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III: Medium: Collaborative Research: Guiding Exploration of Protein Structure Spaces with Deep Learning

III：媒介：协作研究：用深度学习指导蛋白质结构空间探索

基本信息

批准号：
1763233
负责人：
Kenneth De Jong
金额：
$ 49.91万
依托单位：
George Mason University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763233&HistoricalAwards=false
关键词：
III Medium Collaborative Research Guiding

项目摘要

Decades of scientific enquiry beyond molecular biology have demonstrated just how fundamental form is to function. All chemical reactions in the living cell involve molecules bumping and sticking to one another, and molecular form or structure is a central determinant of complementarity and strength of molecular interactions. In particular, by assuming specific structures, proteins are able to regulate diverse processes that maintain and replicate the living cell. With structure determination in laboratories lagging desperately behind the rapidly-growing number of protein-encoding gene sequences by high-throughput sequencing technologies, computational approaches to the problem of protein structure prediction now have a central role in molecular biology. This project advances algorithmic research to address the current impasse in form-function related problems in molecular biology. In particular, the project develops advanced optimization methods to explore the vast protein structure space and leverages information-integration techniques under the deep learning framework to effectively guide the exploration towards biologically-active structures. This project will benefit researchers of diverse sub-communities in computational and biological sciences, result in open source, publicly-available software packages, and provide excellent training and mentoring opportunities for under-represented students at the interface of computational science and computational biology.This project advances algorithmic research in information integration and informatics to address the current impasse in structure-function related problems in computational structural biology. The main focus is on the de-novo protein structure prediction problem, which is central to inferring biological activities of a rapidly-growing number of protein-encoding gene sequences. The proposed research generalizes the problem of exploring and obtaining a comprehensive view of a protein's structure space as that of computing a diverse ensemble of constraint-satisfying structures and then leveraging information-integration techniques to guide the exploration to regions of the structure space relevant for biological activity. The research proposes hybrid stochastic optimization algorithms for comprehensive exploration of protein structure spaces, deep convolutional neural networks for better assessment of structure nativeness, and combines the two in an information-integration algorithmic framework to guide the exploration of a structure space towards native structures. By doing so, the proposed research investigates a direction complementary to physics-based treatments, proposing to supplant such treatments with machine-learned models of nativeness. The research will benefit researchers in machine learning, stochastic optimization, and information integration with application-driven interests in molecular modeling, protein structure prediction, and modeling of complex, dynamic systems. The research will be disseminated via various venues, including an open-source software package, and will provide training opportunities for under-represented students of all levels at the interface of optimization, deep learning, and computational biology.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.

几十年来，分子生物学以外的科学探索已经证明了形式对功能的重要性。活细胞中的所有化学反应都涉及分子相互碰撞和粘附，分子的形式或结构是分子相互作用的互补性和强度的核心决定因素。特别是，通过假设特定的结构，蛋白质能够调节维持和复制活细胞的各种过程。随着实验室中的结构测定严重落后于通过高通量测序技术快速增长的蛋白质编码基因序列的数量，蛋白质结构预测问题的计算方法现在在分子生物学中发挥着核心作用。该项目推进了算法研究，以解决目前分子生物学中形式-功能相关问题的僵局。特别是，该项目开发了先进的优化方法来探索广阔的蛋白质结构空间，并利用深度学习框架下的信息集成技术，有效地指导对生物活性结构的探索。该项目将使计算和生物科学中不同子社区的研究人员受益，产生开源，公开可用的软件包，并为在计算科学和计算生物学的界面上代表性不足的学生提供良好的培训和指导机会。该项目推进了信息集成和信息学的算法研究，以解决当前结构-计算结构生物学中的功能相关问题。主要的重点是从头蛋白质结构预测问题，这是中央推断的生物活性的快速增长的蛋白质编码基因序列的数量。建议的研究概括的问题，探索和获得一个全面的看法，蛋白质的结构空间的计算一个不同的合奏约束满足结构，然后利用信息集成技术，以指导探索区域的结构空间相关的生物活性。该研究提出了用于全面探索蛋白质结构空间的混合随机优化算法，用于更好地评估结构原生性的深度卷积神经网络，并将两者结合在信息集成算法框架中，以引导结构空间对原生结构的探索。通过这样做，拟议的研究调查了一个与基于物理的治疗互补的方向，提出用机器学习的原生模型取代这种治疗。该研究将使机器学习，随机优化和信息集成方面的研究人员受益，并对分子建模，蛋白质结构预测和复杂动态系统建模产生应用驱动的兴趣。该研究将通过各种渠道传播，包括开源软件包，并将为各级学生提供优化，深度学习和计算生物学接口的培训机会。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Balancing multiple objectives in conformation sampling to control decoy diversity in template-free protein structure prediction

DOI：
10.1186/s12859-019-2794-5
发表时间：
2019-04
期刊：
BMC Bioinformatics
影响因子：
3
作者：
Ahmed Bin Zaman;Amarda Shehu
通讯作者：
Ahmed Bin Zaman;Amarda Shehu

Graph Neural Networks in Predicting Protein Function and Interactions

图神经网络预测蛋白质功能和相互作用

DOI：
10.1007/978-981-16-6054-2_25
发表时间：
2021
期刊：
and Applications
影响因子：
0
作者：
Kabir, Anowarul;Shehu, Amarda
通讯作者：
Shehu, Amarda

Using Sequence-Predicted Contacts to Guide Template-free Protein Structure Prediction

DOI：
10.1145/3307339.3342175
发表时间：
2019-09
期刊：
Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
影响因子：
0
作者：
Ahmed Bin Zaman;Prasanna Parthasarathy;Amarda Shehu
通讯作者：
Ahmed Bin Zaman;Prasanna Parthasarathy;Amarda Shehu

Graph Representation Learning for Protein Conformation Sampling

蛋白质构象采样的图表示学习