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, Frster 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.

Public Health Relevance

We propose to extend IMP, an open source computer program for determining the structures of macromolecu- lar machines based on varied data from multiple experimental methods. The resulting structures will allow us to better understand the workings of the cell, both under normal and disease conditions.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM083960-10
Application #
9397550
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Krepkiy, Dmitriy
Project Start
2008-04-01
Project End
2020-12-31
Budget Start
2018-01-01
Budget End
2018-12-31
Support Year
10
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of California San Francisco
Department
Pharmacology
Type
Schools of Pharmacy
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94118
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