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Distance-based ab initio protein structure prediction

基于距离的从头算蛋白质结构预测

基本信息

批准号：
10418784
负责人：
Jianlin Cheng
金额：
$ 34.21万
依托单位：
UNIVERSITY OF MISSOURI-COLUMBIA
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10418784
关键词：
3-Dimensional Amino Acid Sequence Architecture Area Artificial Intelligence Attention Biomedical Research C-terminal Collaborations Communities Complex Computational Biology Computing Methodologies Conflict (Psychology)Dependence Development Genome Hereditary Disease Homo Maps Methods Modeling Modernization Mutate Performance Play Problem Solving Protein Engineering Protein Region Proteins Recurrence Renaissance Residual state Role Scientist Sequence Alignment Signal Transduction Site Structure Techniques Technology Tertiary Protein Structure Weight X-Ray Crystallography base biophysical techniques comparative convolutional neural network cost deep learning design drug development empowered experience experimental study improved learning network learning strategy long short term memory monomer novel novel strategies open source tool protein data bank protein function protein protein interaction protein structure protein structure prediction reconstruction recurrent neural network self assembly structural biology success three dimensional structure three-dimensional modeling tool

项目摘要

Project Summary Predicting the three-dimensional structures of proteins without using known structures from the Protein Data Bank (PDB) as templates (ab initio) remains a grand challenge of computational biology. Whereas template-based modeling is now a mature field, ab initio modeling is a comparatively nascent one, especially for large proteins with complex topologies and multiple domains. The need for advances in ab initio modeling is evident. A lot of protein sequences do not have (recognizable) templates in the PDB, and the pace of experimental structure determination is incommensurate with the scale of the problem. Herein, we propose a new approach to ab initio modeling that consists of novel deep learning architectures to predict inter- residue distances and domain boundaries as well as robust, iterative optimization methods to construct tertiary structures from the predicted distances. This project builds on the success of our current R01, particularly the outstanding performance of the Cheng group in the 2018 worldwide protein structure prediction experiment – CASP13 – where our MULTICOM suite ranked among the top three tertiary structure predictors, alongside Google DeepMind’s AlphaFold. The methods will be implemented as open-source tools for the emerging field of distance-based ab initio protein structure modeling. We will apply the methods to study protein homo-oligomers and self-assemblies, based on our novel discovery that the quaternary structure contacts within homo-oligomers can be predicted by deep learning methods from the co-evolutionary signals embedded in multiple sequence alignments of protein monomers. Furthermore, we will apply the methods to predict the folds, functional sites, superfamilies, and protein-protein interactions of proteins that contain “essential Domains of Unknown Function” (eDUFs), a group of evolutionarily conserved, essential proteins that represents an important uncharted region of protein function/fold space. The predictions for a diverse and representative subset of eDUFs will be experimentally validated through a unique collaboration with the structural biology group of Dr. Tanner.

项目总结