AF: Small: Algorithms for Accurate Prediction of Protein Interaction Sites by Integrating Sequence, Structure, and Network Data

蛋白质是细胞的主要构件和功能分子，然而基因组中编码的大多数蛋白质的生物学功能还没有得到很好的描述。结构基因组学的最新进展产生了关于单个蛋白质的三维结构的丰富数据。与此同时，蛋白质很少在细胞中单独作用；相反，它们形成了蛋白质-蛋白质相互作用和其他类型的生物分子相互作用的复杂网络，从中出现了错综复杂但强大的细胞行为。确定参与这些生物分子相互作用的氨基酸位置是了解蛋白质功能的分子基础的重要第一步。尽管它们具有生物学意义，但这些介导蛋白质-蛋白质相互作用的氨基酸位点很难通过实验加以阐明。需要计算算法来准确预测这些站点。这项建议的目标是开发新的计算算法，集成广泛的公开可用的蛋白质序列、结构和网络数据，以准确预测介导蛋白质相互作用的氨基酸位点。特别是，将开发两种新的算法来准确和高效地识别蛋白质表面进化慢于预期的氨基酸残基，以及富含在具有共同相互作用伙伴的非同源蛋白质中的短序列基序。这些氨基酸残基和序列基序是调节蛋白质相互作用的有力候选者。该方案的一个创新和统一的特点是，两种算法都将考虑到蛋白质三维结构对这些氨基酸位点施加的强大空间约束。所提出的工作具有重要意义，因为它解决了分子系统生物学中的一个基本问题：识别调节生物网络的氨基酸残基和序列基序。该提案的执行将提供一套算法、工具和数据集，以最大限度地提高高通量方法对系统和网络生物学研究的影响，研究人员可以使用这些方法来解决从生物医学到进化的各种问题。最后，该建议开发了一种新的计算范式，它集成了广泛的生物数据(蛋白质序列、蛋白质相互作用网络图和蛋白质三维结构)，以高精度预测参与蛋白质相互作用的氨基酸位点。这些解决计算生物学基本问题的新算法直接为NSF CESE/CCF计划的核心任务做出了贡献。这项拟议的研究将进一步加强波士顿大学生物信息学计划中的PI和波士顿大学医学院中的合作实验者之间的跨学科联系。这些联系为研究生和本科生提供了宝贵的跨学科研究经验的机会。教育计划旨在连接K-12、本科生和研究生级别的生物、化学和计算机科学的传统教学和指导方法，并将最新的研究成果和方法带到课堂上。他将继续在波士顿大学生物信息学项目的课程开发和改进中发挥关键作用。

Proteins are the main building blocks and functional molecules of the cell, yet the biological function of most proteins encoded in genomes are not well characterized. Recent advances in structural genomics have generated a wealth of data regarding the three-dimensional structure of individual proteins. At the same time, proteins rarely act alone in the cell; rather, they form complex networks of protein-protein interactions and other types of biomolecular interactions from which intricate yet robust cellular behavior emerges. Identifying amino acid sites that are involved in these biomolecular interactions is an essential first step towards understanding the molecular basis of protein function. Despite their biological significance, these amino acid sites mediating protein-protein interactions are difficult to elucidate experimentally. Computational algorithms are needed to accurately predict these sites.Intellectual Merit. The objective of this proposal is to develop novel computational algorithms that integrate a wide spectrum of publicly available protein sequence, structure, and network data to accurately predict amino acid sites mediating protein interaction. In particular, two new algorithms will be developed to accurately and efficiently identify amino acid residues on the surface of proteins that evolve more slowly than expected, as well as short sequence motifs that are enriched among non-homologous proteins with a common interacting partner. These amino acid residues and sequence motifs are strong candidates for mediating protein interactions. An innovative and unifying feature of this proposal is that both algorithms will take into account the powerful spatial constraints on these amino acid sites imposed by the three-dimensional structure of proteins.The proposed work is significant in that it addresses a fundamental question in molecular systems biology: identifying amino acid residues and sequence motifs that mediate biological networks. The execution of this proposal will provide a set of algorithms, tools, and datasets that maximize the impact of high-throughput approaches on systems and network biology research, which can be used by researchers to address a wide variety of questions ranging from biomedical to evolutionary. Finally, this proposal develops a novel computational paradigm that integrates a wide spectrum of biological data (protein sequences, protein-protein interaction network graphs, and protein three-dimensional structures) to predict amino acid sites mediating protein interaction with high accuracy. These novel algorithms for fundamental problems in computational biology contribute directly to the core mission of the NSF CISE/CCF program.Broader Impacts. The proposed research will further strengthen the interdisciplinary ties between the PI in the Boston University Bioinformatics Program and the collaborating experimentalists in the Boston University School of Medicine. These ties provide invaluable opportunities for cross-disciplinary research experiences for both graduate and undergraduate trainees.The educational plan aims to bridge traditional teaching and mentoring methods between biology, chemistry, and computer science at the K-12, undergraduate, and graduate levels, and to bring the latest research findings and methods to the classroom. He will continue to play a key role in the curriculum development and improvement of the Bioinformatics Program at Boston University.