CAREER: Sparse Spatial Reasoning for High-Throughput Protein Structure Determination

职业：用于高通量蛋白质结构测定的稀疏空间推理

• 批准号: 0237654
• 负责人: Christopher Bailey-Kellogg
• 金额: $ 48.81万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-01 至 2005-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0237654&HistoricalAwards=false
• 关键词: CAREER; Sparse; Spatial; Reasoning; Throughput

This is a Faculty Early Career Development (CAREER) award. The research will develop new methods for analyzing the structure of protein molecules, interpreting spatial data sets containing significant noise and sparse information content. Despite many experimental and computational advances, traditional structure determination protocols remain very difficult, expensive, and time-consuming. Consequently, in order to increase the throughput of structure determination, researchers are pursuing minimalist techniques that provide much less structure information much faster; examples include mutation studies, indicating at which positions amino acid substitutions significantly affect the protein's function; cross-linking mass spectrometry, providing crude proximity information for some positions in the protein; and electron microscopy, elucidating the protein's surface/volume at relatively low resolution. These minimalist experiments then place more burden on associated algorithms for experiment planning and data interpretation.This project pursues new theory, representations, and algorithms to address data interpretation and experiment design problems in domains characterized by sparse spatial data. A significant component of the research is the case study application of minimalist protein structure determination. The education plan addresses the need to build bridges between computer science and the life sciences in order to attack problems of this combined computational-experimental kind.A spatial reasoner will be developed, leveraging key problem structure to efficiently and effectively plan and interpret experiments. It will represent data, models, and biophysical knowledge with multi-level, multi-dimensional topological and geometric objects and constraints. This representation will allow algorithms to match features of data and models, overcome problems of noise and scarcity by uncovering consistent feature sets, target clarifying queries in response to conflicts, and plan additional experiments. This approach will thus support closed-loop integration of modeling and experiment -- experimental evidence will trigger evaluation of model features and even optimization of models themselves, while model analysis will trigger specific data interpretation questions and even new experiments.The education component of this project brings together students from computer science and the life sciences to train them for interdisciplinary computational biology research. Additional and revised coursework, to be developed in conjunction with the Computer Science Department and Computational Science and Engineering program at Purdue, will combine advanced computational techniques and biological applications. The training will provide life science students with the necessary algorithmic background and computer science students with the necessary exposure to and experience with motivating biological problems. Research opportunities, course projects, and other learning opportunities will further involve students in the many challenging and fascinating biological problems requiring advanced computational techniques.This CAREER award recognizes and supports the early career-development activities of a teacher-scholar who is likely to become an academic leader of the twenty-first century. The research will lead to scientific contributions in the structural and functional understanding of biomolecular machinery. The challenges faced in developing, applying, and extending algorithms for this application will lead to core contributions in reasoning about physical systems, where many similar tasks in planning, modeling, predicting, and controlling face similar problems with sparse, noisy spatial data.

这是教师早期职业发展(Career)奖。这项研究将开发新的方法来分析蛋白质分子结构，解释包含显著噪声和稀疏信息量的空间数据集。尽管在实验和计算方面取得了许多进展，但传统的结构确定协议仍然非常困难、昂贵和耗时。因此，为了增加结构测定的吞吐量，研究人员正在寻求提供更少结构信息的最低限度技术；例如，突变研究，表明氨基酸取代对蛋白质功能有显著影响的位置；交联质谱，提供蛋白质中某些位置的粗略接近信息；以及电子显微镜，以相对较低的分辨率阐明蛋白质的表面/体积。这些极简实验给实验规划和数据解释的相关算法带来了更多的负担。本项目寻求新的理论、表示和算法来解决稀疏空间数据领域的数据解释和实验设计问题。这项研究的一个重要组成部分是最低限度蛋白质结构测定的案例研究应用。该教育计划解决了在计算机科学和生命科学之间建立桥梁的需要，以解决这种计算-实验相结合的问题。将开发一个空间推理机，利用关键问题结构来高效地计划和解释实验。它将用多层次、多维的拓扑和几何对象和约束来表示数据、模型和生物物理知识。这种表示将允许算法匹配数据和模型的特征，通过发现一致的特征集来克服噪声和稀缺性问题，针对响应冲突的澄清查询，并计划额外的实验。因此，这种方法将支持建模和实验的闭环集成--实验证据将引发对模型特征的评估，甚至模型本身的优化，而模型分析将引发特定的数据解释问题，甚至新的实验。该项目的教育部分汇集了计算机科学和生命科学的学生，为他们进行跨学科的计算生物学研究进行培训。新增和修订的课程将与普渡大学计算机科学系和计算科学与工程项目一起开发，将结合先进的计算技术和生物应用。培训将为生命科学专业的学生提供必要的算法背景，为计算机科学专业的学生提供必要的接触和经验，以激发生物问题。研究机会、课程项目和其他学习机会将进一步使学生参与许多具有挑战性和引人入胜的生物学问题，需要先进的计算技术。这一职业奖表彰和支持有可能成为21世纪学术领袖的教师-学者的早期职业发展活动。这项研究将在理解生物分子机械的结构和功能方面做出科学贡献。为这一应用程序开发、应用和扩展算法所面临的挑战将导致在关于物理系统的推理方面做出核心贡献，在物理系统中，许多类似的任务在规划、建模、预测和控制方面面临着与稀疏、噪声空间数据类似的问题。