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I-TASSER I-TASSER-MTD C-I-TASSER CR-I-TASSER QUARK C-QUARK LOMETS MUSTER CEthreader SEGMER DeepFold DeepFoldRNA FoldDesign COFACTOR COACH MetaGO TripletGO IonCom FG-MD ModRefiner REMO DEMO DEMO-EM SPRING COTH Threpp PEPPI BSpred ANGLOR EDock BSP-SLIM SAXSTER FUpred ThreaDom ThreaDomEx EvoDesign BindProf BindProfX SSIPe GPCR-I-TASSER MAGELLAN ResQ STRUM DAMpred

TM-score TM-align US-align MM-align RNA-align NW-align LS-align EDTSurf MVP MVP-Fit SPICKER HAAD PSSpred 3DRobot MR-REX I-TASSER-MR SVMSEQ NeBcon ResPRE TripletRes DeepPotential WDL-RF ATPbind DockRMSD DeepMSA FASPR EM-Refiner GPU-I-TASSER

BioLiP E. coli GLASS GPCR-HGmod GPCR-RD GPCR-EXP Tara-3D TM-fold DECOYS POTENTIAL RW/RWplus EvoEF HPSF THE-DB ADDRESS Alpaca-Antibody CASP7 CASP8 CASP9 CASP10 CASP11 CASP12 CASP13 CASP14

The Zhang Lab On-line Service System contains:

Questions and issues can be reported and discussed in the Service System Discussion Board.


I. Protein Structure and Function Prediction Services (folding, threading, potential, contact, torsion, docking etc)

      Introduction: I-TASSER server is an Internet service for protein structure and function predictions. Models are built based on multiple-threading alignments by LOMETS and iterative TASSER simulations. I-TASSER (as 'Zhang-Server') was ranked as the No 1 server in recent CASP7 and CASP8 experiments. The server is in active development with the goal to provide accurate structural and function predictions using state-of-the-art algorithms.
      References:
      • Ambrish Roy, Alper Kucukural, Yang Zhang. I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols, vol 5, 725-738 (2010). (download the PDF file).
      • Yang Zhang. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, vol 9, 40 (2008). (download the PDF file).




      Introduction: I-TASSER-MTD is multi-domain version of I-TASSER. For a given sequence, it first predicts the domain boundaries by FUpred and ThreaDom based on the deep-learning contact-map prediction and multiple threading alignments. Next, the structure model of each individual domain is constructed independently by I-TASSER guided by the deep learning predicted spatial restraints. Finally, the individual domain models are assembled into full-length structure by DEMO under guidance of quaternary structural templates and deep-learning distance profiles. Meanwhile, the protein functions at both domain level and full-chain level are annotated by COFACTOR based on structures, sequences, and protein-protein interaction networks.
      References:
      • Xiaogen Zhou, Wei Zheng, Yang Li, Robin Pearce, Chengxin Zhang, Eric W. Bell, Guijun Zhang, and Yang Zhang. I-TASSER-MTD: A deep-learning based platform for multi-domain protein structure and function prediction, Nature Protocols, in press, 2022.




      Introduction: C-I-TASSER server is an extension of I-TASSER for contact-assisted protein structure and function predictions. By integrating deep-learning contact-maps, C-I-TASSER provides more accurate structure predictions than I-TASSER, especially for the targets that lack homologous templates in the PDB.
      References:
      • 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). [PDF] [Support Information]




      Introduction: QUARK is a computer algorithm for ab initio protein folding and protein structure prediction, which aims to construct the correct protein 3D model from amino acid sequence only. QUARK models are built from a small fragments (1-20 residues long) by replica-exchange Monte Carlo simulation under the guide of an atomic-level knowledge-based force field. QUARK was ranked as the No 1 server in Free-modeling (FM) in CASP9. Since no global template information is used in QUARK simulation, the server is suitable for proteins which are considered without homologous templates.
      References:
      • D. Xu, Y. Zhang, Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins, 2012, 80: 1715-1735 (download the PDF file and Support Information).




      Introduction: C-QUARK is an extension of QUARK for contact-assisted ab initio protein folding and protein structure prediction. By integrating deep-learning contact-maps, C-QUARK can provide more accurate 3D structure modeling than QUARK for nearly all test cases.
      References:
      • S. M. Mortuza, Wei Zheng, Chengxin Zhang, Yang Li, Robin Pearce, Yang Zhang. C-QUARK: Template-free protein structure modeling using low-accuracy contact-map prediction. Nature Communications, in press, 2021.




      Introduction: LOMETS (Local Meta-Threading-Server) is a locally installed meta-server for protein structure prediction. It generates 3D models by collecting consensus target-to-template alignments from 9 locally-installed threading programs (FUGUE, HHsearch, PAINT, PPA-I, PPA-II, PROSPECT2, SAM-T02, SPARKS, SP3).
      References:
      • S. Wu, Y. Zhang. LOMETS: A local meta-threading-server for protein structure prediction. Nucleic Acids Research 2007; 35: 3375-3382 (download the PDF file).




      Introduction: COACH is a meta-server approach to protein-ligand binding site prediction. Starting from given structure of target proteins, COACH will generate complementray ligand binding site predictions using two comparative methods, TM-SITE and S-SITE, which recognize ligand-binding templates from the BioLiP database by substructure and binding-specific sequence-profile comparisons. These predictions will be combined with results from other methods (including COFACTOR, FINDSITE and ConCavity to generate final ligand binding site predictions. Users are also allowed to input primary sequence, where I-TASSER will be used to generate 3D models first which are then fed into the COACH pipeline for ligand-binding site prediction.
      References:
      • Jianyi Yang, Ambrish Roy, and Yang Zhang. Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment, Bioinformatics, 29:2588-2595 (2013). [PDF] [Support Information] [Server]




