ;
3   def datfil `my2dfile.dat'
4   def matrix `my2dfile.mat'
5   def d1zfil 2048
6   def d2zfil 2048
7   def numd1 512
8   ;
9   cmx
10  bld &matrix 2 &d1zfil &d2zfil 0 y
11  mat &matrix w
12  ;
13  def temph0 -63.7941
14  def temph1 -130.5324
15  ;
16  def datsiz &d1zfil
17  def datype 1
18  set 1
19  ss 2048 60
20  stb 1
21  ;
22  ; D1 processing
23  ;
24  ty  D1(t2) Processing.
25  ty ---------------------
26  ;
27  cl
28  for row 1 &numd1
29   esc out
30   if &out ne 0 quit
31   rn &datfil
32   if &status ne 0 eof
33   def phase0 &temph0
34   def phase1 &temph1
35   def datype 1
36   bc 0.2
37   lpf 32 16 8 1
38   cnv 0 48
39   mwb 1
40   ft
41   ph
42   red
43   sto 0 &row
44   ty Row=&row $
45  next
46 eof:
47  if &status ne 0 then
48   def status 0
49   ty End-of-file on record &row
50  eif
51  ty  D1(t2) transform completed.
52  ty --------------------------------
53 quit:
54  cmx
55  def status 0
56 ret
57  end

These lines define a series of symbols whose values determine how the data processing is to be performed. Note that it is not necessary to define these symbols separately in the beginning of the macro. You can choose to enter processing specific parameters on the individual command lines. However, grouping important symbol definitions in the beginning of a macro tends to remind the user which parameter values may need to changed for different processing sessions.

Lines 9-12:

The cmx command is used to close any open matrix files. Then the bld command is used to create an empty matrix file of the appropriate size. The zero in the bld command line indicates that the matrix to be created will be real. Most multidimensional data processing is done with real matrix files. The mat command is then given to open the matrix that was just created with write access.

Lines 13-14:

These two lines define a pair of symbols (temph0 and temph1) which are used to store the zero and first order phasing parameters. It is necessary to store the desired phasing parameters in temporary symbols because later when the input data files are read in the phase0 and phase1 symbols will be overwritten by the phasing values that have been stored in the input data file.

Lines 16-20:

This section of the macro illustrates how to set up an apodization function in buffer 1. Then later in the processing loop section of the macro each FID will be multiplied by the contents of buffer 1. This is a more efficient method of doing the apodization compared to recalculating the appropriate apodization function for each FID. The first couple lines define the datsiz symbol to be the appropriate number of complex data points and the datype symbol to one which indicates complex data. The set command then sets all the real values in the workspace to one. The appropriate apodization function is then performed and the result is stored in buffer 1.

Lines 24-25:

These lines print out a message to the textport that the D1 processing loop is about to begin.

Line 27:

The cl command is used to close any open data file. This ensures that a subsequent read command (re or rn) will read the first record of the data file.

Lines 28-45:

This section of the macro forms the main processing loop. This loop is executed once for each data file in the input data set.

Lines 29-30:

These two lines of code show how the esc command can be used to allow the user to interrupt a macro during execution. Note, that this checking will slow down the processing, therefore you may want to consider not to use it. Also, there is another possibility in Felix to stop a macro execution--hitting the <Ctrl> and \ keys together will stop macro execution. The esc command monitors keyboard input for the escape character. When the escape key is pressed the user defined symbol (in this case the out symbol) is set to one. An if statement then transfers control out of the loop when the out symbol is no longer equal to zero.

Lines 31-32:

These two lines read the next data set from the input data file and check the status of the read command. The re command is used to read old format data and the rn command is used to read new format data. If the read command is successful the status symbol is set to zero. If the read command is not successful because for example the user attempted to read more data sets than were present in the input data file then the status symbol will be set to a non-zero value. If the read is not successful then control transfers to outside the loop.

Lines 33-34:

These two lines make sure that the phase0 and phase1 symbols which are used by the subsequent phasing command (ph) are set correctly based on the saved phasing parameters (temph0 and temph1). This is necessary as stated above because when a read command is given (re or rn) the phase0 and phase1 symbol values will be overwritten with the phasing values that are stored in the input data file.

Line 35:

This line sets the datype symbol to one which represents complex data. This is required so that subsequent processing operations will operate properly.

Lines 36-41:

These are the main processing steps. The data is baseline corrected, first point is corrected, solvent is removed, apodized, ft'd and phased. You would customize this section of the macro to correctly process your data. Note that apodization is performed by multiplying by buffer 1. Buffer 1 contains the apodization function that was set up earlier in the macro.

Lines 42-44:

The data is then reduced so that only the real part of the FID is retained. This real data is then stored to the matrix. A type command (ty) is used to print out the current row so that the user can monitor the progress of the macro. It is important to note, that the actual output to the textport is a rather time consuming step, and the processing can be speed up by 10 - 20% if one chooses not to use it!

Lines 46-50:

If a read command should fail (such as by trying to read more input data sets than are present in the input data file) then control transfers to this section of the macro. A message is printed telling the user on which record the failed read occurred.

Lines 53-57:

If the macro finishes normally or the user exits the macro early then control transfers to this section. A cmx command is given to close the open matrix. The status symbol is set to zero indicating a normal exit and control is returned to the menu interface.

