Prediction of Protein-Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations

Experimental and computational insights of Albizia amara phytoconstituents targeting anthranilate phosphoribosyltransferase from Malassezia globosa

The fungus Malassezia globosa is often responsible for superficial mycoses posing significant treatment challenges because of the unfavourable side effects of available antifungal drugs. To reduce potential hazards to the host and overcome these hurdles, new therapeutic medicines must be developed that selectively target enzymes unique to the pathogen. This study focuses on the enzyme anthranilate phosphoribosyltransferase (AnPRT), which is vital to M. globosa's tryptophan production pathway. To learn more about the function of the AnPRT enzyme, we modeled, validated, and simulated its structure. Moreover, many bioactive components were found in different extracts from the plant Albizia amara after phytochemical screening. Interestingly, at doses ranging from 500 to 2000 µg/ml, the chloroform extract showed significant antifungal activity, with inhibition zones measured between 11.0 ± 0.0 and 25.6 ± 0.6 mm. According to molecular docking analyses, the compounds from the active extract, particularly 2-tert-Butyl-4-isopropyl-5-methylphenol, interacted with the AnPRT enzyme's critical residues, ARG 205 and PHE 214, with an effective binding energy of -4.9 kcal/mol. The extract's revealed component satisfies the requirements for drug-likeness and shows promise as a strong antifungal agent against infections caused by M. globosa. These findings imply that using plant-derived chemicals to target the AnPRT enzyme is a viable path for the creation of innovative antifungal treatments.

Synthesis of C-glycoside analogues of isopropyl β-D-1-thiogalactopyranoside (IPTG) and 1-β-D-galactopyranosyl-2-methylpropane. Conformational analysis and evaluation as inhibitors of the lac repressor in E. coli and as galactosidase inhibitors

Isopropyl 1-thio-β-D-galactopyranoside (IPTG, 1) is used widely as an inducer of protein expression in E. coli and 1-β-D-galactopyranosyl-2-methylpropane (2), a C-glycoside analogue of 1, has also been identified as an inducer. Here, synthesis and study of mimetics of 1 and 2, 1-β-D-galactopyranosyl-2-methylpropan-1-ols and two cyclic acetals derivatives, that constrain the presentation of the iPr group in various geometries is described. Conformational analysis of C-glycosides in protic solvent is performed using (i) Desmond metadynamics simulations (OPLS4) and (ii) use of 3JHH values obtained by 1H-NMR spectroscopy. 1-β-D-Galactopyranosyl-2-methylpropane (2) is an effective protein expression inducer when compared to the new mimetics, which were less effective or did not induce expression. 1-β-D-Galactopyranosyl-2-methylpropane (2) led to significantly reduced proteolysis during protein expression, compared to IPTG suggesting that recombinant protein purification will be easier to achieve with 2, yielding proteins with higher quality and activity. IPTG reduced bacterial growth to a greater degree than 2 compared to the control. IPTG's isopropyl group was observed by molecular dynamics (MD) simulations to be flexible in the binding pocket, deviating from its crystal structure binding mode, without impacting other interactions. The MD simulations predicted that 1-β-D-galactopyranosyl-2-methylpropane (2) was more likely than IPTG to bind the repressor with a conformation favoured in protic solvent, while maintaining interactions observed for IPTG. MD simulations predicted that isobutanol derivatives may disrupt interactions associated with IPTG's binding mode. The compounds were also evaluated as inhibitors of galactosidases, with 2 being the more potent inhibitor of the E. coli β-galactosidase. The constrained cyclic acetals showed similar inhibition constants to IPTG indicating E. coli β-galactosidase can recognize galactopyranoses with varying presentation of the iPr group.

Discovery of a urea-based hit compound as a novel inhibitor of transforming growth factor-β type 1 receptor: in silico and in vitro studies

Transforming growth factor β type 1 receptor (TGFβR1), a crucial serine-threonine kinase, is central to the TGFβ/Smad signaling pathway, governing cellular processes like growth, differentiation, apoptosis, and immune response. This pathway is closely linked to the epithelial-mesenchymal transition (EMT) process, which plays an important role in the metastasis of hepatocellular carcinoma (HCC). To date, only limited inhibitors targeting TGFβR1 have entered clinical trials, yet they encounter challenges, notably high toxicity, in clinical applications. Herein, an efficient virtual screening pipeline was developed. Eighty compounds were screened from a pool of over 17 million molecules based on docking scores and binding free energy. Four compounds were manually selected with the assistance of enhanced sampling method BPMD (binding pose metadynamics). The binding stability of these four compounds complexed with TGFβR1 was subsequently studied through long-timescale conventional molecular dynamics simulations. The three most promising compounds were subjected to in vitro bioactivity assays. Cpd272 demonstrated moderate inhibitory activity against TGFβR1, with an IC50 value of 1.57 ± 0.33 μM. Moreover, it exhibited cytotoxic effects on human hepatocellular carcinoma cell line Bel-7402. By shedding light on the binding mode of the receptor-ligand complexes, Cpd272 was identified as a hit compound featuring a novel urea-based scaffold capable of effectively inhibiting TGFβR1.

