Protein structure refinement through effective sampling and scoring. Detailed structural information is essential in understanding biological processes in detail and in allowing the rational development of therapeutic strategies against a variety of diseases. Experimental methods allow the accurate determination of high-resolution structures, but are encumbered by significant effort and experimental constraints. As an alternative, computational methods can predict protein structures to some degree of accuracy. However, it has remained a challenge to routinely predict protein structures at near-experimental accuracy. A high level of resolution may be reached through refinement of initial models. Successful protein structure refinement requires sampling methods that can generate native-like conformations and scoring methods that are able to identify the most native structures from a set of candidates without any knowledge of the true experimental structure. In order to achieve these goals novel protein structure prediction and refinement protocols are developed. In particular, effective conformational sampling strategies based on existing and new methods with constraints to reduce conformational search space are introduced;novel statistical methods to enhance and combine existing scoring functions in the selection of refined models from a set of decoys are developed;and an intermediate resolution model PRIMO is developed to obtain a better balance between energetic accuracy, model resolution, and sampling efficiency. These new methods are combined into an integrated refinement strategy and applied in the context of an automated protein structure pipeline.

Public Health Relevance

New computational methods for the accurate prediction of protein structures are developed as an alternative to experimental approaches. Such structural information is crucial in understanding detailed biological mechanisms and allowing the development of therapeutic strategies against a variety of diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
3R01GM084953-05S1
Application #
8848261
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Wehrle, Janna P
Project Start
2008-09-01
Project End
2015-05-31
Budget Start
2012-09-01
Budget End
2015-05-31
Support Year
5
Fiscal Year
2014
Total Cost
$83,148
Indirect Cost
$27,609
Name
Michigan State University
Department
Biochemistry
Type
Schools of Arts and Sciences
DUNS #
193247145
City
East Lansing
State
MI
Country
United States
Zip Code
48824
Heo, Lim; Feig, Michael (2018) PREFMD: a web server for protein structure refinement via molecular dynamics simulations. Bioinformatics 34:1063-1065
Heo, Lim; Feig, Michael (2018) What makes it difficult to refine protein models further via molecular dynamics simulations? Proteins 86 Suppl 1:177-188
Dutagaci, Bercem; Heo, Lim; Feig, Michael (2018) Structure refinement of membrane proteins via molecular dynamics simulations. Proteins 86:738-750
Feig, Michael; Yu, Isseki; Wang, Po-Hung et al. (2017) Crowding in Cellular Environments at an Atomistic Level from Computer Simulations. J Phys Chem B 121:8009-8025
Dutagaci, Bercem; Wittayanarakul, Kitiyaporn; Mori, Takaharu et al. (2017) Discrimination of Native-like States of Membrane Proteins with Implicit Membrane-based Scoring Functions. J Chem Theory Comput 13:3049-3059
Huang, Jing; Rauscher, Sarah; Nawrocki, Grzegorz et al. (2017) CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods 14:71-73
Feig, Michael (2017) Computational protein structure refinement: Almost there, yet still so far to go. Wiley Interdiscip Rev Comput Mol Sci 7:
Nawrocki, Grzegorz; Wang, Po-Hung; Yu, Isseki et al. (2017) Slow-Down in Diffusion in Crowded Protein Solutions Correlates with Transient Cluster Formation. J Phys Chem B 121:11072-11084
Dutagaci, Bercem; Sayadi, Maryam; Feig, Michael (2017) Heterogeneous dielectric generalized Born model with a van der Waals term provides improved association energetics of membrane-embedded transmembrane helices. J Comput Chem 38:1308-1320
Dutagaci, Bercem; Feig, Michael (2017) Determination of Hydrophobic Lengths of Membrane Proteins with the HDGB Implicit Membrane Model. J Chem Inf Model 57:3032-3042

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