# Computational Structure-Based Protein Design

> **NIH NIH R01** · DUKE UNIVERSITY · 2020 · $354,038

## Abstract

Project Summary. Computational structure-based protein design is a transformative field with exciting
prospects for advancing both basic science and translational medical research. My laboratory has developed
new protein design algorithms and used them to predict MRSA resistance to new antibiotics; design a broadly
neutralizing antibody VRC07-523LS against HIV with unprecedented breadth and potency that is now in
clinical trials; design protein-peptide interactions to treat cystic fibrosis; perform antigenicity-guided structural
design of HIV gp140 envelope protein (Env) trimer constructs to delineate mechanism and fix conformation;
and design a new antigenic membrane-bound membrane proximal external region (MPER) trimer for
examining immunogenic responses to the HIV viral coat protein gp41. Central to protein design methodology
is the need to optimize the amino acid sequence, placement of side chains, and backbone conformations in
protein structures. By developing advanced search and scoring algorithms for combinatorial optimization of
protein and ligand structure and sequence, we showed that desired structure, affinity, and activity can be
designed by (a) modeling improved molecular flexibility and (b) exploiting ensembles of structures for accurate
predictions. Our suite of algorithms has mathematical guarantees on the solution quality (up to the accuracy of
the input model, which includes the initial structures, molecular flexibility to be modeled, and an empirical
molecular mechanics energy function). Specifically, our algorithms guarantee to compute the global minimum
energy conformation (GMEC), a gap-free list of sequences and structures in order of predicted energy, and a
provably-good approximation to the binding affinity by bounding partition functions over molecular ensembles.
We propose to build on our foundation of protein design algorithms, called OSPREY, and apply them in areas
of biochemical and pharmacological importance. We will (1) predict future resistance mutations in protein
targets of novel drugs; (2) design inhibitors of protein:protein interactions to target today’s “undruggable”
proteins; and (3) use OSPREY to redesign and improve broadly neutralizing HIV antibodies. Improvements to
our protein design algorithms will be implemented to improve accuracy and scope, and we will advance the
state of the art in protein design by making algorithmic and modeling improvements to accomplish the Aims (1-
3) above, including: the modeling of more protein/ligand flexibility and improved energy functions during large-
scale design; new combinatorial optimization and energy-fitting methods to accelerate the design search; and
design of affinity and specificity using novel multi-state design algorithms that model thermodynamic molecular
ensembles. We will test our design predictions prospectively, by making novel predicted mutant proteins and
performing biochemical, biological, and structural studies. We will also validate our algorithm...

## Key facts

- **NIH application ID:** 9915930
- **Project number:** 5R01GM078031-11
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Bruce R. Donald
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $354,038
- **Award type:** 5
- **Project period:** 2008-04-15 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9915930

## Citation

> US National Institutes of Health, RePORTER application 9915930, Computational Structure-Based Protein Design (5R01GM078031-11). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9915930. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*