Computational identification and analysis of CNP0269688 as a natural product inhibitor disrupting the interaction between the HIV matrix domain and tRNA

Our research is dedicated to combating HIV by targeting its Matrix (MA) domain, which is crucial for viral assembly and replication. This strategy specifically aims to interrupt early-stage infection and deter drug resistance by focusing on this essential domain. Due to the MA domain's conservation across different HIV strains, our approach promises broad-spectrum efficacy, which is particularly crucial in regions marked by significant genetic diversity and resistance issues. In our study, we introduce CNP0269688, a natural product that exhibits high affinity for the HIV-1 Matrix. Through detailed molecular dynamics simulations, we have assessed the compound's structural stability and interaction dynamics, particularly its potential to hinder Protein-tRNA interactions. This analysis lays the groundwork for future experimental investigations. Our efforts are steps toward enhancing HIV treatment, reducing viral transmission, and curbing drug resistance, with the ultimate aim of controlling and eradicating the pandemic, thereby contributing significantly to public health and scientific advancement.

Discovery of novel FGF trap small molecules endowed with anti-myeloma activity

Fibroblast growth factors (FGFs) act as proangiogenic and mitogenic cytokines in several cancers, including multiple myeloma (MM). Indeed, corrupted FGF autocrine and paracrine secretion induces an aberrant activation of the FGF receptor (FGFR) signaling sustaining cancer cell spreading and resistance to pharmacological treatments. Thus, FGF traps may represent a promising anti-cancer strategy to hamper the ligand-dependent activation of the FGF/FGFR system. We previously identified NSC12 as the first orally available small molecule FGF trap able to inhibit the growth and progression of several FGF-dependent tumor models. NSC12 is a pregnenolone derivative carrying a 1,1-bis-trifluoromethyl-1,3-propanediol chain in position 17 of the steroid nucleus. Investigation of structure-activity relationships (SARs) provided more potent and specific NSC12 steroid derivatives and highlighted that the C17-side chain is pivotal for the FGF trap activity. Here, a scaffold hopping approach allowed to obtain two FGF trap compounds (22 and 57) devoid of the steroid nucleus and able to efficiently bind FGF2 and to inhibit FGFR activation in MM cells. Accordingly, these compounds exert a potent anti-tumor activity on MM cell lines both in vitro and in vivo and on MM patient-derived primary cells, strongly affecting the survival of both proteasome-inhibitor sensitive and resistant MM cells. These results propose a new therapeutic option for relapsed/refractory MM patients and set the bases for the development of novel FGF traps prone to chemical diversification to be used in the clinic for the treatment of those tumors in which the FGF/FGFR system plays a pivotal role, including MM.

Modulation of the Meisenheimer complex metabolism of nitro-benzothiazinones by targeted C-6 substitution

Tuberculosis, caused by Mycobacterium tuberculosis, remains a major public health concern, demanding new antibiotics with innovative therapeutic principles due to the emergence of resistant strains. Benzothiazinones (BTZs) have been developed to address this problem. However, an unprecedented in vivo biotransformation of BTZs to hydride-Meisenheimer complexes has recently been discovered. Herein, we present a study of the influence of electron-withdrawing groups on the propensity of HMC formation in whole cells for a series of C-6-substituted BTZs obtained through reductive fluorocarbonylation as a late-stage functionalization key step. Gibbs free energy of reaction and Mulliken charges and Fukui indices on C-5 at quantum mechanics level were found as good indicators of in vitro HMC formation propensity. These results provide a first blueprint for the evaluation of HMC formation in drug development and set the stage for rational pharmacokinetic optimization of BTZs and similar drug candidates.

Leveraging binding pose metadynamics to optimise target fishing predictions for three diverse ligands and their true targets

Computational target fishing plays an important role in target identification, particularly in drug discovery campaigns utilizing phenotypic screening. Numerous approaches exist to predict potential targets for a given ligand, but true targets may be inconsistently ranked. More advanced simulation methods may provide benefit in such cases by reranking these initial predictions. We evaluated the ability of binding pose metadynamics to improve the predicted rankings for three diverse ligands and their six true targets. Initial predictions using pharmacophore mapping showed no true targets ranked in the top 50 and two targets each ranked within the 50-100, 100-150, and 250-300 ranges respectively. Following binding pose metadynamics, ranking of true targets improved for four out of the six targets and included the highest ranked predictions overall, while rankings deteriorated for two targets. The revised rankings predicted two true targets ranked within the top 50, and one target each within the 50-100, 100-150, 150-200, and 200-250 ranges respectively. The findings of this study demonstrate that binding pose metadynamics may be of benefit in refining initial predictions from structure-based target fishing algorithms, thereby improving the efficiency of the target identification process in drug discovery efforts.

Potent, Selective Agonists for the Cannabinoid-like Orphan G Protein-Coupled Receptor GPR18: A Promising Drug Target for Cancer and Immunity

The human orphan G protein-coupled receptor GPR18, activated by Δ9-tetrahydrocannabinol (THC), constitutes a promising drug target in immunology and cancer. However, studies on GPR18 are hampered by the lack of suitable tool compounds. In the present study, potent and selective GPR18 agonists were developed showing low nanomolar potency at human and mouse GPR18, determined in β-arrestin recruitment assays. Structure-activity relationships were analyzed, and selectivity versus cannabinoid (CB) and CB-like receptors was assessed. Compound 51 (PSB-KK1415, EC50 19.1 nM) was the most potent GPR18 agonist showing at least 25-fold selectivity versus CB receptors. The most selective GPR18 agonist 50 (PSB-KK1445, EC50 45.4 nM) displayed >200-fold selectivity versus both CB receptor subtypes, GPR55, and GPR183. The new GPR18 agonists showed minimal species differences, while THC acted as a weak partial agonist at the mouse receptor. The newly discovered compounds represent the most potent and selective GPR18 agonists reported to date.

