INSTALLATION AND IMPLEMENTATION OF I-TASSER-MTD SUITE (Copyright 2021 by Zhang Lab, University of Michigan, All rights reserved) (Version 1.0, 2021/09/16) 1. What is I-TASSER-MTD Suite? The I-TASSER-MTD (previously called I-TASSER-MD) Suite is a composite package of programs for protein structure modeling from cryo-EM denisty maps. The Suite includes the following programs: a) I-TASSER-MTD: A hierarchical program for multi-domain protein structure modeling b) FUpred: A deep-learning based program for domain boundary prediction c) ThreaDom: A threading template based program for domain boundary prediction d) DeepMSA: A program for multiple sequence alignmnet generation e) DeepPotential: A deep residual neural-network algorithm for inter-residue spatial restraints prediction f) D-I-TASSER: A single-domain protein structure modeling algorithm using deep leraning predicted spatial restraints g) DEMO: A program for multi-domain protein structure assembly h) LOMETS2: A meta-approach included in I-TASSER for threading templates identification i) FASPR: A program for protein side-chain packing 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) COACH: A function annotation program based on COFACTOR, TM-SITE and S-SITE n) COFACTOR: A program for ligand-binding site, EC number & GO term prediction 2. How to install the I-TASSER-MTD Suite? a) download the I-TASSER-MTD Suite 'I-TASSER-MD-1.0.tar.gz' from https://zhanggroup.org/I-TASSER-MD/download/ and unpack 'I-TASSER-MD-1.0.tar.gz by > tar -zxvf I-TASSER-MD-1.0.tar.gz The root path of this package is called $pkgdir, e.g. /home/yourname/I-TASSER-MD-1.0. You should have all the programs under this directory. You can install the package at any location on your computer. b) Download I-TASSER-MD library files from https://zhanggroup.org/I-TASSER-MD/download/ A script 'download_lib.pl' is provided in the package for automated library download and update of the libraries. The library needs about 150GB of the disk space. 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 'I-TASSER-MD-1.0.tar.gz' 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 I-TASSER-MD 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/ DeepMSA/bin/esl-sfetch to create .ssi index for uniref90 and metaclust, here $pkgdir means the path where you put the I-TASSER-MD 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/run_I-TASSER-MD.py, 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 the message board: https://zhanggroup.org/forum/ ####################################################### # # # 4. Installation and implementation of I-TASSER-MD # # # ####################################################### 4.1. Introduction of I-TASSER-MD I-TASSER-MD is a hierarchical protocol to predict structures and functions of multi-domain proteins. It first predicts the domain bounaries by FUpred and ThreaDom based on the deep learning contact map prediction and the multiple template alignment. Meanwhile, residue- residue spatial restraints are generated by the deep convolutional neural-network according to the multiple sequence alignment constructed from the whole-genome and metagenome databases. Model of each inividual domain is then independently constructured by I-TASSER guided by the deep learning predicted spatial restraints. Next, the inividual domain models are assembled into full-length structure by DEMO under the guidance of knowledge-based inter-domain protentials and deep-learning distance profiles. Finally, the protein function on both domain level and full-chain level are annotated by COFACTOR based on structures, sequences, and protein-protein interaction networks. Large-scale benchmark tests have shown significant advantage of I-TASSER-MD over traditional protein structure prediction methods for high-accuracy multi-domain protein structure modeling. 4.2. How to run I-TASSER-MD? a) Main script for running I-TASSER-MD is $pkgdir/run_I-TASSER-MD.py, where "$pkgdir" is the location of run_I-TASSER-MD.py script. Run it directly without arguments will output the help information. b) The following arguments must be set (mandatory arguments). One example is: "$pkgdir/run_I-TASSER-MD.py protein_name input_dir sequence [Options]" 'protein_name' is the name of the folder containg the protein sequence and cryo-EM density map 'input_dir' is the directory which contains the query folder 'sequence' is the directory of your query sequence c) Other arguments are optional whose default values have been set. User can reset one or more of them. One example of command line is: "$pkgdir/run_I-TASSER-MD.py protein_name input_dir sequence -template XXX.pdb" -template Provide the template strcuture to guide the domain assembly. The tmeplate should be in PDB format. -deepdist [no or yes], flag of predicted distance by DomainDist to guide the assembly. The default value is "yes". -EMmap The cryo-EM density map in MRC or CCP4 format. -reso The resolution of the density map. -CLink The cross link data (follw the format provided on websever). -expdom Provide the