Protein‐Structure Prediction by Recombination of Fragments

The field of protein‐structure prediction has been revolutionized by the application of “mix‐and‐match” methods both in template‐based homology modeling and in template‐free de novo folding. Consensus analysis and recombination of fragments copied from known protein structures is currently the only approach that allows the building of models that are closer to the native structure of the target protein than the structure of its closest homologue. It is also the most successful approach in cases in which the target protein exhibits a novel three‐dimensional fold. This review summarizes the recent developments in both template‐based and template‐free protein structure modeling and compares the available methods for protein‐structure prediction by recombination of fragments. A convergence between the “protein folding” and “protein evolution” schools of thought is postulated.

[1]  Jonathan Casper,et al.  Combining local‐structure, fold‐recognition, and new fold methods for protein structure prediction , 2003, Proteins.

[2]  Paul W. Fitzjohn,et al.  In silico protein recombination: enhancing template and sequence alignment selection for comparative protein modelling. , 2003, Journal of molecular biology.

[3]  T. Blundell,et al.  Conformational analysis and clustering of short and medium size loops connecting regular secondary structures: A database for modeling and prediction , 1996, Protein science : a publication of the Protein Society.

[4]  D. T. Jones,et al.  A new approach to protein fold recognition , 1992, Nature.

[5]  Jens Meiler,et al.  Rosetta predictions in CASP5: Successes, failures, and prospects for complete automation , 2003, Proteins.

[6]  Arne Elofsson,et al.  3D-Jury: A Simple Approach to Improve Protein Structure Predictions , 2003, Bioinform..

[7]  P. Munson,et al.  Simplified representation of proteins. , 1996, Journal of biomolecular structure & dynamics.

[8]  A. Kolinski.  Protein modeling and structure prediction with a reduced representation. , 2004, Acta biochimica Polonica.

[9]  J. Skolnick,et al.  Development and large scale benchmark testing of the PROSPECTOR_3 threading algorithm , 2004, Proteins.

[10]  C. Ponting,et al.  On the evolution of protein folds: are similar motifs in different protein folds the result of convergence, insertion, or relics of an ancient peptide world? , 2001, Journal of structural biology.

[11]  Baldomero Oliva,et al.  An automated classification of the structure of protein loops. , 1997, Journal of molecular biology.

[12]  Shoji Takada,et al.  Optimizing physical energy functions for protein folding , 2003, Proteins.

[13]  S. Subramaniam,et al.  Natural coordinate representation for the protein backbone structure , 2002, Proteins.

[14]  L. Chew,et al.  Unit‐vector RMS (URMS) as a tool to analyze molecular dynamics trajectories , 1999, Proteins.

[15]  D. Eisenberg,et al.  Assessment of protein models with three-dimensional profiles , 1992, Nature.

[16]  J. Skolnick,et al.  TOUCHSTONE: An ab initio protein structure prediction method that uses threading-based tertiary restraints , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[17]  S. Wodak,et al.  Modelling the polypeptide backbone with 'spare parts' from known protein structures. , 1989, Protein engineering.

[18]  Liam J McGuffin,et al.  Assembling novel protein folds from super‐secondary structural fragments , 2003, Proteins.

[19]  A. Sali,et al.  Protein Structure Prediction and Structural Genomics , 2001, Science.

[20]  Marcin Feder,et al.  A “FRankenstein's monster” approach to comparative modeling: Merging the finest fragments of Fold‐Recognition models and iterative model refinement aided by 3D structure evaluation , 2003, Proteins.

[21]  T. A. Jones,et al.  Determination of three‐dimensional protein structures from nuclear magnetic resonance data using fragments of known structures , 1987, Proteins.

[22]  Janusz M. Bujnicki,et al.  COLORADO3D, a web server for the visual analysis of protein structures , 2004, Nucleic Acids Res..

[23]  A Maritan,et al.  Recurrent oligomers in proteins: An optimal scheme reconciling accurate and concise backbone representations in automated folding and design studies , 2000, Proteins.

[24]  V. Thorsson,et al.  HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. , 2000, Journal of molecular biology.

[25]  C Kooperberg,et al.  Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. , 1997, Journal of molecular biology.

[26]  D. Baker,et al.  Modeling structurally variable regions in homologous proteins with rosetta , 2004, Proteins.

[27]  Shoji Takada,et al.  A Reversible Fragment Assembly Method for De Novo Protein Structure Prediction , 2003 .

[28]  A. Elofsson,et al.  Can correct protein models be identified? , 2003, Protein science : a publication of the Protein Society.

[29]  Daniel Fischer,et al.  3D‐SHOTGUN: A novel, cooperative, fold‐recognition meta‐predictor , 2003, Proteins.

[30]  D Fischer,et al.  LiveBench‐1: Continuous benchmarking of protein structure prediction servers , 2001, Protein science : a publication of the Protein Society.

[31]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[32]  A. Sali,et al.  Comparative protein structure modeling by iterative alignment, model building and model assessment. , 2003, Nucleic acids research.

[33]  D Baker,et al.  Local sequence-structure correlations in proteins. , 1996, Current opinion in biotechnology.

[34]  David Baker,et al.  Protein Structure Prediction Using Rosetta , 2004, Numerical Computer Methods, Part D.

[35]  A. Godzik,et al.  Sequence-structure matching in globular proteins: application to supersecondary and tertiary structure determination. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[36]  David Baker,et al.  Protein structure prediction and analysis using the Robetta server , 2004, Nucleic Acids Res..

[37]  A Valencia,et al.  A neural network approach to evaluate fold recognition results , 2003, Proteins.

[38]  Ceslovas Venclovas,et al.  Assessment of progress over the CASP experiments , 2003, Proteins.