      Introduction: COFACTOR is an automated method for biological function annotation of protein molecules, based on protein 3D structures. When user provides a structure model of the target protein, COFACTOR will match the target proteins to the known proteins (templates) in three comprehensive protein function libraries by global and local structure comparisons. Functional insights, including ligand-binding site, gene-ontology term, and enzyme classification, are then derived from the best template proteins of the highest confidence score (C-score). The COFACTOR algorithm was ranked as the best method for ligand-binding site predictions in the community-wide CASP9 experiments.
      References:
      • Ambrish Roy, Jianyi Yang, and Yang Zhang. COFACTOR: An accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Research, 40:W471-W477 (2012). (download the PDF file)
      • Ambrish Roy, Yang Zhang. Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement. Structure, 20: 987-997 (2012) (download the PDF file and Support Information)
      • Chengxin Zhang, Peter L. Freddolino, Yang Zhang COFACTOR: improved protein function prediction by combining structure, sequence, and protein-protein interaction information. Nucleic Acids Research, 45: W291-299 (2017). (download the PDF file and Support Information)




      Introduction: MetaGO is an algorithm for predicting Gene Ontology (GO) of proteins. It consists of three pipelines to detect functional homologs through local and global structure alignments, sequence and sequence profile comparison, and parter's-homology based protein-protein interaction mapping. The final function insights are a combination of the three pipelines through logistic regression.
      References:
      • Chengxin Zhang, Peter L. Freddolino, and Yang Zhang. MetaGO: Predicting Gene Ontology of non-homologous proteins through low-resolution protein structure prediction and protein-protein network mapping. Journal of Molecular Biology, 430: 2256-2265 (2018). [PDF] [Support Information] [Server]




      Introduction: MUSTER (MUlti-Sources ThreadER) is a new protein threading algorithm to identify the template structures from the PDB library. It generate sequence-template alignments by combining sequence profile-profile alignment with multiple structural information.
      References:
      • S. Wu, Y. Zhang. MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins: Structure, Function, and Bioinformatics 2008; 72: 547-556. (download the PDF file)




      Introduction: CEthreader (Contact Eigenvector-based threader) is a threading method for protein fold recognition. It enhances sequence-template alignment accuracy by integrating sequence profile information with contact-map that is predicted from deep-learning.
      References:
      • 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). [PDF] [Support Information]




      Introduction: SEGMER is a segmental threading algorithm designed to recoginzing substructure motifs from the Protein Data Bank (PDB) library. It first splits target sequences into segments which consists of 2-4 consecutive or non-consecutive secondary structure elements (alpha-helix, beta-strand). The sequence segments are then threaded through the PDB to identify conserved substructures. It often identifies better conserved structure motifs than the whole-chain threading methods, especially when there is no similar global fold existing in the PDB.
      References:
      • S. Wu, Y. Zhang. SEGMER:identifying protein sub-structural similarity by segmental threading. Structure, vol 18, 858-867 (2010). (download the PDF file)




      Introduction: FG-MD is a molecular dynamics (MD) based algorithm for high-resolution protein structure refinement. Given an initial protein or protein complex 3D model (either in C-alpha or full-atom), FG-MD first identifies analogous fragments from the PDB by the structural alignment program TM-align. Spatial restraints extracted from the fragments are then used to guide the molecular dynamics simulations. In general, FG-MD aims to refine the initial models closer to the native structure. It also improves the local geometry of the structures by removing the steric clashes and improving the torsion angle and the hydrogen-binding networks.
      References:
      • Jian Zhang, Yu Liang, Yang Zhang. Atomic-Level Protein Structure Refinement Using Fragment-Guided Molecular Dynamics Conformation Sampling. Structure, 19: 1784-1795, 2011 (Download the PDF file and the Support Information).




      Introduction: ModRefiner is an algorithm for atomic-level, high-resolution protein structure refinement. It can start from either C-alpha trace, main-chain model or full-atomic model. Both side-chain and backbone atoms are completely flexible during structure refinement simulations, where conformational search is guided by a composite of physics- and knowledge-based force field. ModRefiner has an option to allow for the assignment of a second structure which will be used as a reference to which the refinement simulations are driven. One aim of ModRefiner is to draw the initial starting models closer to their native state. It also generates significant improvement in physical quality of local structures.
      References:
      • Dong Xu and Yang Zhang. Improving Physical Realism and Structural Accuracy of Protein Models by a Two-step Atomic-level Energy Minimization, Biophysical Journal, vol 101, 2525-2534 (2011) (Download the PDF file).




      Introduction: REMO is a new algorithm for constructing protein atomic structures from C-alpha traces by optimizing the backbone hydrogen-bonding networks.
      References:
      • Yunqi Li and Yang Zhang. REMO: A new protocol to refine full atomic protein models from C-alpha traces by optimizing hydrogen-bonding networks. Proteins, 2009, 76: 665-676. (download the PDF file).




      Introduction: DEMO (Domain Enhanced MOdeling) is a method for automated assembly of full-length structural models of multi-domain proteins, starting from individual domain structures.
      References:
      • X Zhou, J Hu, C Zhang, G Zhang, Y Zhang. Assembling multidomain protein structures through analogous global structural alignments. Proceedings of the National Academy of Sciences, 116: 15930-15938 (2019). [PDF] [Support Information]




      Introduction: SPRING is a template-base algorithm for protein-protein structure prediction. It first threads one chain of the protein complex through the PDB library with the binding parters retrieved from the original oligomer entries. The complex models associated with another chain is deduced from a pre-calculated look-up table, with the best orientation selected by the SPRING-score which is a combination of threading Z-score, interface contacts, and TM-align match between monomer-to-dimer templates.
      References:
      • Aysam Guerler, Brandon Govindarajoo and Yang Zhang. Mapping monomeric threading to protein-protein structure prediction, Journal of Chemical Information and Modeling 2013, 53: 717-725. (Download the PDF file).




      Introduction: COTH (CO-THreader) is a multiple-chain protein threading algorithm to identify and recombine the protein complex structures from both tertiary and complex structure libraries. It first generates complex query-template alignments by sequence profile-profile alignment assisted by the ab initio binding-site predictions from BSpred. The monomer structures from tertiary template library are then combined into the complex framework by structure superposition.
      References:
      • S Mukherjee, Y Zhang Protein-protein complex structure prediction by multimeric threading and template recombination. Structure, vol 19, 955-966 (2011) (Download the PDF file and Supporting Information).




