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Exploring the Structure Space of Wildtype Ras Guided by Experimental Data

Authors:

Amarda ShehuAuthors Info & Claims

BCB'13: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

Pages 756 - 763

https://doi.org/10.1145/2506583.2506706

Published: 22 September 2013 Publication History

Abstract

The Ras enzyme mediates critical signaling pathways in cell proliferation and development by transitioning between GTP- (active) and GDP-bound (inactive) states. Many cancers are linked to specific Ras mutations affecting its conformational switching between active and inactive states. A detailed understanding of the sequence-structure-function space in Ras is missing. In this paper, we provide the first steps towards such an understanding. We conduct a detailed analysis of X-ray structures of wildtype and mutant variants of Ras. We embed the structures onto a low-dimensional structure space by means of Principal Component Analysis (PCA) and show that these structures are energetically feasible for wildtype Ras. We then propose a probabilistic conformational search algorithm to further populate the structure space of wildtype Ras. The algorithm explores a low-dimensional map as guided by the principal components obtained through PCA. Generated conformations are rebuilt in all-atom detail and energetically refined through Rosetta in order to further populate the structure space of wildtype Ras with energetically-feasible structures. Results show that a variety of novel structures are revealed, some of which reproduce experimental structures not subjected to the PCA but withheld for the purpose of validation. This work is a first step towards a comprehensive characterization of the sequence-structure space in Ras, which promises to reveal novel structures not probed in the wet laboratory, suggest new mutations, propose new binding sites, and even elucidate unknown interacting partners of Ras.

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Cited By

Sapin EDe Jong KShehu A(2018)From Optimization to MappingIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.262874515:3(719-731)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1109/TCBB.2016.2628745
Molloy KClausen RShehu A(2015)A stochastic roadmap method to model protein structural transitionsRobotica10.1017/S026357471500105834:8(1705-1733)Online publication date: 11-Dec-2015
https://doi.org/10.1017/S0263574715001058
Clausen RShehu ABaldi PWang W(2014)A multiscale hybrid evolutionary algorithm to obtain sample-based representations of multi-basin protein energy landscapesProceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/2649387.2649390(269-278)Online publication date: 20-Sep-2014
https://dl.acm.org/doi/10.1145/2649387.2649390

Index Terms

Exploring the Structure Space of Wildtype Ras Guided by Experimental Data
1. Applied computing
  1. Life and medical sciences
2. Mathematics of computing
  1. Probability and statistics

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cover image ACM Conferences

BCB'13: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

September 2013

987 pages

ISBN:9781450324342

DOI:10.1145/2506583

Copyright © 2013 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 September 2013

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BCB'13: ACM-BCB2013

September 22 - 25, 2013

Wshington DC, USA

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BCB'13 Paper Acceptance Rate 43 of 148 submissions, 29%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Sapin EDe Jong KShehu A(2018)From Optimization to MappingIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.262874515:3(719-731)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1109/TCBB.2016.2628745
Molloy KClausen RShehu A(2015)A stochastic roadmap method to model protein structural transitionsRobotica10.1017/S026357471500105834:8(1705-1733)Online publication date: 11-Dec-2015
https://doi.org/10.1017/S0263574715001058
Clausen RShehu ABaldi PWang W(2014)A multiscale hybrid evolutionary algorithm to obtain sample-based representations of multi-basin protein energy landscapesProceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/2649387.2649390(269-278)Online publication date: 20-Sep-2014
https://dl.acm.org/doi/10.1145/2649387.2649390

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