MANUAL OF CR-I-TASSER SUITE
(Copyright 2017 by Zhang Lab, University of Michigan, All rights reserved)
(Version 1.0, 2021/01/01)
1. What is CR-I-TASSER Suite?
The CR-I-TASSER Suite is a composite package of programs for protein
structure prediction guided by cryo-EM density map. The Suite
includes the following programs:
a) CR-I-TASSER: A hierarchical program for templates refinement and protein structure prediction guided by cryo-EM density map
b) DeepMSA: A program for multiple sequence alignmnet generation
c) MUSTER: A threading program for protein template identification
d) CEthreader: A contact-based threading program for protein template identification
e) LOMETS2: A meta-server approach consisting of multiple threading programs
f) ResTriplet,TripletRes,ResPre,ResPLM and DeepPLM: Deep-learning-based programs for residue-residue contact prediction
g) SPICKER: A clustering program for structure decoy selection
h) HAAD: Quickly adding hydrogen atoms to protein heavy atom structure
i) EDTSurf: Construct triangulated surfaces of protein molecules
j) ModRefiner: Construct and refine atomic model from C-alpha traces
k) NWalign: Protein sequence alignments by Needleman-Wunsch algorithm
l) PSSpred: A program for Protein Secondary Structure PREDiction
m) ResQ: An algorithm to estimate B-factor and residue-level error of models
2. How to install the CR-I-TASSER Suite?
a) download the CR-I-TASSER Suite 'CR-I-TASSER-1.0.tar.bz2' from
http://zhanglab.dcmb.med.umich.edu/CR-I-TASSER/download
and unpack 'CR-I-TASSER-1.0.tar.bz2 by
> tar -xvf CR-I-TASSER-1.0.tar.bz2
The root path of this package is called $pkgdir, e.g.
/home/yourname/CR-I-TASSER-1.0. You should have all the programs under this
directory. You can install the package at any location on your computer. The
unpacking should be very fast (<2 minutes).
b) Download CR-I-TASSER library files from
https://zhanglab.dcmb.med.umich.edu/CR-I-TASSER/download
A script 'download_lib.pl' is provided in the package for automated
library download and update of the libraries.
We recommend putting the library files under the path /home/yourname/ITLIB.
c) Third-party software installation:
While the majority of programs in the package 'CR-I-TASSER-1.0.tar.bz2' are
developed in the Zhang Lab herein the permission of use is released,
there are some programs and databases (including blast, nr, GOparser,
uniclust30,uniref90 and metaclust) which were developed by third-party groups.
A default version of blast and nr are included in the package. It is user's obligation to obtain
license permission from the developers for all the third-party software
before using them. In addition, your system needs to have Java, python2,
python3 (which supports pytorch >1.1.0) installed.
To use DeepMSA, you need download uniclust30, uniref90 and metaclust from
http://gwdu111.gwdg.de/~compbiol/uniclust/2017_04/uniclust30_2017_04_hhsuite.tar.gz ,
ftp://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref90/uniref90.fasta.gz ,
and https://metaclust.mmseqs.org/2017_05/metaclust_2017_05.fasta.gz. after you unpack them,
put the entire folder to the CR-I-TASSER library folder, (i.e. where the folder you put your PDB, MTX, DEP folders).
Then rename the folder uniclust30_xxx_xxx to uniclust30, uniref90_xxx to uniref90, metaclust_xxx to metaclust.
Then use $pkgdir/contact/DeepMSA/bin/esl-sfetch to create .ssi index for uniref90 and metaclust, here
$pkgdir means the path where you put the CR-I-TASSER suite package.
For example, if the uniref90 database in uniref90 folder is named as uniref90.fasta,
then go to uniref90 folder, run $pkgdir/contact/DeepMSA/bin/esl-sfetch --index uniref90.fasta, you will find
a new file named as uniref90.fasta.ssi after the command done. Then do the same thing to metaclust database.