JD-02, a novel Hsp90 inhibitor, induces ROS/SRC axis-dependent cytoprotective autophagy in colorectal cancer cells

Heat shock protein 90 (Hsp90) is a tumor marker that accelerates cancer growth by disrupting protein homeostasis. However, concerns such as low clinical efficacy and drug resistance continue to be obstacles to the successful marketing of Hsp90 inhibitors. The cytoprotective function of autophagy has been identified as one of the mechanisms by which tumor cells gain resistance to chemotherapy. JD-02 was identified as a new Hsp90 inhibitor that suppressed colorectal cancer (CRC) growth by lowering client protein levels in vivo and in vitro. We found that JD-02 increased cellular autophagy, which inhibited apoptosis. JD-02 enhanced cytoprotective autophagy and regulated apoptotic suppression by increasing intracellular reactive oxygen species and inhibiting SRC protein levels, as demonstrated by quantitative proteomics, bioinformatic analysis, western blotting, and flow cytometry. This effect was reversed by autophagy inhibition. Therefore, due to the synergistic effects of Hsp90 and autophagy inhibitors in efficiently activating apoptotic pathways, they could potentially serve as promising therapeutic options for CRC.

Chaetoxylariones A-G: undescribed chromone-derived polyketides from co-culture of Chaetomium virescens and Xylaria grammica enabled via the molecular networking strategy

By co-culturing two endophytic fungi (Chaetomium virescens and Xylaria grammica) collected from the medicinal and edible plant Smilax glabra Roxb. and analyzing them with MolNetEnhancer module on GNPS platform, seven undescribed chromone-derived polyketides (chaetoxylariones A-G), including three pairs of enantiomer ones (2a/2b, 4a/4b and 6a/6b) and four optical pure ones (1, 3, 5 and 7), as well as five known structural analogues (8-12), were obtained. The structures of these new compounds were characterized by NMR spectroscopy, single-crystal X-ray diffraction, 13C NMR calculation and DP4+ probability analyses, as well as the comparison of the experimental electronic circular dichroism (ECD) data. Structurally, compound 1 featured an unprecedented chromone-derived sulfonamide tailored by two isoleucine-derived δ-hydroxy-3-methylpentenoic acids via the acylamide and NO bonds, respectively; compound 2 represented the first example of enantiomeric chromone derivative bearing a unique spiro-[3.3]alkane ring system; compound 3 featured a decane alkyl side chain that formed an undescribed five-membered lactone ring between C-7' and C-10'; compound 4 contained an unexpected highly oxidized five-membered carbocyclic system featuring rare adjacent keto groups; compound 7 featured a rare methylsulfonyl moiety. In addition, compound 10 showed a significant inhibition towards SW620/AD300 cells with an IC50 value of PTX significantly decreased from 4.09 μM to 120 nM, and a further study uncovered that compound 10 could obviously reverse the MDR of SW620/AD300 cells.

Discovery of novel positive allosteric modulators targeting GluN1/2A NMDARs as anti-stroke therapeutic agents

Excitotoxicity due to excessive activation of NMDARs is one of the main mechanisms of neuronal death during ischemic stroke. Previous studies have suggested that activation of either synaptic or extrasynaptic GluN2B-containing NMDARs results in neuronal damage, whereas activation of GluN2A-containing NMDARs promotes neuronal survival against ischemic insults. This study applied a systematic in silico, in vitro, and in vivo approach to the discovery of novel and potential GluN1/2A NMDAR positive allosteric modulators (PAMs). Ten compounds were obtained and identified as potential GluN1/2A PAMs by structure-based virtual screening and calcium imaging. The neuroprotective activity of the candidate compounds was demonstrated in vitro. Subsequently, compound 15 (aegeline) was tested further in the model of transient middle cerebral artery occlusion (tMCAO) in vivo, which significantly decreased cerebral infarction. The mechanism by which aegeline exerts its effect on allosteric modulation was revealed using molecular dynamics simulations. Finally, we found that the neuroprotective effect of aegeline was significantly correlated with the enhanced phosphorylation of cAMP response element-binding protein (CREB). Our study discovered the neuroprotective effect of aegeline as a novel PAM targeting GluN1/2A NMDAR, which provides a potential opportunity for the development of therapeutic agents for ischemic stroke.

Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention

Identifying and discovering druggable protein binding sites is an important early step in computer-aided drug discovery, but it remains a difficult task where most campaigns rely on a priori knowledge of binding sites from experiments. Here, we present a binding site prediction method called Graph Attention Site Prediction (GrASP) and re-evaluate assumptions in nearly every step in the site prediction workflow from data set preparation to model evaluation. GrASP is able to achieve state-of-the-art performance at recovering binding sites in PDB structures while maintaining a high degree of precision which will minimize wasted computation in downstream tasks such as docking and free energy perturbation.

