Abstract
Incorporating receptor flexibility in small ligand-protein docking still poses a challenge for proteins undergoing large conformational changes. In the absence of bound structures, sampling conformers that are accessible by apo state may facilitate docking and drug design studies. For this aim, we developed an unbiased conformational search algorithm, by integrating global modes from elastic network model, clustering and energy minimization with implicit solvation. Our dataset consists of five diverse proteins with apo to complex RMSDs 4.7–15 Å. Applying this iterative algorithm on apo structures, conformers close to the bound-state (RMSD 1.4–3.8 Å), as well as the intermediate states were generated. Dockings to a sequence of conformers consisting of a closed structure and its “parents” up to the apo were performed to compare binding poses on different states of the receptor. For two periplasmic binding proteins and biotin carboxylase that exhibit hinge-type closure of two dynamics domains, the best pose was obtained for the conformer closest to the bound structure (ligand RMSDs 1.5–2 Å). In contrast, the best pose for adenylate kinase corresponded to an intermediate state with partially closed LID domain and open NMP domain, in line with recent studies (ligand RMSD 2.9 Å). The docking of a helical peptide to calmodulin was the most challenging case due to the complexity of its 15 Å transition, for which a two-stage procedure was necessary. The technique was first applied on the extended calmodulin to generate intermediate conformers; then peptide docking and a second generation stage on the complex were performed, which in turn yielded a final peptide RMSD of 2.9 Å. Our algorithm is effective in producing conformational states based on the apo state. This study underlines the importance of such intermediate states for ligand docking to proteins undergoing large transitions.
Highlights
Proteins undergo conformational changes of varying degrees, in order to perform their cellular functions
Our current study presents an unbiased atomistic conformer generation algorithm by integration of Elastic network model (ENM) with energy minimization and clustering, which is especially beneficial for proteins undergoing large conformational transitions
Large conformational changes (RMSD > 4 Å) between unbound and bound forms of receptor are detected in all cases, and these are shown in the left panels of Fig 1
Summary
Proteins undergo conformational changes of varying degrees, in order to perform their cellular functions. The flexible nature of proteins is one of the challenging problems in drug design studies since even small conformational changes can affect the nature of ligand-protein interactions [1]. Accounting for large conformational changes upon binding is still challenging [3] and efficient computational algorithms are necessary to sample protein conformations for more accurate prediction of binding sites and affinities in docking studies. It is a computationally efficient tool that can provide insight about the protein functional dynamics and conformational changes that take place upon ligand binding. ENM has been widely used in protein-protein docking to account for the global backbone conformational changes [3,11,12,13], as well as in the optimization of complexes fitted in electron-density maps and in protein-DNA model refinement [13,14]
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