Processing the D2 dimension using macros

1   c**simpled2.mac
2   cmx
3   def matrix `my2dfile.mat'
4   mat &matrix w
5   ;
6   ty D2(t1) Processing.
7   ty ---------------------
8   ;
9   for col 1 &d1size
10    esc out
11    if &out ne 0 quit
12    loa &col 0
13    def datype 1
14    def datsiz 512
15    zf 1024
16    ss 256 90
17    ft
18    ph
19    red
20    sto &col 0
21    ty Col=&col $
22  nex
23 quit:
24  ty D2(t1) transform completed.
25  ty ------------------------------
26  cmx
27 ret
28  end

The cmx command is used to close any open matrix files. Then the mat command is used to open the appropriate matrix file for writing.

Lines 9-22:

This section forms the main processing loop. Since this macro operates on the D2 dimension a column must be processed for each D1 point value. Note that the for loop increments the symbol col from one to the value of the symbol d1size. When a matrix is opened the value of the symbols d1size, d2size etc. are set to the number of points in the corresponding dimension. Thus the symbol d1size is automatically set to the number of points in the D1 dimension and is therefore a logical choice to use for the maximum value of the for loop.

Lines 10-11:

These two lines of code show how the esc command can be used to allow the user to interrupt a macro during execution. Note, that this checking will slow down the processing, therefore you may want to consider not to use it. Also, there is another possibility in Felix to stop a macro execution--hitting the <Ctrl> and \ keys together will stop macro execution. The esc command monitors keyboard input for the escape character. When the escape key is pressed the user defined symbol (in this case the out symbol) is set to one. An if statement then transfers control out of the loop when the out symbol is no longer equal to zero.

Line 12:

The loa command is used to load the next column into the workspace for processing.

Lines 13-15:

Since this is States data the datype symbol is set to one indicating complex data. Now that the data is complex the data size (datsiz symbol) must be reduced by half because there are only half as many complex points compared to the number of points in the vector when it was first read in as real. If the data size is not reduced by half then the second half of the workspace will contain invalid data. The data size is then doubled by zero-filling so that the second half of the FID will contain zeros. The data size in complex points (datsiz) is now equal to the number of points in the D2 dimension of the matrix.

Lines 16-18:

The data is then apodized, ft'd and phased. Note that the apodization is over 256 complex points. This is due to the fact that the 512 FIDs collected in the D2 dimension correspond to 256 complex points in D2. Because this is States data a complex ft is performed.

Lines 19-21:

The data is then reduced so that only the real part of the FID is retained. The data size (datsiz parameter) remains unchanged and is equal to the number of points in the D2 dimension of the matrix. This real data is then stored to the matrix. A type command (ty) is used to print out the current column so that the user can monitor the progress of the macro. It is important to note, that the actual output to the textport is a rather time consuming step, and the processing can be speed up by 10 - 20% if one chooses not to use it!

Lines 23-28:

If the macro finishes normally or the user exits the macro early then control transfers to this section. A cmx command is given to close the open matrix. Control is returned to the menu interface.

Processing 2D data with the supplied macros

The precoded 2D processing macros are accessed with the Macro/2D Data Processing command. This command brings forth first a file selection dialog box from where you can select the data type: Felix old and new data (.dat) file, Felix matrix file (.mat), Bruker fid or ser file, Varian fid file or JEOL Alpha or Lambda file, and the file name. If Felix can access the data then it brings up a second dialog box with the header parameters--the acquisition parameters can be valid only if the appropriate files are present (for Varian the procpar file and for Bruker the acqus and the acqu2s file. For Bruker the program also attempts to read the pdata/1/procs and proc2s files.) In this dialog box you can update the header parameters if they show incorrect values.

When you select D1 dimension for processing you are presented with a menu of choices which define how the D1 dimension is to be processed. For more information on each of the various processing options see Chapter 3, Processing, Visualization, and Analysis Interface (1D/2D/ND). When you select OK the macro first builds a real matrix of the size that you have specified with the Dimension 1 Size and Dimension 2 Size parameters. The macro then reads in each of FIDs from the given input file, processes the data according to the options you have selected and saves the processed vector to the matrix. The macro completes when each of the FIDs as specified by the D2 Parameters Data Size header parameter have been processed.

When the D1 macro has finished you will have a Felix matrix file (normally with a .mat extension) with each of the D1(t2) vectors processed. At this point you can process the D2(t1) vectors by selecting again the Macro/2D Data Processing command and choosing the matrix from the previous processing. Then you are again presented with a menu of choices which in this case defines how the D2 dimension will be processed. When you click OK to begin processing the D2 dimension the macro will open the matrix you have selected, read in each of the D2 vectors into the workspace, process each vector according to the options you have selected, save the processed vector back to the matrix and then close the matrix. When the D2 processing macro completes you will have a Felix matrix file where all of the D1 and D2 vectors have been processed.

