D3SC: Mining for mechanistic information to predict protein function

项目名称：D3SC：挖掘机制信息以预测蛋白质功能蛋白质在细胞中执行各种基本功能，包括作为酶催化化学反应。美国东北大学化学部的生命过程化学项目将资助东北大学的Mary Jo Ondrechen博士、Penny Beuning博士和Deniz Erdogomus博士开发新的方法，从蛋白质的三维结构来预测蛋白质的功能。这个计算问题是基因组学的一个主要挑战——研究DNA序列及其蛋白质产物。基因组学的研究为当前和未来的巨大创新打开了大门，造福社会，在粮食生产、能源、经济、环境和健康等各个领域。在这个项目中，化学性质被计算出来，并与机器学习算法相结合，以确定蛋白质结构中活性氨基酸的特定生化作用，然后导致蛋白质功能的预测。这些功能的预测是通过直接生化分析和配体结合研究，对选定的情况进行实验测试。博士生和本科生研究实习生，包括那些在STEM领域代表性不足的少数群体，正在通过该项目接受培训，成为计算化学、信息学、机器学习和生物化学领域的高素质科学家。这些技能对新英格兰地区的高科技经济和美国在全球经济中的竞争力至关重要。计算预测单个氨基酸在蛋白质结构中的生化功能作用是全新的。从计算化学中获得的属性的预测能力正在通过机器学习方法得到增强，包括支持向量机（SVM）和图卷积神经网络（GCNN）。改进的、实验测试的蛋白质功能预测方法对基因组测序和结构基因组学（SG）计划的大量数据的解释做出了重大贡献。这个项目的一个重要特点是，它将蛋白质结构中氨基酸的计算化学性质纳入更传统的信息学方法中，以预测功能，而目前大多数方法纯粹是基于信息学的方法。该项目的独特之处在于，它在蛋白质功能预测问题中使用原子尺度上计算的化学反应性和静电特征来获得残基特异性的机制信息。具有跨不同结构折叠匹配功能类型的能力，即既没有序列相似性也没有3D结构相似性的情况下，对于未知或不确定功能的SG蛋白，可靠地分配生化功能的能力大大增加。这项工作还有助于更好地了解酶的工作原理以及特定氨基酸残基如何实现其催化能力。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。

Project Title: D3SC: Mining for Mechanistic Information to Predict Protein FunctionProteins perform a variety of essential functions in a cell, including catalyzing chemical reactions as enzymes. With this award, the Chemistry of Life Processes Program in the Chemistry Division is funding Dr. Mary Jo Ondrechen, Dr. Penny Beuning and Dr. Deniz Erdogomus at Northeastern University to develop new ways to predict the function of a protein from its three-dimensional structure. This computational problem is a major challenge in genomics - the study of DNA sequences and their protein products. Research in genomics is opening the door to tremendous current and future innovations to benefit society, in areas as diverse as food production, energy, the economy, the environment, and health. In this project, chemical properties are computed and coupled with machine learning algorithms to identify the specific biochemical roles for the active amino acids in a protein structure, which then leads to the prediction of the protein's function. These predictions of function are tested experimentally by direct biochemical assays and by ligand binding studies, for selected cases. Doctoral students and undergraduate research interns, including those from minority groups that are underrepresented in STEM fields, are being trained through this project to become highly qualified scientists in the areas of computational chemistry, informatics, machine learning, and biochemistry. These skills are vital to the regional high-tech economy of New England and to United States competitiveness in the global economy. The computational prediction of biochemical functional roles of individual amino acids in a protein structure is entirely new. The predictive power of properties obtained from computational chemistry are being enhanced by machine learning approaches, including Support Vector Machines (SVM) and Graph Convolutional Neural Networks (GCNN). Improved, experimentally tested methods for the prediction of protein function contribute significantly to the interpretation of the massive quantities of data from genome sequencing and Structural Genomics (SG) initiatives. A significant feature of this project is that it incorporates computed chemical properties of the amino acids in a protein structure into more conventional informatics methods to predict function, whereas most current methods are purely informatics-based approaches. This project is unique in that it employs computed chemical reactivity and electrostatic features on the atomic scale in the protein function prediction problem to obtain residue-specific mechanistic information. With the capability to match functional types across different structural folds, i.e. cases with neither sequence nor 3D structure similarity, the ability to assign biochemical function reliably is substantially increased for SG proteins of unknown or uncertain function. This work also leads to better understanding of how enzymes work and of how specific amino acid residues achieve their catalytic power.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.