Exploring Kinase Asp-Phe-Gly (DFG) Loop Conformational Stability with AlphaFold2-RAVE

Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular, cancers. The ubiquitousness and structural similarities of kinases make specific and effective drug design difficult. In particular, conformational variability due to the evolutionarily conserved Asp-Phe-Gly (DFG) motif adopting in and out conformations and the relative stabilities thereof are key in structure-based drug design for ATP competitive drugs. These relative conformational stabilities are extremely sensitive to small changes in sequence and provide an important problem for sampling method development. Since the invention of AlphaFold2, the world of structure-based drug design has noticeably changed. In spite of it being limited to crystal-like structure prediction, several methods have also leveraged its underlying architecture to improve dynamics and enhanced sampling of conformational ensembles, including AlphaFold2-RAVE. Here, we extend AlphaFold2-RAVE and apply it to a set of kinases: the wild type DDR1 sequence and three mutants with single point mutations that are known to behave drastically differently. We show that AlphaFold2-RAVE is able to efficiently recover the changes in relative stability using transferable learned order parameters and potentials, thereby supplementing AlphaFold2 as a tool for exploration of Boltzmann-weighted protein conformations (Meller, A.; Bhakat, S.; Solieva, S.; Bowman, G. R. Accelerating Cryptic Pocket Discovery Using AlphaFold. J. Chem. Theory Comput. 2023, 19, 4355-4363).

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.

Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation

Free energy perturbation (FEP) remains an indispensable method for computationally assaying prospective compounds in advance of synthesis. However, before FEP can be deployed prospectively, it must demonstrate retrospective recapitulation of known experimental data where the subtle details of the atomic ligand-receptor model are consequential. An open question is whether AlphaFold models can serve as useful initial models for FEP in the regime where there exists a congeneric series of known chemical matter but where no experimental structures are available either of the target or of close homologues. As AlphaFold structures are provided without a bound ligand, we employ induced fit docking to refine the AlphaFold models in the presence of one or more congeneric ligands. In this work, we first validate the performance of our latest induced fit docking technology, IFD-MD, on a retrospective set of public experimental GPCR structures with 95% of cross-docks producing a pose with a ligand RMSD ≤ 2.5 Å in the top two predictions. We then apply IFD-MD and FEP on AlphaFold models of the somatostatin receptor family of GPCRs. We use AlphaFold models produced prior to the availability of any experimental structure from this family. We arrive at FEP-validated models for SSTR2, SSTR4, and SSTR5, with RMSE around 1 kcal/mol, and explore the challenges of model validation under scenarios of limited ligand data, ample ligand data, and categorical data.

Pinpointing top inhibitors for GSK3β from pool of indirubin derivatives using rigorous computational workflow and their validation using molecular dynamics (MD) simulations

Glycogen synthase kinase-3β (GSK3β) is a pivotal protein kinase implicated in a spectrum of debilitating diseases, encompassing cancer, diabetes, and neurodegenerative disorders. While the therapeutic potential of GSK3β inhibition is widely recognized, there remains an unmet need for a rigorous, systematic analysis probing the theoretical inhibition dynamics of a comprehensive library of indirubin derivatives against GSK3β using advanced computational methodologies. Addressing this gap, this study embarked on an ambitious endeavor, leveraging indirubin-a renowned scaffold-as a template to curate a vast library of 1000 indirubin derivatives from PubChem. These were enriched with varied substitutions and modifications, identified via a structure similarity search with a Tanimoto similarity threshold of 85%. Harnessing a robust virtual screening workflow, we meticulously identified the top 10 contenders based on XP docking scores. Delving deeper, we gauged the binding free energy differentials (ΔGBind) of these hits, spotlighting the top three compounds that showcased unparalleled binding prowess. A comparative pharmacophore feature mapping with the reference inhibitor OH8, co-crystallized with GSK3β (PDB ID: 6Y9R), was undertaken. The binding dynamics of these elite compounds were further corroborated with 100 ns molecular dynamics simulations, underlining their stable and potent interactions with GSK3β. Remarkably, our findings unveil that these indirubin derivatives not only match but, in certain scenarios, surpass the binding affinity and specificity of OH8. By bridging this research chasm, our study amplifies the therapeutic promise of indirubin derivatives, positioning them as frontrunners in the quest for groundbreaking GSK3β inhibitors, potentially revolutionizing treatments for a myriad of ailments.

Virtual Screening Process: A Guide in Modern Drug Designing

Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule's ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.

Three Decades of Targeting Falcipains to Develop Antiplasmodial Agents: What have we Learned and What can be Done Next?

Malaria is a devastating infectious disease that affects large swathes of human populations across the planet's tropical regions. It is caused by parasites of the genus Plasmodium, with Plasmodium falciparum being responsible for the most lethal form of the disease. During the intraerythrocytic stage in the human hosts, malaria parasites multiply and degrade hemoglobin (Hb) using a battery of proteases, which include two cysteine proteases, falcipains 2 and 3 (FP-2 and FP-3). Due to their role as major hemoglobinases, FP-2 and FP-3 have been targeted in studies aiming to discover new antimalarials and numerous inhibitors with activity against these enzymes, and parasites in culture have been identified. Nonetheless, cross-inhibition of human cysteine cathepsins remains a serious hurdle to overcome for these compounds to be used clinically. In this article, we have reviewed key functional and structural properties of FP-2/3 and described different compound series reported as inhibitors of these proteases during decades of active research in the field. Special attention is also paid to the wide range of computer-aided drug design (CADD) techniques successfully applied to discover new active compounds. Finally, we provide guidelines that, in our understanding, will help advance the rational discovery of new FP-2/3 inhibitors.