Checking/examining the data as it is processed

Whether you choose to process your data using the precoded processing macros or your own custom macros the process of transforming your data in the D1 dimension will be basically the same. The macro will generally first build a new real matrix to hold the data, read in each FID from the input data file into the workspace, process each FID in turn and store the result to the matrix. At this point in the processing you will have a Felix matrix file where all of the D1 vectors have been processed but none of the D2 vectors have been processed. You need to examine the matrix at this point to make sure that the D1 processing is acceptable. It is a good idea to open the matrix file, extract 1D slices (using the View/Draw 1D Slices command) and then examine the individual 1D vectors using commands such as View/Limits/Manual Limits. The View/Draw 1D Slices command is especially helpful because it allows you, through the use of a slider, to scan through the individual D1 vectors in order to determine if the data needs to be reprocessed. If you decide that the basic processing operations such as apodization and solvent suppression were performed correctly then you will generally not have to fully reprocess the D1 dimension. It is possible to rephase or baseline flatten the existing data. To rephase the D1 dimension you could use the Macro/Phase command. To baseline flatten a matrix that has already been transformed in the D1 dimension you could use one of the Macro/Baseline commands. You could of course write your own macro to rephase or baseline correct the data in the D1 dimension. Once you have determined that the D1 dimension has been processed correctly and you have reprocessed the data if necessary it is then a good idea to look at some of the D2 vectors. Select some peaks in the first transformed row that you expect will have strong cross peaks and note the point numbers. Alternatively you could use the Vertical 1D Slice icon to select an initial column vector to view, then use the left and right arrows on the keyboard to scan through a series of column vectors noting the D1 point numbers for a few columns which show a strong interferogram. It is often desirable to load these selected columns into the workspace using the View/Limits/Manual Limits command and then save them to Felix data files. Use these saved column vectors to determine the optimum processing parameters for the D2 dimension. Once you have decided on the optimum processing parameters for the D2 dimension you can move on to processing all of the D2 vectors using your own macros or the precoded processing macros.

Whether you process the D2 dimension with your own macros or the precoded macros the basic process is the same. The macro will open the appropriate matrix, read in each column vector into the workspace one by one, process the vector, save the vector back to the matrix and close the matrix file. After you have transformed the D2 dimension you need to examine the individual D2 vectors using either the View/Limits/Manual Limits or the Vertical 1D Slice icon. If the D2 vectors need to be rephased or baseline corrected you can do this with your own macros or using the precoded macros in the Macro pulldown as discussed above for the D1 dimension.

Task: Analysis of relaxation data

It is assumed that R1 and R1 relaxation data and heteronuclear NOE were measured as a series of 2D HSQC (or equivalent) spectra (Skelton et al. 1993). You will have to pick peaks in one of the relaxation spectra or in an equivalent HSQC. Peak assignments are helpful, but not necessary.

The first step in the relaxation analysis is to evaluate peak heights or peak volumes in a series of 2D spectra using the Measure/Relaxation/Measure Heights/Volumes command. In practice, peak heights have proved more reliable and yield better signal to noise ratios (Skelton et al. 1993). Felix automatically centers the peak boxes (in case they were picked in another spectrum and are slightly off) and then determines the peak heights or volumes in the relaxation spectra.

In the case of peak volume determination, the raw volumes are optimized by fitting a two-dimensional gaussian function to the spectra and deriving volumes from the fit. In the first spectrum of the series, peak widths are optimized, then peak amplitudes and finally both are optimized simultaneously. In subsequent spectra, centers and widths are left as they are, and only amplitudes are adjusted. In the case of peak height determination when peak boxes overlap, the peak box is iteratively shrunk by one point in each dimension until no other peak box overlaps it, to prevent crosstalk from any overlapping peak maxima.

Thus volumes and heights are accurate within the limits of the methodology even for overlapped peaks, and the procedure requires no interaction on your part. Peak heights or volumes are stored into a regular Felix volume table, but the last row holds relaxation delays instead of mixing times.

The next step is to estimate the signal to noise ratio from one or more duplicate spectra, using the Measure/Relaxation/Signal/Noise Ratio command. The average standard deviations obtained for these spectra are interpolated to the other points in the series as appropriate. The time courses of peak heights/volumes you obtain in this way can be visualized and plotted by:

1.   Selecting the Measure/Relaxation/View Timecourse via Cursor command and then selecting a peak with the mouse.

2.   Selecting the Measure/Relaxation/View Timecourse via Item command and entering a peak number.

Duplicate peak volumes and heights are stored in their own volume table which is essentially a copy of the original volume table, with the appropriate column overwritten with values from the duplicate spectrum. Uncertainties are found in the last row of this table.

After obtaining peak heights and volumes and their errors, these time series can be fitted to an appropriate exponential function (using the Measure/Relaxation/Fit R1/R2/NOE command). In the case of R1 data, this is a general exponential function:

Eq. 1            

In the case of R2 data, where in theory the values decay to zero, the simpler equation:

Eq. 2            

Heteronuclear NOE data is analyzed in a single step using the Measure/Relaxation/Fit R1/R2/NOE command. After obtaining the file names for the spectra obtained with and without 1H saturation, Felix again centers the peak boxes (see above) and then proceeds to determine peak heights or volumes as desired by the user. Next, two duplicate spectra are analyzed to derive the uncertainty of the measurement. The NOE is the ratio of the volumes obtained with and without 1H saturation.

In addition to generating R1, R2 and NOE values, the relaxation analysis tools provided in Felix let you prepare input data files for the program Modelfree authored by Professor Arthur G. Palmer of Columbia University (accessed via the Measure/Relaxation/Modelfree Input command). Modelfree is downloadable from HTTP://cpmcnet.columbia.edu/dept/gsas/biochem/labs/palmer/. For further details on analyzing relaxation data with the modelfree program, see Mandel et al. (1993).