      Introduction: Threpp is a method for protein-protein interaction (PPI) prediction. Starting from a pair of protein sequences, it does two things: (1), it will judge whether the two proteins interact with each other by calculating the likelihood through a naive Bayes classifier model which combines the Threpp threading score and available high-throughput experimental (HTE) data. (2), it creates the quaternary stuctural models of the PPIs by reassembling the monomeric threading templates with the identified PPI frameworks.
      References:
      • Weikang Gong, Aysam Guerler, Chengxin Zhang, Elisa Warner, Chunhua Li, Yang Zhang. Integrating Multimeric Threading With High-throughput Experiments for Structural Interactome of Escherichia coli . Journal of Molecular Biology, 433: 166944 (2021). [PDF] [Supporting Information]




      Introduction: BSpred is a neural network based algorithm for predicting binding site of proteins from amino acid sequences. The algorithm was extensively trained on the sequence-based features including protein sequence profile, secondary structure prediction, and hydrophobicity scales of amino acids.
      References:
      • S Mukherjee, Y Zhang Protein-protein complex structure prediction by multimeric threading and template recombination. Structure, vol 19, 955-966 (2011) (Download the PDF file and Supporting Information).




      Introduction: ANGLOR is a machine-learning based algorithm for ab initio prediction of protein backbone torsion angles. For a given amino acid sequence, the real-value backbone torsion angles (phi and psi) for each residue are predicted by the combination of the neural network training and the support vector machine.
      References:
      • S. Wu, Y. Zhang. ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction. PLoS ONE 2008; 3: e3400. (download the PDF file)




      Introduction: EDock is method for blind ligand-protein docking. It starts with initial ligand poses generated by a modified graph matching on the predicted binding pockets. Replica-exange Monte Carlo (REMC) simulations are then performed for ligand conformation sampling under the guidance of a physical force field coupled with binding site constraints. The final ligand docking model is selected by a composite knowledge-based score function.
      References:
      • Wenyi Zhang, Eric Bell, Minghao Yin, Yang Zhang. EDock: Blind Protein-ligand Docking by Replica-Exchange Monte Carlo Simulation. Journal of Cheminformatics, 12: 37 (2020). [PDF] [Support Information] [Server]




      Introduction: BSP-SLIM is a blind molecular docking method on low-resolution protein structures. The method first identifies putative ligand binding sites by structurally matching the target to the template holo-structures. The ligand-protein docking conformation is then constructed by local shape and chemical feature complementarities between ligand and the negative image of binding pockets.
      References:
      • Hui Sun Lee and Yang Zhang. BSP-SLIM: A blind low-resolution ligand-protein docking approach using theoretically predicted protein structures, Proteins, 2012, 80:93-110 (download the PDF file).




      Introduction: SAXSTER is a new algorithm to combine small-angle x-ray scattering (SAXS) data and threading for high-resolution protein structure determination. Given a query sequence, SAXSTER first generates a list of template alignments using the MUSTER threading program from the PDB library. The SAXS data will then be used to prioritize the best template alignments based on the SAXS profile match, which are finally used for full-length atomic protein structure construction.
      References:
      • M. dos Reis, R. Aparicio and Y. Zhang. Improving protein template recognition by using small angle X-ray scattering profiles. Biophysical Journal, vol 101, 2770-2781 (2011) (Download the PDF file).




      Introduction: FUpred is a contact map-based protein domain prediction method. It utilizes a recursion strategy to detect domain boundary based on predicted contact-map and secondary structure information.
      References:
      • Wei Zheng, Xiaogen Zhou, Qiqige Wuyun, Robin Pearce, Yang Li, Yang Zhang FUpred: Detecting protein domains through deep-learning based contact map prediction. Bioinformatics, 36: 3749–3757 (2020). [PDF] [Support Information] [Server]




      Introduction: ThreaDom 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. The domain boundary is then assigned based on the multiple sequence alignment between target and template structures, where a confidence score is assigned to each prediction which combines information from template structure, terminal and internal gaps and insertions. ThreaDom is designed to predict both continuous and discontinuous domains.
      References:
      • Z Xue, D Xu, Y Wang, Y Zhang. ThreaDom: Assigning protein domain boundary using multiple threading alignments. Bioinformatics, 29: i247-i256, 2013. [PDF] [Server]




      Introduction: ThreaDomEx is a new version of template-based domain prediction program, which is extended from ThreaDom. Compared to the ThreaDom program, the major new features in ThreaDomEx include: (1) it enables discontinuous domain prediction; (2) it allows manual intervention of domain prediction.
      References:
      • Yan Wang, Jian Wang, Ruiming Li, Qiang Shi, 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-W407, (2017). [PDF] [Server]




      Introduction: EvoDesign is an evolutionary profile based approach to de novo protein design. Starting from a scaffold of target protein structure, EvoDesign first identifies protein families which have similar fold from the PDB library by TM-align. A structural profile is then constructed from the protein templates which is used to guide the conformation search of amino acid sequence space, where physicochemical packing is accommodated by the single-sequence based solvation, torsion angle and secondary structure predictions. The final designed sequence is obtained by clustering all sequence decoys generated during design simulations.
      References:
      • Pralay Mitra, David Shultis and Yang Zhang. EvoDesign: de novo protein design based on structural and evolutionary profiles. Nucleic Acids Research, W273-W280, 2013. [PDF] [Support Information] [Server]




      Introduction: GPCR-I-TASSER is on-line server system specfically designed for predicting 3D structure of G protein-coupled receptors. The target sequence is first threaded through the PDB libary by LOMETS to search for putative templates. If homologous templates are identified, a template-based fragment assembly procedure is used to construct full-length models. In case that no homologous templates are available, an ab initio TM-helix folding procedure is used to assembly the 7-TM-helix bundle from scratch, followed by GPCR-I-TASSER structure reassembly simulation assisted with the sparse mutagensis restraints from GPCR-RD. The final structue models are refined at atomic-level by the fragment-guided molecular dynamic (FG-MD) simulations.
      References:
      • Jian Zhang, Jianyi Yang, Richard Jang, Yang Zhang. GPCR-I-TASSER: A hybrid approach to G protein-coupled receptor structure modeling and the application to the human genome. Structure, 23: 1538-1549 (2015). [PDF] [Support Information] [Server] [Database]