Harnessing the druggability at orthosteric and allosteric sites of PD-1 for small molecule discovery by an integrated in silico pipeline

The PD-1/PD-L1 interaction is a promising target for small molecule inhibitors in cancer immunotherapy, but targeting this interface has been challenging. While efforts have been made to identify compounds that target the orthosteric sites, no reports have explored the potential of small molecules to target the allosteric region of PD-1. Therefore, our study aims to establish a pipeline to identify small molecules that can effectively bind to either the orthosteric or allosteric pockets of PD-1. We categorized the PD-1 interface into two hot-spot zones (P-and N-zones) based on extensive analysis of its structural, dynamical, and energetic properties. These zones correspond to the orthosteric and allosteric PPI sites, respectively, targeted by monoclonal antibodies. We used a guided virtual screening workflow to identify hits from ∼7 million compounds library, which were then clustered based on structural similarity and assessed by interaction fingerprinting. The selective and diverse chemical representatives were subjected to MD simulations and binding energetics calculations to filter out false positives and identify actual binders. Binding poses metadynamics calculations confirmed the stability of the final hits in the pocket. This study emphasizes the need for an integrated pipeline that uses molecular dynamics simulations and binding energetics to identify potential binders for the dynamic PD-1/PD-L1 interface, due to the lack of small molecule co-crystals. Only a few potential binders were discovered from a large pool of molecules targeting both the allosteric and orthosteric zones. Our results suggest that the allosteric site has more potential than the orthosteric site for inhibitor design. The identified "computational hits" hold potential as starting points for in vitro evaluations followed by hit-to-lead optimization. Overall, this study represents an effort to establish a computational pipeline for exploring and enriching both the allosteric and orthosteric sites of PPI interfaces, "a tough but indispensable nut to crack".

Assessing Metadynamics and Docking for Absolute Binding Free Energy Calculations Using Severe Acute Respiratory Syndrome Coronavirus 2 Main Protease Inhibitors

Absolute binding free energy (ABFE) calculations can be an important part of the drug discovery process by identifying molecules that have the potential to be strong binders for a biomolecular target. Recent work has used free energy perturbation (FEP) theory for these calculations, focusing on a set of 16 inhibitors of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). Herein, the same data set is evaluated by metadynamics (MetaD), four different docking programs, and molecular mechanics with generalized Born and surface area solvation. MetaD yields a Kendall τ distance of 0.28 and Pearson r2 of 0.49, which reflect somewhat less accuracy than that from the ABFE FEP results. Notably, it is demonstrated that an ensemble docking protocol by which each ligand is docked into the 13 crystal structures in this data set provides improved performance, particularly when docking is carried out with Glide XP (Kendall τ distance = 0.20, Pearson r2 = 0.71), Glide SP (Kendall τ distance = 0.19, Pearson r2 = 0.66), or AutoDock 4 (Kendall τ distance = 0.21, Pearson r2 = 0.55). The best results are obtained with "superconsensus" docking by averaging the 52 results for each compound using the 4 docking protocols and all 13 crystal structures (Kendall τ distance = 0.18, Pearson r2 = 0.73).

In Silico Exploration of the Trypanothione Reductase (TryR) of L. mexicana

Human leishmaniasis is a neglected tropical disease which affects nearly 1.5 million people every year, with Mexico being an important endemic region. One of the major defense mechanisms of these parasites is based in the polyamine metabolic pathway, as it provides the necessary compounds for its survival. Among the enzymes in this route, trypanothione reductase (TryR), an oxidoreductase enzyme, is crucial for the Leishmania genus' survival against oxidative stress. Thus, it poses as an attractive drug target, yet due to the size and features of its catalytic pocket, modeling techniques such as molecular docking focusing on that region is not convenient. Herein, we present a computational study using several structure-based approaches to assess the druggability of TryR from L. mexicana, the predominant Leishmania species in Mexico, beyond its catalytic site. Using this consensus methodology, three relevant pockets were found, of which the one we call σ-site promises to be the most favorable one. These findings may help the design of new drugs of trypanothione-related diseases.

Design and synthesis of benzofuran- and naphthalene-fused thiazinones as antimycobacterial agents

Benzothiazinones (BTZs) have widely inspired medicinal chemistry and translational research due to their remarkable antitubercular potency and clinical potential. While most structure-activity relationship campaigns have largely focused on lateral chain modifications and substituents on the BTZ core, scaffold hopping strategies have been rarely investigated previously. In this work, we report the first example of ring expansion of the BTZ core toward benzofuran- and naphthalene-fused thiazinones. In vitro testing showed micromolar activity for both compounds, and molecular docking simulations provided insights into their reduced inhibitory capacity toward the enzymatic target (DprE1). Calculated electrochemical potentials revealed a lower susceptibility to reduction as opposed to BTZ drug candidates, in line with the mechanistic requirement for covalent binding.

