Interaction Pattern Based Predictor of Protein Strutures

Summary

Principal Investigator: Jeffrey Skolnick
Abstract: DESCRIPTION (provided by applicant): The goals of this project are to elucidate the fundamental principles underlying protein biochemical function and to apply the resulting insights on the interrelationships of protein structure, function and evolution to functional annotation and to assist in drug discovery. The underlying theme is that many features of protein structure and function arise from their physical properties without selection for function, which evolution then acts to fine tune/optimize. This will be demonstrated by the coincidence of the structural and functional properties of native proteins with artificially generated, homopolypeptide "SYN" protein structures to which protein-like sequences are added based on their stability in a particular fold. We will focus on ligand binding pockets and will explore the relationship between global fold similarity, pocket location/shape and amino acid conservation. A key objective is to demonstrate that ligand binding pocket geometry and sequence can be uncoupled from a protein's global structure, so that functional inference can be made between structurally different proteins. These ideas will extend our FINDSITEcomb Ligand Homology Modeling algorithm by developing approaches that better identify common ligand binding pockets and ligands in proteins having either similar or unrelated global folds. FINDSITEcomb will provide predicted structures, GO function, ligand binding sites, and virtual ligand screening/binding pose predictions. An important application will be to predict off-targets of FDA approved drugs in the human proteome. These off-target predictions will be experimentally tested for a significant number of proteins. To achieve these objectives, four Specific Aims are proposed: 1. Examination of the roles of physics and evolution in determining protein structure and function. 2. Exploiting insights from Aim 1, a new approach to difficult target threading will be developed. 3. The major limitations of FINDSITEcomb will be addressed and the methodology applied to drug repurposing. 4. Using thermal shift assays, FINDSITEcomb's virtual screening predictions will be experimentally tested. The entire computational methodology will be applied to the human proteome and model organisms, and the SUNPRO database will report all results. All tools will be made available on our superPSIFR webserver and as downloadable software, including source code. These aims represent an integrated effort to elucidate the principles underlying protein structure and function and to apply these insights to improve function predictions.
Funding Period: 1994-05-01 - 2017-04-30
more information: NIH RePORT

Top Publications

  1. ncbi WeFold: a coopetition for protein structure prediction
    George A Khoury
    Department of Chemical and Biological Engineering, Princeton University, Princeton
    Proteins 82:1850-68. 2014
  2. pmc FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level
    Michal Brylinski
    Center for the Study of Systems Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 79:735-51. 2011
  3. pmc Structural space of protein-protein interfaces is degenerate, close to complete, and highly connected
    Mu Gao
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, GA 30318, USA
    Proc Natl Acad Sci U S A 107:22517-22. 2010
  4. pmc TASSER_WT: a protein structure prediction algorithm with accurate predicted contact restraints for difficult protein targets
    Seung Yup Lee
    Center for Study of Systems Biology, Georgia Institute of Technology, Atlanta, Georgia, USA
    Biophys J 99:3066-75. 2010
  5. pmc Cross-reactivity virtual profiling of the human kinome by X-react(KIN): a chemical systems biology approach
    Michal Brylinski
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia, USA
    Mol Pharm 7:2324-33. 2010
  6. pmc Comprehensive structural and functional characterization of the human kinome by protein structure modeling and ligand virtual screening
    Michal Brylinski
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    J Chem Inf Model 50:1839-54. 2010
  7. pmc The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement
    Michal Brylinski
    Center for the Study of Systems Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA
    J Struct Biol 173:558-69. 2011
  8. pmc TASSER_low-zsc: an approach to improve structure prediction using low z-score-ranked templates
    Shashi B Pandit
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 78:2769-80. 2010
  9. pmc iAlign: a method for the structural comparison of protein-protein interfaces
    Mu Gao
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA, USA
    Bioinformatics 26:2259-65. 2010
  10. pmc Improving threading algorithms for remote homology modeling by combining fragment and template comparisons
    Hongyi Zhou
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 78:2041-8. 2010

Detail Information

Publications52

  1. ncbi WeFold: a coopetition for protein structure prediction
    George A Khoury
    Department of Chemical and Biological Engineering, Princeton University, Princeton
    Proteins 82:1850-68. 2014
    ..A footprint of all contributions and structures are publicly accessible at http://www.wefold.org...
