Protein structure refinement through effective sampling and scoring Detailed structural information is essential for understanding biological processes and for providing the basis for the development of therapeutic strategies against a variety of diseases. Experimental methods allow the accurate determination of high-resolution structures, but they are encumbered by significant effort and experimental constraints. As an alternative, modern computational methods can predict protein structures to a good degree of accuracy. However, computational models often do not reach experimental accuracy. Computational structure refinement aims at improving initial models towards experimental quality. 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 knowledge of the true experimental structure. Building on past progress with the development of structure refinement methods, new methods are developed to further improve the quality and efficiency of sampling and the selection of native-like structures. A key component is the use of the intermediate resolution model PRIMO in conjunction with fully atomistic models. These methods will be applied to the refinement of both soluble and membrane-bound proteins. The development of a practical protocol that can be made available to the community via a web service is a central goal of this proposal.
Protein structures are essential to fully understand biological processes and as the starting point for rational drug design. The generation of such structures using computers is efficient and well-established but often falls short of reaching the high accuracy of experimentally obtained structures. Computational structure refinement aims at improving initial models towards experimental accuracy. New methods are developed to implement effective refinement methods, including the development of a public web service.
|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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