If you use different version of uniclust30, uniref90 or metaclust, please go to $pkgdir/I-TASSERmod/runI-TASSER.pl,
change the variables
$hhbdbdir = "$libdir/uniclust30";
$jacdbdir = "$libdir/uniref90";
$hmsdbdir = "$libdir/metaclust";
$hhbdb = "$libdir/uniclust30/uniclust30_2017_04";
$jacdb = "$libdir/uniref90/uniref90.fasta";
$hmsdb = "$libdir/metaclust/metaclust.fasta";
3. Bug report:
Please report and post bugs and suggestions at CR-I-TASSER message board:
http://zhanglab.dcmb.med.umich.edu/forum
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# 4. Installation and implementation of CR-I-TASSER #
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4.1. Introduction of CR-I-TASSER
CR-I-TASSER (Cryo-EM Iterative Threading ASSEmbly Refinement) is a new method
extended from I-TASSER for high-accuracy protein structure prediction guided by cryo-EM density map.
Starting from a query sequence and density map, CR-I-TASSER first extracts the information of CA atom
positions by using a 3D-CNN predictor. CR-I-TASSER also generates inter-residue contact-maps
and threading templates, which are then refined by the predicted CA conformation.
The full-length structure models are constructed by reassembling the continuous
fragments excised from the optimized templates through REMC simulations under the
guidance of a composite force field consisting of cryo-EM density map restraints,
contact-map restraints, threading-template-derived contact and distance maps,
and a set of knowledge-based energy terms highly optimized through
large-scale decoy-based energy-RMSD correlations.
4.2. How to run CR-I-TASSER?
a) Main script for running CR-I-TASSER is $pkgdir/I-TASSERmod/runI-TASSER.pl.
Run it directly without arguments will output the help information.
b) The following arguments must be set (mandatory arguments). One example is:
"$pkgdir/I-TASSERmod/runI-TASSER.pl -libdir /home/yourname/ITLIB -seqname example -mapname yourmap.map -datadir /home/yourname/CR-I-TASSER-1.0/example"
-libdir means the path of the template libraries
-seqname means the unique name of your query sequence
-mapname means the name of input density map (e.g. inputmap.map)
-datadir means the directory which contains your sequence (seq.fasta) and density map
c) Other arguments are optional whose default values have been set.
User can reset one or more of them. One example of command line is:
"$pkgdir/I-TASSERmod/runI-TASSER.pl -pkgdir /home/yourname/CR-I-TASSER-1.0 -libdir /home/yourname/ITLIB -seqname example -mapname yourmap.map -datadir /home/yourname/CR-I-TASSER-1.0/example -runstyle parallel -homoflag benchmark -idcut 0.3 -LBS true -EC true -GO true -java_home /usr -python2 /usr/bin/python2 -python3 /usr/bin/python3 -cit true"
-pkgdir means the path of the I-TASSER package. default is to
guess by the location of runI-TASSER.pl script
-java_home means the path contains the java executable "bin/java"
(your system needs to have Java installed)
-runstyle default value is "serial" which means running I-TASSER
simulation sequentially.
"parallel" means running parallel simulation jobs in the
cluster using PBS/torque job scheduling system.
"gnuparallel" means running parallel simulation jobs on
one computer with multiple cores using GNU parallel
-homoflag [real, benchmark],"real" will use all templates, "benchmark"
will exclude homologous templates
-idcut sequence identity cutoff for "benchmark" runs, default
value is 0.3, range is in [0,1]
-ntemp number of top templates output for each threading program,
default is 20, range is in [1,50]
-nmodel number of final models output by I-TASSER, default value
is 5, range is in [1,10]
-LBS [false or true], whether to predict ligand-binding site, default is false
-EC [false or true], whether to predict EC number, default is false
-GO [false or true], whether to predict GO terms, default is false
-restraint1 specify distance/contact restraints (read more at
http://zhanglab.dcmb.med.umich.edu/I-TASSER/option1.html )
-restraint2 specify template with alignment (read more at
http://zhanglab.dcmb.med.umich.edu/I-TASSER/option4.html )
-restraint3 specify template name without alignment (read more at
http://zhanglab.dcmb.med.umich.edu/I-TASSER/option2.html )
-restraint4 specify template file without alignment (read more at
http://zhanglab.dcmb.med.umich.edu/I-TASSER/option3.html )
-temp_excl exclude specific templates from template library (read more
at http://zhanglab.dcmb.med.umich.edu/I-TASSER/option6.html )
-traj this option means to deposit the trajectory files
