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IIBR Informatics: Accurate assessment of protein-DNA complex models and applications

IIBR 信息学：蛋白质-DNA 复杂模型和应用的准确评估

基本信息

批准号：
2051491
负责人：
Jun-tao Guo
金额：
$ 67.37万
依托单位：
University of North Carolina at Charlotte
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051491&HistoricalAwards=false
关键词：
IIBR Informatics Accurate assessment protein

项目摘要

Protein-DNA interactions play crucial roles in many biological processes, such as structure-based drug design and structure-based transcription factor binding site prediction. Transcription factors, a type of proteins, are considered as one of the prime drug targets since mutations on transcription factors and aberrant protein-DNA interactions have been implicated in many diseases including cancer. The major challenges in modeling protein-DNA complexes are accurate assessment of the quality of complex models and a small number of known protein-DNA complex structures. This project aims to better understand protein-DNA interactions from a structural perspective by developing methods for assessment of the quality of protein-DNA complex models. A comprehensive database of protein-DNA complex structure will be constructed for analysis, modeling, and assessment, which will be a valuable resource for the scientific community. The project will actively promote and recruit postdoc and students from underrepresented groups through summer programs. Techniques and results of this project will be integrated into curriculum design to foster creative learning and understanding of relationships among structure modeling, macromolecular interaction, biological function, and biomedical applications. Computational modeling of protein-DNA complexes, including homology modeling and protein-DNA docking, is a cost-efficient alternative to fill the void in the protein-DNA complex structure landscape. The major challenges are accurate assessment of the quality of complex models and the assessment of the similarity between protein-DNA complexes. While standard protocols have been developed for comparing protein-ligand complexes, they are not suitable for assessing the similarity of protein-DNA complexes due to their unique structural and chemical features. DNA has a double-helical structure and the hydrogen bonds between protein and DNA bases are crucial for protein-DNA binding specificity. New methods are clearly needed for accurate comparison of protein-DNA complexes, which in turn can help develop methods for accurate quality assessment of protein-DNA complex models. In this project, a novel method will be developed for accurately capturing the essence of similarity between protein-DNA complex structures and new algorithms will be developed for assessing the quality of protein-DNA models. In addition, these newly developed methods will be applied to assess the quality of homology protein-DNA complex models and structure-based transcription factor-binding site prediction with these models. The datasets and algorithms developed from this project will be made freely available to the research community. The results of the project can be found at https://guolab.uncc.eduThis 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-DNA相互作用在许多生物学过程中起着至关重要的作用，如基于结构的药物设计和基于结构的转录因子结合位点预测。转录因子是蛋白质的一种，由于转录因子的突变和蛋白-DNA相互作用的异常与包括癌症在内的许多疾病有关，因此被认为是主要的药物靶点之一。蛋白质-DNA复合体建模的主要挑战是准确评估复杂模型的质量和少量已知的蛋白质-DNA复合体结构。该项目旨在通过开发评估蛋白质-DNA复合体模型质量的方法，从结构角度更好地了解蛋白质-DNA相互作用。将建立一个全面的蛋白质-DNA复合体结构数据库，用于分析、建模和评估，这将是科学界的宝贵资源。该项目将通过暑期项目积极促进和招收来自代表性不足群体的博士后和学生。该项目的技术和成果将被整合到课程设计中，以促进创造性学习和理解结构建模、大分子相互作用、生物功能和生物医学应用之间的关系。蛋白质-DNA复合体的计算建模，包括同源建模和蛋白质-DNA对接，是填补蛋白质-DNA复合体结构空白的一种经济高效的替代方案。主要的挑战是准确评估复杂模型的质量和评估蛋白质-DNA复合体之间的相似性。虽然已经开发了用于比较蛋白质-配体复合体的标准方法，但由于蛋白质-DNA复合体独特的结构和化学特征，它们不适合于评估其相似性。DNA具有双螺旋结构，蛋白质与DNA碱基之间的氢键对蛋白质-DNA结合的特异性至关重要。显然，需要新的方法来准确比较蛋白质-DNA复合体，这反过来又可以帮助开发准确评估蛋白质-DNA复合体模型质量的方法。在这个项目中，将开发一种新的方法来准确地捕捉蛋白质-DNA复杂结构之间的相似本质，并将开发新的算法来评估蛋白质-DNA模型的质量。此外，这些新开发的方法将被应用于评估同源蛋白质-DNA复合体模型的质量以及利用这些模型进行基于结构的转录因子结合位点预测。该项目开发的数据集和算法将免费提供给研究界。该项目的结果可以在https://guolab.uncc.eduThis上找到，该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。