Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative.

Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.

L-DOPA and Droxidopa: From Force Field Development to Molecular Docking into Human ß2-Adrenergic Receptor.

The increasing interest in the molecular mechanism of the binding of different agonists and antagonists to ß2-adrenergic receptor (ß2AR) inactive and active states has led us to investigate protein-ligand interactions using molecular docking calculations. To perform this study, the 3.2 Å X-ray crystal structure of the active conformation of human ß2AR in the complex with the endogenous agonist adrenaline has been used as a template for investigating the binding of two exogenous catecholamines to this adrenergic receptor. Here, we show the derivation of L-DOPA and Droxidopa OPLS all atom (AA) force field (FF) parameters via quantum mechanical (QM) calculations, molecular dynamics (MD) simulations in aqueous solutions of the two catecholamines and the molecular docking of both ligands into rigid and flexible ß2AR models. We observe that both ligands share with adrenaline similar experimentally observed binding anchor sites, which are constituted by Asp113/Asn312 and Ser203/Ser204/Ser207 side chains. Moreover, both L-DOPA and Droxidopa molecules exhibit binding affinities comparable to that predicted for adrenaline, which is in good agreement with previous experimental and computational results. L-DOPA and Droxidopa OPLS AA FFs have also been tested by performing MD simulations of these ligands docked into ß2AR proteins embedded in lipid membranes. Both hydrogen bonds and hydrophobic interaction networks observed over the 1 µs MD simulation are comparable with those derived from molecular docking calculations and MD simulations performed with the CHARMM FF.

Analysis of L-DOPA and droxidopa binding to human ß2-adrenergic receptor.

Over the last two decades, an increasing number of studies has been devoted to a deeper understanding of the molecular process involved in the binding of various agonists and antagonists to active and inactive conformations of ß2-adrenergic receptor (ß2AR). The 3.2 Å x-ray crystal structure of human ß2AR active state in combination with the endogenous low affinity agonist adrenaline offers an ideal starting structure for studying the binding of various catecholamines to adrenergic receptors. We show that molecular docking of levodopa (L-DOPA) and droxidopa into rigid and flexible ß2AR models leads for both ligands to binding anchor sites comparable to those experimentally reported for adrenaline, namely D113/N312 and S203/S204/S207 side chains. Both ligands have a hydrogen bond network that is extremely similar to those of noradrenaline and dopamine. Interestingly, redocking neutral and protonated versions of adrenaline to rigid and flexible ß2AR models results in binding poses that are more energetically stable and distinct from the x-ray crystal structure. Similarly, lowest energy conformations of noradrenaline and dopamine generated by docking into flexible ß2AR models had binding free energies lower than those of best poses in rigid receptor models. Furthermore, our findings show that L-DOPA and droxidopa molecules have binding affinities comparable to those predicted for adrenaline, noradrenaline, and dopamine, which are consistent with previous experimental and computational findings and supported by the molecular dynamics simulations of ß2AR-ligand complexes performed here.

High Water Density at Non-Ice-Binding Surfaces Contributes to the Hyperactivity of Antifreeze Proteins.

Antifreeze proteins (AFPs) can bind to ice nuclei thereby inhibiting their growth and their hydration shell is believed to play a fundamental role. Here, we use molecular dynamics simulations to characterize the hydration shell of four moderately-active and four hyperactive AFPs. The local water density around the ice-binding-surface (IBS) is found to be lower than that around the non-ice-binding surface (NIBS) and this difference correlates with the higher hydrophobicity of the former. While the water-density increase (with respect to bulk) around the IBS is similar between moderately-active and hyperactive AFPs, it differs around the NIBS, being higher for the hyperactive AFPs. We hypothesize that while the lower water density at the IBS can pave the way to protein binding to ice nuclei, irrespective of the antifreeze activity, the higher density at the NIBS of the hyperactive AFPs contribute to their enhanced ability in inhibiting ice growth around the bound AFPs.

Length-scale dependence of protein hydration-shell density.

Here we present a computational approach based on molecular dynamics (MD) simulation to study the dependence of the protein hydration-shell density on the size of the protein molecule. The hydration-shell density of eighteen different proteins, differing in size, shape and function (eight of them are antifreeze proteins), is calculated. The results obtained show that an increase in the hydration-shell density, relative to that of the bulk, is observed (in the range of 4-14%) for all studied proteins and that this increment strongly correlates with the protein size. In particular, a decrease in the density increment is observed for decreasing protein size. A simple model is proposed in which the basic idea is to approximate the protein molecule as an effective ellipsoid and to partition the relevant parameters, i.e. the solvent-accessible volume and the corresponding solvent density, into two regions: inside and outside the effective protein ellipsoid. It is found that, within the model developed here, almost all of the hydration-density increase is located inside the protein ellipsoid, basically corresponding to pockets within, or at the surface of the protein molecule. The observed decrease in the density increment is caused by the protein size only and no difference is found between antifreeze and non-antifreeze proteins.

