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

活着的生物体受蛋白质和其他大分子之间的特定相互作用的调节。癌症和遗传病是由突变引起的相互作用改变引起的。细胞器和病毒自发地从组成分子的有序组装中构建自己。虽然完全的理解仍然难以捉摸，但这些现象和许多其他现象从根本上取决于分子识别和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。计算机模型提供了解开和分析分子相互作用并产生定量预测的重要手段。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模拟原子分辨率的分子识别过程。这项研究旨在提高计算速度并扩大硬件支持，以便能够筛选更大的候选药物集合，并研究不同条件下的多种蛋白质突变。一个更大的社区对建模软件技术的准确性和可用性的提高将导致新的想法和研究方法，并最终在医学、化学和材料科学方面产生新的发现。这一努力将有助于建立评估环境和临床索赔的电子手段。例如，关于物质毒性的计算证据可以以相同的方式为公共政策提供信息，例如，大气模型目前用于全球气候项目。该项目的目标是结合能量分布分析方法(BEDAM)，这是一种准确的分子结合模型，目前受到计算性能的限制。这项研究的成果包括首次在通用图形处理单元(GPGPU)和许多集成核心(MIC)大规模并行体系结构上部署BEDAM模型。为此，将调整模型的数学公式，以最大限度地利用这些现代计算体系结构的特点。将开发专门的递归计算几何算法，以从并行硬件中提取接近最优的性能。用于处理分子模型及其分析的强大的自动化工具将在大规模应用中到位。将实施可访问的用户界面，以确保该软件的广泛适用性和采用率。这些软件应用程序将在开放源码许可下分发，以促进共享和社区贡献。学生研究助理是研究团队中不可或缺的一部分。在对科学研究做出有意义贡献的同时，来自具有挑战性社会经济背景的学生将获得有助于进入高科技就业市场的计算机编程和软件维护技能。

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.