      Introduction: MAGELLAN (Michigan G protein-coupled Receptor Ligand-Based Virtual Screen) is a ligand-based virtual screening pipeline developed for screening Class-A G protein-coupled receptors (GPCR). The core of this pipeline is the construction of a composite ligand profile, represented by 1024xN matrix, that is collected from homologous ligand-GPCR interactions detected by sequence and structure alignments. Active GPCR compounds are then prioritized by threading the ligand profile through large-scale compound databases.
      References:
      • Wallace K.B. Chan, Yang Zhang. Virtual screening of human Class-A GPCRs using ligand profiles built on multiple ligand-receptor interactions. Journal of Molecular Biology, 432: 4872-4890 (2020). [PDF] [Support Information]




      Introduction: BindProf is a method for predicting free energy changes (ΔΔG) of protein-protein binding interactions upon mutations of residues at the interface. While BindProf adopts a multi-scale approach using multiple sources of information at different levels of structural resolution, a unique feature of BindProf is the inclusion of an interface structural profile score derived from multiple structure alignments from analogous protein-protein interactions.
      References:
      • Jeffrey R. Brender, Yang Zhang. Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles. PLOS Computational Biology, 11: e1004494 (2015). [PDF] [Support Information].




      Introduction: BindProfX is a method to assess protein-protein binding free-energy changes (ΔΔG) induced by single- and multiple-mutations. This is an update on the BindProf method and tries to enhance PPI ΔΔG prediction accuracy using log-odds likelihood and pseudo count techniques.
      References:
      • P Xiong, C Zhang, W Zheng, Y Zhang. BindProfX: Assessing mutation-induced binding affinity change by protein interface profiles with pseudo counts. J Mol Biol. 429: 426-434, 2017. [PDF] [Supplementary Information].




      Introduction: SSIPe is a method to calculate binding affinity changes (ΔΔG) of protein-protein interactions (PPIs) upon mutations at protein-protein interface. The method is a significant extension of BindProf/BindProfX by integrating PPI interface structural profiles with sequence profiles and physics-based physical energy function EvoEF.
      References:
      • X Huang, W Zheng, R Pearce, Y Zhang. SSIPe: accurately estimating protein-protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function. Bioinformatics, 36: 2429-2437 (2020). [PDF] [SI] [Server]




      Introduction: 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 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, with the B-factor of each residue deduced from the experimental records of the top homologous proteins.
      References:
      • Jianyi Yang, Yan Wang, Yang Zhang. ResQ: An approach to unified estimation of B-factor and residue-specific error in protein structure prediction. Journal of Molecular Biology, 428: 693-701 (2016). [PDF] [Support Information] [Server]




      Introduction: IonCom is an ligand-specific method for small ligand (including metal and acid radical ions) binding site prediction. Starting from given sequences or structures of the query proteins, IonCom performs a composite binding-site prediction that combines ab initio training and template-based transferals. To enhance specificity and sensitivity, the server focuses on binding site prediction of thirteen most important small ligand molecules, including nine metal ions (Zn++, Cu+, Fe+, Fe++, Ca++, Mg++, Mn++, Na+, K+) and four acid radical ions (CO3--, NO2-, SO4--, PO4---).
      References:
      • Xiuzhen Hu, Qiwen Dong, Jianyi Yang, Yang Zhang. Recognizing metal and acid radical ion binding sites by integrating ab initio modeling with template-based transferals. Boinformatics, 32: 3260-3269 (2016). [PDF] [Support Information] [Server]




      Introduction: STRUM is a method for predicting the fold stability change (ΔΔG) of protein molecules upon single-point nsSNP mutations. STRUM adopts a gradient boosting regression approch to train the Gibbs free-energy changes on a variety of features at different levels of sequence and structure properties. The unique characteristics of STRUM is the combination of sequence profiles with low-resolution structure models from protein structure prediction, which helps enhance the robustness and accuracy of the method and make it applicable to various protein seqences, including those without experimental structures.
      References:
      • Lijun Quan, Qiang Lv, Yang Zhang. STRUM: Structure-based stability change prediction upon single-point mutation, Boinformatics, 32: 2936-46 (2016). [PDF] [Support Information] [Server]




      Introduction: DAMpred is a method to predict what gene mutations can cause human diseases and what mutations do not do so. Starting with a protein sequence and specified non-synonymous single nucleotide polymorphisms (nsSNPs), DAMpred calculates the probability of the mutations to be deleterious or neutral to human health. The calculation is built on a deep-learning model that integrates three sources of information from sequence profiles, biological assembly and 3D structure model (by I-TASSER), which is trained through a novel Bayes-guided artificial neural network (BANN) algorithm.
      References:
      • Lijun Quan, Hongjie Wu, Qiang Lyu, Yang Zhang. DAMpred: Recognizing disease-associated nsSNPs through Bayes-guided neural-network model built on low-resolution structure prediction of proteins and protein-protein interactions. J Mol Biol, 431: 2449-2459 (2019).
        [PDF] [Support Information] [Server]



       


II. Bioinformatics Tools (structure alignment, sequence alignment, 3D visulization, surface, and clustering, etc)

      Introduction: TM-score is an algorithm to calculate the topological similarity of two protein structures. It can be used to quantitatively access the quality of protein structure predictions relative to the native. Because TM-score weights the close matches stronger than the distant matches, TM-score is more sensitive to the global topology of structures than the often-used root-mean-square deviation (RMSD).
      References:
      • Y. Zhang, J. Skolnick, Scoring function for automated assessment of protein structure template quality. Proteins, 2004 57: 702-710 (download the PDF file and Correction).




      Introduction: TM-align is a computer algorithm for quick and accurate protein structure alignment using dynamic programming and TM-score rotation matrix. An optimal alignment between two proteins, as well as the TM-score, will be reported for each comparison.
      References:
      • Y. Zhang, J. Skolnick, TM-align: A protein structure alignment algorithm based on TM-score. Nucleic Acids Research, 2005 33: 2302-2309 (download the PDF file).




