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

该项目旨在提高预测蛋白质序列变化对三维结构和功能变化影响的现有能力。研究人员关注的是氨基酸的插入和缺失。这种扩展是合理的，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。需要在本项目中开发的能力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刺突蛋白的序列上。最近的报道表明，这些变化正在影响这种蛋白质的结构变化，并可能影响其功能。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，一种有前途的几何处理方法来预测结构的变化，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途的探索性研究。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删除影响的子序列构型，然后通过约束优化方法在整个蛋白质序列的物理现实三级结构中适应这些构型。其次，采用图形刚度分析法对计算结构进行分析，并对刚度数据进行预测。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。研究人员将继续努力，成功指导本科生，包括来自其他STEM专业的本科生。在密切协调下，调查人员将通过几个项目积极与妇女和代表性不足群体的学生合作。其中一个是建立在现有的国家科学基金会奖的基础上的。调查人员还将利用诸如通往学士学位项目的桥梁、马萨诸塞大学波士顿女性科学俱乐部和最大化学生发展倡议等外展努力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.