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

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，例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。转录因子是一类蛋白质，被认为是主要的药物靶点之一，因为转录因子的突变和异常的蛋白质-DNA相互作用与包括癌症在内的许多疾病有关。蛋白质-DNA复合物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复合物结构。该项目旨在通过开发用于评估蛋白质-DNA复合物模型质量的方法，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复合物结构数据库，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募博士后和代表性不足的群体的学生。本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复合物的计算建模，包括同源建模和蛋白质-DNA对接，是一种具有成本效益的替代方案，以填补蛋白质-DNA复合物结构景观中的空白。主要的挑战是准确评估复杂模型的质量和评估蛋白质-DNA复合物之间的相似性。虽然已经开发了用于比较蛋白质-配体复合物的标准方案，但由于其独特的结构和化学特征，它们不适合评估蛋白质-DNA复合物的相似性。DNA具有双螺旋结构，蛋白质和DNA碱基之间的氢键对于蛋白质-DNA结合特异性至关重要。显然需要新的方法来准确比较蛋白质-DNA复合物，这反过来又可以帮助开发用于蛋白质-DNA复合物模型的准确质量评估的方法。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复合物结构之间的相似性的本质，并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复合物模型的质量和基于结构的转录因子结合位点预测与这些模型。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www.example.com上找到https://guolab.uncc.eduThis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。

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.