  2. pmc FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level
    Michal Brylinski
    Center for the Study of Systems Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 79:735-51. 2011
    ..FINDSITE-metal is freely available to the academic community at http://cssb.biology.gatech.edu/findsite-metal/...
  3. pmc Structural space of protein-protein interfaces is degenerate, close to complete, and highly connected
    Mu Gao
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, GA 30318, USA
    Proc Natl Acad Sci U S A 107:22517-22. 2010
    ..Finally, our results provide a structural explanation for the prevalence of promiscuous protein interactions. By side-chain packing adjustments, we illustrate how multiprotein specificity can be attained at a promiscuous interface...
  4. pmc TASSER_WT: a protein structure prediction algorithm with accurate predicted contact restraints for difficult protein targets
    Seung Yup Lee
    Center for Study of Systems Biology, Georgia Institute of Technology, Atlanta, Georgia, USA
    Biophys J 99:3066-75. 2010
    ..4, when F(wt ≥ 3)(cov) > 1.0 and > 0.4, the success rate of TASSER_WT (TASSER_2.0) is 98.8% (76.2%) and 93.7% (81.1%), respectively...
  5. pmc Cross-reactivity virtual profiling of the human kinome by X-react(KIN): a chemical systems biology approach
    Michal Brylinski
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia, USA
    Mol Pharm 7:2324-33. 2010
    ..The constructed cross-reactivity profiles for the human kinome are freely available to the academic community at http://cssb.biology.gatech.edu/kinomelhm/ ...
  6. pmc Comprehensive structural and functional characterization of the human kinome by protein structure modeling and ligand virtual screening
    Michal Brylinski
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    J Chem Inf Model 50:1839-54. 2010
    ..biology.gatech.edu/kinomelhm/ as well as at the ZINC Web site ( http://zinc.docking.org/applications/2010Apr/Brylinski-2010.tar.gz )...
  7. pmc The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement
    Michal Brylinski
    Center for the Study of Systems Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA
    J Struct Biol 173:558-69. 2011
    ..The Binding Site Refinement (BSR) approach is available to the scientific community as a web server that can be accessed at http://cssb.biology.gatech.edu/bsr/...
  8. pmc TASSER_low-zsc: an approach to improve structure prediction using low z-score-ranked templates
    Shashi B Pandit
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 78:2769-80. 2010
    ..Similar improvements are observed with low-ranked templates from SPARKS(2). The template clustering approach could be applied to other modeling methods that utilize multiple templates to improve structure prediction...
  9. pmc iAlign: a method for the structural comparison of protein-protein interfaces
    Mu Gao
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA, USA
    Bioinformatics 26:2259-65. 2010
    ..While there are many structural comparison approaches developed for individual proteins, very few methods are available for protein-protein complexes...
  10. pmc Improving threading algorithms for remote homology modeling by combining fragment and template comparisons
    Hongyi Zhou
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 78:2041-8. 2010
    ..Moreover, the number of foldable targets (TM-score >or= 0.4) increases from least 7.6% for SP(3) to 54% for SPARKS. Thus, FTCOM is a promising approach to template selection. Proteins 2010. (c) 2010 Wiley-Liss, Inc...
  11. pmc PSiFR: an integrated resource for prediction of protein structure and function
    Shashi B Pandit
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA
    Bioinformatics 26:687-8. 2010
    ....
  12. pmc Q-Dock(LHM): Low-resolution refinement for ligand comparative modeling
    Michal Brylinski
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, Georgia 30318, USA
    J Comput Chem 31:1093-105. 2010
    ....
  13. pmc New benchmark metrics for protein-protein docking methods
    Mu Gao
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 79:1623-34. 2011
    ..While the results according to the new scoring scheme are generally consistent with the original CAPRI assessment, the IS-score identifies models whose significance was previously underestimated...
  14. pmc Why not consider a spherical protein? Implications of backbone hydrogen bonding for protein structure and function
    Michal Brylinski
    Center for the Study of Systems Biology, Georgia Institute of Technology, 250 14th St NW, Atlanta, GA 30076, USA
    Phys Chem Chem Phys 13:17044-55. 2011
    ....
  15. pmc A comprehensive survey of small-molecule binding pockets in proteins
    Mu Gao
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia, United States of America
    PLoS Comput Biol 9:e1003302. 2013
    ..Finally, we discuss the implications of our study for the prediction of protein-ligand interactions based on pocket comparison. ..
