BioComp: A Computational Framework for the Characterization of Biological Systems at the Molecular Level

蛋白质建模对于在分子水平上表征生物系统正变得越来越重要。现在人们普遍认为，了解生物功能以及随后的疾病的关键是了解人体工作分子蛋白质的结构、灵活性、动力学和动力学。最近在利用计算分析了解蛋白质的结构和动力学方面取得的成功强调了开发计算方法来探索蛋白质灵活性的基本原理的重要性。用单一构型表示蛋白质的吸引力越来越小，因为这样的表示不能代表能够调节蛋白质功能的不同构象之间的动态相互作用，也不能完全描述蛋白质与邻近分子的相互作用。这一建议将通过几何/机器人和生物物理方法的结合来开发一个计算框架来表征平衡条件下的蛋白质柔性。这项拟议的工作位于计算机科学和现代生物物理学的交汇点，将使两个社区都受益。在计算方面，它将导致新的方法和范例来模拟具有高灵活性、复杂几何、多约束和连续运动的物理系统。成功的机器人学和计算几何学方法将被用来支持生物学应用所需的大规模分析。在生物物理学方面，将开发和测试新的理论和方法来模拟不同分辨率的蛋白质。我们将探讨理论和实验之间的定量联系。这项拟议的工作有可能极大地影响蛋白质系统内部工作的重大悬而未决的问题。

Protein modeling is becoming increasingly important for the characterization of biological systems at the molecular level. It is now widely accepted that the key to understanding biological function, and subsequently disease, is to understand the structure, flexibility, kinetics, and dynamics of the body's worker molecules, proteins. Recent successes in understanding the structure and dynamics of proteins using computational analysis underscore the importance of developing computational methodologies to explore the fundamental principles of protein flexibility. Representing a protein by a single configuration is less and less attractive, as such a representation does not represent the dynamical interplay of different conformations that can regulate protein function and does not fully characterize the protein's interaction with neighboring molecules.This proposal will develop a computational framework for the characterization of protein flexibility at equilibrium conditions through a combination of geometric/robotic and biophysics methods. The proposed work lies at the intersection of computer science and modern biophysics, and will benefit both communities. On the computational side, it will lead to new methodologies and paradigms to model physical systems with high flexibility, complex geometry, multiple constraints, and continuous motion. Successful robotics and computational geometry methods will be adapted to support the large-scale analysis required for biological applications. On the biophysical side, novel theories and methodologies will be developed and tested for modeling proteins at different resolutions. Quantitative connections between theory and experiment will be pursued. The proposed work has the potential to dramatically affect major unresolved problems on the inner workings of protein systems.