Ligand Docking to Intermediate and Close-To-Bound Conformers Generated by an Elastic Network Model Based Algorithm for Highly Flexible Proteins

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.

Molecular mechanisms underlying the impact of mutations in SOD1 on its conformational properties associated with amyotrophic lateral sclerosis as revealed with molecular modelling

“…In the current work, SOD1 dynamics modelling was performed with elastic network (EN) models [ 28 ]. The modelling with EN has also been conducted by other authors, especially for docking [ 29 ], protein intermediate-state detection [ 30 ], and investigating evolutionary aspects of protein functioning [ 31 ]. This approach has also been applied to study large-scale protein dynamics that are challenging for MD [ 32 – 36 ].…”

Conformational dynamics of bacterial trigger factor in apo and ribosome-bound states

“…We further performed an atomistic conformational sampling of the complex based on slowest five modes. Our aim was to observe the extent of TF motions on the 50S using ClustENM, an efficient, unbiased ENM-based technique, which has proven successful in providing atomistic sampling for a variety of highly flexible proteins and the 70S ribosome [ 24 , 42 ].…”

“…We employed our recently developed, iterative and unbiased method ClustENM [ 24 , 42 ] on one TF-50S structure to explore the conformational states intrinsically accessible to the complex, without the presence of polypeptide chain exiting the ribosomal tunnel. The complex used as a starting point corresponds to the docked minimized crystal structure (PDB ID: 1W26 [ 2 ], C_1 in Table 1 ) on 50S, where TF is in an extended conformation.…”

ClustENMD: efficient sampling of biomolecular conformational space at atomic resolution

“…ClustENM hybrid algorithm ( Kurkcuoglu et al , 2016 ) has been introduced for unbiased sampling of the essential subspace spanned by the softest ENM modes through integration with clustering and energy minimization of conformers. Comparison with experimental data has shown the efficiency and utility of ClustENM for investigating highly flexible proteins like calmodulin ( Kurkcuoglu and Doruker, 2016 ; Kurkcuoglu et al , 2016 ) as well as large assemblies such as the ribosome ( Can et al , 2017 ; Guzel et al , 2020 ; Kurkcuoglu et al , 2016 ). More recently, ClustENM conformers have proven to facilitate protein–DNA and protein–protein ensemble docking ( Kurkcuoglu and Bonvin, 2020 ) and prediction of cryptic allosteric pockets ( Kaynak et al , 2020 ).…”