Services and tools developed in the Yang Zhang Lab

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: 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, QUARK Ab Intio Protein Structure Prediction I: Methodology developments (in preparation)
D. Xu, Y. Zhang, QUARK Ab Intio Protein Structure Prediction II: Results of benchmark and blind tests (in preparation)

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: 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: A Roy and Y Zhang, Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement. 2011(Submitted)

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: 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: J Zhang, Y Zhang. High-resolution protein structure refinement using fragment guided molecular dynamics simulations (2011), submitted.

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, 2011 (in press).

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: 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: 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: 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, in press (2011).

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, in press (2011).

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: H S Lee, Y Zhang. BSP-SLIM: A blind low-resolution ligand-protein docking approach using theoretically predicted protein structures (2011) submitted.

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, 2011, in press.

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: 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.

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, (2011, 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: http://zhanglab.ccmb.med.umich.edu/PSSpred

III. Databases and Potentials

Introduction: GPCRRD 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: Jian Zhang, Yang Zhang, "GPCRRD: An experimental restraint database for GPCR structure modeling" 2009 (submitted).

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: The atomic structure decoys of 56 non-homologous small proteins. The backbone structures are generated by the I-TASSER ab initio modeling; the side-chain and other atoms are added using Pulchra.
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)

Introduction: The interaction parameters and the knowledge-based force field used by I-TASSER.
References: 1. 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]
2. Yang Zhang, Jeffrey Skolnick. Automated structure prediction of weakly homologous proteins on a genomic scale. Proceedings of the National Academy of Sciences of USA, vol 101, 7594 (2004). [download the PDF file]
3. 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: Jian Zhang and Yang Zhang, A distance-dependent atomic potential form random-walk ideal chain reference state for protein fold selection and structure prediction. (2009) submitted.

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.