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An evolutionary algorithm to model structural excursions of a protein

Authors:

Emmanuel Sapin,

Kenneth De Jong,

Amarda ShehuAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1669 - 1673

https://doi.org/10.1145/3067695.3082544

Published: 15 July 2017 Publication History

Abstract

Excursions of a protein between different structures at equilibrium are key to its ability to modulate its biological function. The energy landscape, which organizes structures available to a protein by their energetics, contains all the information needed to characterize and simulate structural excursions. Computational research aims to uncover such excursions to complement wet-laboratory studies in characterizing protein equilibrium dynamics. Popular strategies adapt the robot motion planning framework and construct full or partial, structured representations of the energy landscape. In this paper, we present a novel, complementary approach based on evolutionary computation. We propose an evolutionary algorithm that evolves path representations of a specific structural excursion without a priori construction of the energy landscape. Preliminary applications on healthy and pathogenic variants of a protein central to human health are promising and warranting further investigation of evolutionary search techniques for modeling protein structural excursions.

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E. Sapin, K. A. De Jong, and A. Shehu. 2017. From Optimization to Mapping: An Evolutionary Algorithm for Protein Energy Landscapes. IEEE/ACM Trans Comput Biol & Bioinform (2017). in press.

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Cited By

Varela DSantos J(2022)Evolving cellular automata schemes for protein folding modeling using the Rosetta atomic representationGenetic Programming and Evolvable Machines10.1007/s10710-022-09427-x23:2(225-252)Online publication date: 16-Jan-2022
https://doi.org/10.1007/s10710-022-09427-x

Index Terms

An evolutionary algorithm to model structural excursions of a protein
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
    2. Computational biology
      1. Molecular structural biology

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cover image ACM Conferences

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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New York, NY, United States

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Published: 15 July 2017

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GECCO '17

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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Cited By

Varela DSantos J(2022)Evolving cellular automata schemes for protein folding modeling using the Rosetta atomic representationGenetic Programming and Evolvable Machines10.1007/s10710-022-09427-x23:2(225-252)Online publication date: 16-Jan-2022
https://doi.org/10.1007/s10710-022-09427-x

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