ABI Innovation: DeepStruct: Learning representations of protein 3-d structures and their interfaces using deep architectures

ABI 创新：DeepStruct：使用深层架构学习蛋白质 3-d 结构及其界面的表示

• 批准号: 1564840
• 负责人: Asa Ben-Hur
• 金额: $ 57.03万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564840&HistoricalAwards=false
• 关键词: ABI; Innovation; DeepStruct; Learning; representations

Proteins perform many cellular functions, made possible by complex networks of interactions; knowing the location of the interaction sites on the proteins is key for understanding exactly how they work. Important applications include designing drugs and therapeutic agents. Experimental techniques for determining the interfaces between proteins are expensive and time consuming, so computational structural biologists seek to predict these mathematically. Current prediction methods use a limited number of features hand-crafted by an expert. An alternate approach is to learn the important features directly from all of the data, using a method called deep neural networks. This proposal explores a combined approach: use expert intuition for some features but add the power of unsupervised learning with deep neural networks to learn additional, novel features. The results will enrich the way protein structural features are understood in terms of their functional properties, whether those are catalytic sites, protein-binding sites or other sites important to the protein structure. Certainly in the prediction of protein structure itself machine learning scoring methods are showing great promise. Aspects of the research will be used in courses offered through a recently awarded NSF-NRT training grant, The training grant establishes an interdisciplinary program at the interfaces of biology, engineering, math/statistics and computer science. The program prepares students for a variety of career paths. Research and education experiences will provide students with valuable expertise in a computational area that is highly valued by top technology firms, such as Google and Facebook, which have research teams exploring the possibilities of deep neural networks.This work proposes a paradigm shift in the field of protein interface prediction and scoring: from hand-crafted features and standard off-the-shelf classifiers to an approach that augments existing features with automatic learning of the features that characterize the 3-d structures of proteins, combined with the use of learning algorithms that are specifically designed for the characteristics of the problem. The proposed approach has multiple novel aspects: the proposed learning approach leverages information contained in the entire protein data bank (PDB) to learn features that characterize protein structures at multiple scales and levels of abstraction. It introduces a novel neural network architecture and regularization terms that constrain the solution towards biologically relevant results. The primary alternative to this machine learning-based interface prediction uses docking simulations; however, current docking energy functions are not accurate enough, so that a near-native solution is often not ranked high enough on the list of outcomes to be useful. Extensions of the proposed architectures for interface prediction will be employed for re-scoring docking solutions to improve their predictive success. A workflow that integrates docking and machine learning-based interface prediction and scoring is proposed to explore the synergism between these tasks. Information on the progress made on the project is available through the project website: http://www.cs.colostate.edu/~asa/projects.html.

蛋白质通过复杂的相互作用网络实现许多细胞功能；了解蛋白质上相互作用位点的位置是准确理解它们如何工作的关键。重要的应用包括设计药物和治疗剂。确定蛋白质之间界面的实验技术既昂贵又耗时，因此计算结构生物学家寻求用数学方法来预测这些界面。目前的预测方法使用由专家手工制作的有限数量的特征。另一种方法是使用一种称为深度神经网络的方法，直接从所有数据中学习重要的特征。该提案探索了一种组合方法：对某些特征使用专家直觉，但将深度神经网络的无监督学习功能添加到学习额外的新特征中。这些结果将丰富蛋白质结构特征在其功能特性方面的理解方式，无论是催化位点，蛋白质结合位点还是其他对蛋白质结构重要的位点。当然，在蛋白质结构本身的预测中，机器学习评分方法显示出很大的前景。该研究的部分内容将用于最近获得的NSF-NRT培训资助提供的课程，该培训资助建立了一个跨学科的项目，涉及生物学、工程学、数学/统计学和计算机科学。该项目为学生准备了各种各样的职业道路。研究和教育经历将为学生提供计算领域的宝贵专业知识，这一领域受到b谷歌和Facebook等顶级科技公司的高度重视，这些公司的研究团队正在探索深度神经网络的可能性。这项工作提出了蛋白质界面预测和评分领域的范式转变：从手工制作的特征和标准现成的分类器到一种通过自动学习表征蛋白质三维结构的特征来增强现有特征的方法，并结合使用专门为问题特征设计的学习算法。所提出的方法有多个新颖的方面：所提出的学习方法利用包含在整个蛋白质数据库（PDB）中的信息来学习表征多个尺度和抽象水平的蛋白质结构的特征。它引入了一种新的神经网络架构和正则化术语，约束解决方案向生物学相关的结果。这种基于机器学习的界面预测的主要替代方案使用对接模拟；然而，目前的对接能量函数还不够精确，因此一个接近原生的解决方案在结果列表中的排名往往不够高，无法发挥作用。所提出的接口预测架构的扩展将用于对对接解决方案进行重新评分，以提高其预测成功率。提出了一个集成对接和基于机器学习的界面预测和评分的工作流程，以探索这些任务之间的协同作用。有关项目进展的信息可通过项目网站：http://www.cs.colostate.edu/~asa/projects.html获得。