Hybrid Computational Models for Membrane-Protein Interfaces

膜-蛋白质界面的混合计算模型

• 批准号: 2154804
• 负责人: Qiang Cui
• 金额: $ 50万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154804&HistoricalAwards=false
• 关键词: Hybrid; Computational; Models; Membrane; Protein

WIth support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Professor Qiang Cui of Boston University is developing effective computational methods for tackling problems that involve collective behaviors of proteins/peptides at the lipid membrane interface. These problems are difficult to study using existing computational methodologies due to the involvement of multiple length and time scales. Taking advantage of recent progress in machine learning (ML) techniques, Dr. Cui will aim to overcome these challenges to establish efficient and reliable computational models that enable the mechanistic analysis of protein phase separation at cell membrane surface and protein mediated membrane porations, which are critical in important biological processes such as cell signaling, viral infection and synaptic transmission. Cui will also engage in various education and out-reach activities to inspire students of broad backgrounds to pursue a career at the boundary between physical chemistry, computational science, and biology. At the undergraduate level, Professor Cui will endeavor to enhance the integration of computation and basic programming concepts into the chemistry curriculum at Boston University.To accomplish the research goals, the Cui team will effectively integrate recent advances in simulation methodologies in unique biophysical contexts. In one problem, Cui and co-workers will aim to understand how protein-membrane interactions modify the conformational and interaction properties of proteins in the context of liquid-liquid phase separation. The unique angle will be to develop a hybrid ML/MM model in which the protein and its interaction with the membrane environment are described using ML, trained with atomistic simulation data and a reference coarse-grained model; the advantage of this hybrid model is that many-body effects at the coarse-grained level are captured, a feature expected to be essential to the proper description of collective behaviors of proteins in different environments, including phase separation at or wetting of the lipid membrane. In another problem, the challenge is to understand the mechanism by which multiple types of peptides or protein motifs regulate membrane pores. The Cui group will combine finite temperature string and an ML approach to expand the list of collective variables automatically and systematically for evaluating the underlying minimum free energy pathways. The fundamental strategy of combining the strengths of global (string) and local (PIB) enhanced sampling techniques will be potentially applicable to a broad range of problems in which a minimal set of global progress variables is known ahead of time, yet important local degrees of freedom remain obscure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在化学系化学理论、模型和计算方法项目的支持下，波士顿大学崔强教授正在开发有效的计算方法，以解决涉及蛋白质/肽在脂质膜界面的集体行为的问题。这些问题是很难研究使用现有的计算方法，由于涉及多个长度和时间尺度。利用机器学习（ML）技术的最新进展，崔博士将致力于克服这些挑战，建立高效可靠的计算模型，从而实现细胞膜表面蛋白质相分离和蛋白质介导的膜穿孔的机制分析，这在重要的生物过程中至关重要，如细胞信号传导，病毒感染和突触传递。崔还将参与各种教育和外展活动，激励背景广泛的学生在物理化学、计算科学和生物学之间的边界从事职业。在本科阶段，崔教授将奋进于将计算和基本编程概念融入波士顿大学的化学课程。为了实现研究目标，崔教授团队将有效地将模拟方法的最新进展融入独特的生物物理背景。在一个问题中，Cui及其同事的目标是了解蛋白质-膜相互作用如何在液-液相分离的背景下改变蛋白质的构象和相互作用特性。独特的角度将是开发一个混合ML/MM模型，其中蛋白质及其与膜环境的相互作用使用ML描述，使用原子模拟数据和参考粗粒度模型进行训练;这种混合模型的优点是捕获了粗粒度级别的多体效应，这一特征对于正确描述蛋白质在不同环境中的集体行为是必不可少的，包括脂质膜处的相分离或脂质膜的润湿。在另一个问题中，挑战是了解多种类型的肽或蛋白质基序调节膜孔的机制。崔组将结合联合收割机有限温度弦和ML方法，以自动和系统地扩展集体变量列表，用于评估潜在的最小自由能路径。结合全局（字符串）和局部（PIB）增强采样技术的优势的基本策略将潜在地适用于广泛的问题，其中全局进度变量的最小集合是提前已知的，然而，重要的地方自由度仍然模糊不清。这个奖项反映了NSF的法定使命，并被认为是值得通过利用基金会的智力价值进行评估来支持的和更广泛的影响审查标准。