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

结构生物学已经进入了计算建模的时代。计算模型是研究复杂生物大分子（如蛋白质）的结构和动力学的工具，可以更好地理解细胞的性质和机制。计算蛋白质模型由于其效率和可扩展性，可以在全基因组范围内用于在实验结构确定技术不可行或不实用的情况下从序列中预测原子水平的三维蛋白质结构。然而，计算模型往往不能达到生物学相关的实验精度，即所谓的原生状态。计算结构精化的目的是提高这些适度精确的蛋白质模型，使其达到实验质量。然而，结构精化方法常常不能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方法，以大幅提高蛋白质结构的精细化，使蛋白质模型更接近自然状态。将开发开放获取的生物信息学研究基础设施并向公众传播，推进基础生物学研究。此外，这个跨学科项目致力于丰富生物分子模拟和改进方面的知识，使计算和生物学界面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互反耦合来解决结构优化中采样和评分的双重障碍。具体而言，将开发新的数据驱动采样方法，以残基特异性和残基间约束为指导，结合广义集合搜索，使构象采样偏向于自然状态。此外，将制定基于深度学习的新型面向侧链的高分辨率和中分辨率评分功能，以显着提高对原生构象的识别。通过整合新的采样和评分方法，将开发和部署用于结构优化的开放获取生物信息学网络基础设施，使全球生命科学研究人员能够应用这些先进的优化协议，从而增加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是一个用于交互式蛋白质结构操作和优化的视觉模拟器，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http://www.eng.auburn.edu/~dzb0050/.This上找到。该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。

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.