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Guang-Zheng Zhang, De-shuang Huang (2004)
Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding schemeJournal of Computer-Aided Molecular Design, 18
P. Fariselli, O. Olmea, A. Valencia, R. Casadio (2001)
Progress in predicting inter‐residue contacts of proteins with neural networks and correlated mutationsProteins: Structure, 45
M. Punta, Burkhard Rost (2005)
PROFcon: novel prediction of long-range contactsBioinformatics, 21 13
Berman (2000)
The protein data bankNucleic Acids Res, 28
Rotem Rubinstein, A. Fiser (2008)
Predicting disulfide bond connectivity in proteins by correlated mutations analysisBioinformatics, 24 4
Guoli Wang, Roland Dunbrack (2003)
PISCES: a protein sequence culling serverBioinformatics, 19 12
R. MacCallum (2004)
Striped sheets and protein contact predictionBioinformatics, 20 Suppl 1
C. Floudas, H. Fung, S. McAllister, M. Mönnigmann, R. Rajgaria (2006)
Advances in protein structure prediction and de novo protein design : A reviewChemical Engineering Science, 61
Yu‐Ching Chen, Jenn-Kang Hwang (2005)
Prediction of disulfide connectivity from protein sequencesProteins: Structure, 61
Vicatos (2005)
Prediction of distant residue contacts with the use of evolutionary informationProteins, 58
P. Kundrotas, E. Alexov (2006)
Predicting residue contacts using pragmatic correlated mutations method: reducing the false positivesBMC Bioinformatics, 7
George Shackelford, K. Karplus (2007)
Contact prediction using mutual information and neural netsProteins: Structure, 69
Spyridon Vicatos, Y. Kaznessis (2007)
Separating true positive predicted residue contacts from false positive ones in mainly α proteins, using constrained Metropolis MC simulationsProteins: Structure, 70
Wu (2008)
A comprehensive assessment of sequence-based and template-based methods for protein contact predictionBioinformatics, 24
Jianlin Cheng, P. Baldi (2007)
Improved residue contact prediction using support vector machines and a large feature setBMC Bioinformatics, 8
J. Klepeis, C. Floudas (2003)
ASTRO-FOLD: a combinatorial and global optimization framework for Ab initio prediction of three-dimensional structures of proteins from the amino acid sequence.Biophysical journal, 85 4
A. Vullo, Ian Walsh, G. Pollastri (2006)
A two-stage approach for improved prediction of residue contact mapsBMC Bioinformatics, 7
(2007)
BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btn069 Structural bioinformatics A comprehensive assessment of sequence-based and template-based methods for protein contact prediction
W. Kabsch, C. Sander (1983)
Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical featuresBiopolymers, 22
C. Floudas (2007)
Computational methods in protein structure prediction.Biotechnology and bioengineering, 97 2
PROTEINS: Structure, Function, and Bioinformatics 58:935–949 (2005) Prediction of Distant Residue Contacts With the Use of Evolutionary Information
H. Berman, Tammy Battistuz, T. Bhat, Wolfgang Bluhm, Philip Bourne, K. Burkhardt, Zukang Feng, G. Gilliland, L. Iype, Shri Jain, Phoebe Fagan, Jessica Marvin, David Padilla, V. Ravichandran, B. Schneider, N. Thanki, H. Weissig, J. Westbrook, C. Zardecki
Electronic Reprint Biological Crystallography the Protein Data Bank Biological Crystallography the Protein Data Bank
R. Rajgaria, S. McAllister, C. Floudas (2006)
A novel high resolution CαCα distance dependent force field based on a high quality decoy setProteins: Structure, 65
David Jones (1999)
Protein secondary structure prediction based on position-specific scoring matrices.Journal of molecular biology, 292 2
A. Ortiz, A. Kolinski, A. Kolinski, J. Skolnick (1998)
Fold assembly of small proteins using monte carlo simulations driven by restraints derived from multiple sequence alignments.Journal of molecular biology, 277 2
O. Lund, K. Frimand, J. Gorodkin, H. Bohr, J. Bohr, Jan Hansen, S. Brunak (1997)
Protein distance constraints predicted by neural networks and probability density functions.Protein engineering, 10 11
Zhao (2003)
Prediction of contact maps using support vector machinesProceedings of the IEEE Sympesium on Bioinformatics and Bioengineering
C. Anfinsen (1973)
Principles that govern the folding of protein chains.Science, 181 4096
P. Fariselli, R. Casadio (1999)
A neural network based predictor of residue contacts in proteins.Protein engineering, 12 1
Angel Ortiz, A. Kolinski, J. Skolnick (1998)
Nativelike topology assembly of small proteins using predicted restraints in Monte Carlo folding simulations.Proceedings of the National Academy of Sciences of the United States of America, 95 3
Nicholas Hamilton, K. Burrage, M. Ragan, T. Huber (2004)
Protein contact prediction using patterns of correlationProteins: Structure, 56
Jianlin Cheng, P. Baldi
Bioinformatics Original Paper a Machine Learning Information Retrieval Approach to Protein Fold Recognition
Ying Zhao, G. Karypis (2003)
Prediction of contact maps using support vector machinesThird IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.
