Sample-based Models of Protein Structural Transitions-Reference-Cited by-同舟云学术

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Sample-based Models of Protein Structural Transitions

Published:2016-10-02 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
language:
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Author:

Maximova Tatiana¹,Carr Daniel²,Plaku Erion³,Shehu Amarda⁴

Affiliation:

1. Dept. of Computer Science, George Mason University, Fairfax, VA

2. Dept. of Statistics, George Mason University, Fairfax, VA

3. Dept. of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC

4. Dept. of Computer Science, Dept. of Bioengineering, School of Systems Biology, George Mason University, Fairfax, VA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2975167.2975180

Reference36 articles.

1. Motion planning algorithms for molecular simulations: A survey

2. Modeling protein conformational transitions by a combination of coarse-grained normal mode analysis and robotics-inspired methods;Al-Bluwi I.;BMC Struct. Biol.,2013

3. Editorial overview: Theory and simulation: Tools for solving the insolvable

4. Using Motion Planning to Map Protein Folding Landscapes and Analyze Folding Kinetics of Known Native Structures

5. Stochastic Roadmap Simulation: An Efficient Representation and Algorithm for Analyzing Molecular Motion

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1. Equivariant Encoding based GVAE (EqEn-GVAE) for Protein Tertiary Structure Generation;2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2022-12-06

2. Graph Representation Learning for Protein Conformation Sampling;Computational Advances in Bio and Medical Sciences;2022

3. Phenoxyethyl Piperidine/Morpholine Derivatives as PAS and CAS Inhibitors of Cholinesterases: Insights for Future Drug Design;Scientific Reports;2019-12

4. Attenuating dependence on structural data in computing protein energy landscapes;BMC Bioinformatics;2019-06

5. From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes;BMC Genomics;2018-09

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