Predicting and analyzing protein interaction networks

描述（由申请人提供）：分子相互作用是在生物体中执行的所有过程的潜在基础，它们的完整映射将对理解和解释正常和疾病功能有很大帮助。转录调控相互作用特别有趣，因为它们对基因的适当空间和时间调控至关重要。本提案旨在开发几种新的和互补的计算方法来预测转录因子的相互作用和特异性，并揭示它们在生物体中的保护和变异。综上所述，这些方法将极大地扩展我们对真核生物调控网络及其基本原理的认识。我们将设计一种约束优化和统计相结合的方法来预测多结构域C2H2锌指蛋白的dna结合特异性；这些蛋白质构成了真核生物基因组中最大的一类转录因子。我们还将建立一个新的比较序列框架，以确定同源转录因子之间的结合特异性变化，因为网络差异是生物之间和生物内部观察到的表型和功能多样性的基础；该框架将用于探索转录因子的变化在多大程度上影响生物体间的调节网络变异。最后，我们将开发一个跨基因组框架，用于预测具有已知特异性的转录因子的基因组结合位点，以及推断这些转录因子在生物体之间相互作用的分析技术。越来越多的转录因子的dna结合特异性正在被确定，这种大规模的数据为绘制转录因子结合位点和揭示转录因子-转录因子在生物体中的相互作用提供了新的机会；这些相互作用是调控网络的重要组成部分，它们的变异在调控网络分化中起着关键作用。这些目标的成功完成将导致计算方法的产生，这些方法将显著提高转录网络的表征速度，并将揭示其功能和进化的基本方面。所有开发的软件都将向公众开放。

DESCRIPTION (provided by applicant): Molecular interactions are the underlying basis of all processes that are executed in an organism, and their complete mapping would be a great aid in understanding and interpreting both normal and disease functioning. Transcriptional regulatory interactions are of particular interest as they are critical in the proper spatial and temporal regulation of genes. This proposal aims to develop several novel and complementary computational methods for predicting transcription factor interactions and specificities, and for uncovering their conservation and variation across organisms. Taken together, these methods will vastly expand our knowledge of eukaryotic regulatory networks and their underlying principles. We will devise a combined constrained optimization and statistical approach to predict the DNA-binding specificities of multidomain C2H2 zinc finger proteins; these proteins comprise the largest class of transcription factors in eukaryotic genomes. We will also establish a novel comparative sequence framework for determining binding specificity variation amongst homologous transcription factors, as network divergence underlies much of the observed phenotypic and functional diversity between and within organisms; this framework will be applied to explore the extent to which changes in transcription factors can affect regulatory network variation across organisms. Finally, we will develop a cross-genomic framework for predicting genomic binding sites for transcription factors with known specificities, along with analysis techniques for inferring interactions amongst these transcription factors across organisms. The DNA-binding specificities for an increasing number of transcription factors are being determined, and this large-scale data presents new opportunities to map transcription factor binding sites and to uncover transcription factor- transcription factor interactions across organisms; these interactions are an important component of regulatory networks and their variation plays a key role in network divergence. Successful completion of these aims will result in computational methods that will significantly increase the rate with which transcriptional networks are characterized and will reveal fundamental aspects of their functioning and evolution. All developed software will be made publicly available. PUBLIC HEALTH RELEVANCE: Cellular networks underlie all processes that are executed in an organism, and their complete mapping would aid in understanding both normal and disease functioning. The proposed research will yield software for uncovering and characterizing protein interactions and specificities. These computational tools will help to place proteins, including those important for disease, within the broader context of their cellular pathways, thereby expanding our understanding of diseases and providing an important avenue for uncovering putative drug targets.