  16. pmc Interplay of physics and evolution in the likely origin of protein biochemical function
    Jeffrey Skolnick
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA
    Proc Natl Acad Sci U S A 110:9344-9. 2013
    ....
  17. pmc Restricted N-glycan conformational space in the PDB and its implication in glycan structure modeling
    Sunhwan Jo
    Department of Molecular Biosciences and Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, USA
    PLoS Comput Biol 9:e1002946. 2013
    ..Our results suggest that structure prediction/modeling of N-glycans of glycoconjugates using structure database could be effective and different modeling approaches would be needed depending on the availability of template structures...
  18. pmc Are predicted protein structures of any value for binding site prediction and virtual ligand screening?
    Jeffrey Skolnick
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, GA 30318, USA
    Curr Opin Struct Biol 23:191-7. 2013
    ..Thus, despite the widespread belief to the contrary, low-to-moderate resolution predicted structures have considerable utility for biochemical function prediction...
  19. pmc APoc: large-scale identification of similar protein pockets
    Mu Gao
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30076, USA
    Bioinformatics 29:597-604. 2013
    ..An efficient method for comparing these pockets could greatly assist the classification of ligand-binding sites, prediction of protein molecular function and design of novel drug compounds...
  20. pmc FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach
    Hongyi Zhou
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street, N W, Atlanta, Georgia 30318, USA
    J Chem Inf Model 53:230-40. 2013
    ..The FINDSITE(comb) web service is freely available for academic users at http://cssb.biology.gatech.edu/skolnick/webservice/FINDSITE-COMB/index.html...
  21. pmc EFICAz2.5: application of a high-precision enzyme function predictor to 396 proteomes
    Narendra Kumar
    Center for Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA
    Bioinformatics 28:2687-8. 2012
    ..5), that is trained on a significantly larger data set of enzyme sequences and PROSITE patterns. We also present the results of the application of EFICAz(2.5) to the enzyme reannotation of 396 genomes cataloged in the ENSEMBL database...
  22. pmc FINDSITE(X): a structure-based, small molecule virtual screening approach with application to all identified human GPCRs
    Hongyi Zhou
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street, N W, Atlanta, Georgia 30318, United States
    Mol Pharm 9:1775-84. 2012
    ..All predicted structures, virtual screening data and off-target interactions for the 998 human GPCRs are available at http://cssb.biology.gatech.edu/skolnick/webservice/gpcr/index.html ...
  23. pmc The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation
    Mu Gao
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, GA 30318, USA
    Proc Natl Acad Sci U S A 109:3784-9. 2012
    ..We propose that packing nearby protein-protein or domain-domain interfaces is a major route to the formation of ligand-binding pockets...
  24. pmc Further evidence for the likely completeness of the library of solved single domain protein structures
    Jeffrey Skolnick
    Center for the Study of Systems Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, Georgia 30318, USA
    J Phys Chem B 116:6654-64. 2012
    ..45 (0.5) TM-score threshold, essentially all (most) are found in the PDB. Thus, the conclusion that the PDB is likely complete is further supported...
  25. pmc GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction
    Hongyi Zhou
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia, USA
    Biophys J 101:2043-52. 2011
    ..Thus, GOAP is a promising advance in knowledge-based, all-atom statistical potentials. GOAP is available for download at http://cssb.biology.gatech.edu/GOAP...
  26. pmc Comparison of structure-based and threading-based approaches to protein functional annotation
    Michal Brylinski
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA
    Proteins 78:118-34. 2010
    ..Combined evolution/structure-based function assignment emerges as a powerful technique that can make a significant contribution to comprehensive proteome annotation...
  27. pmc Performance of the Pro-sp3-TASSER server in CASP8
    Hongyi Zhou
    Center for Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 77:123-7. 2009
    ..Finally, we analyze the overall performance and highlight some successful predictions of the pro-sp3-TASSER server...
  28. pmc M-TASSER: an algorithm for protein quaternary structure prediction
    Huiling Chen
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia, USA
    Biophys J 94:918-28. 2008
    ..Thus, we have developed a promising approach to predict full-length quaternary structure for proteins that have weak sequence similarity to proteins of solved quaternary structure...