Task: Working with assignment databases

The definition of the project starts with reading in a Insight II or X-PLOR coordinate file of the molecule (or complex) under investigation. This coordinate file does not necessarily need to be the true structure, especially since at the beginning of the assignment the final structure usually is not known. For example, for a protein it can be a simple linear chain, or a B-form of a nucleic acid. As the assignment progresses and refined structures become available, these new coordinates can be read into the project.

The crucial part of residue identification is the library. The library is an ASCII file, where the mean chemical shifts and standard deviations are collected for the usual residues. You can change these values, and can also add non-standard residues to the end of the file. This step is illustrated later. This file is then read into the project and used in the spin system identification step.

You can define the matrices which are then used in the assignment. These matrices should be referenced in ppm, and are in the standard Felix format. In adding the experiments, it is important to define a reasonable tolerance for each axis, since the automated spin system detection routines will heavily rely on them. This tolerance should mirror the uncertainty of the peak positions in each dimension (in ppm). Later on you can delete unused experiments from the database and add new ones. For example, you can start with J spectra (TOCSY, DQF-COSY, HSQC-TOCSY, or CBCA(CO)NH) first to do the sequence specific resonance assignment. Once that is done, you can delete the J-spectra and define NOE-type experiments (e.g., NOESY or HSQC-NOESY) to do peak assignment and restraint generation.

Task: Adding modified residues to the Assign database

The first file is called pd.rdb or rna.rdb. To add for example, an aminobutyric acid residue, you should first notice that the end of the pd.rdb file is:

!
!
! ROOM FOR MORE RESIDUE TYPES ...
!

1.   Each line starting with exclamation mark is a comment. So first you add the formula as a comment:

!
! ABU
!
! H - N    H
!     |    |
! H - Ca - Cb - Cg - H3
!     |    |
! O = C    H
!

2.   The next part of the file is the residue name:

RESIDU ABU X

where X is just a one letter shortcut for the residue name.

3.   Now you need to enter the atoms by first entering the keyword RESATM, then the atom type, atom name (in Insight notation), a number (this field is not active yet), a mean chemical shift, a standard deviation, and finally, if there is more than one atom (e.g., a pseudoatom) in that group, enter the number of atoms.

This section of the file should now look something like this:

RESATM H HN   1  8.16   0.60
RESATM H HA   2  4.30   0.20
RESATM H HB1  3  3.30   0.20
RESATM H HB2  3  3.30   0.20
RESATM H HG*  4  1.10   0.10  3
RESATM N N    8  126.0  5.0
RESATM C CA   10 51.0   5.0
RESATM C C    9  150.0  50.0
RESATM O O    15 0.0    999.0
RESATM C CB   12 26.0   5.0
RESATM C CG   12 17.0   5.0

4.   Next there is another comment line which contains the atoms used to help construct the neighbor matrix.

!      HN HA HB1 HB2 HG* N CA C O CB CG

5.   The next part of the file is the matrix, where each element shows how many bonds are between the constituent atoms (groups).

CONECT 0
CONECT 3  0
CONECT 4  3  0
CONECT 4  3  2  0
CONECT 5  4  3  3  0
CONECT 1  2  3  3  4  0
CONECT 2  1  2  2  3  1  0
CONECT 3  2  3  3  4  2  1  0
CONECT 4  3  4  4  5  3  2  1  0
CONECT 3  2  1  1  2  2  1  2  3  0
CONECT 4  3  2  2  1  3  2  3  4  1  0

6.   Finally a keyword finishes the residue definition:

ENDRES

7.   The other file to edit is the biosym.alias file. You have to append a line to the file which contains the possible names of that residue for all the charged/terminal forms:

ABU ABUN ABUC ABUn 

After these two files are edited, Assign can deal with the new residue type.

After peaks are picked you may want to use a peak optimization step to better define the peak centers/widths/volumes. In order to use it you need to first measure the peak volumes. Sometimes the peak optimizer can merge close peaks, therefore it is advisable to do this step interactively and save the peak entity into a backup file within the database. By defining small regions where the optimizer should work and examining the results you can see if the peaks get misplaced too much or merged unnecessarily, then you can retrieve back the saved copy and skip optimization for those peaks.

Task: Using connected frames to navigate in multiple spectra within Assign

1.   In the dialog box you should leave the First Frame parameter as 1 and the Second Frame parameter as 2.

2.   For the connection method you would push the D1-D2- D3<=>D1-D2-D3 radio button, and then hit OK.

Certainly, if you have 2D, 3D, and 4D spectra to connect, you can use this interface to define quite sophisticated schemas. The following is a theoretical example of how you would connect 2D spectrum with 3D spectra:

1.   Assume you have a 2D ¹⁵N-¹H HSQC spectrum with ¹⁵N as D2 and ¹H as D1. Let's moreover assume that you have a ¹⁵N TOCSY and ¹⁵N NOE spectra, where D1 is the amide ¹H, D2 the full ¹H, and D3 is the ¹⁵N dimension. You want to look through the amide peaks in the HSQC spectrum, and then see the corresponding slices in the TOCSY and NOESY spectra. First you would select from the Experiment table for the Frame 1 the HSQC spectrum, and for Frame 2 and Frame 3 the TOCSY and the NOE spectra, respectively.

