# Using in vivo genetic and physical interaction data for structure determination of protein assemblies

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $403,750

## Abstract

PROJECT SUMMARY
Many proteins function by forming macromolecular assemblies. Describing the structures of these assemblies
in their cellular environment remains challenging. Traditional structural biology approaches may provide high-
resolution atomic structures but usually require purified samples and might describe only a few conformers. We
propose using data from in vivo genetic interaction and quantitative cross-linking mass-spectrometry (qXL-MS)
experiments to build structural models of protein assemblies, empowering the scientific community to address
structural questions that are currently out of reach of traditional structural biology methods. For example, genetic
interaction mapping by point-mutant epistatic miniarray profile (pE-MAP) platform and deep mutational scanning
(DMS) have emerged as powerful tools to interrogate proteins, at a residue resolution, in the context of their
biologically relevant functions. Similarly, in vivo qXL-MS approaches are well-suited to dissect physical
interactions between proteins, a full range of structural dynamics, and conformational changes at residue
resolution. Notably, in vivo genetic interaction and cross-linking experiments can be performed under varying
conditions to determine how protein functional states respond to changes in the cellular environment, a problem
difficult to approach by other methods. However, in vivo genetic interaction and cross-linking datasets are usually
noisy, sparse, and ambiguous, making structural interpretation challenging. To fully realize the potential of in
vivo genetic and physical interaction data, we need new computational methods that maximize the structural
information extracted from these datasets. Here, we propose a comprehensive research program to develop
tools to build integrative/hybrid structure models of stable and transient protein assemblies. We will focus on (1)
developing Bayesian scoring functions that objectively quantify the noise and ambiguity in the in vivo
experimental data, therefore increasing the accuracy and precision of the models; (2) building Bayesian
hierarchical models to represent the ensembles of protein assemblies, therefore allowing the application to
conformational and compositionally heterogeneous systems; and (3) creating validation tools to assess the
precision and accuracy of structural models obtained using in vivo data, therefore allowing judicious use of the
models. Finally, in close collaboration with experimentalists, we will apply these methods to determine the
structures of protein assemblies that have been refractive to traditional structural biology methods, including
vaccinia virus protein assemblies, TRIM5α bound to the HIV-1 capsid, and Ddis shuttling factors associated with
the proteasome. In conclusion, we will expand the scope of structural biology by increasing the variety of input
information used for integrative/hybrid structure modeling and thus allow structural modeling of biological
systems that are not...

## Key facts

- **NIH application ID:** 10714613
- **Project number:** 1R35GM151256-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Ignacia Echeverria Riesco
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $403,750
- **Award type:** 1
- **Project period:** 2023-08-08 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10714613

## Citation

> US National Institutes of Health, RePORTER application 10714613, Using in vivo genetic and physical interaction data for structure determination of protein assemblies (1R35GM151256-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10714613. Licensed CC0.

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