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

蛋白质执行许多细胞功能，这是通过复杂的相互作用网络实现的；了解蛋白质上相互作用部位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。确定蛋白质之间的界面的实验技术既昂贵又耗时，因此计算结构生物学家试图从数学上预测这些界面。目前的预测方法使用的是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名为深度神经网络的方法，直接从所有数据中学习重要特征。这一建议探索了一种组合方法：使用专家直觉来识别某些特征，但增加了使用深度神经网络的无监督学习的能力，以学习更多、新的特征。这些结果将丰富蛋白质结构特征在功能性质方面的理解方式，无论这些特征是催化部位、蛋白质结合部位还是对蛋白质结构重要的其他部位。当然，在蛋白质结构本身的预测方面，机器学习评分方法正显示出巨大的前景。研究的方方面面将用于通过最近授予的NSF-NRT培训补助金提供的课程，该培训补助金在生物学、工程学、数学/统计学和计算机科学的接口上建立了一个跨学科计划。该项目为学生提供各种职业道路的准备。研究和教育经验将为学生提供高度重视的计算领域的宝贵专业知识，这些公司拥有探索深度神经网络可能性的研究团队。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的现成分类器，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，并结合使用专门针对问题特征设计的学习算法。提出的方法具有多个新颖的方面：所提出的学习方法利用整个蛋白质数据库(PDB)中包含的信息来学习在多个尺度和抽象级别上表征蛋白质结构的特征。它引入了一种新的神经网络结构和正则化项，将解决方案限制在生物学相关的结果上。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，当前的对接能量函数不够准确，因此接近本地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。建议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。提出了一种集成对接和基于机器学习的界面预测和评分的工作流，以探索这些任务之间的协同效应。有关该项目进展情况的信息可通过项目网站获取：http://www.cs.colostate.edu/~asa/projects.html.

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.