SI2-SSE: High-Performance Software for Large-Scale Modeling of Binding Equilibria

SI2-SSE：用于结合平衡大规模建模的高性能软件

• 批准号: 1440665
• 负责人: Emilio Gallicchio
• 金额: $ 14.11万
• 依托单位: CUNY Brooklyn College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1440665&HistoricalAwards=false
• 关键词: SI2; SSE; Performance; Software; Large

Living organisms are regulated by specific interactions among proteins and other macromolecules. Cancer and genetic diseases are due to altered interactions caused by mutations. Cellular organelles and viruses spontaneously build themselves from the ordered assembly of component molecules. While a complete understanding remains elusive, these and many other phenomena fundamentally rest on the ability of molecules to recognize and bind specifically to other molecules. The general principles of molecular recognition are being employed to design new drugs, chemical catalysts, and advanced materials. Computer models offer an important mean to disentangle and analyze molecular interactions and to produce quantitative predictions. The main objective of the project is to develop novel algorithms to model molecular recognition processes at atomic resolution on modern parallel computer architectures. This research seeks to increase the speed of the calculations and expand hardware support to enable the screening of larger sets of drug candidates and the study of multiple protein mutations under various conditions. The increased accuracy and availability of modeling software technologies by a larger community will lead to new ideas and research approaches, and ultimately to new discoveries in medicine, chemistry and material science. This effort will contribute to the establishment of in silico means to evaluate environmental and clinical claims. For example computational evidences on toxicity of substances can inform public policy in the same way that, for instance, atmospheric models are currently used for global climate projections.The project targets the Binding Energy Distribution Analysis Method (BEDAM, for short), an accurate model of molecular binding, currently limited by computational performance. Outcomes of this research include deployment the BEDAM model for the first time on General Purpose Graphical Processing Units (GPGPU) and Many Integrated Core (MIC) massively parallel architectures. To this end, the mathematical formulation of the model will be tuned to best utilize the features of these modern computing architectures. Specialized recursive computational geometry algorithms will be developed to extract from the parallel hardware near optimal performance. Robust automated tools for the processing of molecular models and their analysis will be put in place for large scale applications. Accessible user interfaces will be implemented to ensure wide applicability and adoption of the software. These software applications will be distributed under an open source license to promote sharing and community contributions. Student research assistants form an integral part of the research team. While contributing meaningfully to scientific research, students from challenging socioeconomic backgrounds will acquire computer programming and software maintenance skills useful to enter the high tech job market.

生物体受蛋白质和其他大分子之间的特定相互作用调节。癌症和遗传疾病是由于突变引起的相互作用的改变。细胞器和病毒是由组成分子的有序组装自发地建立起来的。虽然完全理解仍然难以捉摸，但这些和许多其他现象从根本上依赖于分子识别和特异性结合其他分子的能力。分子识别的一般原理正被用于设计新药、化学催化剂和先进材料。计算机模型提供了一个重要的手段来解开和分析分子的相互作用，并产生定量预测。该项目的主要目标是开发新的算法，在现代并行计算机体系结构上以原子分辨率模拟分子识别过程。这项研究旨在提高计算速度，扩大硬件支持，从而能够筛选更大的候选药物集，并在各种条件下研究多种蛋白质突变。更大的社区对建模软件技术的准确性和可用性的提高，将带来新的想法和研究方法，并最终在医学、化学和材料科学领域产生新的发现。这项工作将有助于建立计算机手段来评估环境和临床索赔。例如，关于物质毒性的计算证据可以以与目前用于全球气候预测的大气模型相同的方式为公共政策提供信息。该项目针对的是结合能分布分析方法（简称BEDAM），这是一种精确的分子结合模型，目前受计算性能的限制。这项研究的成果包括首次在通用图形处理单元（GPGPU）和多集成核心（MIC）大规模并行架构上部署BEDAM模型。为此，将调整模型的数学公式，以最好地利用这些现代计算体系结构的特性。将开发专门的递归计算几何算法，从接近最佳性能的并行硬件中提取。用于处理分子模型及其分析的强大自动化工具将用于大规模应用。将实施易接近的用户界面，以确保软件的广泛适用性和采用。这些软件应用程序将在开源许可下分发，以促进共享和社区贡献。学生研究助理是研究团队不可分割的一部分。在为科学研究做出有意义的贡献的同时，来自具有挑战性社会经济背景的学生将获得对进入高科技就业市场有用的计算机编程和软件维护技能。