Integrated molecular modeling and dynamics approaches revealed the mechanism of selective inhibition of HDAC6/8

The high structural homology of histone deacetylases 6 and 8 (HDAC6/8) poses a challenge in achieving isoform selectivity and has resulted in adverse side effects due to pan-inhibition in clinical applications. Additionally, the rational design of dual-target inhibitors, centered on HDAC6/8, demands a profound understanding of their selectivity mechanisms. Addressing the urgent need for enhanced specificity in the development of inhibitors targeting specific isoforms, we elucidate the mechanism underpinning the selective inhibition of HDAC6/8 inhibitors through in-silico strategies. The hydrogen bonding interaction with Asp101 and Tyr306 is a key factor that enables compound 12b to selectively inhibit HDAC8. Its favorable spatial orientation places the Cap group of 12b between Tyr306 and Tyr100, resulting in an overall L-shaped conformation. These two factors significantly contribute to the selective inhibitory activity of 12b against HDAC8. The zinc binding group (ZBG) of compound NN-390 forms a hydrogen bond with His610, a key residue of HDAC6, facilitating stable chelation with zinc ions. In addition, the Cap group of NN-390 interacts with Phe620 and Phe680 via van der Waals forces, leading to an overall Y-shaped conformation. The aforementioned factors are the main reasons for the selective inhibition of HDAC6 by NN-390. Furthermore, whether the Cap group is in the para or meta-position will influence the selective inhibition of either HDAC6 or HDAC8. We believe these clues can offer valuable insights for the rational design of selective inhibitors targeting HDAC6/8 and pave the way for rational design of dual-target HDAC6/8-based inhibitors.Communicated by Ramaswamy H. Sarma.

A systematic pipeline of protein structure selection for computer-aided drug discovery: A case study on T790M/L858R mutant EGFR structures

Virtual screening (VS) is a routine method to evaluate chemical libraries for lead identification. Therefore, the selection of appropriate protein structures for VS is an essential prerequisite to identify true actives during docking. But the presence of several crystal structures of the same protein makes it difficult to select one or few structures rationally for screening. Therefore, a computational prioritization protocol has been developed for shortlisting crystal structures that identify true active molecules with better efficiency. As identification of small-molecule inhibitors is an important clinical requirement for the T790M/L858R (TMLR) EGFR mutant, it has been selected as a case study. The approach involves cross-docking of 21 co-crystal ligands with all the structures of the same protein to select structures that dock non-native ligands with lower RMSD. The cross docking performance was then correlated with ligand similarity and binding-site conformational similarity. Eventually, structures were shortlisted by integrating cross-docking performance, and ligand and binding-site similarity. Thereafter, binding pose metadynamics was employed to identify structures having stable co-crystal ligands in their respective binding pockets. Finally, different enrichment metrics like BEDROC, RIE, AUAC, and EF1% were evaluated leading to the identification of five TMLR structures (5HCX, 5CAN, 5CAP, 5CAS, and 5CAO). These structures docked a number of non-native ligands with low RMSD, contain structurally dissimilar ligands, have conformationally dissimilar binding sites, harbor stable co-crystal ligands, and also identify true actives early. The present approach can be implemented for shortlisting protein targets of any other important therapeutic kinases.

Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention

Identifying and discovering druggable protein binding sites is an important early step in computer-aided drug discovery but remains a difficult task where most campaigns rely on a priori knowledge of binding sites from experiments. Here we present a novel binding site prediction method called Graph Attention Site Prediction (GrASP) and re-evaluate assumptions in nearly every step in the site prediction workflow from dataset preparation to model evaluation. GrASP is able to achieve state-of-the-art performance at recovering binding sites in PDB structures while maintaining a high degree of precision which will minimize wasted computation in downstream tasks such as docking and free energy perturbation.

Computational Design, Synthesis, and Biological Evaluation of Diimidazole Analogues Endowed with Dual PCSK9/HMG-CoAR-Inhibiting Activity

Proprotein convertase subtilisin/kexin 9 (PCSK9) is responsible for the degradation of the hepatic low-density lipoprotein receptor (LDLR), which regulates circulating cholesterol levels. Consequently, the PCSK9 inhibition is a valuable therapeutic approach for the treatment of hypercholesterolemia and cardiovascular diseases. In our studies, we discovered Rim13, a polyimidazole derivative reducing the protein-protein interaction between PCSK9 and LDLR with an IC₅₀ of 1.6 μM. The computational design led to the optimization of the shape of the PCSK9/ligand complementarity, enabling the discovery of potent diimidazole derivatives. In fact, carrying out biological assays to fully characterize the cholesterol-lowering activity of the new analogues and using both biochemical and cellular techniques, compound Dim16 displayed improved PCSK9 inhibitory activity (IC₅₀ 0.9 nM). Interestingly, similar to other lupin-derived peptides and their synthetic analogues, some compounds in this series showed dual hypocholesterolemic activity since some of them complementarily inhibited the 3-hydroxy-3-methylglutaryl coenzyme A reductase.

