Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein-Ligand Complexes

Novel Cyclopentaquinoline and Acridine Analogs as Multifunctional, Potent Drug Candidates in Alzheimer's Disease

A series of new cyclopentaquinoline derivatives with 9-acridinecarboxylic acid and a different alkyl chain length were synthesized, and their ability to inhibit cholinesterases was evaluated. All designed compounds, except derivative 3f, exhibited a selectivity for butyrylcholinesterase (BuChE) with IC₅₀ values ranging from 103 to 539 nM. The 3b derivative revealed the highest inhibitory activity towards BuChE (IC₅₀ = 103.73 nM) and a suitable activity against AChE (IC₅₀ = 272.33 nM). The 3f derivative was the most active compound to AChE (IC₅₀ = 113.34 nM) with satisfactory activity towards BuChE (IC₅₀ = 203.52 nM). The potential hepatotoxic effect was evaluated for both 3b and 3f compounds. The 3b and 3f potential antioxidant activity was measured using the ORAC-FL method. The 3b and 3f derivatives revealed a significantly higher antioxidant potency, respectively 35 and 25 higher than tacrine. Theoretical, physicochemical, and pharmacokinetic properties were calculated using ACD Labs Percepta software. Molecular modeling and kinetic study were used to reveal the mechanism of cholinesterase inhibition in the most potent compounds: 3b and 3f.

Allosteric Binding of MDMA to the Human Serotonin Transporter (hSERT) via Ensemble Binding Space Analysis with ΔG Calculations, Induced Fit Docking and Monte Carlo Simulations

Despite the recent promising results of MDMA (3,4-methylenedioxy-methamphetamine) as a psychotherapeutic agent and its history of misuse, little is known about its molecular mode of action. MDMA enhances monoaminergic neurotransmission in the brain and its valuable psychoactive effects are associated to a dual action on the 5-HT transporter (SERT). This drug inhibits the reuptake of 5-HT (serotonin) and reverses its flow, acting as a substrate for the SERT, which possesses a central binding site (S1) for antidepressants as well as an allosteric (S2) one. Previously, we characterized the spatial binding requirements for MDMA at S1. Here, we propose a structure-based mechanistic model of MDMA occupation and translocation across both binding sites, applying ensemble binding space analyses, electrostatic complementarity, and Monte Carlo energy perturbation theory. Computed results were correlated with experimental data (r = 0.93 and 0.86 for S1 and S2, respectively). Simulations on all hSERT available structures with Gibbs free energy estimations (ΔG) revealed a favourable and pervasive dual binding mode for MDMA at S2, i.e., adopting either a 5-HT or an escitalopram-like orientation. Intermediate ligand conformations were identified within the allosteric site and between the two sites, outlining an internalization pathway for MDMA. Among the strongest and more frequent interactions were salt bridges with Glu494 and Asp328, a H-bond with Thr497, a π-π with Phe556, and a cation-π with Arg104. Similitudes and differences with the allosteric binding of 5-HT and antidepressants suggest that MDMA may have a distinctive chemotype. Thus, our models may provide a framework for future virtual screening studies and pharmaceutical design and to develop hSERT allosteric compounds with a unique psychoactive MDMA-like profile.

Electrostatic Complementarity in Structure-Based Drug Design

Optimization of electrostatic complementarity is an important strategy in structure-based drug discovery for improving the affinity of molecules against a specific protein target. In this Miniperspective we identify examples where deliberate optimization of protein-ligand electrostatic complementarity or intramolecular electrostatic interactions gave improvements in target affinity (up to 250-fold), physicochemical properties, in vitro properties, and off-target selectivity. We also look retrospectively at a series of factor Xa inhibitors that show an almost 8000-fold range in potency that can be correlated with the calculated electrostatic potential (ESP) surfaces. Recent developments using a graph-convolutional deep neural network to rapidly generate high quality ESP surfaces have the potential to make this useful tool more accessible for a wider audience within the field of medicinal chemistry.

