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

摘要 我的研究重点是蛋白质结构的统计分析，结构方法的发展 蛋白质和蛋白质复合体的预测，以及计算结构生物学在问题中的应用 癌症研究。在最基本的层面上，我们将现代统计分析方法应用于蛋白质结构 用于结构确定和结构预测的参数，如键角和二面角 软件。我们还试图理解我们观察到的分布和相互依赖背后的物理 实验结构。 我经营着一个分子建模设备，使我在实验细胞和分子生物学方面的同事们 了解生物系统的三维结构，开发和测试生物系统中的假说 他们研究的蛋白质。从任何感兴趣的目标的可用数据中，我们的目标是开发最 可能的生物信息性模型，无论是由低聚物和/或具有小配体的络合物组成 或核酸，或不同的构象和功能状态。要做好这项工作，我们需要了解 在蛋白质晶体中观察到的生物组装，包括同源和杂合低聚物。我们利用了一种结构性的 生物信息学方法，寻找多种晶体形式的特定相互作用和组装的证据 跨越蛋白质家族(或家族对)。这些研究也与预测和解释 临床环境下基因测序中发现的错义突变的功能相关性。 由于它们与癌症治疗相关，我们正在研究抗体的所有可用结构。 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，我们感兴趣的是反式转录过程中的构象变化 自动磷酸化，以及如何开发抑制物来干扰这一过程，特别是对突变的 激活剂。在抗体的情况下，我们正在开发计算抗体设计的方法，既可以是球形的，也可以是 具有线性表位的蛋白质和无序或变性蛋白质，既可作为治疗药物，也可用作试剂 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.