Identification of core therapeutic targets for Monkeypox virus and repurposing potential of drugs against them: An in silico approach

Monkeypox virus (mpox virus) outbreak has rapidly spread to 82 non-endemic countries. Although it primarily causes skin lesions, secondary complications and high mortality (1-10%) in vulnerable populations have made it an emerging threat. Since there is no specific vaccine/antiviral, it is desirable to repurpose existing drugs against mpox virus. With little knowledge about the lifecycle of mpox virus, identifying potential inhibitors is a challenge. Nevertheless, the available genomes of mpox virus in public databases represent a goldmine of untapped possibilities to identify druggable targets for the structure-based identification of inhibitors. Leveraging this resource, we combined genomics and subtractive proteomics to identify highly druggable core proteins of mpox virus. This was followed by virtual screening to identify inhibitors with affinities for multiple targets. 125 publicly available genomes of mpox virus were mined to identify 69 highly conserved proteins. These proteins were then curated manually. These curated proteins were funnelled through a subtractive proteomics pipeline to identify 4 highly druggable, non-host homologous targets namely; A20R, I7L, Top1B and VETFS. High-throughput virtual screening of 5893 highly curated approved/investigational drugs led to the identification of common as well as unique potential inhibitors with high binding affinities. The common inhibitors, i.e., batefenterol, burixafor and eluxadoline were further validated by molecular dynamics simulation to identify their best potential binding modes. The affinity of these inhibitors suggests their repurposing potential. This work can encourage further experimental validation for possible therapeutic management of mpox.

Resolving Protein Conformational Plasticity and Substrate Binding via Machine Learning

A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. Here, we demonstrate that a computational framework coined as RF-TICA-MD, which integrates an ensemble decision-tree-based Random Forest (RF) machine learning (ML) technique with an unsupervised dimension reduction approach time-structured independent component analysis (TICA), provides an efficient and unambiguous solution toward resolving protein conformational plasticity and the substrate binding process. In particular, we consider multimicrosecond-long molecular dynamics (MD) simulation trajectories of a ligand recognition process in solvent-inaccessible cavities of archetypal proteins T4 lysozyme and cytochrome P450cam. We show that in a scenario in which clear correspondence between protein conformation and binding-competent macrostates could not be obtained via an unsupervised dimension reduction approach, an a priori decision-tree-based supervised classification of the simulated recognition trajectories via RF would help characterize key amino acid residue pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction of the selected residue pairs via TICA would then delineate a conformational landscape of protein which is able to demarcate ligand-bound poses from unbound ones. The proposed RF-TICA-MD approach is shown to be data agnostic and found to be robust when using other ML-based classification methods such as XGBoost. As a promising spinoff of the protocol, the framework is found to be capable of identifying distal protein locations which would be allosterically important for ligand binding and would characterize their roles in recognition pathways. A Python implementation of a proposed ML workflow is available in GitHub https://github.com/navjeet0211/rf-tica-md.

Selectivity and Ranking of Tight-Binding JAK-STAT Inhibitors Using Markovian Milestoning with Voronoi Tessellations

Janus kinases (JAK), a group of proteins in the nonreceptor tyrosine kinase (NRTKs) family, play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase-signal transducer and activator of a transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective toward JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK-inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid-enhanced sampling approaches.

AI-driven drug repurposing and binding pose meta dynamics identifies novel targets for Monkeypox virus

Monkeypox virus (MPXV) was confirmed in May 2022 and designated a global health emergency by WHO in July 2022. MPX virions are big, enclosed, brick-shaped, and contain a linear, double-stranded DNA genome as well as enzymes. MPXV particles bind to the host cell membrane via a variety of viral-host protein interactions. As a result, the wrapped structure is a potential therapeutic target. DeepRepurpose, an artificial intelligence-based compound-viral proteins interaction framework, was used via a transfer learning setting to prioritize a set of FDA approved and investigational drugs which can potentially inhibit MPXV viral proteins. To filter and narrow down the lead compounds from curated collections of pharmaceutical compounds, we used a rigorous computational framework that included homology modeling, molecular docking, dynamic simulations, binding free energy calculations, and binding pose metadynamics. We identified Elvitegravir as a potential inhibitor of MPXV virus using our comprehensive pipeline.

Application of Molecular Simulation Methods in Food Science: Status and Prospects

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

MetaDOCK: A Combinatorial Molecular Docking Approach

Molecular docking plays a major role in academic and industrial drug screening and discovery processes. Despite the availability of numerous docking software packages, there is a lot of scope for improvement for the docking algorithms in terms of becoming more reliable to replicate the experimental binding results. Here, we propose a combinatorial or consensus docking approach where complementary powers of the existing methods are captured. We created a meta-docking protocol by combining the results of AutoDock4.2, LeDock, and rDOCK programs as these are freely available, easy to use, and suitable for large-scale analysis and produced better performance on benchmarking studies. Rigorous benchmarking analyses were undertaken to evaluate the scoring, posing, and screening capability of our approach. Further, the performance measures were compared against one standard state-of-the-art commercial docking software, GOLD, and one freely available software, PLANTS. Performances of MetaDOCK for scoring, posing, and screening the protein-ligand complexes were found to be quite superior compared to the reference programs. Exhaustive molecular dynamics simulation and molecular mechanics Poisson-Boltzmann and surface area-based free energy estimation also suggest better energetic stability of the docking solutions produced by our meta-approach. We believe that the MetaDOCK approach is a useful packaging of the freely available software and provides a better alternative to the scientific community who are unable to afford costly commercial packages.

