Abstract not found

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,

ABSTRACT By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C � (“protein blocks”). The dependence that exists between successive blocks is explicitly taken into account. A Bayesian approach based on the relation protein block-amino acid propensity is used for prediction and leads to a success rate close to 35%. Sharing sequence windows associated with certain blocks into “sequence families ” improves the prediction accuracy by 6%. This prediction accuracy exceeds 75 % when keeping the first four predicted protein blocks at each site of the protein. In addition, two different strategies are proposed: the first one defines the number of protein blocks in each site needed for respecting a user-fixed prediction accuracy, and alternatively, the second one defines the different protein sites to be predicted with a user-fixed number of blocks and a chosen accuracy. This last strategy applied to the ubiquitin conjugating enzyme (�/ � protein) shows that 91 % of the sites may be predicted with a prediction accuracy larger than 77 % considering only three blocks per site. The prediction strategies proposed improve our knowledge about sequence-structure dependence and should be very useful in ab initio protein modelling. Proteins 2000;41:271–287. © 2000 Wiley-Liss, Inc. Key words: protein backbone structure; unsupervised classifier; structure-sequence relationships; structure prediction; protein block; Bayesian approach; prediction strategies

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.

Abstract not found

Encoding protein 3D structures into 1D string using short structural prototypes or structural alphabets opens a new front for structure comparison and analysis. Using the well-documented 16 motifs of Protein Blocks (PBs) as structural alphabet, we have developed a methodology to compare protein structures that are encoded as sequences of PBs by aligning them using dynamic programming which uses a substitution matrix for PBs. This methodology is implemented in the applications available in Protein Block Expert (PBE) server. PBE addresses common issues in the field of protein structure analysis such as comparison of proteins structures and identification of protein structures in structural databanks that resemble a given structure. PBE-T provides facility to transform any PDB file into sequences of PBs. PBE-ALIGNc performs comparison of two protein structures based on the alignment of their corresponding PB sequences. PBE-ALIGNm is a facility for mining SCOP database for similar structures based on the alignment of PBs. Besides, PBE provides an interface to a database (PBE-SAdb) of preprocessed PB sequences from SCOP culled at 95 % and of all-against-all pairwise PB alignments at family and superfamily levels. PBE server is freely available at

ABSTRACT An important problem in computational biology is predicting the structure of the large number of putative 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 that 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 hidden Markov models (HMMs). We did not rely on a simple helix-strandcoil definition of secondary structure, but experimented with a variety of local structure descriptions, following a principled protocol to establish which descriptions are most useful for improving fold recognition and alignment quality. On a test set of 1298 nonhomologous proteins, HMMs incorporating a 3-letter STRIDE alphabet improved fold recognition accuracy by 15 % over amino-acid-only HMMs and 23% over PSI-BLAST, measured by ROC-65 numbers. We compared two-track HMMs to amino-acid-only HMMs on a difficult alignment test set of 200 protein pairs (structurally similar with 3–24 % sequence identity). HMMs with a 6-letter STRIDE secondary track improved alignment quality by 62%, relative to DALI structural alignments, while HMMs with an STR track (an expanded DSSP alphabet that subdivides strands into six states) improved by 40 % relative to CE. Proteins 2003;51:504–514. © 2003 Wiley-Liss, Inc. Key words: protein structure prediction; two-track HMM; multitrack HMM; information theory; neural network; alignment; secondary structure

Research article Protein-segment universe exhibiting transitions at intermediate segment length in conformational subspaces

Motivation: The 3D structure of protein sequence A can be assembled by the substructures corresponding to small segments of A. A sequence segment does not take on all the structural fragments and thus it is desirable to build a short customized structural candidate list for each sequence segment. For each sequence segment, these substructures are its “specific structural alphabet”. The smaller these candidate lists are, the faster the protein structure can be constructed; the more accurate these candidate lists are, the more accurate the final protein structure will be. A major obstacle in protein structure prediction is to construct a small set of structural candidates for each segment such that the native structure can be rebuilt from these structural candidates accurately. Results: Based on integer linear programming and incorporating extra structural information, a software package FragShaver is developed.