Structural insights into the discrepant synergistic activity of Codlemone and (Z)-8-dodecenol towards Grapholita molesta pheromones

BACKGROUND

Insect pheromone synergists have been widely used to produce potent pheromone products for environment-friendly pest control. Codlemone (Cod) and (Z)-8-dodecenol (Dod) are two major Grapholita molesta pheromone synergists, with Cod having greater synergism and affinity for G. molesta pheromone binding protein 2 (GmolPBP2). Uncovering structural information key to the different binding affinity of Cod and Dod to GmolPBP2 would gain insights into what causes their synergy activity discrepancy.

RESULTS

Binding modes of the two synergists in the binding pocket of GmolPBP2 were analyzed and compared by molecular dynamics-based approaches. Although Cod and Dod were stabilized in a similar hydrophobic pocket, their interaction details with GmolPBP2 were divergent due to the extra double bond (C10═C11) in Cod. The C10═C11 improved the hydrophobic interactions of Cod with around residues. Such hydrophobic interaction improvement was also reflected in the raised importance of Phe11 in the GmolPBP2-Cod interaction. Not only that, the increased hydrophobic forces introduced by the C10═C11 changed the CH2-OH orientation in the GmolPBP2-Cod complex, which improved the H-bond interaction. Electrostatic complementarity analysis further indicated the positive role of C10═C11 in optimizing GmolPBP2-Cod interaction.

CONCLUSION

The C10═C11 is thought to contribute greatly to Cod's stronger synergy as a group key to the higher GmolPBP2-affinity, based on which the improvement directions for Cod and Dod were addressed as well. Our findings will aid in the development and optimization of more effective pheromone synergists, resulting in more effective pheromone-based pest management.

Computational Molecular Characterization of the Interaction of Acetylcholine and the NMDA Receptor to Explain the Direct Glycine-Competitive Potentiation of NMDA-Mediated Neuronal Currents

The activation of N-methyl-d-aspartate receptor (NMDAR) is triggered by the closure of bilobed (D1 and D2) clamshell-like clefts upon binding glycine (Gly) and glutamate. There is evidence that cholinergic compounds modulate NMDAR-mediated currents via direct receptor-ligand interactions; however, molecular bases are unknown. Here, we first propose a mechanistic structure-based explanation for the observed ACh-induced submaximal potentiation of NMDA-elicited currents in striatal neurons by predicting competitive inhibition with Gly. Then, the model was validated, in principle, by confirming that the coapplication of Gly and ACh significantly reduces these neuronal currents. Finally, we delineate the interplay of ACh with the NMDAR by a combination of computational strategies. Crystallographic ACh-bound complexes were studied, revealing a similar ACh binding environment on the GluN1 subunit of the NMDAR. We illustrate how ACh can occupy X-ray monomeric open, dimeric "semiopen" cleft conformations obtained by molecular dynamics and a full-active cryo-EM NMDAR structure, explaining the suboptimal NMDAR electrophysiological activity under the "Venus Flytrap model". At an evolutionary biology level, the binding mode of ACh coincides with that of the homologous ornithine-bound periplasmic LAO binding protein complex. Our computed results indicate an analogous mechanism of action, inasmuch as ACh may stabilize the GluN1 subunit "semiclosed" conformations by inducing direct and indirect D1-to-D2 interdomain bonds. Additionally, an alternative binding site was detected, shared by the known NMDAR allosteric modulators. Experimental and computed results strongly suggest that ACh acts as a Gly-competitive, submaximal potentiating agent of the NMDAR, possibly constituting a novel chemotype for multitarget-directed drug development, e.g., to treat Alzheimer's, and it may lead to a new understanding of glutamatergic neurotransmission.