experimental domain information including domain definition and PDB domain models if some experimental domain models are available. See the websever or README for the explanation of the format. -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 -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 -run [real, benchmark],"real" will use all templates, "benchmark" will exclude homologous templates -libdir means the path of the template libraries for I-TASSER. The default directory is "$pkgdir/ITLIB". You must use this option to change the path if you did not put it in the default directory. -java_home means the path contains the java executable "bin/java" (your system needs to have Java installed) -python2 path to python 2, for example /usr/bin/python -python3 path to python 3 for distance prediction, need to support pytorch 1.1.0, for example /usr/bin/python3 d) Where are the final predicted results?     The following results are included in "/input_dir/protein_name": "model*.pdb" the final model created by I-TASSER-MD "emmodel*.pdb" the final model refined by ModRefiner "dom*.pdb" the domain model predicted by D-I-TASSER "FUpred.info" the predicted domain boundary "seq.ss" the secondary structure predicted by PSSpred "cscore" the confidence score, estimated TM-score, and estimated RMSD of the final model NOTE: a) Outline of steps for running I-TASSER-MD by 'run_I-TASSER-MD.py': a1) Prase user provided information a2) run 'DeepPotential' to predict inter-residue spatial restraints of the full-chain a3) run 'LOMETS2' to determine the protein type a3) run 'ThreaDom' or 'FUpred' to predict the domain boundary a4) run 'D-I-TASSER' to predict the model of each domain. If the protein is predicted as a single-domain protein, the full-length model will be directly generated by D-I-TASSER a5) run 'DEMO' to assemble all domain models into a full-length model a6) run run 'COACH' and 'COFACTOR' to generate ligand-binding sites, EC number and GO terms predictions. b) 'seq.fasta' is the query sequence file in FASTA format, which is the only needed input file for running I-TASSER-MD. This file should be put in "./input_dir/protein_name" before running this job. c) 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. d) 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. e) If you want to provide the cryo-EM density data to guide the assembly, please use the option "-EMmap" and "-reso" and follw the explanation and example at https://zhanggroup.org/I-TASSER-MD/explanation_EM.html f) If you want to provide the cross link data or contact/distance to guide the assembly, please use the option "CLink" and follw the explanation and example at https://zhanggroup.org/I-TASSER-MD/explanation_CL.html g) If you want to provide the experimental models for some domains, please prepare the file in the following format: The file starts with the domain definition of the query sequence in the first line. The experimental domain information starts from the second line with the residue index range of the domain wirten in the first line. See the detailed explanation and example at https://zhanggroup.org/I-TASSER-MD/explanation_expdom.html 4.3 System requirement: a) x86_64 machine, Linux kernel OS, Free disk space of more than 150G. b) Perl, python, and java interpreters should be installed. 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: https://zhanggroup.org/bbs/?q=node/3561 4.4. How to cite I-TASSER-MD and I-TASSER-MD Suite? Xiaogen Zhou, Wei Zheng, Yang Li, Robin Pearce, Chengxin Zhang, Eric W. Bell, Guijun Zhang, and Yang Zhang. I-TASSER-MD: A deep-learning based platform for multi-domain protein structure and function prediction. Submitted, 2021. Xiaogen Zhou, Jun Hu, Chengxin Zhang, Guijun Zhang, and Yang Zhang. Assembling multidomain protein structures through analogous global structural alignments. Proceedings of the National Academy of Sciences, 116: 15930-15938 (2019) ####################################################### # # # 5. Installation and implementation of FUpred # # # ####################################################### 5.1. Introduction of FUpred FUpred is a contact map-based domain prediction method which utilizes a recursion strategy to detect domain boundary based on predicted contact-map and secondary structure information. Large scale benchmark analysis shows that FUpred has significantly better ability of domain boundary prediction than threading-based method and machine learning-based methods. Particularly, our method has obviously excellent performance in detecting discontinuous domain boundary than current methods. 5.2. How to install FUpred program? When you unpack the I-TASSER-MD Suite, the FUpred program is already installed in $pkgdir/FUpredmod. 