# Computational Design of Protein Structures and Complexes

> **NIH NIH R35** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $804,353

## Abstract

Project Summary/Abstract
Proteins are critical to all biological processes and designed proteins have important applications in research
and medicine. This project focuses on the development and application of computational methods for protein
design. Recently, there has been tremendous progress in the use of deep learning (DL) for protein modeling.
Neural networks have been trained for structure prediction and sequence design that outperform more traditional
molecular mechanics-based models in a variety of benchmarks. The exciting challenge before the field now is
to discover how to extend these methods toward the design of the diverse functionalities and structural features
evident in naturally occurring proteins. In preliminary studies, we developed a protein design pipeline, called
EvoPro, that uses iterative rounds of DL-based structure prediction and sequence design to optimize a
population of sequences for a user-specified design goal. As an initial test, EvoPro was used to create de novo
proteins that bind to a specified surface patch on another protein. Without any affinity maturation, EvoPro
generated several proteins that bind to the target with KDs less than 150 nM. We now aim to test this pipeline on
a diverse set of protein targets, choosing cases where protein binders may be useful as biosensors or
therapeutics. Allostery is a fundamental process used throughout biology for regulating protein function, and
because of technical challenges is an understudied area in protein design. We will use EvoPro to design protein
interactions where the binding of protein A to protein B induces a conformational change in protein B. To enable
this goal, EvoPro will make use of DL models for sequence design that can optimize an amino acid sequence to
be compatible with multiple conformations. Progress in this area will enable the design of protein switches that
respond to positive and negative regulators. Explicit negative design will also be incorporated into the sequence
design models to redesign the specificity of protein-protein interactions and enable the formation of novel protein
assemblies. To improve DL models for sequence design, we are testing graph neural networks that include
explicit representations of side chain atoms during message passing. These models are being trained to
simultaneously predict side chain coordinates and optimize the protein sequence. By pursuing these projects,
we will expand the capabilities of computational protein design and create molecules that can be used to
understand or treat disease.

## Key facts

- **NIH application ID:** 10842592
- **Project number:** 2R35GM131923-06
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** BRIAN A KUHLMAN
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $804,353
- **Award type:** 2
- **Project period:** 2019-06-01 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10842592

## Citation

> US National Institutes of Health, RePORTER application 10842592, Computational Design of Protein Structures and Complexes (2R35GM131923-06). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10842592. Licensed CC0.

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