Haste makes waste: A critical review of docking-based virtual screening in drug repurposing for SARS-CoV-2 main protease (M-pro) inhibition

This review makes a critical evaluation of 61 peer‐reviewed manuscripts that use a docking step in a virtual screening (VS) protocol to predict SARS‐CoV‐2 M‐pro (M‐pro) inhibitors in approved or investigational drugs. Various manuscripts predict different compounds, even when they use a similar initial dataset and methodology, and most of them do not validate their methodology or results. In addition, a set of known 150 SARS‐CoV‐2 M‐pro inhibitors extracted from the literature and a second set of 81 M‐pro inhibitors and 113 inactive compounds obtained from the COVID Moonshot project were used to evaluate the reliability of using docking scores as feasible predictors of the potency of a SARS‐CoV‐2 M‐pro inhibitor. Using two SARS‐CoV‐2 M‐pro structures and five protein‐ligand docking programs, we proved that the correlation between the pIC₅₀ and docking scores is not good. Neither was any correlation found between the pIC₅₀ and the ∆G calculated with an MM‐GBSA method. When a group of experimentally known inactive compounds was added, neither the docking scores or the ∆G were able to distinguish between compounds with or without M‐pro experimental inhibitory activity. Performances improved when covalent and noncovalent inhibitors were treated separately, but were not good enough to fully support using a docking score as a cutoff value for selecting new putative M‐pro inhibitors or predicting the relative bioactivity of a compound by comparison with a reference compound. The two sets of known SARS‐CoV‐2 M‐pro inhibitors presented here could be used for validating future VS protocols which aim to predict M‐pro inhibitors.

Exploring disordered loops in DprE1 provides a functional site to combat drug-resistance in Mycobacterium strains

As an anti-tuberculosis target, DprE1 contains two flexible loops (Loop I and Loop II) which have never been exploited for developing DprE1 inhibitors. Here Leu317 in Loop II was discovered as a new functional site to combat drug-resistance in Mycobacterium strains. Based on TCA1, LZDT1 was designed to optimize the hydrophobic interaction with Leu317. A subsequent biochemical and cellular assay displayed increased potency of LZDT1 in inhibiting DprE1 and killing drug-sensitive/-resistant Mycobacterium strains. The improved activity of LZDT1 and its analogue LZDT2 against multidrug resistant tuberculosis was particularly highlighted. For LZDT1, its enhanced interaction with Leu317 also impaired the drug-insensitivity of DprE1 caused by Cys387 mutation. A new nonbenzothiazole lead (LZDT10) with reduced Cys387-dependence was further produced by optimizing interactions with Leu317, improvement directions for LZDT10 were discussed as well. Our research underscores the value of potential functional sites in disordered loops, and affords a feasible way to develop these functional sites into opportunities for drug-resistance management.

Molecular docking analysis of potential compounds from an Indian medicinal soup "kabasura kudineer" extract with IL-6

The use of "kabasura kudineer" (liquid soup made from Indian medicinal plants) for combating COVID-19 has been common in the states of Tamilnadu and Puducherry, India during the pandemic. Therefore, it is of interest to document the molecular docking analysis of IL-6 inhibitors with potential antiviral compounds from "kabasura kudineer" extract. We show the optimal binding features of gallic acid and luteolin with the Interleukin-6 protein for further consideration.

Efficient Electrocatalytic Approach to Spiro[Furo[3,2-b]pyran-2,5′-pyrimidine] Scaffold as Inhibitor of Aldose Reductase

A continuously growing interest in convenient and ‘green’ reaction techniques encourages organic chemists to elaborate on new synthetic methodologies. Nowadays, organic electrochemistry is a new useful method with important synthetic and ecological advantages. The employment of an electrocatalytic methodology in cascade reactions is very promising because it provides the combination of the synthetic virtues of the cascade strategy with the ecological benefits and convenience of electrocatalytic procedures. In this research, a new type of the electrocatalytic cascade transformation was found: the electrochemical cyclization of 1,3-dimethyl-5-[[3-hydroxy-6-(hydroxymethyl)-4-oxo-4H-pyran-2-yl](aryl)methyl]pyrimidine-2,4,6(1H,3H,5H)-triones was carried out in alcohols in an undivided cell in the presence of sodium halides with the selective formation of spiro[furo[3,2-b]pyran-2,5′-pyrimidines] in 59-95% yields. This new electrocatalytic process is a selective, facile, and efficient way to create spiro[furo[3,2-b]pyran-2,5′-pyrimidines], which are pharmacologically active heterocyclic systems with different biomedical applications. Spiro[furo[3,2-b]pyran-2,5′-pyrimidines] were found to occupy the binding pocket of aldose reductase and inhibit it. The values of the binding energy and Lead Finder’s Virtual Screening scoring function showed that the formation of protein–ligand complexes was favorable. The synthesized compounds are promising for the inhibition of aldose reductase. This makes them interesting for study in the treatment of diabetes or similar diseases.

