Home Research Services Publications People Teaching Job Opening News Lab Only
Online Services

I-TASSER QUARK LOMETS COACH COFACTOR MUSTER SEGMER FG-MD ModRefiner REMO SPRING COTH BSpred SVMSEQ ANGLOR BSP-SLIM SAXSTER ThreaDom ThreaDomEx EvoDesign GPCR-I-TASSER BindProf BindProfX ResQ IonCom STRUM

TM-score TM-align MMalign NWalign EDTSurf MVP MVP-Fit SPICKER HAAD PSSpred 3DRobot I-TASSER-MR NeBcon

BioLiP E. coli GLASS GPCR-HGmod GPCR-RD GPCR-EXP TM-fold DECOYS POTENTIAL RW HPSF CASP7 CASP8 CASP9 CASP10 CASP11 CASP12

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: 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: 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)




      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:
      • 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: 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: 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: 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: 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: 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: 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] [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]



       


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.
      References:
      • Yang Zhang. http://zhanglab.ccmb.med.umich.edu/NW-align/




      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.ccmb.med.umich.edu/PSSpred




      Introduction: 3DRobot is a program for automated generation of diverse and well-packed protein structure decoys. Given a native structure as input, 3DRobot identifies diverse structure scaffolds from the PDB library. Restraint-free fragment reassembly simulations are then performed to construct diverse full-length models. The final decoys are further refined at atomic-level by a two-step iterative energy minimization procedure to improve the hydrogen-binding networks and steric overlaps of the structures. 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: 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: 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]



       

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.ccmb.med.umich.edu/GPCR-EXP/).




      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: 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: 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.


       
Message Board for Zhang Lab Service Systems

yangzhanglabumich.edu | (734) 647-1549 | 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218