2.   In the first dialog box leave 1 for the First Frame and 2 for the Second Frame parameters, respectively. This would connect the HSQC spectrum with the TOCSY. Select the Custom option and hit OK. In the next dialog box you would set:

First Frame 1	Second Frame 2
D1	D1
D2	Null

3.   Set Define Jump on and for Jump Direction set:

First Frame 1	Second Frame 2
D2	D3
Null	Null

4.   Hit OK.

5.   Now you would connect the HSQC spectrum with the NOE spectrum through the first dialog box which should pop up again. Leave the First Frame as 1 and the Second Frame as 2. Select the Custom option and hit OK. In the next dialog box you would set:

First Frame 1	Second Frame 3
D1	D1
D2	Null

6.   Set Define Jump on and for Jump Direction set:

First Frame 1	Second Frame 3
D2	D3
Null	Null

7.   Hit OK.

8.   This setup will make sure that the plot navigation in the HSQC spectrum (e.g., zoom on a peak) will translate to the HSQC- TOCSY and HSQC-NOE spectra. If you hit the period <.> on the keyboard while in the HSQC frame you will be able to select via the cursor a new ¹⁵N position (e.g., clicking on a HSQC peak) which then translates to new planes in the TOCSY and NOE spectra. Moreover if you wish to navigate the TOCSY and NOE spectra together you need to set that option with executing the Preference/Frame Connection command again.

9.   Make Frame 2 current. Select the Preference/Frame Connection command. In the first dialog box leave for First Frame and Second Frame parameters 2 and 3 respectively. Select D1-D2- D3<=>D1-D2-D3 for the method and hit OK. After the dialog box comes back hit Cancel. This way you have connected all three axis of the TOCSY and NOE spectra.

1.   The first type of spin system results from an automated spin system detection. These are called prototype patterns or protos. This system can be intra-residual or can contain spins from neighboring residues, depending on the spectrum and method by which they were collected.

2.   The second type of spin system is the frequency clipboard, which is a unique list of frequencies which originate from a proto or a concatenation of several protos, or which are hand picked from a spectrum. The clipboard is usually the place to delete spurious resonances from the spin system or to add missed resonances manually.

3.   The third stage of the spin system is the so-called pattern. Patterns can be scored against a library to find a type probability score, or they can have sequential neighbors. The frequencies in the pattern may also then be assigned to atoms (or atom groups) in the molecule. Patterns can be edited, but with much more limited functionality.

Task: Manual spin system collection

Task: Automated spin system detection

Tolerances

Another tolerance is also required in the course of automated spin system detection, which in certain cases is used to decide whether a peak belongs to a spin system or not. If you use the homonuclear method with one spectrum, this tolerance will be defined by resolution of the dimension having the worse digital resolution in ppm, usually the F1. If you use multiple spectra, then this tolerance should be defined based on the comparison of the chemical shifts of the peaks belonging to the same connectivity in different spectra.For example, if you measure the position of couple of the "same" peaks in the NOESY and TOCSY spectra, the difference defines the tolerance. Certainly if the automated methods do not give sufficient results, the first thing you should try changing is the tolerance.

It may benefit you to reference one base spectrum and also newly introduced chemical shift ranges in other spectra, according to scientific theory, and reference the other spectra so that known peaks match optimally. This may significantly increase the success of automated spin system detection and peak assignment routines.

Two-dimensional systematic search method

The 2D systematic search method can be used on different combinations of COSY, TOCSY, and NOESY type spectra. Usually in the first iteration for a protein, the spin systems are collected starting from a H-H_N cross peak, then the full spectrum is searched for aligned peaks that define new frequencies. Therefore the seed region should be set to an H-H_N region either above the diagonal or below, depending mainly on resolution and on the presence of possible spurious peaks. The frequency collapse tolerance then defines whether the new frequencies (possibly resulting from different spectra) are unique. It is useful to try several settings for this number, but you can start by guessing how well the corresponding peaks in different spectra are aligned.

• You can run several iterations of spin system detection, and to avoid picking the same systems again and again you can direct the program to discard those peaks from further consideration which are within the tolerance from already-found spin systems.

• By requiring that certain minimum and maximum numbers of frequencies should be found for a prototype pattern you can selectively detect spin systems. For example, by setting the minimum to 2 and maximum to 4, the AMX spin systems can be found (allowing a frequency tolerance of ±1).

• In each loop you can specify how many of the best matching frequencies should be added to the prototype patterns under consideration. A value of 1 usually works well.

• Another important parameter to adjust is the minimum number of contacts (Min # cont). Using this parameter, you can specify that a new frequency will be added to the spin system if there are certain number of cross peaks (contacts) present between this frequency and the frequencies in that particular spin system.

• You can impose chemical shift filters using the #ppm filters active and Filter parameters. These filters insure that a certain prototype pattern will be stored only if the number of frequencies in different regions matches up the conditions you have specified.

These settings must be changed if, for example, you want to find the possible prolines; the seed region should then be at the H-H region, and you must exclude spin systems having frequencies in the aromatic/amide region. Similarly, if you want to search for aromatic side chains, you must use the aromatic region as a seed and exclude the aliphatic region by filtering.

Between iterations you can selectively delete prototype patterns which are clearly not right, thus releasing peaks, or you can delete all of them. Also, it is useful to note that after you assign or define several reliable patterns, then those patterns' frequencies can be copied back to prototype patterns, thus preventing you from re-detecting them in the following iterations.

