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.
|Mirjalili, Vahid; Noyes, Keenan; Feig, Michael (2014) Physics-based protein structure refinement through multiple molecular dynamics trajectories and structure averaging. Proteins 82 Suppl 2:196-207|
|Kar, Parimal; Gopal, Srinivasa Murthy; Cheng, Yi-Ming et al. (2013) PRIMO: A Transferable Coarse-grained Force Field for Proteins. J Chem Theory Comput 9:3769-3788|
|Feig, Michael; Sugita, Yuji (2013) Reaching new levels of realism in modeling biological macromolecules in cellular environments. J Mol Graph Model 45:144-56|
|Panahi, Afra; Feig, Michael (2013) Dynamic Heterogeneous Dielectric Generalized Born (DHDGB): An implicit membrane model with a dynamically varying bilayer thickness. J Chem Theory Comput 9:1709-1719|
|Mirjalili, Vahid; Feig, Michael (2013) Protein Structure Refinement through Structure Selection and Averaging from Molecular Dynamics Ensembles. J Chem Theory Comput 9:1294-1303|
|Harada, Ryuhei; Tochio, Naoya; Kigawa, Takanori et al. (2013) Reduced native state stability in crowded cellular environment due to protein-protein interactions. J Am Chem Soc 135:3696-701|
|Harada, Ryuhei; Sugita, Yuji; Feig, Michael (2012) Protein crowding affects hydration structure and dynamics. J Am Chem Soc 134:4842-9|
|Jaskierny, Adam J; Panahi, Afra; Feig, Michael (2011) Effect of flanking residues on the conformational sampling of the internal fusion peptide from Ebola virus. Proteins 79:1109-17|
|Gopal, Srinivasa M; Mukherjee, Shayantani; Cheng, Yi-Ming et al. (2010) PRIMO/PRIMONA: a coarse-grained model for proteins and nucleic acids that preserves near-atomistic accuracy. Proteins 78:1266-81|
|Zavodszky, Maria I; Stumpff-Kane, Andrew W; Lee, David J et al. (2009) Scoring confidence index: statistical evaluation of ligand binding mode predictions. J Comput Aided Mol Des 23:289-99|