EVA: Large‐scale analysis of secondary structure prediction

EVA is a web‐based server that evaluates automatic structure prediction servers continuously and objectively. Since June 2000, EVA collected more than 20,000 secondary structure predictions. The EVA sets sufficed to conclude that the field of secondary structure prediction has advanced again. Accuracy increased substantially in the 1990s through using evolutionary information taken from the divergence of proteins in the same structural family. Recently, the evolutionary information resulting from improved searches and larger databases has again boosted prediction accuracy by more than 4% to its current height around 76% of all residues predicted correctly in one of the three states: helix, strand, or other. The best current methods solved most of the problems raised at earlier CASP meetings: All good methods now get segments right and perform well on strands. Is the recent increase in accuracy significant enough to make predictions even more useful? We believe the answer is affirmative. What is the limit of prediction accuracy? We shall see. All data are available through the EVA web site at {cubic.bioc.columbia.edu/eva/}. The raw data for the results presented are available at {eva}/sec/bup_common/2001_02_22/. Proteins 2001;Suppl 5:192–199. © 2002 Wiley‐Liss, Inc.

[1]  M. Levitt A simplified representation of protein conformations for rapid simulation of protein folding. , 1976, Journal of molecular biology.

[2]  Burkhard Rost,et al.  Sisyphus and prediction of protein structure , 1997, Comput. Appl. Biosci..

[3]  F S Mathews,et al.  The structure, function and evolution of cytochromes. , 1985, Progress in biophysics and molecular biology.

[4]  M Gerstein,et al.  Advances in structural genomics. , 1999, Current opinion in structural biology.

[5]  B Rost,et al.  Better 1D predictions by experts with machines , 1997, Proteins.

[6]  B. Rost,et al.  Prediction of protein secondary structure at better than 70% accuracy. , 1993, Journal of molecular biology.

[7]  W C Johnson,et al.  The relative order of helical propensity of amino acids changes with solvent environment , 2000, Proteins.

[8]  B. Rost Twilight zone of protein sequence alignments. , 1999, Protein engineering.

[9]  R Zhang,et al.  Skewed distribution of protein secondary structure contents over the conformational triangle. , 1999, Protein engineering.

[10]  Giovanni Soda,et al.  Exploiting the past and the future in protein secondary structure prediction , 1999, Bioinform..

[11]  Geoffrey J. Barton,et al.  JPred : a consensus secondary structure prediction server , 1999 .

[12]  Chris Sander,et al.  Jury returns on structure prediction , 1992, Nature.

[13]  B. Matthews Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[14]  Rita Casadio,et al.  Neural networks to study invariant features of protein folding , 1999 .

[15]  Marc A. Martí-Renom,et al.  EVA: continuous automatic evaluation of protein structure prediction servers , 2001, Bioinform..

[16]  Liam J. McGuffin,et al.  The PSIPRED protein structure prediction server , 2000, Bioinform..

[17]  K Karplus,et al.  Predicting protein structure using only sequence information , 1999, Proteins.

[18]  L Serrano,et al.  Analysis of the effect of local interactions on protein stability. , 1996, Folding & design.

[19]  B. Rost,et al.  Redefining the goals of protein secondary structure prediction. , 1994, Journal of molecular biology.

[20]  R Langridge,et al.  Improvements in protein secondary structure prediction by an enhanced neural network. , 1990, Journal of molecular biology.

[21]  C. Chothia,et al.  Structural patterns in globular proteins , 1976, Nature.

[22]  Malin M. Young,et al.  Predicting allosteric switches in myosins , 1999, Protein science : a publication of the Protein Society.

[23]  A. Szent-Gyorgyi,et al.  Role of proline in polypeptide chain configuration of proteins. , 1957, Science.

[24]  G J Barton,et al.  Evaluation and improvement of multiple sequence methods for protein secondary structure prediction , 1999, Proteins.

[25]  B. Rost PHD: predicting one-dimensional protein structure by profile-based neural networks. , 1996, Methods in enzymology.

[26]  K. Chou,et al.  Prediction of protein secondary structure content. , 1999, Protein engineering.

[27]  G Chelvanayagam,et al.  An analysis of the helix‐to‐strand transition between peptides with identical sequence , 2000, Proteins.

[28]  D T Jones,et al.  Protein secondary structure prediction based on position-specific scoring matrices. , 1999, Journal of molecular biology.

[29]  M. Levitt,et al.  A structural census of the current population of protein sequences. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[30]  P. S. Kim,et al.  Context-dependent secondary structure formation of a designed protein sequence , 1996, Nature.

[31]  Zheng Yuan,et al.  How good is prediction of protein structural class by the component‐coupled method? , 2000, Proteins.

[32]  B Rost,et al.  Bridging the protein sequence-structure gap by structure predictions. , 1996, Annual review of biophysics and biomolecular structure.

[33]  Zhi-Xin Wang,et al.  What Is the Minimum Number of Residues to Determine the Secondary Structural State? , 1999, Journal of protein chemistry.

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

[35]  Malin M. Young,et al.  Predicting conformational switches in proteins , 1999, Protein science : a publication of the Protein Society.

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

[37]  C. Sander,et al.  Database of homology‐derived protein structures and the structural meaning of sequence alignment , 1991, Proteins.

[38]  R. Casadio,et al.  Predictions of protein segments with the same aminoacid sequence and different secondary structure: A benchmark for predictive methods , 2000, Proteins.

[39]  B. Rost,et al.  Alignments grow, secondary structure prediction improves , 2002, Proteins.

[40]  K. Chou,et al.  An optimization approach to predicting protein structural class from amino acid composition , 1992, Protein science : a publication of the Protein Society.

[41]  B. Rost,et al.  A modified definition of Sov, a segment‐based measure for protein secondary structure prediction assessment , 1999, Proteins.

[42]  B. Rost,et al.  Combining evolutionary information and neural networks to predict protein secondary structure , 1994, Proteins.

[43]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[44]  J M Chandonia,et al.  New methods for accurate prediction of protein secondary structure , 1999, Proteins.

[45]  George D. Rose,et al.  A protein taxonomy based on secondary structure , 1999, Nature Structural Biology.

[46]  A. Lesk,et al.  Assessment of novel fold targets in CASP4: Predictions of three‐dimensional structures, secondary structures, and interresidue contacts , 2001, Proteins.

[47]  A. Finkelstein,et al.  Theory of protein secondary structure and algorithm of its prediction , 1983, Biopolymers.

[48]  M Ouali,et al.  Cascaded multiple classifiers for secondary structure prediction , 2000, Protein science : a publication of the Protein Society.

[49]  D Gorse,et al.  Prediction of the location and type of β‐turns in proteins using neural networks , 1999, Protein science : a publication of the Protein Society.

[50]  Roland L. Dunbrack,et al.  CAFASP2: The second critical assessment of fully automated structure prediction methods , 2001, Proteins.

[51]  L Serrano,et al.  Protein engineering as a strategy to avoid formation of amyloid fibrils , 2000, Protein science : a publication of the Protein Society.

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

[53]  F E Cohen,et al.  Evaluation of current techniques for Ab initio protein structure prediction , 1995, Proteins.