Discovery of a simplified deguelin analog as an HSP90 C-terminal inhibitor for HER2-positive breast cancer

A series of O-substituted analogs of the C-ring-truncated scaffold of deguelin designed as heat shock protein 90 (HSP90) C-terminal inhibitors were investigated as novel antitumor agents against human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Among the synthesized compounds, compound 37 displayed significant inhibition in both trastuzumab-sensitive and trastuzumab-resistant breast cancer cells with little cytotoxicity to normal cells. Mechanistic studies of compound 37 carried out by HSP90α C-terminal inhibitor screening, the induction of the heat shock response and downregulation of HSP90 client proteins indicated that the antitumor activity of 37 in breast cancer cells could be attributed to the destabilization and inactivation of HSP90 client proteins by the binding of 37 to the C-terminal domain of HSP90. A molecular docking study of compound 37 with a HSP90 homology model indicated that its S-isomer fit well in the ATP binding site of the C-terminal domain, forming key interactions.

Anti-malarial Drugs are Not Created Equal for SARS-CoV-2 Treatment: A Computational Analysis Evidence

BACKGROUND

The evolution of the pandemic has burdened the national healthcare systems worldwide and at present, there is no preferred antiviral treatment for COVID-19. Recently, the SARS-Cov-2 protease structure was released that may be exploited in in-silico studies in order to conduct molecular docking analysis.

METHODS

In particular, we compared the binding of twoantimalarial drugs, already in use, (i.e. chloroquine and hydroxychloroquine), which showed some potential clinical effects on COVID-19 patients, using ritonavir, lopinavir and darunavir as positive control tree antiviral recognized compounds.

RESULTS

Our results showed that hydroxychloroquine but not chloroquine exhibited a significant binding activity to the main protease similar to that possessed by protease inhibitors tested for other viral infections.

CONCLUSION

Our data suggest that hydroxychloroquine may exert additional direct antiviral activity compared to chloroquine. In the absence of clinical studies comparing the efficacy of these two compounds, hydroxychloroquine may offer additional effects and may be considered as the first choice.

Computational Design of Novel Allosteric Inhibitors for Plasmodium falciparum DegP

The serine protease, DegP exhibits proteolytic and chaperone activities, essential for cellular protein quality control and normal cell development in eukaryotes. The P. falciparum DegP is essential for the parasite survival and required to combat the oscillating thermal stress conditions during the infection, protein quality checks and protein homeostasis in the extra-cytoplasmic compartments, thereby establishing it as a potential target for drug development against malaria. Previous studies have shown that diisopropyl fluorophosphate (DFP) and the peptide SPMFKGV inhibit E. coli DegP protease activity. To identify novel potential inhibitors specific to PfDegP allosteric and the catalytic binding sites, we performed a high throughput in silico screening using Malaria Box, Pathogen Box, Maybridge library, ChEMBL library and the library of FDA approved compounds. The screening helped identify five best binders that showed high affinity to PfDegP allosteric (T0873, T2823, T2801, RJC02337, CD00811) and the catalytic binding site (T0078L, T1524, T2328, BTB11534 and 552691). Further, molecular dynamics simulation analysis revealed RJC02337, BTB11534 as the best hits forming a stable complex. WaterMap and electrostatic complementarity were used to evaluate the novel bio-isosteric chemotypes of RJC02337, that led to the identification of 231 chemotypes that exhibited better binding affinity. Further analysis of the top 5 chemotypes, based on better binding affinity, revealed that the addition of electron donors like nitrogen and sulphur to the side chains of butanoate group are more favoured than the backbone of butanoate group. In a nutshell, the present study helps identify novel, potent and Plasmodium specific inhibitors, using high throughput in silico screening and bio-isosteric replacement, which may be experimentally validated.

Identification of key residues of carboxylesterase PxEst-6 involved in pyrethroid metabolism in Plutella xylostella (L.)

