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Fast and accurate methods for predicting short-range constraints in protein models

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Abstract

Protein modeling tools utilize many kinds of structural information that may be predicted from amino acid sequence of a target protein or obtained from experiments. Such data provide geometrical constraints in a modeling process. The main aim is to generate the best possible consensus structure. The quality of models strictly depends on the imposed conditions. In this work we present an algorithm, which predicts short-range distances between Cα atoms as well as a set of short structural fragments that possibly share structural similarity with a query sequence. The only input of the method is a query sequence profile. The algorithm searches for short protein fragments with high sequence similarity. As a result a statistics of distances observed in the similar fragments is returned. The method can be used also as a scoring function or a short-range knowledge-based potential based on the computed statistics.

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Acknowledgement

This work was partially supported from NIH grant 1R01GM081680-01.

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Authors and Affiliations

  1. Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland

    Dominik Gront & Andrzej Kolinski

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  1. Dominik Gront
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  2. Andrzej Kolinski
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Correspondence to Dominik Gront.

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Gront, D., Kolinski, A. Fast and accurate methods for predicting short-range constraints in protein models. J Comput Aided Mol Des 22, 783–788 (2008). https://doi.org/10.1007/s10822-008-9213-8

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  • DOI: https://doi.org/10.1007/s10822-008-9213-8

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