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

> **NIH NIH R35** · RESEARCH INST OF FOX CHASE CAN CTR · 2021 · $692,227

## Abstract

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.

## Key facts

- **NIH application ID:** 10176529
- **Project number:** 5R35GM122517-05
- **Recipient organization:** RESEARCH INST OF FOX CHASE CAN CTR
- **Principal Investigator:** ROLAND L DUNBRACK
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $692,227
- **Award type:** 5
- **Project period:** 2017-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10176529

## Citation

> US National Institutes of Health, RePORTER application 10176529, Structural bioinformatics of proteins and protein complexes and applications to cancer biology (5R35GM122517-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10176529. Licensed CC0.

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