      Introduction: MM-align is designed to structurally align multimeric protein complexes using heuristic iteration of dynamic programming based on TM-score rotation matrix. The multple chains in each complex are first joined, in every possible order, and then simultaneously aligned with cross-chain alignment prevented. The alignment on interface structures can be enhenced by MM-align by an interface-specific weighting factor. A TM-score is reported for assessing the structural similarity of two complexes.
      References:
      • S. Mukherjee, Y. Zhang, MM-align: a quick algorithm for aligning multiple-chain protein complex structures using iterative dynamic programming. Nucleic Acids Research 2009; 37: e83 (Download PDF file and supporting materials).




      Introduction: RNA-align is an algorithm for comparing 3D structures of RNA molecules. Starting from two RNA structures, RNA-align seeks optimal nucleotide-to-nucleotide alignments based on a heuristic dynamic programming iteration process, assisted by distance-based secondary structure assignments. The structural similarity of the selected alignment is assessed by a renormalized TM-score on the statistics of RNA structures in the PDB, where TM-scoreRNA has values in (0,1] with 1 indicating a perfect structure match and a score ≥0.45 corresponding to a structural similarity of the RNA pairs in the same Rfam family. RNA-align can also be used to compare double-stranded DNA structures.
      References:
      • Sha Gong, Chengxin Zhang, Yang Zhang. RNA-align: quick and accurate alignment of RNA 3D structures based on size-independent TM-scoreRNA. (2019) Bioinformatics, 35: 4459-4461. [PDF] [Supplement]




      Introduction: NW-align is simple and robust alignment program for protein sequence-sequence alignments based on the standard Needleman-Wunsch dynamic programming algorithm. The mutation matrix is from BLOSUM62 with gap openning penaly=-11 and gap extension panalty=-1. The source code of this program can be downloaded at the bottom of the NW-align website, which can be easily modified for different purposes.
      References:
      • Yang Zhang. http://zhanglab.dcmb.med.umich.edu/NW-align/




      Introduction: LS-align is an algorithm designed for atom-level structural comparison of ligand molecules. The target function of LS-align is a combination of inter-atom distance, atom mass, and chemical bond connections; while the final atom-to-atom alignment is generated by maximizing such target function through an enhanced-greedy based, iterative heuristic search algorithm. LS-align program contains two modules: Rigid-LS-align for rigid-body ligand structure comparison; and Flexi-LS-align for flixible structure comparison. In particular, the Flexi-LS-align module seeks for optimal alignments of various alternative conformers of the ligand molecules by rotating flexible bond-angles, which allows the consideration of binding-induced conformational changes in the ligand structural comparison and alignment.
      References:
      • Jun Hu, Zi Liu, Dong-Jun Yu, and Yang Zhang. LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening. Bioinformatics, 34: 2209-2218 (2018). [Download download the PDF file] [Support Information] [Server]




      Introduction: EDTSurf is a open source program to construct triangulated surfaces for macromolecules. It can generate three major macromolecular surfaces of van der Waals surface, solvent-accessible surface and molecular surface (solvent-excluded surface), and identify cavities which are inside of macromolecules.
      References:
      • Dong Xu, Yang Zhang (2009) Generating Triangulated Macromolecular Surfaces by Euclidean Distance Transform. PLoS ONE 4(12): e8140 (download the PDF file).




      Introduction: MVP (Macromolecular Visualization and Processing) is a convenient tool for visualizing macromolecular structures and their derived information. It supports PDB format and EM density maps and has many drawing styles and color modes. It contains lots of convenient features, including computations of triangulated surfaces, depth, principal axes and estimate the secondary structures for protein structures etc.
      References:
      • Dong Xu, Yang Zhang (2009) Generating Triangulated Macromolecular Surfaces by Euclidean Distance Transform. PLoS ONE 4(12): e8140. (download the PDF file). (download the PDF file)




      Introduction: MVP-Fit is a tool to combine and fit multiple monomer structures into EM density maps. While most current tools can only achieve regid-body docking and fitting, MVP-Fit has the advantage to flexibly move and dock the monomer structures into the EM density maps while keeping the physical and geometric restraints of the individual structural models.
      References:
      • Dong Xu, Yang Zhang, MVP-Fit: A Convenient Tool for Flexible Fitting of Protein Domain Structures with Cryo-Electron Microscopy Density Map. In preparation.




      Introduction: SPICKER is a clustering algorithm to identify the near-native models from a pool of protein structure decoys. The cluster is defined by the pair-wise RMSD metrics of the structural decoys.
      References:
      • Y. Zhang, J. Skolnick, SPICKER: Approach to clustering protein structures for near-native model selection, Journal of Computational Chemistry, 2004 25: 865-871. (download the PDF file).




      Introduction: HAAD is a computer algorithm for constructing hydrogen atoms from protein heavy-atom structures. The hydrgen is added by minimizing atomic overlap and encouraging hydrogen bonding.
      References:
      • Yunqi Li, Roy Ambrish and Yang Zhang, HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures, PLoS One, 2009 4: e6701 (download the PDF file).




      Introduction: PSSpred is a multiple neural training algorithm for accurate protein secondary structure prediction. The program is freely downloadable.
      References:
      • Yang Zhang. http://zhanglab.dcmb.med.umich.edu/PSSpred




      Introduction: 3DRobot is a program for automated generation of diverse and well-packed protein structure decoys. 3DRobot aims to provide high-quality protein structural decoy sets for designing and training protein folding force field and folding simulation methods.
      References:
      • Haiyou Deng, Ya Jia, Yang Zhang. 3DRobot: Automated Generation of Diverse and Well-packed Protein Structure Decoys. Boinformatics, 32: 378-87 (2016). [PDF] [Support Information] [Server]




      Introduction: MR-REX is a method for performing molecular replacement in X-ray crystallography diffraction. The method is designed to search for the optimal placement of target structural models through replica-exchange Monte Carlo simulations. The input for the MR-REX program is a protein structure and a cif structure factor file, where output contains multiple structural conformations of the target protein structure placed in the unit cell.
      References:
      • Jouko J. Virtanen, Yang Zhang. MR-REX: Molecular replacement by cooperative conformational search and occupancy optimization on low-accuracy protein models. Acta Crystallographica Section D, 74: 606-620 (2018). [PDF] [Support Information] [Server]




