HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins 1 1Edited by J. Thornton

Bystroff, Christopher; Thórsson, Vésteinn; Baker, David

doi:10.1006/jmbi.2000.3837

Cited by 278 publications

(187 citation statements)

References 32 publications

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“…18 This is because HMMSTR models the possible ways of arranging sequence- , but the alignment below shows only poor sequence similarity (10% identity).…”

Section: Materials and Methods Hmmstr Modelmentioning

confidence: 99%

“…Symbols represent hidden states: circles, predominantly helix; squares, strand; diamonds, loop or turn; yellow, glycine; magenta, proline; blue, nonpolar; green, polar; white, no predominant amino acid. Only high probability connections are shown (Reprinted by permission of the authors 18 ).…”

Section: Remote Homolog Detectionmentioning

confidence: 99%

“…Bystroff et al 18 described a hidden Markov model called HMMSTR (Hidden Markov Model for protein STRucture) that extends and generalizes the I-sites library. HMMSTR models I-sites motifs as words of a higher order grammatical structure.…”

Section: Introductionmentioning

confidence: 99%

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Remote homolog detection using local sequence–structure correlations

et al. 2004

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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.

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“…18 This is because HMMSTR models the possible ways of arranging sequence- , but the alignment below shows only poor sequence similarity (10% identity).…”

Section: Materials and Methods Hmmstr Modelmentioning

confidence: 99%

Section: Remote Homolog Detectionmentioning

confidence: 99%

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Remote homolog detection using local sequence–structure correlations

et al. 2004

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“…24 Each HMMSTR state is a position in an I-sites Library motif. 25 These motifs are short sequence patterns that may fold independently.…”

Section: Hmmstr-cm: Threading and Ab Initio Contact Map Predictionsmentioning

confidence: 99%

Predicting interresidue contacts using templates and pathways

Shao

Bystroff

2003

Proteins

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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.

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“…In addition to providing a tractable model that can be reasoned about, the reduction in parameters lessens the risk of overlearning. However, the leading HMM methods to date [5,18] have not exceeded a Q 3 value of 75%, and SOV scores are often unreported.…”

Section: Introductionmentioning

confidence: 99%

Predicting Secondary Structure of All-Helical Proteins Using Hidden Markov Support Vector Machines

Gassend

O'Donnell

Thies

et al. 2006

Pattern Recognition in Bioinformatics

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Abstract. Our goal is to develop a state-of-the-art secondary structure predictor with an intuitive and biophysically-motivated energy model through the use of Hidden Markov Support Vector Machines (HM-SVMs), a recent innovation in the field of machine learning. We focus on the prediction of alpha helices and show that by using HM-SVMs, a simple 7-state HMM with 302 parameters can achieve a Qα value of 77.6% and a SOVα value of 73.4%. As detailed in an accompanying technical report [11], these performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments).

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HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins 1 1Edited by J. Thornton

Cited by 278 publications

References 32 publications

Remote homolog detection using local sequence–structure correlations

Remote homolog detection using local sequence–structure correlations

Predicting interresidue contacts using templates and pathways

Predicting Secondary Structure of All-Helical Proteins Using Hidden Markov Support Vector Machines

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