A. Ortiz, A. Kolinski, J. Skolnick (1998)
Tertiary structure prediction of the KIX domain of CBP using Monte Carlo simulations driven by restraints derived from multiple sequence alignmentsProteins: Structure, 30
Cheng (2006)
A machine learning information retrieval approach to protein fold recognitionBioinformatics, 22
A. Murzin, S. Brenner, T. Hubbard, C. Chothia (1995)
SCOP: a structural classification of proteins database for the investigation of sequences and structures.Journal of molecular biology, 247 4
Chuang (2003)
Relationship between protein structures and disulfide bonding patternsProteins, 53
Jianlin Cheng, Hiroto Saigo, P. Baldi (2005)
Large‐scale prediction of disulphide bridges using kernel methods, two‐dimensional recursive neural networks, and weighted graph matchingProteins: Structure, 62
Richard Bonneau, I. Ruczinski, J. Tsai, D. Baker (2002)
Contact order and ab initio protein structure predictionProtein Science, 11
O. Olmea, B. Rost, Alfonso Valencia (1999)
Effective use of sequence correlation and conservation in fold recognition.Journal of molecular biology, 293 5
U. Hobohm, C. Sander (1994)
Enlarged representative set of protein structuresProtein Science, 3
Ulrike Göbel, C. Sander, R. Schneider, A. Valencia (1994)
Correlated mutations and residue contacts in proteinsProteins: Structure, 18
Y. Shao, C. Bystroff (2003)
Predicting interresidue contacts using templates and pathwaysProteins: Structure, 53
M. Singer, G. Vriend, R. Bywater (2002)
Prediction of protein residue contacts with a PDB-derived likelihood matrix.Protein engineering, 15 9
Chao-Chun Chuang, Chun-Yin Chen, Jinn-Moon Yang, P. Lyu, Jenn-Kang Hwang (2004)
SHORT COMMUNICATION Relationship Between Protein Structures and Disulfide- Bonding Patterns
D. Horner, W. Pirovano, G. Pesole (2007)
Correlated substitution analysis and the prediction of amino acid structural contactsBriefings in bioinformatics, 9 1
O. Graña, D. Baker, R. MacCallum, J. Meiler, M. Punta, B. Rost, M. Tress, A. Valencia (2005)
CASP6 assessment of contact predictionProteins: Structure, 61
S. McAllister, C. Floudas (2007)
Alpha-helical topology and tertiary structure prediction in globular proteins2007 46th IEEE Conference on Decision and Control
P. Fariselli, O. Olmea, Alfonso Valencia, R. Casadio (2001)
Prediction of contact maps with neural networks and correlated mutations.Protein engineering, 14 11
S. McAllister, B. Mickus, J. Klepeis, C. Floudas (2006)
Novel approach for α‐helical topology prediction in globular proteins: Generation of interhelical restraintsProteins: Structure, 65
A new optimization‐based method is presented to predict the hydrophobic residue contacts in α‐helical proteins. The proposed approach uses a high resolution distance dependent force field to calculate the interaction energy between different residues of a protein. The formulation predicts the hydrophobic contacts by minimizing the sum of these contact energies. These residue contacts are highly useful in narrowing down the conformational space searched by protein structure prediction algorithms. The proposed algorithm also offers the algorithmic advantage of producing a rank ordered list of the best contact sets. This model was tested on four independent α‐helical protein test sets and was found to perform very well. The average accuracy of the predictions (separated by at least six residues) obtained using the presented method was ∼66% for single domain proteins. The average true positive and false positive distances were also calculated for each protein test set and they are 8.87 and 14.67 Å, respectively. Proteins 2009. © 2008 Wiley‐Liss, Inc.
Proteins: Structure Function and Bioinformatics – Wiley
Published: Jan 1, 2009
Keywords: ; ; ; ; ;
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