-light this option means to run I-TASSER in fast mode (each
simulation runs by default 5 hours maximum)
-hours specify maximum hours of simulations (default=5 when -light=true)
-outdir where the final results should be saved (default value is set to data_dir)
-python2 path to python 2, for example /bin/python
-python3 path to python 3 for contact prediction and 3D-CNN CA prediction,
need to support pytorch >1.1.0, for example /bin/python3
-cit whether use predicted contacts guiding the CR-I-TASSER simulation or not (default=true)
NOTE:
a) Outline of steps for running CR-I-TASSER by 'runI-TASSER.pl':
a1) standardize 'seq.fasta' to 'seq.txt' and get the sequence length
a2) run 'deepmsa' to generate deep multiple sequence alignment
run 'psiblast' to generate 'chk', 'out', 'pssm', 'mtx' files
run 'PSSpred' to get 'seq.dat', 'seq.dat.ss'
run 'solve' to get 'exp.dat'
run 'pairmod' to get 'pair1.dat' and 'pair3.dat'
a3) run 'restriplet','tripletres','respre','resplm' and 'deepplm' to predicted contact maps
a4) run 'LOMETS2' threading programs sequentially
run 'mkinit.pl' to generate restraints and refine templates with density map
a5) run CR-I-TASSER simulation
a6) run SPICKER clustering program
run 'get_cscore.pl' to get confidence score
run 'EMrefinement.pl' to get full-atomic models
run 'get_rsq_bfp.pl' to get local accuracy and B-factor estimations
b) 'seq.fasta' is the query sequence file in FASTA format, which is the
only needed input file for running CR-I-TASSER. This file should be
put in $datadir before running this job.
c) CR-I-TASSER structure assembly simulations contains multiple independent
runs by decided by protein type. This number can be modified if the user wants to run
more simulations, especially for big protein without good templates.
d) If working on a cluster with multiple nodes, it is recommended to set
$runstyle="parallel". You need have PBS server installed in your system.
Parallel jobs will run faster since jobs are distributed among different
nodes. The default setting $runstyle="serial" will run all the jobs on a
single computer.
e) If the job has been executed partially and encounter some error, you can
rerun the main script without modification. It will check the existing
files and start from the correct position.
4.3 System requirement:
a) x86_64 machine, Linux kernel OS, Free disk space of more than 60G.
b) Perl and java interpreters should be installed. GO:Parser should be installed
if you want to predict GO terms.
c) Basic compress and decompress package should be installed to support:
tar and bunzip2.
d) If you are using computer clusters, job management software PBS server should
support 'qsub' and 'qstat'. If using other job management software, such as
SGE and Slurm, some changes should be made following the instructions at:
http://zhanglab.dcmb.med.umich.edu/bbs/?q=node/3561
4.4. How to cite CR-I-TASSER and CR-I-TASSER Suite?
1. Xi Zhang, Biao Zhang, Yang Zhang. CR-I-TASSER: Assembling Protein Structures from Cryo-EM Density Maps
using Deep Convolutional Neural Networks. In preparation, 2020.
2. Wei Zheng, Yang Li, Chengxin Zhang, Robin Pearce, S. M. Mortuza, Yang Zhang. Deep-learning contact-map
guided protein structure prediction in CASP13. Proteins: Structure, Function, and Bioinformatics, 87: 1149-1164 (2019).
3. Y Zhang. I-TASSER server for protein 3D structure prediction.
BMC Bioinformatics, 9: 40 (2008).
4. A Roy, A Kucukural, Y Zhang. I-TASSER: a unified platform
for automated protein structure and function prediction.
Nature Protocols, 5: 725-738 (2010).
5. J Yang, R Yan, A Roy, D Xu, J Poisson, Y Zhang. The I-TASSER Suite: Protein structure
and function prediction. Nature Methods, 12: 7-8 (2015)
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# 5. Installation and implementation of contact predictors #
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5.1. Introduction of ResTriplet, TripletRes, ResPRE, ResPLM and DeepPLM
TripletRes and ResTriplet are deep-learning based contact predictors
using three co-evolutionary features: the covariance matrix (COV)
proposed by DeepCov; the precision matrix (PRE) formulated by ResPRE;
and the coupling parameters of the inverse Potts model obtained through
pseudolikelihood maximization (PLM).
ResPRE is our in-house contact-map predictor, which consists of two
consecutive steps of precision matrix-based feature generation and
deep residual neural network-based contact inference.
ResPLM is also an in-house contact-map predictor similar to ResPRE.
