# Structural Bioinformatics of Proteins and Protein Complexes and Applications to Cancer Biology

> **NIH NIH R35** · RESEARCH INST OF FOX CHASE CAN CTR · 2024 · $744,480

## Abstract

Project Summary/Abstract
Structural biology has a fundamental role to play in the advancement of cancer biology and the development of
cancer therapeutics. With the rapid developments in experimental structural determination (both crystallography
and cryo-EM spectroscopy), structure prediction methods (primarily AlphaFold2 and RosettaFold), and molecular
simulation methods, we are poised to bring new levels structural information to cancer research. In this project,
we will analyze the structural variation and dynamics of protein families commonly associated with cancer
development or targets of cancer therapeutics using existing clustering methods for protein loops and new
unsupervised learning techniques from the field of deep learning. We will develop methods for using AlphaFold2
to predict the structures of active and inactive kinases using templates based on our classification of active and
inactive states of kinases and multiple sequence alignments optimized for this task. In relevant cases, these
structure predictions will include the N and C terminal tails and other domains which may interact with the kinase
domains. We will integrate AlphaFold2 structure predictions of protein homo- and heterooligomeric complexes
with our database of common interfaces and assemblies found across the structures of proteins in the PDB.
Interactions observed in crystals that are replicated by AlphaFold2 present well-founded hypotheses for
functional protein interactions. This will be applied specifically for all human kinases where homodimer
interactions play an important role in activation and inhibition. We will continue our structural bioinformatics
studies of antibodies and expand this work to T-cell receptors, and investigate the utility of deep learning methods
for computational antibody and TCR design. Finally, we will bring new structure prediction technologies and our
statistical analysis of protein structures to the ongoing research programs of laboratory and clinical colleagues
at Fox Chase Cancer Center and Temple University School of Medicine.

## Key facts

- **NIH application ID:** 10847375
- **Project number:** 5R35GM122517-07
- **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:** 2024
- **Award amount:** $744,480
- **Award type:** 5
- **Project period:** 2017-04-01 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10847375

## Citation

> US National Institutes of Health, RePORTER application 10847375, Structural Bioinformatics of Proteins and Protein Complexes and Applications to Cancer Biology (5R35GM122517-07). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10847375. Licensed CC0.

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