Author Correction: Transcript expression profiling in two contrasting cultivars and molecular cloning of a SKP-1 like gene, a component of SCF-ubiquitin proteasome system from mungbean Vigna radiate L.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Hydration Shell of Antifreeze Proteins: Unveiling the Role of Non-Ice-Binding Surfaces.

Antifreeze proteins (AFPs) have the ability to inhibit ice growth by binding to ice nuclei. Their ice-binding mechanism is still unclear, yet the hydration layer is thought to play a fundamental role. Here, we use molecular dynamics simulations to characterize the hydration shell of two AFPs and two non-AFPs. The calculated shell thickness and density of the AFPs do not feature any relevant difference with respect to the non-AFPs. Moreover, the hydration shell density is always higher than the bulk density and, thus, no low-density, ice-like layer is detected at the ice-binding surface (IBS) of AFPs. Instead, we observe local water-density differences in AFPs between the IBS (lower density) and the non-IBS (higher density). The lower solvent density at the ice-binding site can pave the way to the protein binding to ice nuclei, while the higher solvent density at the non-ice-binding surfaces might provide protection against ice growth.

Transcript expression profiling in two contrasting cultivars and molecular cloning of a SKP-1 like gene, a component of SCF-ubiquitin proteasome system from mungbean Vigna radiate L.

Protein degradation and turnover under various environmental stresses is basically regulated by ubiquitin-proteasome system (UPS), of which SKP1 is a very essential component. Isolation and cloning of an identified potential stress responsive candidate gene SKP1, was successfully done for the first time to fathom the role of SKP1 in drought tolerance at genetic level in drought tolerant mungbean cultivar Pratap, which was screened after a detailed physio-biochemical screening amongst seven popular mungbean cultivars. The cloned gene SKP1 (accession number KX881912) is 550 bp in length, encodes 114 amino acids. It shows high sequence homology with SKP1 from Zea mays (NP_001148633). The protein expression of isolated SKP1 was confirmed by GUS fused expression using a Histochemical assay under control as well as under drought stress. Further, up-regulation in relative expression level of SKP1 in different plant parts under drought stress confirmed its utility as a potential drought responsive candidate gene certainly demanding extensive genetic research for further incorporation in breeding programs. Moreover, the structure of VrSKP1 (Vigna radiata SKP1) has been modelled, validated and an Essential Dynamics (ED) was done on the Molecular Dynamics (MD) simulation trajectories for filtering large-scale concerted motions. Free-energy calculations on the ED revealed a complex free-energy landscape (FEL) implying the conformational diversity of the modelled VrSPK1 protein.

An in silico approach to understand the structure-function properties of a serine protease (Bacifrinase) from Bacillus cereus and experimental evidence to support the interaction of Bacifrinase with fibrinogen and thrombin.

Microbial fibrinogenolytic serine proteases find therapeutic applications in the treatment of thrombosis- and hyperfibrinogenemia-associated disorders. However, analysis of structure-function properties of an enzyme is utmost important before its commercial application. In this study, an attempt has been made to understand the structure of a fibrinogenolytic protease enzyme, "Bacifrinase" from Bacillus cereus strain AB01. From the molecular dynamics trajectory analysis, the modelled three-dimensional structure of the protease was found to be stable and the presence of a catalytic triad made up of Asp102, His83 and Ser195 suggests that it is a serine protease. To understand the mechanism of enzyme-substrate and enzyme-inhibitor interactions, the equilibrated protein was docked with human fibrinogen (the physiological substrate of this enzyme), human thrombin and with ten selective protease inhibitors. The Bacifrinase-chymostatin interaction was the strongest among the selected protease inhibitors. The serine protease inhibitor phenyl methane sulphonyl fluoride was found to interact with the Ser134 residue of Bacifrinase. Furthermore, protein-protein docking study revealed the fibrinogenolytic property of Bacifrinase and its interaction with Aα-, Bß- and Cγ-chains human fibrinogen to a different extent. However, biochemical analysis showed that Bacifrinase did not hydrolyse the γ-chain of fibrinogen. The in silico and spectrofluorometric studies also showed interaction of Bacifrinase with thrombin as well as fibrinogen with a Kd value of 16.5 and .81 nM, respectively. Our findings have shed light on the salient structural features of Bacifrinase and confirm that it is a fibrinogenolytic serine protease.