ABI Innovation: Deep learning methods for protein bioinformatics

ABI Innovation：蛋白质生物信息学的深度学习方法

• 批准号: 1759934
• 负责人: Jianlin Cheng
• 金额: $ 62.42万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759934&HistoricalAwards=false
• 关键词: ABI; Innovation; Deep; learning; methods

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.

蛋白质序列是生命系统的语言，它编码蛋白质结构和功能，对任何生物体的生存至关重要。因此，了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，传统的蛋白质序列的解释要么是基于人工识别的子序列模式或一些任意的解剖序列到固定大小的序列，这两种方法都不能准确地识别蛋白质序列中所有的语义成分，是相关的结构和功能。在这个项目中，将设计基于深度学习的强大人工智能方法，使它们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。这不仅将提高蛋白质结构和功能特性预测的准确性，而且还提供了一种表示和解释蛋白质生物功能的新方法，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分发开源软件、深度学习和生物信息学研讨会、学生培训、少数民族和女性学生的参与、出版物、演讲、研讨会和高中生外联活动以及与密苏里州立法机构和其他公众成员的精心设计的沟通来扩大。将开发新型深度一维（1D）、2D和3D卷积神经网络，以便在改善多个残基局部结构/功能特性预测的指导下，将任意大小的蛋白质序列或结构翻译为高级特征（二级结构，溶剂可及性，扭转角，无序，接触图，二硫键，β-折叠配对，和蛋白质功能位点）以及全局性质如折叠。用于解释蛋白质序列数据的1D卷积神经网络也将得到长期和短期记忆网络的补充。全面的深度学习模型将通过创新的多任务学习和迁移学习进行训练，以提高预测性能。1D，2D和3D卷积网络将进一步整合，以提高分析蛋白质序列，结构和功能的准确性。1D和3D卷积神经网络是完全原创的，新的2D卷积架构比现有方法更全面，更通用。除了通过新的深度学习架构推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蛋白质的新语义表示，这可能会改变各种蛋白质生物信息学任务，如分类，聚类，比较和排名。该项目的URL是：http://calla.rnet.missouri.edu/cheng/nsf_deepbioinfo.html。该奖项反映了NSF的法定使命，并已被认为值得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的支持。

项目成果

Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps

• DOI: 10.1038/s41598-020-60598-y
• 发表时间: 2020-03-09
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Si, Dong;Moritz, Spencer A.;Cheng, Jianlin
• 通讯作者: Cheng, Jianlin

Geometry-Complete Diffusion for 3D Molecule Generation

• DOI: 10.48550/arxiv.2302.04313
• 发表时间: 2023
• 期刊: ArXiv
• 作者: Alex Morehead;Jianlin Cheng

Geometric Transformers for Protein Interface Contact Prediction

• 发表时间: 2021-10
• 期刊: ArXiv
• 作者: Alex Morehead;Chen Chen-Chen;Jianlin Cheng

Geometry-complete perceptron networks for 3D molecular graphs

• DOI: 10.1093/bioinformatics/btae087
• 发表时间: 2022-11
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Alex Morehead;Jianlin Cheng

DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning

• DOI: 10.48550/arxiv.2205.13594
• 发表时间: 2022-05
• 期刊: ArXiv
• 作者: Elham Soltanikazemi;Rajashree Roy;Farhan Quadir;Nabin Giri;Alex Morehead;Jianlin Cheng