Structural bioinformatics of proteins and protein complexes and applications to cancer biology

摘要 我的研究主要集中在蛋白质结构的统计分析， 蛋白质和蛋白质复合物的预测，以及计算结构生物学在蛋白质和蛋白质复合物中的应用。 癌症研究。在最基本的水平上，我们将现代统计分析方法应用于蛋白质结构 用于结构测定和结构预测的参数，例如键角和二面角 软件我们还试图了解我们在实验中观察到的分布和相互依赖性背后的物理学原理。 实验结构。 我经营着一个分子建模设施，使我的同事在实验细胞和分子生物学 开发和测试假设在生物系统的三维结构的知识， 从任何感兴趣的目标的可用数据中，我们的目标是开发最多的 生物信息模型是可能的，无论其包含寡聚体和/或具有小配体的复合物 或核酸，或不同的构象和功能状态。为了做好这件事，我们需要了解 生物组件，无论是homo-和heterooligomers，在蛋白质晶体中观察。我们利用一个结构 生物信息学方法，寻找多种晶体形式中特定相互作用和组装的证据 跨蛋白质家族（或家族对）。这些研究也与预测和解释 临床环境中基因测序中鉴定的错义突变的功能相关性。 由于它们与癌症治疗的相关性，我们正在研究所有可用的抗体结构 和激酶（蛋白质数据库中最常见的两个结构域），以了解它们的结构 和功能变异。在激酶的情况下，我们感兴趣的是涉及反式构象的变化， 自磷酸化，以及如何抑制剂可能会开发干扰这一过程，特别是突变的 激酶。在抗体的情况下，我们正在开发用于计算抗体设计的方法， 具有线性表位的蛋白质和无序或变性蛋白质， 分子生物学研究。

Abstract My research is focused on statistical analysis of protein structure, development of methods for structure prediction of proteins and protein complexes, and applications of computational structural biology to problems in cancer research. At the most basic level, we apply modern methods of statistical analysis to protein structural parameters such as bond angles and dihedral angles for use in structure determination and structure prediction software. We also seek to understand the physics behind the distributions and interdependencies we observe in experimental structures. I run a Molecular Modeling Facility, enabling my colleagues in experimental cellular and molecular biology to develop and test hypotheses in biological systems with a knowledge of the three-dimensional structures of the proteins they work on. From the available data for any target of interest, we aim to develop the most biologically informative models possible, whether that comprises an oligomer and/or complex with small ligands or nucleic acids, or different conformational and functional states. To do this well, we need to understand the biological assemblies, both homo- and heterooligomers, observed in protein crystals. We utilize a structural bioinformatics approach, seeking evidence for particular interactions and assemblies in multiple crystal forms across a family (or family pair) of proteins. These studies are also relevant to predicting and interpreting the functional relevance of missense mutations identified in gene sequencing in clinical settings. Because of their relevance to cancer therapy, we are studying all of the available structures of antibodies and kinases (the two most common domains in the Protein Data Bank) in order to understand their structural and functional variation. In the case of kinases, we are interested in the conformational changes involved in trans autophosphorylation, and how inhibitors might be developed to interfere with this process, especially for mutated kinases. In the case of antibodies, we are developing methods for computational antibody design to both globular proteins and disordered or denatured proteins with linear epitopes, both as therapeutics and reagents for molecular biology studies.