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

蛋白质和蛋白质复合物的结构生物信息学及其在癌症生物学中的应用

基本信息

批准号：
9900841
负责人：
ROLAND L DUNBRACK
金额：
$ 69.22万
依托单位：
RESEARCH INST OF FOX CHASE CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9900841
关键词：
Antibodies Bioinformatics Biological Cancer Biology Cessation of life Clinical Complex Computer software Computing Methodologies Crystallization Data Disease Epitopes Family Genes Homo Knowledge Ligands Malignant Neoplasms Missense Mutation Modernization Molecular Molecular Biology Molecular Conformation Molecular and Cellular Biology Mutate Mutation Nucleic Acids Patients Phosphotransferases Physics Process Proteins Reagent Research Running Statistical Data Interpretation Statistical Methods Structural Protein Structure Testing Therapeutic United States Variant Work anticancer research biological systems cancer therapy data warehouse design driver mutation globular protein information model inhibitor/antagonist interest method development molecular modeling peripheral blood protein complex protein structure protein structure prediction structural biology three dimensional structure tumor

项目摘要

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.

摘要