# Unraveling molecular and system-level mechanisms of human disease-associated protein mutations

> **NIH NIH R35** · TEXAS ENGINEERING EXPERIMENT STATION · 2021 · $335,245

## Abstract

Project Summary
Although a quickly expanding catalogue of protein mutations have been discovered, including those that cause
human diseases or confer drug resistance, mechanistic understanding and prediction of functional consequences
of these mutations remain rather limited. There is a critical need to develop novel, multiscale computational
frameworks that are rigorous and generalizable to help close the ever-increasing gap between phenotypic data
and mechanistic knowledge about the mutations and to develop effective therapeutic strategies.
 My long-term research goals are two folds: (1) to unravel how a change in protein sequence ripples
through various aspects of molecular and system-levels to a change in cellular function and cause human
diseases or confer drug resistance; and (2) to translate learned mechanistic knowledge to effective drug-design
strategies for human diseases and drug resistance. Toward these goals I propose to advance and combine the
strengths of computational molecular biology (structural modeling and design for proteins or protein
interactions) and computational systems biology (pathway/network-level topology and dynamics) and follow a
novel, formal paradigm to develop methods that systematically identify mechanisms underlying various stages
of consequence propagation (from conformation, molecular and system-level variables, to cellular functions,
where intermediates can be skipped if necessary). In the forward direction of propagating consequences, I will
determine, explain and understand mutational consequences at various levels with machine learning, causal
analysis, and combinatorial optimization, starting with known or modelled changes at various levels. In the
inverse direction of designing consequences, I will follow known or learned mechanistic hypotheses and
develop efficient combinatorial optimization methods to design mutagenesis experiments or screen / design
ligands for desired perturbations at various levels, which would test and refine corresponding hypotheses
directly and translate mechanistic hypotheses to therapeutic strategies. Mechanisms underlying distal
(potentially allosteric) mutations, multiple mutations within a protein or across proteins as well as systems
pharmacology strategies will also be rigorously determined in the process.
 The expected outcomes from the innovative project will include two main components. The first will be
both biophysical principles of proteins and integrative understanding of biomedical systems, which will be
organized in a database for public access. The second will be translating mechanistic insights into therapeutic
intervention strategies to treat diseases. In particular, targeted experiments (mutagenesis or ligand-binding) will
be designed following known or discovered mechanistic hypotheses. This project will involve close
collaboration with structural biologists and clinician-scientists who are active collaborators of the PI.

## Key facts

- **NIH application ID:** 10263154
- **Project number:** 5R35GM124952-05
- **Recipient organization:** TEXAS ENGINEERING EXPERIMENT STATION
- **Principal Investigator:** Yang Shen
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $335,245
- **Award type:** 5
- **Project period:** 2017-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10263154

## Citation

> US National Institutes of Health, RePORTER application 10263154, Unraveling molecular and system-level mechanisms of human disease-associated protein mutations (5R35GM124952-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10263154. Licensed CC0.

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