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.
|Kar, Parimal; Feig, Michael (2017) Hybrid All-Atom/Coarse-Grained Simulations of Proteins by Direct Coupling of CHARMM and PRIMO Force Fields. J Chem Theory Comput 13:5753-5765|
|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|
|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|
|Heo, Lim; Feig, Michael (2017) What makes it difficult to refine protein models further via molecular dynamics simulations? Proteins :|
|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; 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|
|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; Mirjalili, Vahid (2016) Protein structure refinement via molecular-dynamics simulations: What works and what does not? Proteins 84 Suppl 1:282-92|
|Mori, Takaharu; Miyashita, Naoyuki; Im, Wonpil et al. (2016) Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms. Biochim Biophys Acta 1858:1635-51|
|Chodavarapu, Sundari; Jones, A Daniel; Feig, Michael et al. (2016) DnaC traps DnaB as an open ring and remodels the domain that binds primase. Nucleic Acids Res 44:210-20|
Showing the most recent 10 out of 34 publications