Identification of Catechins' Binding Sites in Monomeric Aß42 through Ensemble Docking and MD Simulations.

The assembly of the amyloid-ß peptide (Aß) into toxic oligomers and fibrils is associated with Alzheimer's disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the Aß monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of Aß, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline-in the framework of the ensemble docking strategy-to identify catechins' binding sites in monomeric Aß42. It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aß42. Additionally, it has been found that all the studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of Aß. Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against Aß.

Structural insights into the substrate-binding site of main protease for the structure-based COVID-19 drug discovery.

An attractive drug target to combat COVID-19 is the main protease (Mpro ) because of its key role in the viral life cycle by processing the polyproteins translated from the viral RNA. Studying the crystal structures of the protease is important to enhance our understanding of its mechanism of action at the atomic-level resolution, and consequently may provide crucial structural insights for structure-based drug discovery. In the current study, we report a comparative structural analysis of the Mpro substrate binding site for both apo and holo forms to identify key interacting residues and conserved water molecules during the ligand-binding process. It is shown that in addition to the catalytic dyad residues (His41 and Cys145), the oxyanion hole residues (Asn142-Ser144) and residues His164-Glu166 form essential parts of the substrate-binding pocket of the protease in the binding process. Furthermore, we address the issue of the substrate-binding pocket flexibility and show that two adjacent loops in the Mpro structures (residues Thr45-Met49 and Arg188-Ala191) with high flexibility can regulate the binding cavity' accessibility for different ligand sizes. Moreover, we discuss in detail the various structural and functional roles of several important conserved and mobile water molecules within and around the binding site in the proper enzymatic function of Mpro . We also present a new docking protocol in the framework of the ensemble docking strategy. The performance of the docking protocol has been evaluated in predicting ligand binding pose and affinity ranking for two popular docking programs; AutoDock4 and AutoDock Vina. Our docking results suggest that the top-ranked poses of the most populated clusters obtained by AutoDock Vina are the most important representative docking runs that show a very good performance in estimating experimental binding poses and affinity ranking.

Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem.

Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a solution to this problem, the optimum choice of receptor conformations is still an open question considering the issues related to the computational cost and false positive pose predictions. Here, a combination of ensemble learning and ensemble docking is suggested to rank different conformations of the target protein in light of their importance for the final accuracy of the model. Available X-ray structures of cyclin-dependent kinase 2 (CDK2) in complex with different ligands are used as an initial receptor ensemble, and its redundancy is removed through a graph-based redundancy removal, which is shown to be more efficient and less subjective than clustering-based representative selection methods. A set of ligands with available experimental affinity are docked to this nonredundant receptor ensemble, and the energetic features of the best scored poses are used in an ensemble learning procedure based on the random forest method. The importance of receptors is obtained through feature selection measures, and it is shown that a few of the most important conformations are sufficient to reach 1 kcal/mol accuracy in affinity prediction with considerable improvement of the early enrichment power of the models compared to the different ensemble docking without learning strategies. A clear strategy has been provided in which machine learning selects the most important experimental conformers of the receptor among a large set of protein-ligand complexes while simultaneously maintaining the final accuracy of affinity predictions at the highest level possible for available data. Our results could be informative for future attempts to design receptor-specific docking-rescoring strategies.

Computational evidence of new putative allosteric sites in the acetylcholinesterase receptor.

Acetylcholinesterase (AChE), with a rigid structure and buried active site at the end of a deep narrow gorge, is interesting enough to solve the paradox between high catalytic activity and unavailability of the active site in treatment of Alzheimer's disease (AD). In this way, the blind docking process is performed on an ensemble of AChE structures created with molecular dynamics (MD) simulations to survey the whole space of AChE to find multiple access pathways to the active site and ranking them based on their affinity scores. Our results show that there are other allosteric binding sites in the protein structure whose inhibition, can affect protein function by disrupting the release of the Acetylcholine (AC) degradation products. In this study, inhibitory activities of Hybride14 and two natural compounds (Papaverine and Palmatine) were evaluated for all possible allosteric sites via docking method. The results confirmed the non-competitive inhibition mechanism. The best binding mode for these inhibitors and efficacy of hydrogen bonds and hydrophobic interactions on inhibitory activities of ligands were also disclosed. Furthermore, our studies provide significant molecular insight for AChE inhibition that could aid in the development of new drugs for AD's treatment.

Hydrogen bonding motifs in a hydroxy-bisphosphonate moiety: revisiting the problem of hydrogen bond identification.

Bisphosphonates are important therapeutic agents in bone-related diseases and exhibit complex H-bonding networks. To assess the role of H-bonds in biophosphonate stability, a full conformational search was performed for methylenebisphosphonate (MBP) and 1-hydroxyethylidene-1,1-diphosphonate (HEDP) using the MP2 method in conjunction with the continuum solvation model. The most stable structures and their equilibrium populations were analyzed at two protonation states via assignment of H-bonding motifs to each conformer. Geometrical and topological approaches for the identification and characterization of H-bonds were compared with each other, and some of the important correlations between H-bond features were described over the entire conformational space of a hydroxy-bisphosphonate moiety. The topologically derived H-bond energy obtained from the local density of potential energy at bond critical points shows consistent correlations with other measures such as H-bond frequency shift. An inverse power form without an intercept predicts topological H-bond energies from hydrogen-acceptor distances with an RMS error of less than 1 kcal mol(-1). The consistency of this measure was further checked by building a model that reasonably reproduces the relative stabilities of different conformers from their hydrogen-acceptor distances. In all systems, the predictions of this model are improved by the consideration of weak H-bonds that have no bond critical point.

Coping with the anisotropy in the analytical representation of an ab initio potential energy surface for the Cl2 dimer.

The intermolecular potential energy surface (PES) of the Cl2 dimer is calculated at the MP2/aTZ + b level of ab initio theory. A quantitative measure is proposed for comparison of the anisotropy of PESs of different systems at different intermolecular distances. A high degree of anisotropy at short and intermediate distances results in the failure of fitting strategies that are based on the angular expansion of the potential energy. To tackle this problem, a step-by-step fitting strategy is designed for analytical representation of the PES. The global minimum energy configuration of the dimer is found to be a distorted L-shape structure with a well depth of around 615 cm(-1). The PES is finally scaled to minimize deviations between calculated and experimental second virial coefficients.