The only difference is that ResPLM was trained using the PLM feature.
DeepPLM is our in-house contact-map prediction approach that has the
same deep-learning architecture as ResPRE, except it uses different
features that are generated by CCMpred.
5.2. How to install contact programs?
When you unpack the CR-I-TASSER Suite, ResTriplet, TripletRes, ResPRE, ResPLM and DeepPLM programs are already installed.
5.3. How to run contact programs?
The contact main script is $pkgdir/I-TASSERmod/runCONTACTS.pl. The running
option of this program is similar to that in runI-TASSER.pl. By running
the program without argument, you can print all the running options.
5.4. How to cite contact?
If you are using the TripletRes program, you can cite:
Yang Li, Chengxin Zhang, Eric W Bell, Wei Zheng, Dongjun Yu, Yang Zhang.
Deducing high-accuracy protein contact-maps from a triplet of
coevolutionary matrices through deep residual convolutional networks.
submitted, 2020.
If you are using the ResTriplet program, you can cite:
Yang Li, Chengxin Zhang, Eric W. Bell, Dongjun Yu, Yang Zhang.
Ensembling multiple raw coevolutionary features with deep residual
neural networks for contact-map prediction in CASP13.
Proteins: Structure, Function, and Bioinformatics, 87: 1082-1091 (2019).
If you are using the ResPre program, you can cite:
Yang Li, Jun Hu, Chengxin Zhang, Dong-Jun Yu, and Yang Zhang.
ResPRE: high-accuracy protein contact prediction by coupling precision
matrix with deep residual neural networks. Bioinformatics, 35: 4647-4655 (2019).
If you are using the ResPLM and DeepPLM programs, you can cite:
Wei Zheng, Yang Li, Chengxin Zhang, Robin Pearce, S. M. Mortuza, Yang Zhang.
Deep-learning contact-map guided protein structure prediction in CASP13.
Proteins: Structure, Function, and Bioinformatics, 87: 1149-1164 (2019).
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# 6. Installation and implementation of MUSTER #
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6.1. Introduction of MUSTER
MUSTER (MUlti-Sources ThreadER) is a protein threading algorithm to
identify the template structures from the PDB library. It generates
sequence-template alignments by combining sequence profile-profile
alignment with multiple structural information.
6.2. How to install MUSTER program?
When you unpack the CR-I-TASSER Suite, MUSTER program is already installed.
6.3. How to run MUSTER program?
The MUSTER main script is $pkgdir/I-TASSERmod/runMUSTER.pl. The running
option of this program is similar to that in runI-TASSER.pl. By running
the program without argument, you can print all the running options.
6.4. How to cite MUSTER?
If you are using the MUSTER program, you can cite:
S Wu, Y Zhang. MUSTER: Improving protein sequence profile-profile
alignments by using multiple sources of structure information.
Proteins, 72: 547-556 (2008).
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# 7. Installation and implementation of CEthreader #
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7.1. Introduction of CEthreader
CEthreader is a novel threading algorithm, which first predicts
residue-residue contacts by coupling evolutionary precision matrices with
deep residual convolutional neural-networks. The predicted contact maps
are then integrated with sequence profile alignments to recognize
structural templates from the PDB.
7.2. How to install CEthreader program?
When you unpack the CR-I-TASSER Suite, CEthreader program is already installed.
7.3. How to run CEthreader program?
The CEthreader main script is $pkgdir/I-TASSERmod/runCEthreader.pl. The running
option of this program is similar to that in runI-TASSER.pl. By running
the program without argument, you can print all the running options.
7.4. How to cite CEthreader?
If you are using the CEthreader program, you can cite:
W Zheng, Q Wuyun, Y Li, SM Mortuza, C Zhang, R Pearce, J Ruan, Y Zhang.
Detecting distant-homology protein structures by aligning deep
neural-network based contact maps. PLOS Computational Biology, 15:
e1007411 (2019).
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# 8. Installation and implementation of LOMETS2 #
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8.1. Introduction of LOMETS2
LOMETS2 (Local Meta-Threading-Server) is meta-server approach to protein
fold-recognition. It consists of 11 individual threading programs: CEthreader,
mCEthreader, eCEthreader, MUSTER, PPA, dPPA, dPPA2, sPPA, wPPA, wdPPA, wMUSTER.