Two-dimensional simulated annealing method

Double resonance methods

The first method works on ¹⁵N - ¹H HSQC-TOCSY or ¹⁵N-¹H HMQC-TOCSY spectrums. For this method, the spectrum should contain pseudo-diagonal peaks--that is, H_N-H_N-N peaks--from which then the spin systems will be collected along the side-chain. If the pseudo-diagonal is not well-resolved or if there are missing peaks, you can use the ¹⁵N-¹H HSQC spectrum to help. In that case the program tries to find new frequencies in the 3D from each peak in the 2D spectrum, and stores them as spin systems. Certainly, if the ¹⁵N-¹H HSQC spectrum is not well-resolved, this will be less effective.

In the second spin system detection mode you can use double resonance experiments to extend already existing spin systems--usually these are results of a triple-resonance spin system detection run. In this case, the purpose of the detection is to expand spin systems containing only backbone frequencies to include side-chain information. Currently there is one method implemented in Felix which uses a 3D HCCH-TOCSY spectrum to achieve this. You can either expand spin systems in the prototype pattern stage or in the pattern stage.

Triple resonance methods

There are a variety of triple resonance measurements for making sequence-specific assignments. A number of approaches are implemented for which there are some common elements: usually you must first peak pick several triple resonance spectra, and find out what the uncertainty is between the peak positions within each spectrum for couple of resonances. This will be the spectrum specific tolerance for each axis. Also, you need to find out, simultaneously inspecting the spectra, how well the peaks belonging to the same resonances can be overlaid. For example, you need to know, at a given X residue, what the differences are between the peak frequencies along the H_N and ¹⁵N axis, and C in the HNCO and HNCA and HN(CO)CA spectra (H_N,x-N_x-C_x-1, H_N,x-N_x-C_,x-1 and H_N,x-N_x-C_,x-1, respectively). Those will be the inter-spectral tolerances for the respective axis.

After that you can run one of the spin system detection routines starting with, for example, a small amount less than these inter-spectral tolerances, and then telling the program to iterate and increase the tolerance at each iteration step. Depending on the spectral quality, you can automatically get 50-90% of the theoretical spin systems. Certainly these spin systems may not be perfect, therefore you need to inspect them before promotion and discard or alter any insufficient spin systems. Also, you can manually add missed spin systems to the list of prototype patterns. There is an advantage, since usually triple resonance experiments are measured as (at least) pairs, so if you add spin systems in this stage, you can manually add frequencies belonging to this residue as well as to the neighboring residue. As long as you let the program know what those frequencies are, then Assign will establish these connectivities in the promotion stage.

User-definable automated spin system detection

Besides the predefined combinations of experiments given here, you can design your own spin system detection methods. Typically you would design your own protocol if you have a good sensitivity 2D or 3D double or triple resonance spectrum (e.g., ¹⁵N HSQC or HNCO), known as a "seed" spectrum, together with couple of double or triple resonance 3D "secondary" spectra (e.g., HNCA and HN(CO)CA). The typical procedure is to start from the seed spectrum with each peak as a trial peak and try to collect connected resonances in the secondary spectra. For example, if you want to detect spin systems using a combination of HNCA and HN(CO)CA experiments, you can use the following setup (assuming that the order of dimensions is H_N, N and C):

1.   First you select the Assign/Collect Prototype Patterns/User Settable command and set the Number of steps to 1, since the seed peak selection is considered as zeroth step. Select the HN(CO)CA spectrum as the seed spectrum (Step # 0) and the HNCA spectrum as the first step.

2.   Since you need to match the HN and N frequencies in the two spectra for the corresponding peaks in the second dialog box (Experiment hncoca in Step 0) you select the following for the Match to parameters:

Match to	hnca	null	...	null
hncoca D1	D1	No	...	No
hncoca D2	D2	No	...	No
hncoca D3	No	No	...	No

3.   For the D1 Type select HN, for D2 Type select N and for D3 Type select null. The typical tolerances would be comparable to the differences between the chemical shifts of the same H_N-N- C peak in the two spectra, for example, 0.02, 0.15, and 0.15 ppm for D1 Tol, D2 Tol, and D3 Tol, respectively. You can perform selective detection using chemical shift ranges to reflect that, or you can use the full spectrum using -1 for all Range variables. Typically multiple trials are needed from each peak to find a prototype pattern. The next set of variables reflect this, that is, if with the given tolerances not enough frequencies (Minimum Freqs in Proto) are collected from a particular peak then new iterations are carried out (up to Number of Iterations times) increasing the tolerances by the Tolerance Factor. You can direct also the program to delete peaks which may belong to already detected spin systems (Remove First is then True). In this case you would select 3 for the Number of Iterations, and 1.2 for the factor by which the tolerances are multiplied by after each unsuccessful trial (you need 4 frequencies).

4.   In the next dialog box you must decide how many peaks to store and which frequencies to store. In our case we need to use the D3 frequency (the ¹³C dimension) (Use D3) and you need to store two peaks' frequency (Store 2). The Type variable is active if you select the All option for Store (it is useful in, e.g., a ¹⁵N-HSQC-TOCSY experiment, because the program would not assign the resonances along the TOCSY line but would call them HXs). You set the first peaks frequency to store as the C (this peak in the HNCA spectrum is usually more intense--#1 CA and Attr Larger). The second peaks D3 frequency then results in the C_,i-1 frequency (#2 CA(-1) and Attr Smaller). Besides the magnitude of the intensity, you can use the sign of the intensity as a distinction (as in the case of HNCACB spectrum where the H_N-N-C and H_N-N-C peaks have opposite signs) or use a distinctive chemical shift range.