The long-term and excessive use of insecticides has led to severe environmental problems and the evolution of insecticide resistance in insects. Carboxylesterases (CarEs) are important detoxification enzymes conferring insecticide resistance on insects. Herein, the detoxification process of Plutella xylostella (L.) carboxylesterase 6 (PxEst-6), one representative P. xylostella carboxylesterase, is investigated with cypermethrin, bifenthrin, cyfluthrin and λ-cyhalothrin. RT-qPCR shows that PxEst-6 is highly expressed in the midgut and cuticles of the third instar larvae. Exposure to pyrethroid insecticides resulted in PxEst-6 up-regulation in a short time. Metabolic assays indicate that PxEst-6 has the capacity to metabolize these pyrethroid insecticides. The combination of molecular docking, binding mode analyses and alanine mutations demonstrated that His451, Lys458 and Gln431 were key residues of PxEst-6 for metabolizing pyrethroids and the acetate groups derived from pyrethroids were key sites for being metabolized by PxEst-6. H451- and K458-derived hydrogen bond (H-bond) interactions with the pyrethroid acetate groups and the polar interactions with the pyrethroid acetate group provided by the Q431 sidechain were crucial to the pyrethroids' metabolism by PxEst-6. Our study contributes to revealing the reasons for pyrethroid resistance in P. xylostella, and provides a fundamental basis for the development of novel pyrethroid insecticides.

Rapid Optimization of the Metabolic Stability of a Human Immunodeficiency Virus Type-1 Capsid Inhibitor Using a Multistep Computational Workflow

Poor metabolic stability of the human immunodeficiency virus type-1 (HIV-1) capsid (CA) inhibitor PF-74 is a major concern in its development toward clinical use. To improve on the metabolic stability, we employed a novel multistep computationally driven workflow, which facilitated the rapid design of improved PF-74 analogs in an efficient manner. Using this workflow, we designed three compounds that interact specifically with the CA interprotomer pocket, inhibit HIV-1 infection, and demonstrate enantiomeric preference. Moreover, using this workflow, we were able to increase the metabolic stability 204-fold in comparison to PF-74 in only three analog steps. These results demonstrate our ability to rapidly design CA compounds using a novel computational workflow that has improved metabolic stability over the parental compound. This workflow can be further applied to the redesign of PF-74 and other promising inhibitors with a stability shortfall.

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.

Two 20-Residue-Long Peptides Derived from Plasmodium vivax Merozoite Surface Protein 10 EGF-Like Domains Are Involved in Binding to Human Reticulocytes

Plasmodium parasites’ invasion of their target cells is a complex, multi-step process involving many protein-protein interactions. Little is known about how complex the interaction with target cells is in Plasmodium vivax and few surface molecules related to reticulocytes’ adhesion have been described to date. Natural selection, functional and structural analysis were carried out on the previously described vaccine candidate P. vivax merozoite surface protein 10 (PvMSP10) for evaluating its role during initial contact with target cells. It has been shown here that the recombinant carboxyl terminal region (rPvMSP10-C) bound to adult human reticulocytes but not to normocytes, as validated by two different protein-cell interaction assays. Particularly interesting was the fact that two 20-residue-long regions (³⁸⁸DKEECRCRANYMPDDSVDYF⁴⁰⁷ and ⁴¹⁵KDCSKENGNCDVNAECSIDK⁴³⁴) were able to inhibit rPvMSP10-C binding to reticulocytes and rosette formation using enriched target cells. These peptides were derived from PvMSP10 epidermal growth factor (EGF)-like domains (precisely, from a well-defined electrostatic zone) and consisted of regions having the potential of being B- or T-cell epitopes. These findings provide evidence, for the first time, about the fragments governing PvMSP10 binding to its target cells, thus highlighting the importance of studying them for inclusion in a P. vivax antimalarial vaccine.

