ABI Innovation: Computational methods for macromolecular binding analysis

在分子生物学领域，迫切需要能够帮助阐明大分子结合结构基础的工具。该项目将开发用于自动发现结构和物理化学元素的计算方法，有助于大分子结合的亲和力和特异性。表示蛋白质结构的图模型和基于图核的机器学习方法将有助于结合位点的分析和预测。所提出的图形模型将提供一个简洁的数据结构来编码一系列与分子相互作用相关的结构和物理性质。特别是，这些模型将反映蛋白质结构的灵活性。提出的创新的基于图核的方法将研究结合位点的模块化组织，并发现与模块相关的特征模式。在这项工作中产生的方法和程序将有助于研究人员确定结合位点的位置和阐明结合机制。全面的跨学科教育和推广计划将针对本科生、毕业生和工业界的研究人员。跨学科生物信息学课程将汇集来自生命科学和计算机科学的学生，并引领北达科他州立大学（NDSU）生物信息学课程的发展。

In the field of molecular biology, there is an urgent need for tools that can help elucidate the structural basis for macromolecular binding. This project will develop computational methods for automated discovery of structural and physical-chemical elements contributing to the affinity and specificity of macromolecular binding. Graph models for the representation of protein structures and graph kernel-based machine-learning methods will add to the analysis and prediction of binding sites. The proposed graph models will provide a succinct data structure to encode a range of structural and physical properties germane to molecular interactions. Particularly, the models will reflect the flexibility of protein structures. The proposed innovative graph-kernel-based approach will investigate the modular organization of binding sites and discover characteristic patterns associated with the modules. The methods and procedures produced in this work will assist researchers in pinpointing the location of the binding sites and elucidating the binding mechanism.Comprehensive interdisciplinary educational and outreach plans will target undergraduates, graduates, and researchers from industry. Interdisciplinary bioinformatics courses will bring together students from life sciences and computer science and spearhead the curriculum development for the bioinformatics program at North Dakota State University (NDSU).