Task: Semiautomated spin system detection

1.   First you select the Assign/Collect Prototype Patterns/Semiautomated Setup command and set the Number of frames/steps to 2, putting the HSQC in Frame 1,and the HSQC-TOCSY in Frame 2.

2.   In the second dialog box you select the following for the Connect to parameters:

Connect to	#2	#3	#4
Frame 1 D1	D1	No	No
Frame 1 D2	No	No	No

3.   Now you set the Slice Position in Frame 1 parameter to D2, the Sliceplane in #2 to Along D3. That means that you want to use the D2 coordinate of the HSQC spectrum to select a plane in HSQC-TOCSY along D3.

4.   Next you must specify how to match the different frequencies. In this case you need to match the D1 of HSQC to D1 of the TOCSY, and the D2 of HSQC to D3 of the TOCSY spectrum (HN to HN and ¹⁵N to ¹⁵N). You also need to specify the spin types you expect in this first spectrum together with search tolerances: HN with 0.02 and N with 0.1.

5.   The number of iterations can be set with a tolerance factor, which will specify that the spin system collection be tried that many times, increasing the tolerances by that many times (you can set it to 3 and 1.4, for example).

6.   In the third dialog box you would set the Orientation parameter D1-D2 to D3: 118 ppm. You would set the Connect to parameters as follows:

Connect to	#1	#3	#4
Frame 2 D1	D1	No	No
Frame 2 D2	No	No	No
Frame 2 D3	No	No	No

7.   Now set the Use parameter to D2 since you want to collect new frequencies along D2 of the HSQC-TOCSY spectrum (with D1 and D3 defined by the HSQC) and Store All with type HX.

Task: Spin system extension

The majority of the automated methods in the Assign module collect spin systems containing backbone atoms. In order to automatically extend the spin systems to include atoms from the side chain you may want to run the routines in the Assign/Collect Prototype Patterns/Extend Prototype Patterns command. To illustrate this method let's assume that the spin systems were collected so that the prototype patterns contain the H_N, N, C, and C shifts. Then you can use a 3D HCCH-TOCSY spectrum to automatically find the corresponding H, H frequencies. In the Extend Prototype Pattern Using HCCH-TOCSY command then you would select appropriate tolerances for the search along the ¹³C and ¹H dimensions (taking into account how well the C_a and C_b resonances align between the spectra they were detected from (e.g., CBCANH) and this spectrum). You have to set how many times the extension will be attempted from each prototype pattern (Number of iterations) and how much to increase the tolerances each time (Tolerance factor). You also have to define the primary and secondary search dimensions for protons, as well as the carbon dimension. The primary proton dimension is where the heteronuclear transfer was done, and the secondary dimension is where the TOCSY was done.

You can use the extend option to add H_,i-1 and H_,i-1 frequencies to spin systems containing H_N and N frequencies using the HBHA(CO)NH spectrum (Extend Prototype Pattern Along One Axis).

Task: Spin system promotion

One way is to copy the protos one by one, using the clipboard. This is the preferred method for spin systems detected in homonuclear or heteronuclear double resonance spectra, since it is wise to visually inspect and correct the results of automated methods. After inspecting and correcting the frequency clipboard you can verify that the new "pattern" will be unique using the fuzzy algebra comparison command (Assign/Frequency Clipboard/Compare Frequencies).

The other way, which might be more dangerous if you did not verify the prototype patterns, is to copy all prototype patterns to patterns directly. If you choose this method you must delete false prototype patterns before copying. This latter mode is preferred for prototype patterns resulting from triple resonance heteronuclear spectra, since the neighbor information contained in protos detected in triple resonance spectra can be preserved this way.

Task: Spin system identification

The next step in the assignment is to identify the spin systems--the patterns resulting from an automated or manual search. This can be done in Assign based on all atom chemical shifts contained in the database, by using either a simple scoring algorithm or by using a probability distribution of C/C chemical shifts (Grzesiek and Bax, 1993). This latter method gives better scores, since the proton chemical shifts are not so well dispersed for different residue types, but this method can only be used if labeled proteins are accessible.

The all-atom method gives scores that are not very distinguishable from each other, but combined with the sequential probability scores it still gives a good starting result in the sequential assignment generation step. To help with the assignment, it is advisable to manually unset scores based on a DQF spectrum of the very unlikely residue type probabilities.

Task: Establishing connectivities

You can use the Assign/Neighbor/Find Neighbor Via 2D NOE or 3D NOE command to connect the aromatic side chains with the aliphatic side chains. You will usually end up with prototype patterns having the aliphatic and aromatic part of the same residue as two different entries. Therefore you promote them to separate patterns. Then you must find which aliphatic side chain has several contacts to the H's (of Tyr's or Phe's) which should be the root frequency for that particular pattern. If you find the correct connection (possibly visually inspecting by tiling) then you must merge the two patterns (you can use the Assign/Frequency Clipboard/Copy Pattern To Clipboard and then the Assign/Frequency Clipboard/Copy Clipboard To Pattern command). Lastly, you must delete the purely aromatic side chain pattern(s).

Task: Sequential assignment

The other approach would be to assign shorter stretches (20-30 residues), still keeping the neighbor probability score comparatively small (e.g., 0.1). This method has the disadvantage that you can not assume that the first solution is the correct one, therefore you must check all high scoring possibilities.

