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Computational Methods to Study the Structure and Dynamics of Biomolecules and Biomolecular Processes pp 25–53Cite as

Coarse-Grained Protein Models in Structure Prediction

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Part of the book series: Springer Series in Bio-/Neuroinformatics ((SSBN,volume 1))

Abstract

The knowledge of the three-dimensional structure of proteins is crucial for understanding many important biological processes. Most biologically important proteins are too large to handle for the classical simulation tools. In such cases, coarse-grained (CG) models nowadays offer various opportunities for efficient conformational sampling and thus prediction of the three-dimensional structure. A variety of CG models have been proposed, each based on a similar framework consisting of a set of conceptual components such as protein representation, force field, sampling, etc. In this chapter we discuss these components, highlighting ideas which have proven to be the most successful. As CG methods are usually part of multistage procedures, we also describe approaches used for the incorporation of homology data and all-atom reconstruction methods.

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

    Maciej Blaszczyk, Dominik Gront, Sebastian Kmiecik, Katarzyna Ziolkowska, Marta Panek & Andrzej Kolinski

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    Adam Liwo

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Blaszczyk, M., Gront, D., Kmiecik, S., Ziolkowska, K., Panek, M., Kolinski, A. (2014). Coarse-Grained Protein Models in Structure Prediction. In: Liwo, A. (eds) Computational Methods to Study the Structure and Dynamics of Biomolecules and Biomolecular Processes. Springer Series in Bio-/Neuroinformatics, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28554-7_2

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