Modeling Macromolecular Structures and Motions: Computational Methods for Sampling andAnalysis of Energy Landscapes

Kevin Molloy, N. Akhter, Amarda Shehu
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引用次数: 0

Abstract

With biomolecular structure recognized as central to understanding mechanisms in the cell, dry laboratories have spent significant efforts on modeling and analyzing structure and dynamics. While significant advances have been made, particularly in the design of sophisticated energetic models and molecular representations, such efforts are experiencing diminishing returns. One of the culprits is the low exploration capability of Molecular Dynamics- and Monte Carlo-based exploration algorithms. The impasse has attracted AI researchers bringing complementary tools, such as randomized search and stochastic optimization. The tutorial introduces students and researchers to stochastic optimization treatments and methodologies for understanding and elucidating the role of biomolecular structure and dynamics in function. In addition, the tutorial allows attendees to connect between structures, motions, and function via analysis tools that take an energy landscape view of the relationship between biomolecular structure, dynamics, and function. The presentation is enhanced via open-source software that permit hands-on exercises, which benefits both students and senior researchers keen to make their own contributions.
模拟大分子结构和运动:能量景观采样和分析的计算方法
随着生物分子结构被认为是理解细胞机制的核心,干燥实验室已经在结构和动力学建模和分析方面付出了巨大的努力。虽然已经取得了重大进展,特别是在复杂的能量模型和分子表征的设计方面,但这些努力正在经历收益递减的过程。其中一个原因是基于分子动力学和蒙特卡罗的探测算法的探测能力较低。这一僵局吸引了人工智能研究人员,他们带来了随机搜索和随机优化等补充工具。本教程向学生和研究人员介绍随机优化处理和方法,以理解和阐明生物分子结构和动力学在功能中的作用。此外,该教程允许与会者通过分析工具将结构,运动和功能联系起来,这些工具可以从能量景观的角度看待生物分子结构,动力学和功能之间的关系。通过允许动手练习的开源软件增强了演示,这对学生和渴望做出自己贡献的高级研究人员都有好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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