The third approach was named "iterative assignment by consensus" (Kleywegt et al 1993), which means that the assignment generation starts with restricting the neighbor probability score to high values and letting the Min length of assigned stretches be relatively small. Thus well-connected stretches are found first, then the neighbor score is gradually relaxed and the minimum length is increased; longer, well-defined stretches are assigned. This procedure continues while no new assignments can be obtained. Then you might try to specifically assign the remaining stretches. At all stages the consensus means that those residues are assigned which are conserved in majority of generated assignments.

The Assign/Sequential/Simulated Annealing command can be run on any set of homonuclear or heteronuclear patterns. It uses only the type and neighbors scores, obtained by any method, to find the sequence-specific assignment. Optionally, previous assignments are loaded and respected. The amino-acid type and/or residue number are considered assigned for a pattern if they are consistent over all frequencies of the pattern (unique or specified assignments).

After careful inspection of the patterns and scoring of types and neighbors, the process might be run on the full sequence. Then you might inspect the result, modify it using the Pattern Assign functions, perhaps try another run, and identify some satisfactory parts from the scores listed. You should then discard the ambiguous assignments and rerun the program with the correct residues used as anchor points. If several such iterative processes still fail to determine unambiguously the complete assignment, then some additional information should be input, like a more accurate scoring or some new patterns.

The results are stored into assignment pointers for all frequencies of the patterns (and set as the current specified frequencies). Note that there should not be any residue named "null" in the molecule, else its assignment will be discarded.

Optionally, some parameters of the simulated annealing might be adjusted (scaled by a factor of 0.1 to 10) according to the complexity of the problem:

• Initial temperature, number of iterations: if most parts of the sequence are well-defined these parameters can be decreased to speed up the program.

• Sequential/Individual factor: weight is accorded to the neighbors information, relative to the spin system fit scores.

Task: Peak assignment

You can approach the automated assignment in different ways: you can use a linear chain as a model and only assign intra-residual and sequential peaks, as well as peaks having unique frequency assignments (that is, peaks which have only one possible assignment in each dimension), or you can use a homology or a low resolution starting model (for example, from X-Ray crystallography), and assign based on those distances. There is also a choice which allows you to do ambiguous assignments and then later use those assignments as overlap restraints in SA or rMD protocols.

After a set of assignments are generated and (and possibly manually verified), structures are generated based on those. You can refine your assignments based on the new model or based on hot-spots (where restraints are highly violated or the structure is bad).

Task: Restraint generation

If you use a 2D NOESY spectrum, you may want to control which peaks' volumes (Symmetry Selection) are converted to distance restraints. Use All converts all peaks (and then only restraints are generated for the first occurrence of the symmetric pair), Select Regions uses different sides of the diagonal in certain regions of the spectrum, and Use Weaker uses only the weaker peak of a symmetry-related pair.

Converting the calculated distance to a restraint can happen in many different ways (Method): Exact Distance, S-M-W Bins, VdW-Exact, Percentage. The most dangerous method is using the exact distance as a restraint (Exact Distance)--in that case the lower and upper bound becomes that calculated distance. The most popular method is to carry out a categorization into the strong, medium, and weak categories (S-M-W Bins), and to store the corresponding bounds in the restraint. The third method is to set the lower bound to the Van der Waals distance and the upper bound to the calculated distance (VdW-Exact). In the fourth method (Percentage) you specify a percentage by which the calculated distance will be decreased and increased to yield the lower and upper bounds.

There is always a danger in doing a totally automated conversion--there can be overlapped peaks where the measured volumes are not representing the real volumes. To avoid over-restraining when using such peaks, Felix can handle this type of peak differently, if you use the Partial Overlap option to check how much the area of integration is overlapped for each peak. You can then define a threshold (Area Threshold) by which the algorithm can skip to generate restraints from those overlapped peaks (Discard) or you can use different method to generate bounds from calculated distances, (e.g., Use as Qual, which generates only qualitative upper bound restraints).

Task: Checking and redefining restraints

Those hot-spots can be a result of misassignment or overlapped peaks. In the case of misassignment you use a list to reassign (or unassign) those peaks. You can use a simple ASCII file which just has the two (or three or four, depending on dimensionality) names in a row separated by a blank. You can also create a list from within Insight II using the following recipe:

1.   First load the restraints on all the refined molecules in Insight II using the NMR_Refine/Restraints/Read molname* command.

2.   Execute the NMR_Refine/Distance/List command. On the resulting output file run, the provided numvioltofelix script redirects the output into another file. This is the file you can use in the Filename parameter.

3.   Proceed similarly to the previously described Manual Assign Singly command, but instead of selecting the peaks by cursor the peaks automatically will be centered in your current frame and the corresponding dialog box will be brought up.

1.   Load the restraints on all the refined molecules in Insight II using the NMR_Refine/Restraints/Read <molname*> command.

2.   Execute the NMR_Refine/Distance/List command. On the resulting output file run, the provided numvioltofelix script redirects the output into another file.

3.   Use the Measure/DISCOVER Restraints/NOE Distance Redefine command on this file. You should specify the Restraint entity you wish to work with (usually the msi:noe_dist), and the Buildup Rate Calculation Method. The program brings up a violation table through which you can zoom on each peak for which the defined restraint was violated one by one, and can report the calculated distance. The table also contains the restrained values, and the violation statistics.

4.   From the violation table you can redefine the restraints or delete restraints or delete assignments.