  29. ncbi Analysis of TASSER-based CASP7 protein structure prediction results
    Hongyi Zhou
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 69:90-7. 2007
    ..For the more difficult targets, TASSER with modest human intervention performed better in comparison to its server counterpart, MetaTASSER, which used a limited time simulation...
  30. ncbi What is the relationship between the global structures of apo and holo proteins?
    Michal Brylinski
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 70:363-77. 2008
    ..These results have applications to protein structure prediction, particularly in the context of protein domain assembly, if additional information concerning ligand binding is exploited...
  31. pmc Ab initio protein structure prediction using chunk-TASSER
    Hongyi Zhou
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA, USA
    Biophys J 93:1510-8. 2007
    ..Chunk-TASSER is approximately 11% (10%) better than TASSER for the total TM-score of the first (best of top five) models. Chunk-TASSER is fully automated and can be used in proteome scale protein structure prediction...
  32. ncbi Development and benchmarking of TASSER(iter) for the iterative improvement of protein structure predictions
    Seung Yup Lee
    Center for the Study of Systems Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 68:39-47. 2007
    ..6 to 84.0%. These results suggest that TASSER(iter) can provide more reliable predictions for targets of Medium difficulty, a range that had resisted improvement in the quality of protein structure predictions...
  33. pmc High precision multi-genome scale reannotation of enzyme function by EFICAz
    Adrian K Arakaki
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    BMC Genomics 7:315. 2006
    ..We have performed a reannotation of 245 genomes using an updated version of EFICAz, a highly precise method for enzyme function prediction...
  34. pmc TASSER-Lite: an automated tool for protein comparative modeling
    Shashi Bhushan Pandit
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA
    Biophys J 91:4180-90. 2006
    ..TASSER-Lite is provided on the web at (http://cssb.biology.gatech.edu/skolnick/webservice/tasserlite/index.html)...
  35. ncbi Efficient prediction of nucleic acid binding function from low-resolution protein structures
    Andras Szilagyi
    Center of Excellence in Bioinformatics, University at Buffalo, State University of New York, 901 Washington St, Buffalo, NY 14203, USA
    J Mol Biol 358:922-33. 2006
    ..The accuracy of our method is close to another, published method that uses all-atom structures, time-consuming calculations and information on conserved residues...
  36. ncbi In quest of an empirical potential for protein structure prediction
    Jeffrey Skolnick
    Center of Excellence in Bioinformatics, University at Buffalo, 901 Washington Street, Buffalo, NY 14203, USA
    Curr Opin Struct Biol 16:166-71. 2006
    ..This represents significant progress and suggests applications to proteome-scale structure prediction...
  37. pmc Structure modeling of all identified G protein-coupled receptors in the human genome
    Yang Zhang
    Center of Excellence in Bioinformatics, University at Buffalo, Buffalo, New York, USA
    PLoS Comput Biol 2:e13. 2006
    ..All predicted GPCR models are freely available for noncommercial users on our Web site (http://www.bioinformatics.buffalo.edu/GPCR)...
  38. pmc On the origin and highly likely completeness of single-domain protein structures
    Yang Zhang
    Center of Excellence in Bioinformatics, University at Buffalo, State University of New York, 901 Washington Street, Buffalo, NY 14203, USA
    Proc Natl Acad Sci U S A 103:2605-10. 2006
    ..Thus, the presence of active-site-like geometries also seems to be a consequence of the packing of compact, secondary structural elements. These results have significant implications for the evolution of protein structure and function...
  39. pmc Protein model quality assessment prediction by combining fragment comparisons and a consensus C(alpha) contact potential
    Hongyi Zhou
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Proteins 71:1211-8. 2008
    ..83 for the 98 targets, which is better than those of other quality assessment methods that participated in CASP7. Our method also outperforms the other methods by about 3% as assessed by the total GDT-score of the selected top models...
  40. pmc A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation
    Michal Brylinski
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, GA 30318, USA
    Proc Natl Acad Sci U S A 105:129-34. 2008
    ..Furthermore, the chemical properties of template-bound ligands can be used to select ligand templates associated with the binding site. In most cases, FINDSITE can accurately assign a molecular function to the protein model...
  41. pmc FINDSITE: a threading-based approach to ligand homology modeling
    Michal Brylinski
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia, United States of America
    PLoS Comput Biol 5:e1000405. 2009
    ..Thus, the rather accurate, computationally inexpensive FINDSITE(LHM) algorithm should be a useful approach to assist in the discovery of novel biopharmaceuticals...