5.3. How to run FUpred program? Usage: $pkgdir/FUpredmod/run_FUpred.pl protein_name input_dir To run FUpred, you need to prepare following input files: 'protein_name'--Mandatory, the name of the folder containg the sequence and density map 'input_dir'-----Mandatory, the directory which contains the query folder Output files of FUpred include: 'FUpred.info'---The predicted domain boundary 'FUpred.2c'-----The FUscore for continuous domain detection 'FUpred.2d'-----The FUscore for discontinuous domain detection A detailed readme file can be found at https://zhanglab.dcmb.med.umich.edu/FUpred/download/FUpred/readme.txt 5.4. How to cite FUpred? If you are using the FUpred program, you can cite: Wei Zheng, Xiaogen Zhou, Qiqige Wuyun, Robin Pearce, Yang Li and Yang Zhang. FUpred: Detecting protein domains through deep-learning based contact map prediction. Bioinformatics, 36: 3749–3757, 2020. ####################################################### # # # 6. Installation and implementation of ThreaDom # # # ####################################################### 6.1. Introduction of ThreaDom ThreaDom (Threading-based Protein Domain Prediction) is a template-based algorithm for protein domain boundary prediction. Given a protein sequence, ThreaDom first threads the target through the PDB library to identify protein template that have similar structure fold. A domain conservation score (DCS) will be calculated for each residue which combines information from template domain structure, terminal and internal gaps and insertions. Finally, the domain boundary information is derived from the DCS profile distributions. ThreaDom is designed to predict both continuous and discontinuous domains. 6.2. How to install ThreaDom program? When you unpack the I-TASSER-MD Suite, the ThreaDom program is already installed in $pkgdir/ThreaDommod. 6.3. How to run ThreaDom program? Usage: $pkgdir/ThreaDommod/runThreaDom.pl protein_name input_dir -libdir libdir To run ThreaDom, you need to prepare following input files: 'protein_name'--Mandatory, the name of the folder containg the sequence and density map 'input_dir'-----Mandatory, the directory which contains the query folder 'libdir'--------Mandatory, the path of the template libraries Output file of ThreaDom include: 'protein_name.sd'---The predicted domain boundary A detailed readme can be found in th package. 6.4. How to cite ThreaDom? If you are using the ThreaDom program, you can cite: Yan wang, Jian Wang, Qiang Shi, Ruiming Li，Zhidong Xue, Yang Zhang. ThreaDomEx: a unified platform for predicting continuous and discontinuous protein domains by multiple-threading and segment assembly. Nucleic acids research. 45: W400-407, 2017. ####################################################### # # # 7. Installation and implementation of DeepMSA # # # ####################################################### 7.1. 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. 7.2. How to install DeepMSA program? When you unpack the I-TASSER-MD Suite, DeepMSA program is already installed. 7.3. 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. 7.4. 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). ####################################################### # # # 8. Installation and implementation of DeepPotential# # # ####################################################### 8.1. Introduction of DeepPotential DeepPotential is a method to predict the inter-residue spatial restraints including distances, inter-residue torsion angles, and hydrogen-bonding networks based on the ensemble of two complementary coevolution features coupling with deep residual networks. 8.2. How to install DeepPotential? When you unpack the I-TASSER-MD Suite, the DeepPotential program is already installed in $pkgdir/distance/DeepPotential. 8.3. How to run DeepPotential program? Usage: runDistPre.pl -s protein_name -outdir input_dir [Options] To run DeepPotential, you need to prepare following input files: 'protein_name'--Mandatory, the name of the folder containg the sequence named as "seq.txt" 'input_dir'-----Mandatory, the directory which contains the query folder Output file of DeepPotential include: 'distance_pca_*.txt'---The predicted CA atom distance 'distance_pcb_*.txt'---The predicted CB atom distance 'distance_pomg_20.txt, distance_pphi_20.txt, and distance_ptheta_20.txt'---The predicted torsion angles 'distance_paa_.txt, distance_pbb_.txt, distance_pcc_.txt'---The predicted hydrogen-bonding networks 'distance_ca_contact.txt'---The predicted CA contact 'distance_cb_contact.txt'---The predicted CB contact A detailed readme can be found in th package. 8.4. How to cite DeepPotential? If you are using the DeepPotential program, you can cite: Li Yang, Zhang Chengxin, Zheng Wei, Zhou Xiaogen, Bell W. Eric, Yu Dongjun and Zhang Yang, Protein inter-residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14. Proteins: Structure, Function, and Bioinformatics, doi:https://doi.org/10.1002/prot.26211, 2021. ####################################################### # # # 9. Installation and implementation of D-I-TASSER # # # ####################################################### 9.1. Introduction of D-I-TASSER I-TASSER (Iterative Threading ASSEmbly Refinement) is a method for high-accuracy protein structure and function prediction. Starting from a query sequence, I-TASSER first generates inter-residue restraints by multiple deep neural-network predictors. It then identifies structural templates from the PDB by multiple threading approach LOMETS2, with full-length atomic models assembled by DeepPotential spatial restraints guided replica-exchange Monte Carlo simulations. 