EAGER: III: Collaborative Research: In silico Algorithm for Assessing the Effects of Amino Acid Insertion and Deletion Mutations

EAGER：III：协作研究：评估氨基酸插入和缺失突变影响的计算机算法

• 批准号: 2031260
• 负责人: Nurit Haspel
• 金额: $ 7.27万
• 依托单位: University of Massachusetts Boston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031260&HistoricalAwards=false
• 关键词: EAGER; III; Collaborative; Research; silico

This project proposes to advance current capabilities in predicting the impact of changes to protein sequence to changes in three-dimensional structure and function. The investigators focus on amino-acid insertions and deletions. This expansion is warranted, as changes to DNA include not just mutations but also insertions and deletions of entire fragments. A timely example of the need for the capabilities that will be developed in this project is the fast evolution observed in near real-time in SARS-Cov2. Many of the changes are concentrated in the sequence of the spike protein that binds to the lung receptors. Recent reports indicate that such changes are affecting changes to the structure of this protein and possibly its function. The problem of predicting the impact of changes to sequence to the structure and function of a protein is a hallmark problem in molecular biology. In this project, the investigators combine sequence data, a promising geometric treatment to predict changes to structure, and informatics techniques for predicting changes to function. The activities in this project constitute promising exploratory research.This project makes two key contributions. First, an inverse kinematics approach predicts sub-sequence configurations impacted by the insertion or deletion of amino acids and then accommodates such configurations via a constrained optimization approach in a physically-realistic tertiary structure of the entire protein sequence. Second, graph-rigidity analysis is employed to analyze computed structures and predict rigidity data. Coupled with evolutionary conservation, the data are employed to train machine learning models capable of predicting the stability of sequences of interest. The investigators have a track record of collaborative dissemination. The developed algorithms will be made freely available to the scientific community. In addition, the investigators have made increasing student diversity one of their top priorities. The investigators will continue to expand their efforts in successful mentoring of undergraduate students, including undergraduates from other STEM majors. In close coordination, the investigators will actively work with women and students from under-represented groups through several programs. One of them builds off an existing NSF award. The investigators will also leverage outreach efforts such as the Bridges to the Baccalaureate program, the University of Massachusetts Boston Women in Science club, and the Initiative for Maximizing Student Development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目建议提高目前预测蛋白质序列变化对三维结构和功能变化的影响的能力。研究人员的重点是氨基酸的插入和缺失。这种扩展是有必要的，因为DNA的变化不仅包括突变，还包括整个片段的插入和删除。这个项目中将开发的能力需求的一个及时例子是在SARS-Cov2中几乎实时观察到的快速演变。许多变化集中在与肺受体结合的尖峰蛋白序列上。最近的报告表明，这种变化正在影响这种蛋白质的结构变化，可能还会影响其功能。预测序列变化对蛋白质结构和功能的影响是分子生物学中的一个标志性问题。在这个项目中，研究人员结合了序列数据，一种有前途的预测结构变化的几何处理方法，以及预测功能变化的信息学技术。这个项目中的活动构成了有前景的探索性研究。这个项目有两个关键贡献。首先，逆运动学方法预测受氨基酸插入或缺失影响的子序列构型，然后通过约束优化方法在整个蛋白质序列的物理现实的三级结构中适应这样的构型。其次，采用图-刚度法对计算结构进行分析，并对计算结果进行刚度数据预测。结合进化守恒，这些数据被用来训练能够预测感兴趣序列的稳定性的机器学习模型。调查人员有合作传播的记录。开发的算法将免费提供给科学界。此外，调查人员已将增加学生多样性作为他们的首要任务之一。调查人员将继续加大力度，成功辅导本科生，包括其他STEM专业的本科生。在密切的协调下，调查人员将通过几个项目积极与代表不足群体的妇女和学生合作。其中一个是在现有的NSF奖项的基础上建立起来的。调查人员还将利用外展努力，如通往文凭项目的桥梁，马萨诸塞大学波士顿女性科学俱乐部，以及最大化学生发展倡议。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。