  42. pmc EFICAz2: enzyme function inference by a combined approach enhanced by machine learning
    Adrian K Arakaki
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    BMC Bioinformatics 10:107. 2009
    ..To improve EFICAz's performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment...
  43. pmc FINDSITE: a combined evolution/structure-based approach to protein function prediction
    Jeffrey Skolnick
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology 250 14th St NW, Atlanta, GA 30318, USA
    Brief Bioinform 10:378-91. 2009
    ..Importantly, FINDSITE gives comparable results when high-resolution experimental structures as well as predicted protein models are used...
  44. pmc Protein structure prediction by pro-Sp3-TASSER
    Hongyi Zhou
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia, USA
    Biophys J 96:2119-27. 2009
    ..A server that implements the above algorithm is available at http://cssb.biology.gatech.edu/skolnick/webservice/pro-sp3-TASSER/. The source code is also available upon request...
  45. pmc Identification of metabolites with anticancer properties by computational metabolomics
    Adrian K Arakaki
    Center for the Study of Systems Biology, Georgia Institute of Technology, Atlanta, Georgia, USA
    Mol Cancer 7:57. 2008
    ..The fact that metabolites can affect the cancer process on so many levels suggests that the change in concentration of some metabolites that occurs in cancer cells could have an active role in the progress of the disease...
  46. pmc Benchmarking of TASSER_2.0: an improved protein structure prediction algorithm with more accurate predicted contact restraints
    Seung Yup Lee
    Center for the Study of Systems Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA
    Biophys J 95:1956-64. 2008
    ..3% (64.7%) and 40.8% (35.5%) for the Hard targets (incorrect templates/alignments). For Easy targets (good templates/alignments), the success rate slightly increases from 86.3% to 88.4%...
  47. pmc Q-Dock: Low-resolution flexible ligand docking with pocket-specific threading restraints
    Michal Brylinski
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, Georgia 30318, USA
    J Comput Chem 29:1574-88. 2008
    ..The success rate of Q-Dock employing a pocket-specific potential is 6.3 times higher than that previously reported for the Dolores method, another low-resolution docking approach...
  48. pmc Structure-based classification of 45 FK506-binding proteins
    J A Somarelli
    Department of Biological Sciences, OE304, Florida International University, Miami, Florida 33199, USA
    Proteins 72:197-208. 2008
    ..The docking models also indicate the presence of a helix-loop-helix (HLH) region within a subset of FKBPs, which may be responsible for the interaction between this group of proteins and nucleic acids...
  49. pmc Fast procedure for reconstruction of full-atom protein models from reduced representations
    Piotr Rotkiewicz
    Burnham Institute for Medical Research, 10901 N Torrey Pines Road, La Jolla, California 92037, USA
    J Comput Chem 29:1460-5. 2008
    ..The method is implemented as a standalone program that is available for download from http://cssb.biology.gatech.edu/skolnick/files/PULCHRA...
  50. pmc Assessment of programs for ligand binding affinity prediction
    RyangGuk Kim
    Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street, Atlanta, Georgia 30318, USA
    J Comput Chem 29:1316-31. 2008
    ....
  51. ncbi Benchmarking of dimeric threading and structure refinement
    Vera Grimm
    Center of Excellence in Bioinformatics, University at Buffalo, Buffalo, New York, USA
    Proteins 63:457-65. 2006
    ..Preliminary results for three full-length dimeric models generated with the TASSER method show on average a significant improvement of the final model over the initial template...

Research Grants30

  1. Structural Genomics on Membrane Proteins
    Wayne A Hendrickson; Fiscal Year: 2013
    ..The project will be managed to optimize output and to integrate effectively with the PSI-Biology network and with other membrane protein structure efforts. ..
  2. The Virtual Physiological Rat Project
    Daniel A Beard; Fiscal Year: 2013
    ..This proposal targets the grand challenge of understanding complex multi-faceted disease phenotypes through experiments and simulations that capture the complex genotype-environment-phenotype relationship. ..
  3. Exploring Design Principles of Cellular Control Circuits
    Wendell A Lim; Fiscal Year: 2013
    ..abstract_text> ..
  4. Center for the Spatiotemporal Modeling of Cell Signaling (STMC)
    Bridget S Wilson; Fiscal Year: 2013
    ..The Center will strongly support translation of new technical and computational tools to other signaling systems linked to human disease, especially other immune diseases and cancer. ..