Molecular determinant of substrate binding and specificity of cytochrome P450 2J2

Cytochrome P450 2J2 (CYP2J2) is responsible for the epoxidation of endogenous arachidonic acid, and is involved in the metabolism of exogenous drugs. To date, no crystal structure of CYP2J2 is available, and the proposed structural basis for the substrate recognition and specificity in CYP2J2 varies with the structural models developed using different computational protocols. In this study, we developed a new structural model of CYP2J2, and explored its sensitivity to substrate binding by molecular dynamics simulations of the interactions with chemically similar fluorescent probes. Our results showed that the induced-fit binding of these probes led to the preferred active poses ready for the catalysis by CYP2J2. Divergent conformational dynamics of CYP2J2 due to the binding of each probe were observed. However, a stable hydrophobic clamp composed of residues I127, F310, A311, V380, and I487 was identified to restrict any substrate access to the active site of CYP2J2. Molecular docking of a series of compounds including amiodarone, astemizole, danazol, ebastine, ketoconazole, terfenadine, terfenadone, and arachidonic acid to CYP2J2 confirmed the role of those residues in determining substrate binding and specificity of CYP2J2. In addition to the flexibility of CYP2J2, the present work also identified other factors such as electrostatic potential in the vicinity of the active site, and substrate strain energy and property that have implications for the interpretation of CYP2J2 metabolism.

Long-Range Charge Reorganization as an Allosteric Control Signal in Proteins

A new mechanism of allostery in proteins, based on charge rather than structure, is reported. We demonstrate that dynamic redistribution of charge within a protein can control its function and affect its interaction with a binding partner. In particular, the association of an antibody with its target protein antigen is studied. Dynamic charge shifting within the antibody during its interaction with the antigen is enabled by its binding to a metallic surface that serves as a source for electrons. The kinetics of antibody–antigen association are enhanced when charge redistribution is allowed, even though charge injection happens at a position far from the antigen binding site. This observation points to charge-reorganization allostery, which should be operative in addition or parallel to other mechanisms of allostery, and may explain some current observations on protein interactions.

Quantum Chemical and Molecular Dynamics Studies of MUC1 Calix[4,8]arene Scaffold Based Anticancer Vaccine Candidates

Functional antitumor vaccine constructs are the basis for active tumor immunotherapy, which is useful in the treatment of many types of cancers. MUC1 is one key glycoprotein for targeting and designing new strategies for multicomponent vaccines. Two self-adjuvant tetravalent vaccine candidates were prepared by clustering four or eight PDTRP MUC1 core epitope sequences on calixarene scaffolds. In this work, the different activities of two molecules with calix[4]arene and calix[8]arene skeleton are rationalized. Quantum mechanics, docking, and molecular dynamics structural optimization were first carried out followed by metadynamics to calculate the energy profiles. Further insights were obtained by complementarity studies of molecular fields. The molecular modeling results are in strong agreement with the experimental in vivo immunogenicity data. In conclusion, the overall data shows that, in the designing of anticancer vaccines, scaffold flexibility has a pivotal role in obtaining a suitable electrostatic, hydrophobic, and steric complementarity with the biological target.

Computational Studies towards the Identification of Novel Rhodopsin-Binding Compounds as Chemical Chaperones for Misfolded Opsins

Accumulation of misfolded and mistrafficked rhodopsin on the endoplasmic reticulum of photoreceptor cells has a pivotal role in the pathogenesis of retinitis pigmentosa and a subset of Leber’s congenital amaurosis. One potential strategy to reduce rhodopsin misfolding and aggregation in these conditions is to use opsin-binding compounds as chemical chaperones for opsin. Such molecules have previously shown the ability to aid rhodopsin folding and proper trafficking to the outer cell membranes of photoreceptors. As means to identify novel chemical chaperones for rhodopsin, a structure-based virtual screening of commercially available drug-like compounds (300,000) was performed on the main binding site of the visual pigment chromophore, the 11-cis-retinal. The best 24 virtual hits were examined for their ability to compete for the chromophore-binding site of opsin. Among these, four small molecules demonstrated the ability to reduce the rate constant for the formation of the 9-cis-retinal-rhodopsin complex, while five molecules surprisingly enhanced the formation of this complex. Compound 7, 13, 20 and 23 showed a weak but detectable increase in the trafficking of the P23H mutant, widely used as a model for both retinitis pigmentosa and Leber’s congenital amaurosis, from the ER to the cell membrane. The compounds did not show any relevant cytotoxicity in two different human cell lines, with the only exception of 13. Based on the structures of these active compounds, a series of in silico studies gave important insights on the potential structural features required for a molecule to act either as chemical chaperone or as stabiliser of the 11-cis-retinal-rhodopsin complex. Thus, this study revealed a series of small molecules that represent a solid foundation for the future development of novel therapeutics against these severe inherited blinding diseases.

