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Novel Perspectives on Protein Structure Prediction

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Abstract

Our understanding of the protein structure prediction problem is evolving. Recent experimental insights into the protein folding mechanism suggest that many polypeptides may adopt multiple conformations. Consequently, modeling and prediction of an ensemble of configurations is more relevant than the classical approach that aims to compute a single structure for a given sequence. In this chapter, we review recent algorithmic advances which enable the application of statistical mechanics techniques to predicting these structural ensembles. These techniques overcome the limitations of costly folding simulations and allow a rigorous model of the conformational landscape. To illustrate the strength and versatility of this approach, we present applications of these algorithms to various typical protein structure problems ranging from predicting residue contacts to experimental X-ray crystallography measures.

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Authors and Affiliations

  1. Department of Mathematics & Computer Science and AI Lab, MIT, Cambridge, MA, USA

    Bonnie Berger

  2. School of Computer Science, McGill University, Montreal, QC, Canada

    Jéerôme Waldispühl

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  1. Bonnie Berger
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  2. Jéerôme Waldispühl
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Correspondence to Bonnie Berger .

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  1. Dept. Computer Science, Virginia Tech, Torgersen Hall 2050, Blacksburg, 24061-0106, Virgin Islands, USA

    Lenwood S. Heath

  2. Dept. Computer Science, Virginia Tech, Torgersen Hall 2050, Blacksburg, 24061-0106, Virgin Islands, USA

    Naren Ramakrishnan

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Berger, B., Waldispühl, J. (2010). Novel Perspectives on Protein Structure Prediction. In: Heath, L., Ramakrishnan, N. (eds) Problem Solving Handbook in Computational Biology and Bioinformatics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09760-2_9

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