9.2. How to install I-TASSER program? When you unpack the I-TASSER-MD Suite, the D-I-TASSER program is already installed in $pkgdir/I-TASSERmod. 9.3. How to run I-TASSER program? The I-TASSER main script is $pkgdir/I-TASSERmod/runD-I-TASSER.pl. The running option of this program is similar to run_I-TASSER-MD.py. By running the program without argument, you can print all the running options. A detailed readme can be found in th package. 9.4. How to cite I-TASSER? If you are using the I-TASSER program, you can cite: 1. Wei Zheng, Chengxin Zhang, Yang Li, Robin Pearce, Eric W. Bell, Yang Zhang. Folding non-homology proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Reports Methods, 1: 100014 (2021). 2. Y Zhang. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, 9: 40 (2008). 3. A Roy, A Kucukural, Y Zhang. I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols, 5: 725-738 (2010). 4. 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) ####################################################### # # # 10. Installation and implementation of DEMO # # # ####################################################### 10.1. Introduction of DEMO DEMO (Domain Enhanced MOdeling) is a method for automated assembly of full-length structural models of multi-domain proteins. Starting from individual domain structures, DEMO first identify quaternary structure templates that have similar component domains by domain-level structural alignments using TM-align. Replica-exchange Monte Carlo simulations are then used to assemble full-length models, as guided by the inter-domain distance profiles collected from the top-ranked quaternary templates. The final models with the lowest energy are selected from Monte Carlo trajectories, followed by atomic-level refinments using fragment-guided molecular dynamics simulations. DEMO can be used to assemble domains from either experimental or predicted models for proteins with both continuous and discontinuous domain architectures. 10.2. How to install DEMO program? When you unpack the I-TASSER-MD Suite, DEMO programs are already installed. 10.3. How to run DEMO program? Usage: $pkgdir/DEMOmod/DEMO sequence domain_folder [Options] To run DeepPotential, you need to prepare following input files: 'sequence'-------Mandatory, the full-length sequence of the target 'domain_folder'--Mandatory, the directory which contains the domain model named as "dom1.pdb, dom2.pdb,..." Output file of DEMO include: 'fmodel*.pdb'----The full-length model assembled by DEMO 10.4. How to cite DEMO? If you are using the DEMO program, you can cite: Xiaogen Zhou, Jun Hu, Chengxin Zhang, Guijun Zhang, and Yang Zhang. Assembling multidomain protein structures through analogous global structural alignments. Proceedings of the National Academy of Sciences, 116: 15930-15938 (2019) ####################################################### # # # 11. Installation and implementation of LOMETS2 # # # ####################################################### 11.1. 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. 11.2. How to install LOMETS2 program? When you unpack the I-TASSER-MD Suite, LOMETS2 programs are already installed. 11.3. 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. 11.4. 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). ####################################################### # # # 12. Installation and implementation of FASPR # # # ####################################################### 12.1. Introduction of FASPR FASPR is a method for structural modeling of protein side-chain conformations. Starting from a backbone structure, FASPR samples the side-chain rotamers for each amino acid from the Dunbrack 2010 rotamer library with the atomic interaction energies calculated using an optimized scoring function extended from EvoEF2, where side-chain packing search is performed using a deterministic searching algorithm combining self-energy checking, dead-end elimination theorems, and tree decomposition. 12.2. How to install FASPR program? When you unpack the I-TASSER-MD Suite, FASPR program is already installed at $pkgdir/Assbmod/bin/FASPR 12.3. How to run FASPR program? Usage: FASPR input.pdb output.pdb To run FASPR, you need to prepare following input files: 'input.pdb' Mandatory, input pdb file for side-chain packing. '-s' Optional, the sequence of the input.pdb Output files of FASPR include: 'output.pdb' output pdb file of the FASPR with side-chain packaged. A detailed readme file can be found in the FASPR package 12.4. How to cite FASPR? If you are using the FASPR program, you can cite: Xiaoqiang Huang, Robin Pearce, Yang Zhang. FASPR: an open-source tool for fast and accurate protein side-chain packing. Bioinformatics (2020) 36: 3758-3765. ####################################################### # # # 