CAREER: Mapping Biological Networks

获得生物体的基因组序列提供了一幅蓝图，但我们还不知道如何解读。调控DNA编码的指令仍然特别模糊。这个CAREER项目将推进生物网络的绘制。蛋白质转录因子和DNA调控元件之间的特定相互作用将完全通过基因组序列和推断的蛋白质结构来预测，使用全原子，明确的溶剂模拟转录因子与DNA的结合。分子力场和模拟算法的进步、计算机速度的提高以及转录因子家族内的同源结合模式使这成为一个可行的目标。新的算法将用于蛋白质- dna模拟和结合位点预测。将确定选定基因家族成员的结合位点，以实现对基因组内每个家族成员的预测。在互补的努力中，人类蛋白质-蛋白质相互作用网络将在结构域水平上进行分析，并预测蛋白质复合物内蛋白质亚基的拓扑组织。该项目将通过传播研究计划生成的算法和数据集，包括蛋白质- dna和蛋白质-蛋白质相互作用的公共数据库，产生更广泛的影响。该项目的科学成果包括改进转录调控网络的贝叶斯预测先验，与结合位点分析的实验方法的协同作用，生物特异性研究的预期进展，以及大规模人类蛋白质相互作用网络的发布。对本科生、研究生和博士后科研人员进行指导和培训；将在本科和研究生阶段开发新的课程材料，这对快速发展的计算生物学和生物信息学领域至关重要；外展项目将为公立高中学生提供科学丰富和指导，并为他们的教师提供专业发展机会。所有结果将通过适当渠道传播，包括同行评议的出版物、会议、讲习班、免费提供的软件和数据库以及在线课程材料。

Obtaining the genome sequence of an organism provides a blueprint, but not one that we yet know how to read. The instructions encoded by regulatory DNA remain particularly obscure. This CAREER project will advance the mapping of biological networks. Specific interactions between protein transcription factors and DNA regulatory elements will be predicted purely from genome sequence and inferred protein structure using all-atom, explicit solvent simulations of transcription factors bound to DNA. Advances in molecular force-fields and simulation algorithms, increases in computer speed, and homologous binding modes within transcription factor families make this a feasible goal. New algorithms will be developed for protein-DNA simulations and binding site predictions. Binding sites for members of selected gene families will be determined, with the vision of enabling a prediction for every family member within a genome. In a complementary effort, the human protein-protein interaction network will be analyzed at the domain level and the topological organization of protein subunits within protein complexes will be predicted. This project will have broader impact through dissemination of algorithms and data sets generated by the research plan, including public databases of protein-DNA and protein-protein interactions. Scientific outgrowths of this project include improved priors for Bayesian prediction of transcriptional regulatory networks, synergy with experimental methods for binding site analysis, anticipated advances in organism-specific research, and release of a large-scale human protein interaction network. Undergraduate, graduate and postdoctoral researchers will be mentored and trained; new course material will be developed at the undergraduate and graduate levels, essential for the rapidly evolving area of computational biology and bioinformatics; and outreach programs will provide science enrichment and mentoring to public high school students and professional development opportunities for their teachers. All results will be disseminated through appropriate channels, including peer-reviewed publications, conferences, workshops, freely available software and databases, and on-line course material.