# Computational Tools for Protein Complex Structure Prediction from MS Data

> **NIH NIH P41** · OHIO STATE UNIVERSITY · 2022 · $116,710

## Abstract

TR&D 5: Project Summary. The proposed Resource for Native Mass Spectrometry Guided Structural Biology
aims to develop advanced MS techniques for the structural characterization of biomacromolecules such as
protein:protein, membrane protein:lipid, and RNA:protein complexes. Experimental development in the resource
will focus on effective separations methods to purify and deliver native proteins to the MS, effective surface
induced dissociation methods for non-covalent interface cleavages and UVPD for covalent fragmentation of
native protein complexes, and measurement of the intact complexes and dissociation products (subcomplexes
and covalent fragments) with ion mobility MS (for conformations and conformational changes e.g., upon ligand
binding) and/or high resolution MS. Valuable structural information about macromolecular complexes will be
obtained. However, there is currently no automated way of generating structural restraints from the MS data,
and those restraints are generally insufficient to generate high accuracy complex structures from the data alone.
In TR&D 5, we are proposing that, in combination with novel computational methods, the restraints from SID and
IM, combined with restraints from established methods such as hydrogen deuterium exchange (HDX) and
covalent labeling (CL), are sufficient for improved macromolecular complex structure prediction. We will develop
tools to automatically extract restraints from experimental MS data and incorporate them into the Rosetta
structure prediction tools to guide protein complex structure prediction. The proposed research is structured into
two main stages.
Aim 1. We will develop computational tools for macromolecular complex structure prediction from solution
measurements that are monitored by MS (H/D exchange and covalent labeling). We will implement quantitative
covalent labeling and HDX exposure constraints into the Rosetta docking algorithm, such that it is driven by
agreement with the exposure pattern of the docked subunits. This aim use complexes as testbeds or will be
applied to predict structures from HDX and CL data for complexes from DBPs 1, 2, 3, 7 and 8
Aim 2. We will develop computational tools for macromolecular complex structure prediction from the surface-
induced dissociation and collision cross sections from ion mobility experiments. We will develop new Rosetta
docking scores that measure the agreement of complex models with the SID and IM CCS data. TR&D 5 is tightly
integrated with the other TR&Ds because it aims to extend the applicability of the developed experimental
methods by tailoring computational methods that allow structural modeling based on the experimental data. This
aim will use SID onset energies, oligomeric products generated, and CCS values to test the procedure and to
predict structures by using data from DBPs 1, 2, 3, 7 and 10.

## Key facts

- **NIH application ID:** 10441403
- **Project number:** 5P41GM128577-05
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Steffen Lindert
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $116,710
- **Award type:** 5
- **Project period:** 2018-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10441403

## Citation

> US National Institutes of Health, RePORTER application 10441403, Computational Tools for Protein Complex Structure Prediction from MS Data (5P41GM128577-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10441403. Licensed CC0.

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