      Introduction: I-TASSER-MR is a pipeline designed to determine protein structure by combining I-TASSER and molecular replacement (MR). Starting from the amino acid sequence and X-ray diffraction data, 3D models are first constructed by iterative threading assembly refinement simulation (I-TASSER). The phase information of X-ray diffraction is then decided by molecular replacement through an iterative editing procedure that progressively truncates the unreliably modeled regions. Finally, atomic models are constructed using the Phenix.autobuild program.
      References:
      • Y. Wang, J. Virtanen, Z. Xue, J. J. G. Tesmer and Y. Zhang. Using iterative fragment assembly and progressive sequence truncation to facilitate phasing and crystal structure determination of distantly related proteins. Acta Cryst. (2016). D72, 616-628 [PDF] [Support Information] [Server]
      • Yan Wang, Jouko Virtanen, Zhidong Xue, Yang Zhang. I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation. Nucleic Acids Research, 45: W429-W434 (2017). [PDF] [Server]




      Introduction: SVMSEQ is a new algorithm for protein residue-residue contact prediction using Support Vector Machines.
      References:
      • S. Wu, Y. Zhang. A comprehensive assessment of sequence-based and template-based methods for protein contact prediction. Bioinformatics, vol 24, 924-931 (2008). (download the PDF file)




      Introduction: NeBcon (Neural-network and Bayes-classifier based contact prediction) is a hierarchical algorithm for sequence-based protein contact map prediction. It first uses the naive Bayes classifier theorem to calculate the posterior probability of eight machine-learning and co-evoluation based contact prodiction programs (SVMSEQ, BETACON, SVMcon, PSICOV, CCMpred, FreeContact, MetaPSICOV, and STRUCTCH). Final contact maps are then created by neural network machine that trains the posterior probability scores with intrinsic structural features from secondary structure, solvent accessibility, and Shannon entropy of multiple sequence alignments.
      References:
      • Baoji He, S. M. Mortuza, Yanting Wang, Hong-Bin Shen, Yang Zhang. NeBcon: Protein contact map prediction using neural network training coupled with naïve Bayes classifiers. Bioinformatics, : doi: 10.1093/bioinformatics/btx164 (2017). [PDF] [Support Information] [Server]




      Introduction: ResPRE is an algorithm for protein residue-residue contact-map prediction. Starting from a query sequence, multiple sequence alignments (MSAs) are collected from sequence databases. The inverse covariance matrix, or precision matrix, of the MSAs are then used to train the contact models through deep residual convolutional neural networks.
      References:
      • Y Li, J Hu, C Zhang, D Yu, Y Zhang ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks. Bioinformatics, 35: 4647-4655 (2019). [PDF] [Support Information].




      Introduction: TripletRes is a method for protein inter-residue contact prediction. For a query sequence, TripletRes starts with the collection of deep multiple sequence alignments (MSAs) through whole-genome and metagenome sequence databases. Next, three complimentary coevolutionay feature matrices (covariance martrix, precision matrix and the pseudolikelihood maximization) extracted from the MSAs are used to create contact-map models through deep residual convolutional neural network training.
      References:
      • Yang Li, Chengxin Zhang, Eric W. Bell, Wei Zheng, Xiaogen Zhou, Dongjun Yu, Yang Zhang. Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks. PLoS Computational Biology, e1008865 (2021) [PDF] [Supporting Information]




      Introduction: WDL-RF (weighted deep learning and random forest) is a novel pipeline for bioactivity prediction of GPCR-associated ligand molecules. In commercial drug design, virtual screening is acceptable only when the prediction accuracy is high. One of the outstanding issues with the bioactivity modeling is that the input to the model, a ligand, can be of arbitrary size, but most of the current predictors can only handle inputs of a fixed size. WDL-RF builds on a novel two-stage algorithm, with molecular fingerprint generated through a weighted deep learning method, followed by random forest based bioactivity assignments. The pipelins allows high-accuracy end-to-end learning of prediction pipelines whose inputs are of arbitrary size. The large-scale benchmark tests showed that the WDL-RF model has an average root-mean square error 1.42 and correlation coefficient 0.78, compared to the experimental measurements.
      References:
      • Jiansheng Wu, Qiuming Zhang, Weijian Wu, Tao Pang, Haifeng Hu, Wallace K.B. Chan, Xiaoyan Ke, Yang Zhang WDL-RF: Predicting Bioactivities of Ligand Molecules Acting with G Protein-coupled Re-ceptors by Combining Weighted Deep Learn-ing and Random Forest. Bioinformatics, 34: 2271-2282 (2018). [Download PDF] [Support Information)] [Server)]




      Introduction: ATPbind is a meta-server approach to protein-ATP binding site prediction. Starting from given structure of query protein, ATPbind will identify the ATP-binding sites by using SVM to integrate the outputs of two template-based predictors, i.e., S-SITEatp (the extension of S-SITE) and TM-SITEatp (the extension of TM-SITE), and three discriminative sequence-driven features, i.e., position specific scoring matrix (PSSM), predicted secondary structure, and predicted solvent accessibility. Users are also allowed to input primary sequence, where I-TASSER will be used to generate 3D model first which are then fed into the ATPbind pipeline for protein-ATP binding site prediction. After protein-ATP binding site prediction, the ATPbind server implements a new binding pocket clustering scheme, PocHunter, to identify the pockets based on the predicted binding sites.
      References:
      • Jun Hu, Yang Li, Yang Zhang, Dongjun Yu ATPbind: accurate protein-ATP binding site prediction by combining sequence-profiling and structure-based comparisons. Journal of Chemical Information and Modeling, 58: 501-510 (2018). (Download PDF and Support Information).




      Introduction: DockRMSD is a program for the calculation of RMSD (root-mean-square deviation) between two poses of the same ligand molecule docked on the same protein structure without the assumption of known atomic ordering between the two files. This is achieved by recursively determining all possible atomic mappings between the two poses given their respective atomic bonding networks, and returning the mapping whose RMSD is the lowest. This is particularly relevant for comparing ligands with symmetric structure (e.g., benzene ring) as a simiple comparison based on default atomic ordering does not result in the minimum RMSD.
      References:
      • Eric W. Bell, Yang Zhang DockRMSD: an Open-Source Tool for Atom Mapping and RMSD Calculation of Symmetric Molecules through Graph Isomorphism.
        J Cheminformatics, 11: 40 (2019). [PDF] [Server]




      Introduction: DeepMSA is a tool to create high quality multiple sequence alignment based on three large-scale sequence libraries from whole-genome (Uniclust30 and UniRef90) and database (Metaclust) databases.
      References:
      • C Zhang, W Zheng, SM Mortuza, Y Li, Y Zhang. DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins. Bioinformatics. [PDF] [Support Information 1, 2].