Pharmacological properties and biochemical mechanisms of μ-opioid receptor ligands might be due to different binding poses: MD studies

Background: Central and peripheral analgesia without adverse effects relies on the identification of μ-opioid agonists that are able to activate 'basal' antinociceptive pathways. Recently developed μ-selective benzomorphan agonists that are not antagonized by naloxone do not activate G-proteins and β-arrestins. Which pathways do μ receptors activate? How can each of them be selectively activated? What role is played by allosteric binding sites? Methodology & results: Molecular modeling studies characterize the amino acid residues involved in the interaction with various classes of endogenous and exogenous ligands and with agonists and antagonists. Conclusions: Critical binding differences between various classes of agonists with different pharmacological profiles have been identified. MML series binding poses may be relevant in the search for an antinociception agent without side effects.

Protein electrostatics: From computational and structural analysis to discovery of functional fingerprints and biotechnological design

Computationally driven engineering of proteins aims to allow them to withstand an extended range of conditions and to mediate modified or novel functions. Therefore, it is crucial to the biotechnological industry, to biomedicine and to afford new challenges in environmental sciences, such as biocatalysis for green chemistry and bioremediation. In order to achieve these goals, it is important to clarify molecular mechanisms underlying proteins stability and modulating their interactions. So far, much attention has been given to hydrophobic and polar packing interactions and stability of the protein core. In contrast, the role of electrostatics and, in particular, of surface interactions has received less attention. However, electrostatics plays a pivotal role along the whole life cycle of a protein, since early folding steps to maturation, and it is involved in the regulation of protein localization and interactions with other cellular or artificial molecules. Short- and long-range electrostatic interactions, together with other forces, provide essential guidance cues in molecular and macromolecular assembly. We report here on methods for computing protein electrostatics and for individual or comparative analysis able to sort proteins by electrostatic similarity. Then, we provide examples of electrostatic analysis and fingerprints in natural protein evolution and in biotechnological design, in fields as diverse as biocatalysis, antibody and nanobody engineering, drug design and delivery, molecular virology, nanotechnology and regenerative medicine.

In Silico computational screening of Kabasura Kudineer - Official Siddha Formulation and JACOM against SARS-CoV-2 spike protein

Background

Siddha Medicine is a valuable therapeutic choice which is classically used for treating viral respiratory infections, this principle of medicine is proven to contain antiviral compounds.

Objective

The study is aimed to execute the In Silico computational studies of phytoconstituents of Siddha official formulation Kabasura Kudineer and novel herbal preparation - JACOM which are commonly used in treating viral fever and respiratory infectious diseases and could be affective against the ongoing pandemic novel corona virus disease SARS-CoV-2.

Method

Cresset Flare software was used for molecular docking studies against the spike protein SARS-CoV-2 (PDB ID: 6VSB). Further, we also conducted insilico prediction studies on the pharmacokinetics (ADME) properties and the safety profile in order to identify the best drug candidates by using online pkCSM and SwissADME web servers.

Results

Totally 37 compounds were screened, of these 9 compounds showed high binding affinity against SARS-CoV-2 spike protein. All the phytoconstituents were free from carcinogenic and tumorigenic properties. Based on these, we proposed the new formulation called as “SNACK–V”

Conclusion

Based on further experiments and clinical trials, these formulations could be used for effective treatment of COVID-19.

