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CAREER: Variational Bridge between Knowledge-based and Physics-based Models - Applications to Ubiquilin Interactions

职业：基于知识和基于物理的模型之间的变分桥梁 - 泛素相互作用的应用

基本信息

批准号：
1053970
负责人：
William Noid
金额：
$ 32.5万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2011
资助国家：
美国
起止时间：
2011-02-01 至 2017-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1053970&HistoricalAwards=false
关键词：
CAREER Variational Bridge between Knowledge

项目摘要

The objective of this CAREER project, jointly funded by Molecular Biophysics in the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences and the Physics of Living Systems Program in the Division of Physics in the Mathematical and Physical Sciences Directorate, is to develop a physics-based coarse-grained force field that can be widely applicable for modeling proteins and regulation of protein-protein interactions. Although simulation methods that evolve the classical dynamics of atomically detailed models have contributed considerable insight into the structure and equilibrium fluctuations of single proteins, the computational expense of all-atom models limits their application for more complex systems. These limitations have motivated tremendous interest in coarse-grained (CG) models that provide much greater computational efficiency, while still providing a detailed description of protein structure. Many CG models can be broadly categorized as either knowledge-based or physics-based models. Knowledge-based models are empirically developed using statistics compiled from experimentally-determined structures present in the protein databank (PDB). In contrast, physics-based protein models are developed using more rigorous theories and, consequently, hold the potential for greater accuracy. This project bridges the gap between knowledge- and physics-based models by employing a rigorous statistical mechanics framework to develop protein models from PDB structures. This project will first quantify and systematically improve the approximations employed in current knowledge-based methods. These results will guide the development of a highly efficient CG model that accurately describes the structure and interactions for a wide range of proteins. The PI will develop a pioneering intergenerational science club (ISC) to capitalize upon the vastly underused expertise of emeritus faculty to strengthen local intergenerational community, enrich K-12 education in underserved rural Pennsylvania, and promote an active lifestyle of life-long learning. The ISC will be initially developed as series of lectures for local retirees given by emeritus Penn State faculty, but will develop laboratory components and also additional outreach to K-12 students. This program will provide unique intergenerational mentorship for students of all ages and employ discovery-based education to inform the general electorate about the impact of cutting-edge scientific research upon modern society. The Penn State Outreach Office and the Pennsylvania Cooperative Extension will publish and distribute the program further propagating its impact. This project will also develop interdisciplinary physical chemistry curriculum and supplemental lectures to empower chemistry students with the rigorous mathematical and physical tools necessary for addressing the fundamental scientific challenges of the future.

这个CAREER项目的目标是由生物科学理事会分子和细胞生物科学部的分子生物物理学和数学和物理科学理事会物理学部的生命系统物理学项目共同资助，旨在开发一种基于物理学的粗粒力场，可广泛适用于蛋白质建模和蛋白质-蛋白质相互作用的调节。虽然演化原子详细模型的经典动力学的模拟方法对单个蛋白质的结构和平衡波动有相当大的贡献，但全原子模型的计算费用限制了它们在更复杂系统中的应用。这些限制激发了人们对粗粒度（CG）模型的极大兴趣，这些模型提供了更高的计算效率，同时仍然提供了蛋白质结构的详细描述。许多CG模型可以大致分为基于知识或基于物理的模型。基于知识的模型是使用从蛋白质数据库（PDB）中存在的实验确定的结构编译的统计数据根据经验开发的。相比之下，基于物理学的蛋白质模型是使用更严格的理论开发的，因此具有更高准确性的潜力。该项目通过采用严格的统计力学框架从PDB结构开发蛋白质模型，弥合了基于知识和基于物理的模型之间的差距。该项目将首先量化和系统地改进目前基于知识的方法中使用的近似值。这些结果将指导一个高效的CG模型的开发，该模型可以准确地描述各种蛋白质的结构和相互作用。 PI将开发一个开创性的代际科学俱乐部（ISC），利用退休教师的广泛未充分利用的专业知识，加强当地的代际社区，丰富宾夕法尼亚州服务不足的农村地区的K-12教育，并促进终身学习的积极生活方式。 ISC最初将作为宾夕法尼亚州立大学退休教师为当地退休人员举办的系列讲座开发，但将开发实验室组件，并为K-12学生提供额外的服务。该计划将为所有年龄段的学生提供独特的代际指导，并采用基于发现的教育，向广大选民介绍尖端科学研究对现代社会的影响。宾夕法尼亚州外联办公室和宾夕法尼亚州合作推广将出版和分发该计划，进一步宣传其影响。该项目还将开发跨学科的物理化学课程和补充讲座，使化学专业的学生能够掌握应对未来基本科学挑战所需的严格的数学和物理工具。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Energetic and entropic considerations for coarse-graining

DOI：
10.1140/epjb/s10051-021-00153-4
发表时间：
2021-07
期刊：
The European Physical Journal B
影响因子：
0
作者：
Katherine M. Kidder;R. Szukalo;W. Noid
通讯作者：
Katherine M. Kidder;R. Szukalo;W. Noid

Rigorous progress in coarse-graining

粗粒度的严格进展

DOI：
发表时间：
2024
期刊：
Annual review of physical chemistry
影响因子：
14.7
作者：
Noid, W.G.;Szukalo, R.J.;Kidder, K.M.;Lesniewski, M.C.
通讯作者：
Lesniewski, M.C.

Extended Ensemble Approach to Transferable Potentials for Low-Resolution Coarse-Grained Models of Ionomers

低分辨率粗粒度离聚物模型可转移势的扩展系综方法

DOI：
10.1021/acs.jctc.6b01160
发表时间：
2017
期刊：
Journal of Chemical Theory and Computation
影响因子：
5.5
作者：
Rudzinski, Joseph F.;Lu, Keran;Milner, Scott T.;Maranas, Janna K.;Noid, William G.
通讯作者：
Noid, William G.

Exploring the landscape of model representations

DOI：
10.1073/pnas.2000098117
发表时间：
2020-09-29
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
影响因子：
11.1
作者：
Foley, Thomas T.;Kidder, Katherine M.;Noid, W. G.
通讯作者：
Noid, W. G.