# An effective statistical inference framework to develop innovative compensations for protein mutations

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $243,840

## Abstract

Understanding protein mutations requires learning which specific mutations disrupt a protein's function and offers the
opportunity to further understand how to restore the normal function by introducing additional mutations. The approach
here builds upon analyzing the huge protein sequence and structure data with two novel statistical frameworks, including
high-dimensional Potts model inference with structure information, and inference on matrix-valued partial correlations,
to develop quantitative predictions of which mutants disrupt function with a new uncertainty measure and models the
mutation finesses by integrating the sequence and structure data. Together these innovative approaches with the
uncertainty quantification will enable the prediction of compensatory mutations and the final construction of a protein
mutant atlas that broadly disseminates the collective mutation information. The project will learn which mutants disrupt
protein function, and what additional mutants will restore function. This project will demystify the interdependencies
within the sequences to yield a deeper understanding of how protein mutations can change phenotypes. Preliminary
results demonstrate how mutations that are intrinsically destabilizing, and destructive can persist but be neutralized by
the introduction of additional compensatory mutations. The major aims of this project are to reliably distinguish
between the neutral and deleterious mutations and learn how to repair these problems by introducing additional
compensating mutations, by applying the new statistical inference frameworks. The tools developed in this project will
have the power to make direct connections between gene mutations and changes in phenotypes. This is a highly
interdisciplinary collaboration essential for establishing meaningful assessments of protein mutations and that will
develop an important tool for informed protein editing.

## Key facts

- **NIH application ID:** 11042932
- **Project number:** 1R01GM157600-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Zhao Ren
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $243,840
- **Award type:** 1
- **Project period:** 2024-09-13 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11042932

## Citation

> US National Institutes of Health, RePORTER application 11042932, An effective statistical inference framework to develop innovative compensations for protein mutations (1R01GM157600-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11042932. Licensed CC0.

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