Unraveling Plant Natural Chemical Diversity for Drug Discovery Purposes

The screening and testing of extracts against a variety of pharmacological targets in order to benefit from the immense natural chemical diversity is a concern in many laboratories worldwide. And several successes have been recorded in finding new actives in natural products, some of which have become new drugs or new sources of inspiration for drugs. But in view of the vast amount of research on the subject, it is surprising that not more drug candidates were found. In our view, it is fundamental to reflect upon the approaches of such drug discovery programs and the technical processes that are used, along with their inherent difficulties and biases. Based on an extensive survey of recent publications, we discuss the origin and the variety of natural chemical diversity as well as the strategies to having the potential to embrace this diversity. It seemed to us that some of the difficulties of the area could be related with the technical approaches that are used, so the present review begins with synthetizing some of the more used discovery strategies, exemplifying some key points, in order to address some of their limitations. It appears that one of the challenges of natural product-based drug discovery programs should be an easier access to renewable sources of plant-derived products. Maximizing the use of the data together with the exploration of chemical diversity while working on reasonable supply of natural product-based entities could be a way to answer this challenge. We suggested alternative ways to access and explore part of this chemical diversity with in vitro cultures. We also reinforced how important it was organizing and making available this worldwide knowledge in an "inventory" of natural products and their sources. And finally, we focused on strategies based on synthetic biology and syntheses that allow reaching industrial scale supply. Approaches based on the opportunities lying in untapped natural plant chemical diversity are also considered.

Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning

Accurate and efficient prediction of protein-ligand interactions has been a long-lasting dream of practitioners in drug discovery. The insufficient treatment of hydration is widely recognized to be a major limitation for accurate protein-ligand scoring. Using an integration of molecular dynamics simulations on thousands of protein structures with novel big-data analytics based on convolutional neural networks and deep Taylor decomposition, we consistently identify here three different patterns of hydration to be essential for protein-ligand interactions. In addition to desolvation and water-mediated interactions, the formation of enthalpically favorable networks of first-shell water molecules around solvent-exposed ligand moieties is identified to be essential for protein-ligand binding. Despite being currently neglected in drug discovery, this hydration phenomenon could lead to new avenues in optimizing the free energy of ligand binding. Application of deep neural networks incorporating hydration to docking provides 89% accuracy in binding pose ranking, an essential step for rational structure-based drug design.

Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural Network

Inspecting protein and ligand electrostatic potential (ESP) surfaces in order to optimize electrostatic complementarity is a key activity in drug design. These ESP surfaces need to reflect the true electrostatic nature of the molecules, which typically means time-consuming high-level quantum mechanics (QM) calculations are required. For interactive design much faster alternative methods are required. Here, we present a graph convolutional deep neural network (DNN) model, trained on ESP surfaces derived from high quality QM calculations, that generates ESP surfaces for ligands in a fraction of a second. Additionally, we describe a method for constructing fast QM-trained ESP surfaces for proteins. We show that the DNN model generates ESP surfaces that are in good agreement with QM and that the ESP values correlate well with experimental properties relevant to medicinal chemistry. We believe that these high-quality, interactive ESP surfaces form a powerful tool for driving drug discovery programs forward. The trained model and associated code are available from https://github.com/AstexUK/ESP_DNN.

Solvent Screening in Zwitterions Analyzed with the Fragment Molecular Orbital Method

Based on induced solvent charges, a new model of solvent screening is developed in the framework of the fragment molecular orbital combined with the polarizable continuum model. The developed model is applied to analyze interactions in a prototypical zwitterionic system, sodium chloride in water, and it is shown that the large underestimation of the interaction in the original solvent screening based on local charges is successfully corrected. The model is also applied to a complex of the Trp-cage (PDB: 1L2Y ) miniprotein with an anionic ligand, and the physical factors determined protein-ligand binding in solution are unraveled.

Getting Docking into Shape Using Negative Image-Based Rescoring

The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in structure-based drug discovery. To remedy this problem, elaborate rescoring and postprocessing schemes have been developed with a varying degree of success, specificity, and cost. The negative image-based rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets. The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein's ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is directly used in the similarity comparison to rank the explicit docking poses. Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six popular docking softwares, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA, using five validated benchmark sets. Overall, the results indicate that R-NiB outperforms the default docking scoring consistently and inexpensively, demonstrating that the methodology is ready for wide-scale virtual screening usage.