[39]  J F Boisvieux,et al.  Hidden Markov model approach for identifying the modular framework of the protein backbone. , 1999, Protein engineering.

[40]  T. Blundell,et al.  Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.

[41]  A. Liwo,et al.  Ab initio simulations of protein-folding pathways by molecular dynamics with the united-residue model of polypeptide chains. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[42]  A. Lesk,et al.  Structural determinants of the conformations of medium‐sized loops in proteins , 1989, Proteins.

[43]  Tin Wee Tan,et al.  ANTIMIC: a database of antimicrobial sequences , 2004, Nucleic Acids Res..

[44]  A. Sali,et al.  Statistical potentials for fold assessment , 2009 .

[45]  A Kolinski,et al.  Dynamic Monte Carlo simulations of a new lattice model of globular protein folding, structure and dynamics. , 1991, Journal of molecular biology.

[46]  David T. Jones.  Successful ab initio prediction of the tertiary structure of NK‐lysin using multiple sequences and recognized supersecondary structural motifs , 1997, Proteins.

[47]  Yoshio Umezawa,et al.  A high-throughput screening of genes that encode proteins transported into the endoplasmic reticulum in mammalian cells , 2005, Nucleic acids research.

[48]  B Rost,et al.  Progress of 1D protein structure prediction at last , 1995, Proteins.

[49]  J. Söding,et al.  More than the sum of their parts: On the evolution of proteins from peptides , 2003, BioEssays : news and reviews in molecular, cellular and developmental biology.

[50]  Roland L. Dunbrack,et al.  CAFASP3: The third critical assessment of fully automated structure prediction methods , 2003, Proteins.

[51]  Dominique Douguet,et al.  Easier threading through web-based comparisons and cross-validations , 2001, Bioinform..

[52]  Bruno Contreras-Moreira,et al.  Novel use of a genetic algorithm for protein structure prediction: Searching template and sequence alignment space , 2003, Proteins.

[53]  Christopher Bystroff,et al.  Fully automated ab initio protein structure prediction using I-STES, HMMSTR and ROSETTA , 2002, ISMB.

[54]  T. A. Jones,et al.  Using known substructures in protein model building and crystallography. , 1986, The EMBO journal.

[55]  J. Skolnick,et al.  Automated structure prediction of weakly homologous proteins on a genomic scale. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[56]  C. Anfinsen,et al.  The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. , 1961, Proceedings of the National Academy of Sciences of the United States of America.

[57]  A. Lesk,et al.  The relation between the divergence of sequence and structure in proteins. , 1986, The EMBO journal.

[58]  M. Levitt,et al.  A lattice model for protein structure prediction at low resolution. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[59]  S. Sun.  Reduced representation approach to protein tertiary structure prediction: statistical potential and simulated annealing. , 1995, Journal of Theoretical Biology.

[60]  C Bystroff,et al.  Blind predictions of local protein structure in CASP2 targets using the I‐sites library , 1997, Proteins.

[61]  H A Scheraga,et al.  Recent developments in the theory of protein folding: searching for the global energy minimum. , 1996, Biophysical chemistry.

[62]  M. Levitt,et al.  Small libraries of protein fragments model native protein structures accurately. , 2002, Journal of molecular biology.

[63]  David C. Jones,et al.  GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. , 1999, Journal of molecular biology.

[64]  Adam Godzik,et al.  Fold recognition methods. , 2005, Methods of biochemical analysis.

[65]  J. Skolnick,et al.  The PDB is a covering set of small protein structures. , 2003, Journal of molecular biology.

[66]  G R Marshall,et al.  Molecular modeling in drug design. , 2003, Psychopharmacology series.

[67]  J Lundström,et al.  Pcons: A neural‐network–based consensus predictor that improves fold recognition , 2001, Protein science : a publication of the Protein Society.

[68]  Manuel C. Peitsch,et al.  Protein Modeling by E-mail , 1995, Bio/Technology.

[69]  C. Anfinsen.  Principles that govern the folding of protein chains. , 1973, Science.

[70]  Christopher A. Voigt,et al.  Protein building blocks preserved by recombination , 2002, Nature Structural Biology.

[71]  Yang Zhang,et al.  The protein structure prediction problem could be solved using the current PDB library. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[72]  J L Sussman,et al.  A 3D building blocks approach to analyzing and predicting structure of proteins , 1989, Proteins.

[73]  Janusz M. Bujnicki,et al.  GeneSilico protein structure prediction meta-server , 2003, Nucleic Acids Res..

[74]  Arne Elofsson,et al.  MaxSub: an automated measure for the assessment of protein structure prediction quality , 2000, Bioinform..

[75]  Daniel Fischer,et al.  Modeling three‐dimensional protein structures for CASP5 using the 3D‐SHOTGUN meta‐predictors , 2003, Proteins.

[76]  N. Grishin,et al.  Practical lessons from protein structure prediction , 2005, Nucleic acids research.

[77]  Arne Elofsson,et al.  Structure prediction meta server , 2001, Bioinform..

[78]  A. Liwo,et al.  Calculation of protein conformation by global optimization of a potential energy function , 1999, Proteins.

[79]  Yang Zhang,et al.  SPICKER: A clustering approach to identify near‐native protein folds , 2004, J. Comput. Chem..

[80]  Julian Lee,et al.  PROTEINS: Structure, Function, and Bioinformatics 56:704–714 (2004) Prediction of Protein Tertiary Structure Using PROFESY, a Novel Method Based on Fragment Assembly and , 2022 .

[81]  D Baker,et al.  Global properties of the mapping between local amino acid sequence and local structure in proteins. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[82]  P Rotkiewicz,et al.  Generalized comparative modeling (GENECOMP): A combination of sequence comparison, threading, and lattice modeling for protein structure prediction and refinement , 2001, Proteins.