# IMP: Software for Hybrid Determination of Macromolecular Assembly Structures

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $326,510

## Abstract

Project Summary
The broad goal is to develop and apply computational methods for building structural models of proteins and
their assemblies. These models can give insights into how the assemblies work, how they evolved, how they
can be controlled, and how similar functionality can be designed. One successful approach, integrative struc-
ture determination, casts the building of such models as a computational optimization problem where
knowledge about the assembly is encoded into the scoring function used to evaluate candidate models. We
propose to extend and enhance the Integrative Modeling Platform (IMP; http://integrativemodeling.org) that
provides programmatic support for developing and distributing integrative structure modeling protocols. IMP
allows representing molecules at multiple resolutions, using spatial restraints from many types of data, and
searching for solutions by a variety of sampling algorithms. So far, it has been applied mostly to electron mi-
croscopy, mass spectrometry, small angle X-ray scattering, Förster resonance energy transfer, crosslinking,
and various proteomics data. IMP is easily extensible to add support for new data sources and algorithms, and
is distributed under an open source license. Here, we propose to extend IMP to address a greater range of bio-
logical problems and make it more generally useful to the scientific community. Specifically, in Aim 1, we will
design and test a molecular representation, a scoring function, and a conformational sampling scheme suitable
for modeling based in part on hydrogen deuterium exchange data, determined either by nuclear magnetic res-
onance spectroscopy or mass spectrometry; the scoring function will rely on a Bayesian approach to extract
the maximum structural and dynamic information from the data. In Aim 2, we will focus on optimizing system
representations for integrative structure determination. In particular, we will explore how to find an optimal
coarse-grained representation, given the input information, by sampling alternative representations relying on
several methods, including a Bayesian inference approach. In Aim 3, we will maximize the impact of IMP on
the community, by delivering a well-tested and maintained software package that is documented with mailing
lists, examples, demonstrations at local and external workshops, and hosting of select users at UCSF, and by
pursuing closer integration with other software packages and community resources, including databases such
as the Protein Data Bank, structure viewers such as Chimera, and web portals such as the Protein Model Por-
tal. The proposed aims are informed by and will shape the nascent Worldwide Protein Data Bank effort on rep-
resenting, validating, archiving, and disseminating integrative structure models produced by the community.

## Key facts

- **NIH application ID:** 9838757
- **Project number:** 5R01GM083960-12
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** ANDREJ SALI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $326,510
- **Award type:** 5
- **Project period:** 2008-04-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9838757

## Citation

> US National Institutes of Health, RePORTER application 9838757, IMP: Software for Hybrid Determination of Macromolecular Assembly Structures (5R01GM083960-12). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9838757. Licensed CC0.

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