The mCEthreader and eCEthreader are variances of CEthreader which includes
different scoring functions. The last 7 programs are variances of MUSTER
which includes different optimized energy terms.
8.2. How to install LOMETS2 program?
When you unpack the CR-I-TASSER Suite, LOMETS2 programs are already installed.
8.3. How to run LOMETS2 program?
The LOMETS2 main script is $pkgdir/I-TASSERmod/runLOMETS.pl. The running
option of this program is similar to that in 'runI-TASSER.pl'. By running
the program without argument, you can print all the running options.
8.4. How to cite LOMETS2?
If you are using the LOMETS2 program, you can cite:
Wei Zheng, Chengxin Zhang, Qiqige Wuyun, Robin Pearce, Yang Li, Yang Zhang.
LOMETS2: improved meta-threading server for fold-recognition and
structure-based function annotation for distant-homology proteins.
Nucleic Acids Research, 47: W429-W436 (2019)
S Wu, Y Zhang. LOMETS: A local meta-threading-server for protein
structure prediction. Nucleic Acids Research, 35: 3375-3382 (2007).
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# 9. Installation and implementation of DeepMSA #
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9.1. Introduction of DeepMSA
DeepMSA is a new open-source method for sensitive MSA construction,
which has homolo- gous sequences and alignments created from multi-sources
of whole-genome and metagenome databases through complementary hidden
Markov model algorithms.
9.2. How to install DeepMSA program?
When you unpack the CR-I-TASSER Suite, DeepMSA program is already installed.
9.3. How to run DeepMSA program?
The DeepMSA main script is $pkgdir/contact/DeepMSA/scripts/build_MSA.py. The running
option of this program is similar to that in runI-TASSER.pl. By running
the program without argument, you can print all the running options.
9.4. How to cite DeepMSA?
If you are using the DeepMSA program, you can cite:
C Zhang, W Zheng, S M Mortuza, Y Li, Y Zhang. DeepMSA: constructing
deep multiple sequence alignment to improve contact prediction and
fold-recognition for distant-homology proteins. Bioinformatics 36:
2105-2112 (2020).
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# 10. Installation and implementation of SPICKER #
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10.1. Introduction of SPICKER
SPICKER is a clustering algorithm to identify the near-native models
from a pool of protein structure decoys.
10.2. How to install SPICKER program?
When you unpack the CR-I-TASSER Suite, SPICKER program is already installed
at $pkgdir/I-TASSERmod/spicker45d
10.3. How to run SPICKER program?
To run SPICKER, you need to prepare following input files:
'rmsinp'---Mandatory, length of protein & piece for RMSD calculation;
'seq.dat'--Mandatory, sequence file, for output of PDB models.
'tra.in'---Mandatory, list of trajectory names used for clustering.
In the first line of 'tra.in', there are 3 parameters:
par1: number of decoy files
par2: 1, default cutoff, best for decoys from template-based
modeling;
-1, cutoff based on variation, best for decoys from
ab initio modeling.
par3: 1, closc from all decoys; -1, closc clustered decoys
From second lines are file names which contain coordinates
of 3D structure decoys. All these files are mandatory. See
attached 'rep1.tra1' for the format of decoys.
'CA'-------Optional, native structure, for comparison to native.
Output files of SPICKER include:
'str.txt'-----list of structure in cluster;
'combo*.pdb'--PDB format of cluster centroids;
'closc*.pdb'--PDB format of structures closest to centroids;
'rst.dat'-----summary of clustering results;
A detailed readme file can be found at
http://zhanglab.dcmb.med.umich.edu/SPICKER/readme
10.4. How to cite SPICKER?
If you are using the SPICKER program, you can cite:
Y Zhang, J Skolnick, SPICKER: Approach to clustering protein structures
for near-native model selection, Journal of Computational Chemistry,
25: 865-871 (2004).
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# 11. Installation and implementation of HAAD #
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11.1. Introduction of HAAD
HAAD is a computer algorithm for constructing hydrogen atoms from
protein heavy-atom structures. The hydrogen is added by minimizing
atomic overlap and encouraging hydrogen bonding.
11.2. How to install HAAD program?
When you unpack the CR-I-TASSER Suite, HAAD program is already installed
at $pkgdir/abs/mybin/HAAD
11.3. How to run HAAD program?
Hydrogen atoms in a PDB file(xx.pdb) can be added by running
"./HAAD xx.pdb", the output is "xx.pdb.h".
