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Data Management for Molecule Simulation : A Throughput-Oriented Approach

分子模拟的数据管理：一种面向吞吐量的方法

基本信息

批准号：
10299289
负责人：
Yicheng Tu
金额：
$ 28.77万
依托单位：
UNIVERSITY OF SOUTH FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-22 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10299289
关键词：
ACE2 Algorithms Biological Biological Models Biological Process Biomimetic Materials Collaborations Communities Computer Simulation Computer software Data Data Analyses Data Analytics Data Management Resources Data Set Databases Development Distributed Systems Docking Genetic Diseases Human Individual Knowledge Discovery Letters Location Medical Research Medicine Methodology Methods Modeling Modernization Molecular Motion Names Online Systems Outcome Output Pathology Performance Pharmaceutical Preparations Play Process Publishing Research Research Personnel Resources Role SARS-CoV-2 infection SARS-CoV-2 spike protein Sampling Stream Structural Models Structure Structure-Activity Relationship Supercomputing System Systems Analysis Techniques Testing Thermodynamics Time Validation Work active control analytical tool base big data management cloud based computerized data processing computing resources cost data management design distributed data falls genetic disorder diagnosis improved large datasets molecular dynamics molecular scale nano novel parallel computer programs query optimization receptor simulation simulation software software systems task analysis tool tool development wide area network

项目摘要

Project Summary We will design and develop a molecular simulation data management system, we call P2DMS, to analyze large dis- tributed molecular dynamics (MD) simulation data. The salient features of the system include: (1) a push-based local query engine design that handles data in a batch processing manner and processes many queries at the same time; (2) optimized MD analytics tools using modern many-core hardware such as GPUs; and (3) efﬁcient management and access to distributed data over wide area networks, which is quite common for large scale MD simulations. This will be done by building a data analysis layer on top of state-of-the-art distributed big data management systems. The out- come of this project will not only improve the efﬁciency of MD data processing, but also enable new knowledge discov- ery that is currently regarded difﬁcult or infeasible. In particular, we will integrate the P2DMS program into existing MD simulation packages, and validate the new design with important real-world biological and MD methodological prob- lems. In particular, we will (1) model the structure-function relationships of how the spike protein of SARS-CoV-2 inter- acts with the human angiotensin converting enzyme 2 (ACE2) receptor; and (2) enhance the performance of a recently developed parameter optimization software for active control of MD simulations.

项目总结