13. Installation and implementation of ModRefiner # # # ####################################################### 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 I-TASSER-MD 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 I-TASSER-MD package, e.g. '/home/yourname/I-TASSER-MD-1.0' MD: directory which contains the initial model, e.g. '/home/yourname/I-TASSER-MD/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) ####################################################### # # # 14. Installation and implementation of NWalign # # # ####################################################### 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 I-TASSER-MD 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 ####################################################### # # # 15. Installation and implementation of PSSpred # # # ####################################################### 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 I-TASSER-MD 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 I-TASSER-MD package. 15.4 How to cite PSSpred? If you are using the PSSpred program, you can cite: http://zhanglab.dcmb.med.umich.edu/PSSpred ####################################################### # # # 16. Installation and implementation of COFACTOR # # # ####################################################### 16.1 Introduction of COFACTOR COFACTOR is a structure-based method for biological function annotation of protein molecules. COFACTOR threads the structure through three comprehensive function libraries by local and global structure matches to identify functional sites and homology. Functional insights, including ligand-binding site, gene-ontology terms and enzyme classification, will be derived from the best functional homology template. The COFACTOR algorithm was ranked as the best method for function prediction in the community-wide CASP9 experiments. 16.2 How to install COFACTOR program? When you unpack the C-I-TASSER Suite, COFACTOR program is already installed at $pkgdir/COFACTOR 16.3 How to use COFACTOR program? $pkgdir/I-TASSERmod/runCOFACTOR.pl 16.4 How to interpret the results If your input data is at $datadir/model1.pdb, the output of COFACTOR will be at $datadir/model1/cofactor: (1)List of similar structures in PDB: similarpdb_model1.lst. The columns are (PDB_ID, TM-score, RMSD, Cov, Seq_id) (2)Ligand-binding sites: BSITE_model1/Bsites_model1.dat. The columns are (Rank, C-score, PDB_ID, TM-score, RMSD, Seq_id, Cov, Lig_name, SITE_num, BS-score, LTM, BS_ID, BS_cov,BS_err, BS_ID1,BS_ID2, Binding residues) (3)EC number: ECsearchresult_model1.dat The columns are (PDB_ID, TM-score, RMSD, Seq_ID, Cov, EC-score, EC number, Active site residues) (4)GO terms: GOsearchresult_model1.dat. The columns are (PDB_ID, TM-score, RMSD, Seq_ID, Cov, GO-score, GO terms) 16.5 How to cite COFACTOR? If you are using the COFACTOR program, you can cite: 1. A Roy, J Yang, Y Zhang. COFACTOR: An accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Research, 40:W471-W477 (2012). 2. J Yang, A Roy, Y Zhang. BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions. Nucleic Acids Research, 41: D1096-D1103 (2013). ####################################################### # # # 17. Installation and implementation of COACH # # # ####################################################### 17.1 Introduction of COACH COACH is a meta-server approach to protein function annotations. Starting from given structure of target proteins, COACH will generate complementary ligand binding site predictions using two comparative methods: TM-SITE and S-SITE, which recognize ligand-binding templates from the BioLiP protein function database by binding-specific substructure and sequence profile comparisons. These predictions will be combined with results from COFACTOR to generate multiple function annotations, including ligand-binding sites, enzyme commission and gene ontology terms. 17.2 How to install COACH program? When you unpack the C-I-TASSER Suite, COACH program is already installed at $pkgdir/COACH 17.3 How to use COACH program? $pkgdir/I-TASSERmod/runCOACH.pl 17.4 How to interpret the results If your input data is at $datadir/model1.pdb, the output of COACH will be at $datadir/model1/coach: (1) Ligand-binding sites: Bsites.dat. The columns are (C-score, cluster_densitiy, product_of_top_templates_zscore, Binding residues) (2) Detailed clustering information: Bsites.inf, Bsites.clr, which list the templates used in the cluster that generates the prediction in (1). (3) Ligand-protein complex structures are with name: CH_complex*.pdb (4) Predicions from COFACTOR, TM-SITE, and S-SITE are at, respectively: $datadir/model1/cofactor $datadir/model1/tmsite $datadir/ssite 17.5 How to cite COACH? If you are using the COACH program, you can cite: 1. J Yang, A Roy, Y Zhang. Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics, 29:2588-2595 (2013). 2. J Yang, A Roy, Y Zhang. BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions. Nucleic Acids Research, 41: D1096-D1103 (2013).