ABI Innovation: Deep learning methods for protein bioinformatics

蛋白质序列是生命系统的一种语言，它编码对任何有机体的生存至关重要的蛋白质结构和功能。因此，理解蛋白质序列如何描述功能和结构是生物学研究中的一个基本问题。然而，对蛋白质序列的传统解释要么是基于人工识别的子序列模式，要么是将序列任意分解成固定大小的子序列；这两种方法都不能准确地识别蛋白质序列中与其结构和功能相关的所有语义成分。在这个项目中，基于深度学习的强大的人工智能方法将被设计成能够自动将蛋白质序列映射到与蛋白质结构和功能相关的有意义的高级语义特征。这不仅将提高预测蛋白质结构和功能性质的准确性，而且为表达和解释蛋白质的生物学功能提供了一种新的方法，改变了蛋白质数据的解释方式。基础研究的影响将通过向其他研究人员传播开放源码软件、关于深度学习和生物信息学的研讨会、学生培训、少数民族和女性学生的参与、出版物、演讲、研讨会和针对高中生的外联活动，以及与密苏里州立法机构和其他公众的精心设计的沟通来扩大。在研究期间，将开发新的深一维(1D)、2D和3D卷积神经网络，以将任意大小的蛋白质序列或结构转换为高级特征，指导改进对多种残基相关的局部结构/功能性质(二级结构、溶剂可获得性、扭转角、无序度、接触图、二硫键、β-折叠配对和蛋白质功能位点)以及折叠等全球属性。用于解释蛋白质序列数据的一维卷积神经网络也将得到长期和短期记忆网络的补充。综合深度学习模型将通过创新的多任务学习和迁移学习来训练，以提高预测性能。进一步整合一维、二维和三维卷积网络，提高蛋白质序列、结构和功能分析的准确性。一维和三维卷积神经网络是完全独创的，新的二维卷积结构比现有的方法更全面和通用。除了通过新的深度学习结构改进经典的蛋白质预测任务外，深度学习模型自动提取的隐藏特征将提供一种新的蛋白质语义表示，这可能会改变各种蛋白质生物信息学任务，如分类、聚类、比较和排序。这个项目的网址是：http://calla.rnet.missouri.edu/cheng/nsf_deepbioinfo.html。这个奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Protein sequence is a language of a living system, which encodes protein structure and function critical for the survival of any organism. Therefore, understanding how protein sequence describes function and structure is a fundamental problem in biological research. Yet, the traditional interpretation of protein sequences is 3ased on either manual identification of sub-sequence patterns or some arbitrary dissection of a sequence into subsequences of fixed size; neither approach can accurately recognize all of the semantic components in a protein sequence that are relevant to its structure and function. In this project, powerful artificial intelligence methods, based on deep learning, will be designed such that they can automatically map protein sequences into high-level semantic features that are meaningful when related to protein structure and function. This will not only improve the accuracy of predicting protein structural and functional properties, but also provide a new way of representing and interpreting proteins biological function, transforming how protein data are interpreted. The impact of the basic research will be broadened through open source software dissemination to other researchers, seminars on deep learning and bioinformatics, student training, involvement of minority and female students, publications, presentations, workshops, and outreach activities for high school students, as well as thoughtfully crafted communication with the Missouri state legislature, and other members of the general public.During the research, novel deep one-dimensional (1D), 2D, and 3D convolutional neural networks will be developed to translate protein sequences or structures of arbitrary size into high-level features under the guidance of improving the prediction of multiple residue-wise local structural/functional properties (secondary structures, solvent accessibility, torsion angle, disorder, contact map, disulfide bonds, beta-sheet pairings, and protein functional sites) as well as global properties such as folds. The 1D convolutional neural network for interpreting protein sequence data will also be supplemented by the long- and short-term memory networks. The comprehensive deep learning models will be trained by innovative multi-task learning and transfer learning to enhance prediction performance. The 1D, 2D and 3D convolutional networks will be further integrated to improve the accuracy of analyzing protein sequence, structure and function. The 1D and 3D convolutional neural networks are completely original, and the new 2D convolutional architecture is more comprehensive and versatile than existing approaches. In addition to advancing the classic protein prediction tasks through the novel deep learning architectures, the hidden features automatically extracted by the deep learning models will provide a new semantic representation of proteins, which will likely transform various protein bioinformatics tasks such as classification, clustering, comparison, and ranking. The URL of this project is: http://calla.rnet.missouri.edu/cheng/nsf_deepbioinfo.html .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.