. Affiliations 1 CUBIC, Columbia Univ., Dept. of Biochemistry and Molecular Biophysics, 650 West 168th Street, New York, NY 10032, USA 2 Univ. of California, Irvine, Dept. of Information and Computer Science, Institute of Genomics and Bioinformatics, Irvine, CA92697, USA 3 European Bioinformatics Institute, Genome Campus, Hinxton, Cambs CB10 1SD, England 4 The Sanger Centre, Wellcome Trust Geno...

An evolutionary model that combines protein secondary structure and amino acid replacement is introduced. It allows likelihood analysis of aligned protein sequences and does not require the underlying secondary (or tertiary) structures of these sequences to be known. One component of the model describes the organization of secondary structure along a protein sequence and another specifies the e...

We address the problem of protein secondary structure prediction with Hidden Markov Models. A 21-state model is built using biological knowledge and statistical analysis of sequence motifs in regular secondary structures. Sequence family information is integrated via the combination of independent predictions of homologous sequences and a weighting scheme. Prediction accuracy with single sequen...

We present a new automatic algorithm, named VoTAP (Voronoï Tessellation Assignment Procedure), which assigns secondary structures of a polypeptide chain using the list of alpha-carbon coordinates. This program uses three-dimensional Voronoï tessellation. This geometrical tool associates with each amino acid a Voronoï polyhedron, the faces of which unambiguously define contacts between residues....

The role of the rigidity of a peptide chain in its equilibrium dynamics is investigated within a realistic model with stringent microscopically derived coupling interaction potential and effective on-site potential. The coupling interaction characterizing the chain rigidity and the effective on-site potentials are calculated for three main types of protein secondary structure. The coupling inte...

Simple hidden Markov models are proposed for predicting secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies in a narrow range, we ignore the duration effect of length distribution, and focus on inclusion of short range correlations of residues and of conformation states in the models. Conformation-independent and -dependent ami...

Proteins modulate the majority of all biological functions and are primarily composed of highly organized secondary structural elements such as helices, turns and sheets. Many of these functions are affected by a small number of key protein-protein contacts, often involving one or more of these well-defined structural elements. Given the ubiquitous nature of these protein recognition domains, t...

The DSSP program assigns protein secondary structure to one of eight states. This discrete assignment cannot describe the continuum of thermal fluctuations. Hence, a continuous assignment is proposed. Technically, the continuum results from averaging over ten discrete DSSP assignments with different hydrogen bond thresholds. The final continuous assignment for a single NMR model successfully re...

We present a new automatic algorithm, named VoTAP (Voronoı̈ Tessellation Assignment Procedure), which assigns secondary structures of a polypeptide chainusing the list of -carbon coordinates. This program uses three-dimensional Voronoı̈ tessellation. This geometrical tool associateswith each amino acid aVoronoı̈ polyhedron, the faces of which unambiguously define contacts between residues. Thanks ...