CAREER: Bringing Models to Native: Open Access Bioinformatics for Protein Structure Refinement

职业：将模型引入本地：用于蛋白质结构细化的开放获取生物信息学

• 批准号: 2208679
• 负责人: Debswapna Bhattacharya
• 金额: $ 55.73万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208679&HistoricalAwards=false
• 关键词: CAREER; Bringing; Models; Native; Open

Structural biology has entered an era of computational modeling. Computational models serve as vehicles for studying the structure and dynamics of complex biological macromolecules, such as proteins, to better understand the properties and mechanisms of cells. Computational protein modeling, due to its efficiency and scalability, can be used on a genome-wide scale to predict atomic-level three-dimensional protein structures from sequences when experimental structure determination techniques are not feasible or practical. However, computational models often do not reach biologically relevant experimental accuracy, the so-called native states. Computational structure refinement aims at improving these moderately accurate protein models by driving them towards experimental quality. However structure refinement methods often fail to bring models close enough to the native state, and worse, sometimes drive them away from native. This project will develop novel computational and data-driven methods to substantially improve protein structure refinement, bringing protein models closer to the native states. An open access bioinformatics research infrastructure will be developed and publicly disseminated, advancing basic biological research. Additionally, this interdisciplinary project has a deep commitment to enriching knowledge in biomolecular simulation and refinement, benefiting researchers and students in multiple communities at the interface of computing and biology.This project aims to address the dual barriers of sampling and scoring in structure refinement by exploiting reciprocal coupling of data-driven sampling and deep learning-based scoring. Specifically, new data-driven sampling methods guided by residue-specific and inter-residue restraints with generalized ensemble search will be developed to bias conformational sampling towards the native state. Additionally, novel side-chain oriented high- and intermediate-resolution scoring functions powered by deep learning will be formulated to significantly improve the recognition of native-like conformations. An open access bioinformatics cyberinfrastructure for structure refinement will be developed and deployed by integrating the new sampling and scoring methods, enabling worldwide community of life science researchers to apply these advanced refinement protocols, thereby multiplying the impact of the project on basic biological research. The project facilitates simulation-based learning through the development of PolyFold, a visual simulator for interactive protein structure manipulation and refinement, with an inclusive commitment to engage general public in science and technology. Results of this project, including the open access bioinformatics research and educational resources, can be found at http://www.eng.auburn.edu/~dzb0050/.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.

结构生物学已经进入了计算建模的时代。计算模型作为研究复杂生物大分子（如蛋白质）的结构和动力学的工具，以更好地了解细胞的特性和机制。计算蛋白质建模，由于其效率和可扩展性，可用于在全基因组范围内预测原子水平的三维蛋白质结构的序列时，实验结构测定技术是不可行的或实用的。然而，计算模型往往达不到生物相关的实验精度，即所谓的自然状态。计算结构精化旨在通过将这些适度精确的蛋白质模型推向实验质量来改进它们。然而，结构细化方法往往无法使模型足够接近原生状态，更糟糕的是，有时会使它们远离原生状态。该项目将开发新的计算和数据驱动的方法，以大大提高蛋白质结构的细化，使蛋白质模型更接近天然状态。将开发和公开传播一个开放获取的生物信息学研究基础设施，以推进基础生物学研究。此外，该跨学科项目致力于丰富生物分子模拟和精细化的知识，使计算和生物学接口的多个社区的研究人员和学生受益。该项目旨在通过利用数据驱动的采样和基于深度学习的评分的相互耦合来解决结构精细化中采样和评分的双重障碍。具体而言，新的数据驱动的采样方法指导下的残留特定和残留间的限制与广义集成搜索将开发偏向构象采样对原生状态。此外，将制定由深度学习提供支持的新型侧链导向的高分辨率和中等分辨率评分函数，以显着提高对天然构象的识别。通过整合新的采样和评分方法，将开发和部署一个用于结构优化的开放式生物信息学网络基础设施，使世界各地的生命科学研究人员能够应用这些先进的优化协议，从而扩大该项目对基础生物学研究的影响。该项目通过开发PolyFold来促进基于模拟的学习，PolyFold是一种用于交互式蛋白质结构操纵和细化的视觉模拟器，并致力于让公众参与科学和技术。该项目的成果，包括开放获取的生物信息学研究和教育资源，可以在www.example.com上找到http://www.eng.auburn.edu/~dzb0050/.This奖项反映了NSF的法定使命，并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来支持。

项目成果

QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks

• DOI: 10.1093/bioinformatics/btaa455
• 发表时间: 2020-07-01
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Shuvo, Md Hossain;Bhattacharya, Sutanu;Bhattacharya, Debswapna
• 通讯作者: Bhattacharya, Debswapna

20th International Workshop on Data Mining in Bioinformatics (BIOKDD 2021)

第二十届生物信息学数据挖掘国际研讨会 (BIOKDD 2021)

• DOI: 10.1145/3447548.3469442
• 发表时间: 2021
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Yan, Da;Qin, Steve;Bhattacharya, Debswapna;Chen, Jake;Zaki, Mohammed J.
• 通讯作者: Zaki, Mohammed J.

iQDeep: an integrated web server for protein scoring using multiscale deep learning models

iQDeep：使用多尺度深度学习模型进行蛋白质评分的集成网络服务器

• DOI: 10.1016/j.jmb.2023.168057
• 发表时间: 2023
• 期刊: Journal of Molecular Biology
• 影响因子: 5.6
• 作者: Shuvo, Md Hossain;Karim, Mohimenul;Bhattacharya, Debswapna
• 通讯作者: Bhattacharya, Debswapna

Guest Editorial for Selected Papers From BIOKDD 2021

BIOKDD 2021 精选论文的客座社论

• DOI: 10.1109/tcbb.2022.3208759
• 发表时间: 2022
• 期刊: IEEE/ACM Transactions on Computational Biology and Bioinformatics
• 作者: Yan, Da;Qin, Zhaohui S.;Bhattacharya, Debswapna;Chen, Jake Y.
• 通讯作者: Chen, Jake Y.