      Introduction: 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 dead-end elimination and tree decomposition. The large-scale benchmark tests showed that FASPR outperforms the current state-of-the-art protein side-chain packers on both native and non-native backbones with higher accuracy in terms of side-chain dihedral angle (Chi1-4) recovery rate and RMSD. FASPR is also much faster than these packers and packs 379 protein structures within 0.6 min in the becnhmark tests.
      References:
      • Xiaoqiang Huang, Robin Pearce, Yang Zhang. FASPR: an open-source tool for fast and accurate protein side-chain packing. Bioinformatics, 36: 3758–3765 (2020). [PDF] [Support Information] [Server]




      Introduction: EM-Refiner is a method for Monte Carlo based protein structure refinement using Cryo-EM density map. The pipeline consists of three steps of structure-map superposition, rigid-body fragment adjustments, and atomic-level structure refinement. During the refinement simulations, the backbone structures are kept flexible with movements guided by a composite of physics- and knowledge-based force field, integrated with model-map correlations. The pipeline is fully automated and suitable for the protein targets with low-to-medium resolution Cryo-EM density map data.
      References:
      • Biao Zhang, Xi Zhang, Robin Pearce, Hongbin Shen, Yang Zhang. A new protocol for atomic-level protein structure modeling and refinement using low-to-medium resolution cryo-EM density maps. Journal of Molecular Biology, 432: 5365-5377 (2020) [PDF] [Support Information]



       

III. Databases and Potentials

      Introduction: BioLiP is a manually curated database for high-quality, biologically relevant ligand-protein binding interactions. The data is collected primarily from the Protein Data Bank (PDB), with biological insights mined from literature and other specific databases, followed by both computational and manual verifications.
      References:
      • Jianyi Yang, Ambrish Roy, and Yang Zhang. BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions, Nucleic Acids Research, 41:D1096-D1103, 2013. (Download the PDF file).




      Introduction: This is a database for protein structure, function and interaction network modeling of the entire genome of Escherichia coli bacterium. The 3D structures of the sequences are generated by I-TASSER and QUARK and the structures of interactions modeled by Spring.
      References:
      • Dong Xu, Yang Zhang. Ab Initio Structure Prediction for Escherichia coli: Towards Genome-wide Protein Structure Modeling and Fold Assignment. Scientific Reports, 3: 1895 (2013). [PDF] [Support Information] [Database]
      • Aysam Guerler, Elisa Warner and Yang Zhang. Genome-wide prediction and structural modeling of protein-protein interactions in Escherichia coli. 2013, submitted.




      Introduction: GLASS (GPCR-Ligand Association) database is a manually curated repository for experimentally-validated GPCR-ligand interactions. Along with relevant GPCR and chemical information, GPCR-ligand association data are extracted and integrated into GLASS from literature and public databases.
      References:
      • WK Chan, H Zhang, J Yang, JR Brender, Y Zhang. GLASS: A comprehensive database for experimentally-validated GPCR-ligand associations. Bioinformatics, 31: 3035-3042 (2015). [PDF] [Support Information] [Database]




      Introduction: GPCR-HGmod is a database of 3D structural models for all G protein-coupled receptors (GPCRs) in the human genome, which were generated by the GPCR-I-TASSER method. Due to the sensitivity of the models to template library, the database is updated every six months in case that new GPCR experiment structures are solved.
      References:
      • Jian Zhang, Jianyi Yang, Richard Jang, Yang Zhang GPCR-I-TASSER: A hybrid approach to G protein-coupled receptor structure modeling and the application to the human genome. Structure, 23: 1538-1549 (2015). [PDF] [Support Information] [Server] [Database]




      Introduction: GPCR-RD is a primiary database of experimental restraints for G protein-coupled receptors (GPCRs) which are systematically collected from literature and experimental reports. It contains thousands of spatial restraints from mutagenesis, disulfide mapping distances, electron cryomicroscopy, and FTIR experiments. The data can be conveniently used for assisting GPCR structure prediction and functional annotations.
      References:
      • J Zhang, Y Zhang, GPCRRD: G protein-coupled receptor spatial restraint database for 3-D structure modeling and function annotation Bioinformatics, 2010,26(23):3004-3005. (download the PDF file)




      Introduction: GPCR-EXP is manually curated database that contains all G protein-coupled receptors that have been solved so far. The database is updated weekly. Each entry contains information of PDB ID, resolution, release date, biological name and literature associated with the GPCR.
      References:
      • Jianyi Yang and Yang Zhang. GPCR-EXP: a manually curated database for experimentally solved GPCR structures, 2014 (http://zhanglab.dcmb.med.umich.edu/GPCR-EXP/).




      Introduction: The ocean harbors a huge diversity of microbes that provide nearly half of the primary energy production on this planet, through either photosynthesis or chemosynthesis. Tara-3D exploits to model structural and function modeling of unknown Pfam families by combining the marine microbiome with new techniques from deep-learning contact-map prediction and ab initio folding simulations, followed by structure-based function annotation.
      References:
      • Y Wang, Q Shi, P Yang, C Zhang, SM Mortuza, Z Xue, K Ning, Y Zhang Fueling ab initio folding with marine metagenomics enables structure and function predictions of new protein families. Genome Biology, 20: 229 (2019). [PDF] [Support Information 1, 2]




      Introduction: TM-fold is a on-line server to estimate the posterior possibility of two protein structures belonging to the same family. For a given pair of protein structures, this server is to calculate the structural similarity by structural alignment algorithms, and report a posterior probability for the structures belonging to the same SCOP/CATH Fold family.
      References:
      • J Xu, Y Zhang, How significant is a protein structure similarity with TM-score=0.5? Bioinformatics, 2010, doi:10.1093. (download the PDF file).




