Development of rare-event sampling techniques for predicting structures and free energies of crystal polymorphs and oligopeptides

开发罕见事件采样技术来预测晶体多晶型物和寡肽的结构和自由能

• 批准号: 1565980
• 负责人: Mark Tuckerman
• 金额: $ 58万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565980&HistoricalAwards=false
• 关键词: Development; rare; event; sampling; techniques

Mark Tuckerman of New York University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry Division to develop methods and software for the prediction of molecular crystal structure. This award is cofunded by the CISE/ACI Software Reuse Venture Fund. In the science of materials, ordered arrays of molecules forming structures known as molecular crystals play an essential role in the pharmaceutical, electronics, and defense industries. Often, the crucial question is which crystals should be made for a particular application. It is worth noting that one of the most widely used pharmaceutical molecular crystals, aspirin, was discovered essentially by accident. Typically, in crystal engineering, it is necessary to screen large databases of potential candidate compounds. Unfortunately, making and characterizing molecular crystals in the laboratory is generally time consuming and costly, rendering a trial-and-error approach through such a database impractical. How many more important molecular crystal systems might be discovered if a systematic, targeted approach could be applied? Theory and computation, which can, in principle, rapidly predict molecular crystal structures and their properties, are uniquely poised to play a key role in creating such a targeted approach. What is needed, however, are robust algorithms for making these predictions. The Tuckerman group develops computational techniques and software for predicting the crystal structures a given compound can form and ranking them according to a thermodynamic property known as free energy, which has been recognized in the scientific community as the proper figure of merit for such a ranking but has remained an elusive property to determine. The Tuckerman group also adapts these algorithms for studying the conformational preferences of short chains of amino acids known as oligopeptides in order to explore the role these important biological molecules play in immunogenicity and the design of new classes of pharmaceuticals. Tuckerman and his coworkers are engaged in many software activities including developing a computer package for crystal structure prediction, improving the efficiency of their molecular dynamics software, PINY-MD and continuing to contribute software to many community software codes. All of the software developed in this project is made available to the broader research community. The basic properties of molecular materials in the solid state are often strongly influenced by the details of their crystal structures and the existence of polymorphs. Experimental determination of these structures is costly and time-consuming, which places increased importance on the role of theory and computation. Similarly, the biochemical function of small oligopeptides, from immunogenicity to inhibition, is affected by their equilibrium conformations in different environments. Computational prediction of structure in complex systems such as these is challenging due to the so-called rare-event sampling problem on a rough potential energy landscape, which arises when attempting to study the equilibrium thermodynamics and kinetics of many complex systems. Roughness on an energy surface refers to the existence of high barriers to conformational and structural changes. The Tuckerman group has proposed to develop robust free-energy based enhanced sampling algorithms and software for overcoming the rare-event problem that arises in the crystal structure prediction and conformational sampling of oligopeptides, thereby allowing favored structures to be identified and thermodynamically ranked in an efficient manner. In the proposed methods, the free energy landscape is expressed in terms of select set of collective variables (CVs) designed to distinguish the different structural motifs in these systems. The CVs are first be subject to new surface navigation techniques in order to identify the minima and saddles points, collectively referred to as "landmarks" on the landscape, and then targeted for enhanced sampling in order to produce the free energy ranking of the landmarks. The new techniques are applied to predict the crystal structures and polymorphs of both rigid and flexible small organic molecules, to study the conformational free energy landscape of an immunogenic peptide binding to the major histocompatibility complex, and to understand the influence of mechanical force on the unfolding mechanism of â-hairpin peptide. Software creation will be accelerated via hackathons organized by the Tuckerman group. Education of students in rare-event methods is aided through workshops organized at New York University's global campus sites. Finally, the Tuckerman group reaches out to underrepresented groups via national organizations having a presence in New York City in order to help devise and participate in STEM-related educational activities.

纽约大学的马克·塔克曼（Mark Tuckerman）得到了化学理论，模型和计算方法计划的奖项，用于开发用于预测分子晶体结构的方法和软件。该奖项由CISE/ACI软件Reuse Venture Fund Cofund。在材料科学中，被订购的分子阵列形成称为分子晶体的结构在药品，电子和防御工业中起着至关重要的作用。通常，至关重要的问题是为特定应用制定哪些晶体。值得注意的是，使用最广泛使用的药物分子晶体，阿司匹林，是偶然发现的。通常，在晶体工程中，有必要筛选潜在候选化合物的大型数据库。不幸的是，实验室中的分子晶体制作和表征通常是耗时且昂贵的，通过这样的数据库进行了试验的方法，这是不切实际的。如果可以采用系统的，有针对性的方法，可以发现多少个更重要的分子晶体系统？原则上可以快速预测分子晶体结构及其特性的理论和计算是独特的中毒，可以在创建这种目标方法中发挥关键作用。但是，需要做出这些预测的强大算法。 Tuckerman组开发了计算技术和软件，以预测给定化合物可以根据称为自由能的热力学特性形成和​​对它们进行排名，该化合物在科学界被认为是这种排名的适当功绩，但仍然是一个难以捉摸的特性。塔克曼组还适应了这些算法，以研究称为寡肽的短链链的会议偏好，以探索这些重要的生物分子在免疫原性中起起作用的作用和新的药品的设计。塔克曼（Tuckerman）和他的同事从事许多软件活动，包括开发用于晶体结构预测的计算机软件包，提高其分子动力学软件Piny-MD的效率，并继续为许多社区软件代码贡献软件。该项目中开发的所有软件均可供更广泛的研究社区。固态中分子材料的基本特性通常受到其晶体结构的细节和多晶型物的存在的强烈影响。这些结构的实验确定是昂贵且耗时的，这在理论和计算的作用上提高了重要性。同样，从免疫原性到抑制的小寡肽的生化功能受到不同环境中等效构象的影响。诸如此类复杂系统中结构的计算预测由于在试图研究许多复杂系统的平衡热力学和动力学时会出现的所谓稀有事实采样问题而受到挑战。能量表面的粗糙度是指存在构象和结构变化的高障碍。塔克曼（Tuckerman Group）提议开发强大的基于自由能的增强采样算法和软件，以克服在晶体结构预测和寡肽的会议预测中产生的罕见事实问题，从而允许对偏爱的结构进行有效的方式鉴定并以有效的方式鉴定出热情的结构。在提议的方法中，自由能环境是根据精选的集体变量集（CV）表示的，旨在区分这些系统中的不同结构基序。 CV首先要受到新的表面导航技术的约束，以识别最小值和马鞍点，并在景观上统称为“地标”，然后针对增强的采样以产生地标的自由能排名。新技术用于预测刚性和柔性小有机分子的晶体结构和多晶型物，以研究与主要组织相容性复合物结合的免疫原性肽的会议自由能景观，并了解机械力对机械力对速率肽的外向机制的影响。软件创建将通过Tuckerman Group组织的黑客马拉松加速。通过在纽约大学的全球校园网站组织的研讨会来帮助学生对稀有事实方法的教育。最后，塔克曼集团通过在纽约市拥有的国家组织与代表性不足的团体接触，以帮助设计和参加与STEM相关的教育活动。