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131
Review: Protein Secondary Structure Prediction Continues to Rise
- J. Struct. Biol
, 2001
"... f prediction accuracy? We shall see. 2001 Academic Press INTRODUCTION History. Linus Pauling correctly guessed the formation of helices and strands (14, 15) (and falsely hypothesized other structures). Three years before Pauling's guess was verified by the publications of the first X-ray stru ..."
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Cited by 181 (22 self)
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f prediction accuracy? We shall see. 2001 Academic Press INTRODUCTION History. Linus Pauling correctly guessed the formation of helices and strands (14, 15) (and falsely hypothesized other structures). Three years before Pauling's guess was verified by the publications of the first X-ray structures (16, 17), one group had already ventured to predict secondary structure from sequence (18). The first-generation prediction methods following in the 1960s and 1970s were all based on single amino acid propensities (19). The second-generation methods dominating the scene until the early 1990s used propensities for segments of 3--51 adjacent residues (19). Basically any imaginable theoretical algorithm had been applied to the problem of predicting secondary structure from sequence. However, it seemed that prediction accuracy stalled at levels slightly above 60% (percentage of residues predicted correctly in one of the three states: helix, strand, and other). The reason for this limit was the
Hidden markov models that use predicted local structure for fold recognition: alphabets of backbone geometry
- Proteins
, 2003
"... An important problem in computational biology is predicting the structure of the large number of pu-tative proteins discovered by genome sequencing projects. Fold-recognition methods attempt to solve the problem by relating the target proteins to known structures, searching for template proteins hom ..."
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Cited by 71 (11 self)
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An important problem in computational biology is predicting the structure of the large number of pu-tative proteins discovered by genome sequencing projects. Fold-recognition methods attempt to solve the problem by relating the target proteins to known structures, searching for template proteins homologous to the target. Remote homologs which may have significant structural similarity are often not detectable by sequence similarities alone. To address this, we incorporated predicted local structure, a generalization of secondary structure, into two-track profile HMMs. We did not rely on a simple helix-strand-coil definition of secondary structure,
EVA: Large-Scale Analysis of Secondary Structure Prediction
, 2001
"... EVAisaweb-basedserverthat evaluatesautomaticstructurepredictionservers continuouslyandobjectively.SinceJune2000,EVA collectedmorethan20,000secondarystructurepredictions. TheEVAsetssufficedtoconcludethatthe fieldofsecondarystructurepredictionhasadvancedagain. Accuracyincreasedsubstantiallyin the1990s ..."
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Cited by 54 (10 self)
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EVAisaweb-basedserverthat evaluatesautomaticstructurepredictionservers continuouslyandobjectively.SinceJune2000,EVA collectedmorethan20,000secondarystructurepredictions. TheEVAsetssufficedtoconcludethatthe fieldofsecondarystructurepredictionhasadvancedagain. Accuracyincreasedsubstantiallyin the1990sthroughusingevolutionaryinformation takenfromthedivergenceofproteinsinthesame structuralfamily.Recently,theevolutionaryinformationresultingfromimprovedsearchesandlarger databaseshasagainboostedpredictionaccuracyby morethan4%toitscurrentheightaround76%ofall residuespredictedcorrectlyinoneofthethree states:helix,strand,orother.Thebestcurrent methodssolvedmostoftheproblemsraisedat earlierCASPmeetings:Allgoodmethodsnowget segmentsrightandperformwellonstrands.Isthe recentincreaseinaccuracysignificantenoughto makepredictionsevenmoreuseful?Webelievethe answerisaffirmative.Whatisthelimitofprediction accuracy?Weshallsee.Alldataareavailable throughtheEVAwebsiteat{cubic.bioc.columbia. edu/eva/}.Therawdatafortheresultspresentedare availableat{eva}/sec/bup_common/2001_02_22/. Proteins2001;Suppl5:192--199.2002Wiley-Liss,Inc. Keywords:automaticevaluation;large-scaleassessment; proteinstructureprediction
A hidden Markov model derived structural alphabet for proteins
- J Mol Biol
, 2004
"... Understanding and predicting protein structures depend on the complexity and the accuracy of the models used to represent them. We have recently set up a Hidden Markov Model to optimally compress protein three-dimensional conformations into a one-dimensional series of letters of a structural alphabe ..."
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Cited by 52 (10 self)
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Understanding and predicting protein structures depend on the complexity and the accuracy of the models used to represent them. We have recently set up a Hidden Markov Model to optimally compress protein three-dimensional conformations into a one-dimensional series of letters of a structural alphabet. Such a model learns simultaneously the shape of representative structural letters describing the local conformation and the logic of their connections, i.e. the transition matrix between the letters. Here, we move one step further and report some evidence that such a model of protein local architecture also captures some accurate amino acid features. All the letters have specific and distinct amino acid distributions. Moreover, we show that words of amino acids can have significant propensities for some letters. Perspectives point towards the prediction of the series of letters describing the structure of a protein from its amino acid sequence. D 2005 Elsevier B.V. All rights reserved.
Predicting interresidue contacts using templates and pathways
- Proteins
, 2003
"... ABSTRACT We present a novel method, HMMSTR-CM, for protein contact map predictions. Contact potentials were calculated by using HMMSTR, a hidden Markov model for local sequence structure correlations. Targets were aligned against protein templates using a Bayesian method, and contact maps were gener ..."