In "xx.pdb.h", the label in column 57 presents the label for the atoms
that have been added by HAAD. When the value of the label is less
than 2, the position of the added atom has higher confidence.
11.4. How to cite HAAD?
If you are using the HAAD program, you can cite:
Y Li, A Roy, Y Zhang, HAAD: A Quick Algorithm for Accurate Prediction
of Hydrogen Atoms in Protein Structures, PLoS One, 4: e6701 (2009).
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# 12. Installation and implementation of EDTSurf #
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12.1. Introduction of EDTSurf
EDTSurf is a program to construct triangulated surfaces for macromolecules.
It generates three major macromolecular surfaces: van der Waals surface,
solvent-accessible surface and molecular surface (solvent-excluded
surface). EDTsurf also identifies cavities which are inside of
macromolecules.
12.2. How to install EDTSurf program?
When you unpack the CR-I-TASSER Suite, EDTSurf program is already installed
at $pkgdir/bin/EDTSurf
12.3. How to use EDTSurf program?
EDTSurf -i inputfile ...
Specific options:
-o prefix of output files (default is the prefix of inputfile)
-t triangulation type, 1-MC 2-VCMC (default is 2)
-s surface type, 1-VWS 2-SAS 3-MS (default is 3)
-c color mode, 1-pure 2-atom 3-chain (default is 2)
-p probe radius, float point in [0,2.0] (default is 1.4)
-h inner or outer surface for output, 1-inner and outer 2-outer
3-inner (default is 1)
-f scale factor, float point in (0,20.0] (default is 4.0)
Molecule is scaled by this factor to fit in a bounding box. Scale
factor is the larger the better, but will increase the memory use.
Our strategy is first enlarging the molecule to check if it exceeds
the maximum bounding box. If yes, then reset a proper scale factor
to fit the molecule in the maximum bounding box.
By running EDTSurf itself, it will print out a brief description on how
to use the program. A detail description of EDTSurf is available at
http://zhanglab.dcmb.med.umich.edu/EDTSurf/
12.4. How to cite EDTSurf?
If you are using the EDTSurf program, you can cite:
D Xu, Y Zhang, Generating Triangulated Macromolecular Surfaces by Euclidean
Distance Transform. PLoS ONE 4: e8140 (2009).
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# 13. Installation and implementation of ModRefiner #
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13.1. Introduction of ModRefiner
ModRefiner is a standalone program for atomic-level protein structure
construction and refinement. It includes two steps: (1) construct
main-chain models from C-alpha trace; (2) build side-chain models
and atomic-level structure refinement.
13.2. How to install ModRefiner program?
When you unpack the CR-I-TASSER Suite, ModRefiner program is already installed
at $pkgdir/I-TASSERmod/ModRefiner.pl
13.3. How to use ModRefiner program?
ModRefiner supports following four options:
a) add side-chain heavy atoms to main-chain model without refinement
> ModRefiner.pl 1 ID MD IM ON
b) build main-chain model from C-alpha trace model
> ModRefiner.pl 2 ID MD IM RM ON
c) build full-atomic model from main-chain model
> ModRefiner.pl 3 ID MD IM RM ON
d) build full-atomic model from C-alpha trace model
> ModRefiner.pl 4 ID MD IM RM ON
ID: the path of the CR-I-TASSER package, e.g. '/home/yourname/CR-I-TASSER-1.0'
MD: directory which contains the initial model, e.g. '/home/yourname/CR-I-TASSER/5.0/example'
IM: the initial model to be refined, e.g. 'mode1.pdb'
RM: reference model that refined model is driven to, e.g. 'combo1.pdb'.
Only CA trace is needed and the length can be not full which will make
the refinement of the missing region flexible. If you don't have the
reference model, use the name of IM instead.
ON: the output name of the refined model, e.g. 'model1_ref.pdb'
By running the program without argument, you can print a brief description
of how to use the program.
13.4. How to cite ModRefiner?
If you are using the ModRefiner program, you can cite:
D Xu, Y Zhang. Improving the Physical Realism and Structural Accuracy of
Protein Models by a Two-step Atomic-level Energy Minimization.
