# EmCAST: Stabilizing Proteins and Tuning Dynamics with High Precision and Accuracy

> **NIH NIH R01** · UNIVERSITY OF MONTANA · 2022 · $293,348

## Abstract

Project Summary
There are no reliable methods to stabilize proteins with high accuracy. Currently available methods have
standard errors between observed and predicted effects of mutations on protein stability that range from 1 to 3
kcal/mol. Given the importance of protein stability for biomedical applications such as the shelf-life and
immunogenicity of protein-based pharmaceuticals, development of reliable methods to stabilize proteins with
high accuracy is critical. To address this deficit in current knowledge, we have developed EmCAST (Empirical
C-Alpha Stability Tool) and have shown that it can double the stability of a small three helix bundle, UBA(1),
with four mutations. For a set of eight single, double, triple, and quadruple mutant variants that contain
combinations of these four mutations, the average error between predicted and observed stability was 0.13
kcal/mol, a vast improvement over existing methods to predict stabilizing mutations. EmCAST relies on two
important innovations: 1) use of an empirical potential derived from a database of the alpha carbon (C)
dihedral angle preferences for all possible four-residue sequences extracted from the 2018 release of the
Protein Data Bank and 2) selection of surface-exposed sites for introduction of stabilizing mutations. In the
proposed work, we will demonstrate that EmCAST can be an effective tool to stabilize a broad range of protein
folds and that it can be used to tune the position of protein conformational switches and hence control protein
function. We will also release, maintain, and upgrade a web service so that the protein biochemistry community
can readily access and use this valuable tool.
We will accomplish these goals in the context of the following Aims:
 • In Aim 1, Rational Stabilization of Pure  and  Domains, we will show that EmCAST can stabilize a
 set of four additional helical domains with high accuracy and that it can also be applied to stabilization
 of -sheet domains. Predicted stabilizations for these proteins range from 2.5 to 6 kcal/mol.
 • In Aim 2, Stabilization of Mixed / Domains and Large Folds, we apply EmCAST to stabilize a set
 of four more complex folds that include both -helix and -sheet structure with sequence lengths up to
 270 amino acids. EmCAST predicts stabilizations of 3 to 5 kcal/mol for the selected proteins.
 • In Aim 3, Regulating Loop Dynamics and Tuning the Position of Conformational Switches, we
 will show that EmCAST can be applied to differential stabilization of alternate conformers of proteins,
 allowing for tuning of protein function.

## Key facts

- **NIH application ID:** 10566514
- **Project number:** 1R01GM148610-01
- **Recipient organization:** UNIVERSITY OF MONTANA
- **Principal Investigator:** BRUCE E BOWLER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $293,348
- **Award type:** 1
- **Project period:** 2022-09-24 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10566514

## Citation

> US National Institutes of Health, RePORTER application 10566514, EmCAST: Stabilizing Proteins and Tuning Dynamics with High Precision and Accuracy (1R01GM148610-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10566514. Licensed CC0.

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