      Introduction: This dataset contains two sets of structure decoys generated by ab initio I-TASSER simulations. The first set contains raw decoys by I-TASSER on 56 small proteins. The second set includes the non-redundant structure decoys for the same 56 proteins with the models refined by quick molecular dynamic simulations.
      References:
      • Sitao Wu, Jeffrey Skolnick, Yang Zhang: Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biology 2007, 5: 17. (download PDF file)
      • J Zhang and Y Zhang, A Distance-Dependent Atomic Potential Derived from Random-Walk Ideal Chain Reference State for Protein Fold Selection and Structure Prediction. PLoS One, vol 5, e15386 (2010). (download the PDF file).




      Introduction: The interaction parameters and the knowledge-based force field used by I-TASSER.
      References:
      • Yang Zhang, Andrzej Kolinski, Jeffrey Skolnick. Touchstone II: A new approach to ab initio protein Structure Prediction. Biophysical Journal, vol 85, 1145 (2003). [download the PDF file]
      • Sitao Wu, Jeffrey Skolnick, Yang Zhang. Ab initio modeling of small proteins by iterative TASSER simulations BMC Biology, vol 5, 17 (2007). [download the PDF file]




      Introduction: RW is distance-dependent atomic potential for protein structure modeling and structure decoy recognition. It is calculated from 1,383 high-resolution PDB structures using an ideal random-walk chain as the reference state.
      References:
      • J Zhang and Y Zhang, A Distance-Dependent Atomic Potential Derived from Random-Walk Ideal Chain Reference State for Protein Fold Selection and Structure Prediction. PLoS One, vol 5, e15386 (2010). (download the PDF file).




      Introduction: EvoEF is a physics- and knowledge-based energy function including two versions: (1) EvoEF1 contains energy terms optimized on thermodynamics mutation data (ΔΔG) and (2) EvoEF2 contains energy terms optimized for protein sequence design. EvoEF1 performs better than EvoEF2 on ΔΔG estimation while EvoEF2 significantly outperforms EvoEF2 on de novo protein sequence design.
      References:
      • Xiaoqiang Huang, Robin Pearce, Yang Zhang. EvoEF2: accurate and fast energy function for computational protein design. Bioinformatics, 36: 1135?1142 (2020). [PDF] [Support Information] [Server]




      Introduction: HPSF (Human Proteome Structure and Function) is a database of structure and function annotations on the 'missing proteins' of the human proteome. The missing proteins that have not been validated at protein level are first extracted from the neXtProt database. The structure folding simulations are then generated by I-TASSER with all homologous templates excluded from the threading libraries. Finally, the functional insights of each protein are provided by the structure-based function annotation tool, COFACTOR.
      References:
      • Qiwen Dong, Rajasree Menon, Gilbert S. Omenn, Yang Zhang. Structural Bioinformatics Inspection of neXtProt PE5 Proteins in the Human Proteome. Journal of Proteome Research, 14: 3750-3761 (2015) (download the PDF file).




      Introduction: THE-DB contains structure models obtained by threading programs (MUSTER, SPARKS-X, and HHsearch) for E. coli K12 and Human genomes.
      References:
      • Justin S. Diamond, Yang Zhang. THE-DB: a threading model database for comparative protein structure analysis of the E. coli K12 and human proteomes. Database, 2018: bay090 (2018). [PDF] [Database]




      Introduction: COVID-19 database contains 3D structural models and function annotation for all proteins encoded by the genome of SARS-CoV-2, which is a novel coronavirus that has caused the COVID-19 pandemic. The structure models are generated by the C-I-TASSER pipeline, which utilizes deep convolutional neural-network based contact-map predictions to guide the I-TASSER fragment assembly simulations.
      References:
      • Chengxin Zhang, Wei Zheng, Xiaoqiang Huang, Eric W Bell, Xiaogen Zhou, Yang Zhang Protein structure and sequence re-analysis of 2019-nCoV genome refutes snakes as its intermediate host or the unique similarity between its spike protein insertions and HIV-1. Journal of proteome research, 19: 1351-1360 (2020). [PDF] [Support Information] [Database]




      Introduction: An automated assessment of protein structure predictions generated by 189 human and server groups in the CASP7 experiments. The assessment is based on TM-score, MaxSub and GDT-TS score where 124 domains are split into HA (high accuracy), TBM (template-based modeling), and FM (free-modeling) targets.



      Introduction: An automated assessment of protein structure predictions generated by 81 server groups in the CASP8 experiments. The assessment is based on TM-score, MaxSub and GDT-TS score where 172 domains are split into Easy and Hard targets.



      Introduction: An automated assessment of protein structure predictions generated by 81 server groups in the CASP9 experiments. The assessment is based on TM-score, MaxSub and GDT-TS score where 144 domains are split into Easy and Hard targets.



      Introduction: An automated assessment of protein structure predictions generated by the server groups in the CASP10 and CASP_ROLL experiments. The assessment is based on TM-score, GDT-TS/HA and H-bond scores, where targets are first split into domains which are then categoried into Easy and Hard groups.



      Introduction: An automated assessment of protein structure predictions generated by the server groups in the CASP11 experiments. The assessment is based on TM-score, GDT-TS/HA and H-bond scores, where targets are first split into domains by manual view and categoried into Easy and Hard groups.



      Introduction: An automated assessment of protein structure predictions generated by the server groups in the CASP12 experiments. The assessment is based on TM-score, GDT-TS/HA and H-bond scores, where targets are first split into domains by manual view and categoried into Easy and Hard groups.



      Introduction: An automated assessment of protein structure predictions generated by the server and human groups in the CASP13 experiments. The assessment is based on TM-score, GDT-TS/HA and H-bond scores, where targets are first split into domains using the definition of CASP13 assessors, and then categoried into Easy and Hard groups based on average TM-score of the best 50% of server models.



      Introduction: An automated assessment of protein structure predictions generated by the server groups in the CASP14 experiments. The assessment is based on TM-score, GDT-TS/HA and H-bond scores, where targets are first split into domains using the definition of CASP14 assessors, and then categoried into Easy and Hard groups based on average TM-score of the best 50% of server models.


       
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