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Cited by 48 (5 self)
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ABSTRACT We present a novel method, HMMSTR-CM, for protein contact map predictions. Contact potentials were calculated by using HMMSTR, a hidden Markov model for local sequence structure correlations. Targets were aligned against protein templates using a Bayesian method, and contact maps were generated by using these alignments. Contact potentials then were used to evaluate these templates. An ab initio method based on the target contact potentials using a rule-based strategy to model the protein-folding pathway was developed. Fold recognition and ab initio methods were combined to produce accurate, protein-like contact maps. Pathways sometimes led to an unambiguous prediction of topology, even without using templates. The results on CASP5 targets are discussed. Also included is a brief update on the quality of fully automated ab initio predictions using the I-sites server. Proteins 2003;53:497–502.
An HMM posterior decoder for sequence feature prediction that includes homology information
, 2005
"... Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil--coil structures, protein secondary structure or genes, extra support can be gained from homologs. ..."
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Cited by 46 (3 self)
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Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil--coil structures, protein secondary structure or genes, extra support can be gained from homologs.
Remote homolog detection using local sequence-structure correlations
- Proteins
, 2004
"... ABSTRACT Remote homology detection refers to the detection of structural homology in proteins when there is little or no sequence similarity. In this article, we present a remote homolog detection method called SVM-HMMSTR that overcomes the reliance on detectable sequence similarity by transforming ..."
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Cited by 34 (1 self)
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ABSTRACT Remote homology detection refers to the detection of structural homology in proteins when there is little or no sequence similarity. In this article, we present a remote homolog detection method called SVM-HMMSTR that overcomes the reliance on detectable sequence similarity by transforming the sequences into strings of hidden Markov states that represent local folding motif patterns. These state strings are transformed into fixeddimension feature vectors for input to a support vector machine. Two sets of features are defined: an order-independent feature set that captures the amino acid and local structure composition; and an order-dependent feature set that captures the sequential ordering of the local structures. Tests using the Structural Classification of Proteins (SCOP) 1.53 data set show that the SVM-HMMSTR gives a significant improvement over several current methods. Proteins 2004;57:518–530. © 2004 Wiley-Liss, Inc. Key words: remote homology; local structure; support vector machines; hidden Markov model; protein folding; I-sites; HMMSTR
Ab initio construction of polypeptide fragments: accuracy of loop decoy discrimination by an all-atom statistical potential and the AMBER force field with the Generalized Born solvation model
- Proteins
, 2003
"... ABSTRACT We describe a novel method to generate ensembles of conformations of the main-chain atoms {N, C�,C,O,C�} for a sequence of amino acids within the context of a fixed protein framework. Each conformation satisfies fundamental stereochemical restraints such as idealized geometry, favorable �/ ..."
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Cited by 33 (4 self)
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ABSTRACT We describe a novel method to generate ensembles of conformations of the main-chain atoms {N, C�,C,O,C�} for a sequence of amino acids within the context of a fixed protein framework. Each conformation satisfies fundamental stereochemical restraints such as idealized geometry, favorable �/ � angles, and excluded volume. The ensembles include conformations both near and far from the native structure. Algorithms for effective conformational sampling and constant time overlap detection permit the generation of thousands of distinct conformations in minutes. Unlike previous approaches, our method samples dihedral angles from fine-grained �/ � state sets, which we demonstrate is superior to exhaustive enumeration from coarse �/ � sets. Applied to a large set of loop structures, our method samples consistently near-native conformations, averaging 0.4, 1.1, and 2.2 A ˚ mainchain root-mean-square deviations for four, eight, and twelve residue long loops, respectively. The ensembles make ideal decoy sets to assess the discriminatory power of a selection method. Using these decoy sets, we conclude that quality of anchor geometry cannot reliably identify near-native conformations, though the selection results are comparable to previous loop prediction methods. In a subsequent study (de Bakker et al.: Proteins 2003;51: 21–40), we demonstrate that the AMBER forcefield with the Generalized Born solvation model identifies near-native conformations significantly better than previous methods. Proteins 2003;51:41–55. © 2003 Wiley-Liss, Inc. Key words: conformational sampling; conformational search algorithms; anchor geometry; decoy sets; discrete state sets; loop modeling
Striped sheets and protein contact prediction
- Bioinformatics
, 2004
"... Accepted for oral presentation at ISMB/ECCB 2004 ..."
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SAM-T08, HMM-based Protein Structure Prediction
, 2009
"... The SAM-T08 web server is a protein-structure prediction server that provides several useful intermediate results in addition to the final predicted 3D structure: three multiple sequence alignments of putative homologs using different iterated search procedures, prediction of local structure feature ..."
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Cited by 30 (4 self)
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The SAM-T08 web server is a protein-structure prediction server that provides several useful intermediate results in addition to the final predicted 3D structure: three multiple sequence alignments of putative homologs using different iterated search procedures, prediction of local structure features including various backbone and burial properties, calibrated E-values for the significance of template searches of PDB, and residue-residue contact predictions. The server has been validated as part of the CASP8 assessment of structure prediction as having good performance across all classes of predictions.