Biophysical Journal, 101: 2525-2534 (2011)
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# 14. Installation and implementation of NWalign #
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14.1. Introduction of NWalign
NW-align is simple and robust alignment program for protein
sequence-to-sequence alignments based on the standard Needleman-Wunsch
dynamic programming algorithm. The mutation matrix is from BLOSUM62
with gap opening penalty=-11 and gap extension penalty=-1.
14.2. How to install NWalign program?
When you unpack the CR-I-TASSER Suite, NWalign program is already installed
at $pkgdir/bin/align.
14.3. How to use NWalign program?
> align F1.fasta F2.fasta (align two sequences in fasta file)
> align F1.pdb F2.pdb 1 (align two sequences in PDB file)
> align F1.fasta F2.pdb 2 (align Sequence 1 in fasta and 2 in pdb)
> align GKDGL EVADELVSE 3 (align sequences typed by keyboard)
> align GKDGL F.fasta 4 (align Seq-1 by keyboard and 2 in fasta)
> align GKDGL F.pdb 5 (align Seq-1 by keyboard and 2 in pdb)
By running the program itself, it will print out the usage options of
the program.
14.4. How to cite NWalign?
There is no published paper associated with this program. If you are using
the NWalign program, you can cite it as
Y Zhang, http://zhanglab.dcmb.med.umich.edu/NW-align
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# 15. Installation and implementation of PSSpred #
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15.1 Introduction of PSSpred
PSSpred (Protein Secondary Structure PREDiction) is a simple neural network
training algorithm for accurate protein secondary structure prediction. It first
collects multiple sequence alignments using PSI-BLAST. Amino-acid frequency and
log-odds data with Henikoff weights are then used to train secondary structure,
separately, based on the Rumelhart error back propagation method. The final
secondary structure prediction result is a combination of 7 neural network
predictors from different profile data and parameters.
15.2 How to install PSSpred program?
When you unpack the CR-I-TASSER Suite, NWalign program is already installed
at $pkgdir/PSSpred
15.3 How to use PSSpred program?
$pkgdir/PSSpred/mPSSpred.pl seq.txt $pkgdir $libdir
Please note that 'seq.txt' should be in current directory and the script will
generate two files 'seq.dat' and 'seq.dat.ss' in the current folder. Here,
$pkgdir is the root path of CR-I-TASSER package.
15.4 How to cite PSSpred?
If you are using the PSSpred program, you can cite:
http://zhanglab.dcmb.med.umich.edu/PSSpred
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# 16. Installation and implementation of ResQ #
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16.1 Introduction of ResQ
ResQ is a method for estimating B-factor and residue-level quality in protein
structure prediction, based on local variations of modelling simulations and
the uncertainty of homologous alignments. Given a protein structure model,
ResQ first identifies a set of homologous and/or analogous templates from
the PDB by threading and structure alignment techniques. The residue-level
modeling errors are then derived by support vector regression that was
trained on the local structural and alignment variations of the templates,
with the B-factor of each residue deduced from the experimental records of
the top homologous proteins.
16.2 How to install ResQ program?
When you unpack the CR-I-TASSER Suite, ResQ program is already installed at
$pkgdir/ResQ.
16.3 How to use ResQ program?
There are two methods to run ResQ depending on how your models were generated.
1) If your models were generated by CR-I-TASSER, you can run the script of
$pkgdir/ResQ/runResQ_IT.pl to predict B-factor and local structure errors.
The only argument required is the directory of the CR-I-TASSER decoys. You
can read more at the head of this script to get more information about
its input.
2) If your models were not generated by CR-I-TASSER, you can run the script
$pkgdir/ResQ/runResQ.pl to predict B-factor and local structure errors.
It will automatically run LOMETS2 to generate the threading alignment
file 'init.dat'. LOMETS2 is included in this package.
16.4 What is the output of ResQ?
For CR-I-TASSER models, the output of ResQ is:
rsq_bfp_new.dat
For other models, the output of ResQ is:
1) global.txt for global accuracy estiamtion
2) local.txt for local error and B-factor estimation
16.5 How to cite ResQ?
If you are using the ResQ program, you can cite:
1. J Yang, Y Wang, Y Zhang. ResQ: Approach to unified estimation of B-factor
and residue-specific error in protein structure prediction,
Journal of Molecular Biology, 428: 693-701 (2016).