  5. In Silico and in Vitro Investigation of Non-Conserved Interaction Characteristics
    David A Liberles; Fiscal Year: 2013
    ..Similarly important this approach may help flag instances where model organism-based inferences to human diseases are unlikely to hold true. ..
  6. The MIT Center for Single-Cell Dynamics in Cancer (SCDC)
    Scott R Manalis; Fiscal Year: 2013
    ..These facilities and all reagents generated by the cores will be made available to other PS-OCs. ..
  7. COMPUTER SIMULATION THEORY OF GLOBULAR PROTEIN DYNAMICS
    Jeffrey Skolnick; Fiscal Year: 2013
    ..Each Aim is part of a more comprehensive objective to increase our understanding of subcellular processes and biochemical function that will help accelerate drug discovery as well as provide general biochemical insights. ..
  8. Comparative genomics of protein structure and function
    Olivier Lichtarge; Fiscal Year: 2013
    ....
  9. Membrane Protein Structural Dynamics Consortium
    EDUARDO A PEROZO; Fiscal Year: 2013
    ..abstract_text> ..
  10. University of Maryland Greenebaum Cancer Center Support Grant
    Kevin J Cullen; Fiscal Year: 2013
    ..Reflecting our remarkable and continued growth, UMGCC seeks to renew its CCSG to enhance and expand its efforts in high-quality and clinically relevant cancer research. ..
  11. Center for Structure of Membrane Proteins
    Robert M Stroud; Fiscal Year: 2013
    ..3.1, one of the world's most productive protein crystallography facilities. Overall, the combined expertise of principal investigators provides a unique environment to achieve the proposed aims. ..
  12. PHENIX: new methods for automation in macromolecular crystallography
    PAUL DAVID ADAMS; Fiscal Year: 2013
    ..Program Director/Principal Investigator (Last, First, Middle): Adams, Paul D. Use only if additional space is needed to list additional ..
  13. New England Regional Center of Excellence in Biodefense and Emerging Infectious D
    Dennis L Kasper; Fiscal Year: 2013
    ..NERCE will also continue its Developmental Projects program and Career Development in Biodefense program in an effort to initiate new research efforts and to attract new investigators to this field. ..
  14. Molecular Analyses and Interventions for Biodefense and Emerging Pathogens
    Olaf Schneewind; Fiscal Year: 2013
    ..Research and training at the GLRCE is governed by a mechanism involving ongoing review of scientific excellence and translational goals, inter-institutional advisory boards and external scientific advisory bodies. ..
  15. IkB/NF-kB Recognition In Silico, In Vitro and In Vivo
    Elizabeth A Komives; Fiscal Year: 2013
    ....
  16. New Computational Methods for Data-driven Protein Structure Prediction
    Jinbo Xu; Fiscal Year: 2013
    ..Protein modeling is also widely applied in the pharmaceutical industry and integrated into most stages of pharmaceutical research. ..
  17. Model-based predictions of responses RTK Pathway therapies
    Joe W Gray; Fiscal Year: 2013
    ..abstract_text> ..
  18. Computational methods for structural-functional studies of proteins
    Nick V Grishin; Fiscal Year: 2013
    ..We will improve alignment accuracy and using the new method will analyze kinases, which are a medically important group of enzymes attracting high interest because of their relevance to many diseases, cancer in particular. ..
  19. Study of biological evolution of structure and function in proteins
    Eugene I Shakhnovich; Fiscal Year: 2013
    ..Successful completion of these studies will advance our ability to extract functionally relevant signals from genomic sequences by putting their analysis on firm evolutionary and Biophysical ground. ..
  20. Structural Genomics of Eukaryotic Domain Families
    Gaetano T Montelione; Fiscal Year: 2013
    ..abstract_text> ..
  21. The Transmembrane Protein Center
    Brian G Fox; Fiscal Year: 2013
    ..Expanded options for crystallization screening, access to synchrotrons, and improvements in software used to solve structures will also contribute to the increased throughput necessary to achieve PSI:Biology goals. ..
  22. Predicting Protein-DNA Interactions with Structural Models
    Philip Bradley; Fiscal Year: 2013
    ..The eventual goal of these studies will be to predict the sites in the genome at which MyoD and other key regulatory proteins exert their effect. ..