Identifying Dopamine D3 Receptor Ligands through Virtual Screening and Exploring the Binding Modes of Hit Compounds

The dopamine D3 receptor (D3R) is an important central nervous system target for treating various neurological diseases. D3R antagonists modulate the improvement of psychostimulant addiction and relapse, while D3R agonists can enhance the response to dopaminergic stimulation and have potential applications in treating Parkinson’s disease, which highlights the importance of identifying novel D3R ligands. Therefore, we performed auto dock Vina-based virtual screening and D3R-binding-affinity assays to identify human D3R ligands with diverse structures. All molecules in the ChemDiv library (>1,500,000) were narrowed down to a final set of 37 molecules for the binding assays. Twenty-seven compounds exhibited over 50% inhibition of D3R at a concentration of 10 μM, and 23 compounds exhibited over 70% D3R inhibition at a concentration of 10 μM. Thirteen compounds exhibited over 80% inhibition of D3R at a concentration of 10 μM and the IC50 values were measured. The IC50 values of the five compounds with the highest D3R-inhibition rates ranged from 0.97 μM to 1.49 μM. These hit compounds exhibited good structural diversity, which prompted us to investigate their D3R-binding modes. After trial and error, we combined unbiased molecular dynamics simulation (MD) and molecular mechanics generalized Born surface area (MM/GBSA) binding free-energy calculations with the reported protein−ligand-binding pose prediction method using induced-fit docking (IFD) and binding pose metadynamics (BPMD) simulations into a self-consistent and computationally efficient method for predicting and verifying the binding poses of the hit ligands to D3R. Using this IFD-BPMD-MD-MM/GBSA method, we obtained more accurate and reliable D3R−ligand-binding poses than were obtained using the reported IFD-BPMD method. This IFD-BPMD-MD-MM/GBSA method provides a novel paradigm and reference for predicting and validating other protein−ligand binding poses.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Metadynamics simulations leveraged by statistical analyses and artificial intelligence-based tools to inform the discovery of G protein-coupled receptor ligands

G Protein-Coupled Receptors (GPCRs) are a large family of membrane proteins with pluridimensional signaling profiles. They undergo ligand-specific conformational changes, which in turn lead to the differential activation of intracellular signaling proteins and the consequent triggering of a variety of biological responses. This conformational plasticity directly impacts our understanding of GPCR signaling and therapeutic implications, as do ligand-specific kinetic differences in GPCR-induced transducer activation/coupling or GPCR-transducer complex stability. High-resolution experimental structures of ligand-bound GPCRs in the presence or absence of interacting transducers provide important, yet limited, insights into the highly dynamic process of ligand-induced activation or inhibition of these receptors. We and others have complemented these studies with computational strategies aimed at characterizing increasingly accurate metastable conformations of GPCRs using a combination of metadynamics simulations, state-of-the-art algorithms for statistical analyses of simulation data, and artificial intelligence-based tools. This minireview provides an overview of these approaches as well as lessons learned from them towards the identification of conformational states that may be difficult or even impossible to characterize experimentally and yet important to discover new GPCR ligands.

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein-Ligand Binding Poses

Predicting the correct pose of a ligand binding to a protein and its associated binding affinity is of great importance in computer-aided drug discovery. A number of approaches have been developed to these ends, ranging from the widely used fast molecular docking to the computationally expensive enhanced sampling molecular simulations. In this context, methods such as coarse-grained metadynamics and binding pose metadynamics (BPMD) use simulations with metadynamics biasing to probe the binding affinity without trying to fully converge the binding free energy landscape in order to decrease the computational cost. In BPMD, the metadynamics bias perturbs the ligand away from the initial pose. The resistance of the ligand to this bias is used to calculate a stability score. The method has been shown to be useful in reranking predicted binding poses from docking. Here, we present OpenBPMD, an open-source Python reimplementation and reinterpretation of BPMD. OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. We also investigated the role of accurate water positioning on the performance of the algorithm and showed how the combination with a grand-canonical Monte Carlo algorithm improves the accuracy of the predictions.

Pathfinder-Driven Chemical Space Exploration and Multiparameter Optimization in Tandem with Glide/IFD and QSAR-Based Active Learning Approach to Prioritize Design Ideas for FEP+ Calculations of SARS-CoV-2 PLpro Inhibitors

A global pandemic caused by the SARS-CoV-2 virus that started in 2020 and has wreaked havoc on humanity still ravages up until now. As a result, the negative impact of travel restrictions and lockdowns has underscored the importance of our preparedness for future pandemics. The main thrust of this work was based on addressing this need by traversing chemical space to design inhibitors that target the SARS-CoV-2 papain-like protease (PLpro). Pathfinder-based retrosynthesis analysis was used to generate analogs of GRL-0617 using commercially available building blocks by replacing the naphthalene moiety. A total of 10 models were built using active learning QSAR, which achieved good statistical results such as an R2 > 0.70, Q2 > 0.64, STD Dev < 0.30, and RMSE < 0.31, on average for all models. A total of 35 ideas were further prioritized for FEP+ calculations. The FEP+ results revealed that compound 45 was the most active compound in this series with a ΔG of −7.28 ± 0.96 kcal/mol. Compound 5 exhibited a ΔG of −6.78 ± 1.30 kcal/mol. The inactive compounds in this series were compound 91 and compound 23 with a ΔG of −5.74 ± 1.06 and −3.11 ± 1.45 kcal/mol. The combined strategy employed here is envisaged to be of great utility in multiparameter lead optimization efforts, to traverse chemical space, maintaining and/or improving the potency as well as the property space of synthetically aware design ideas.

Exploring new catechin derivatives as SARS-CoV-2 Mpro inhibitors from tea by molecular networking, surface plasma resonance, enzyme inhibition, induced fit docking, and metadynamics simulations

SARS-CoV-2 M^pro (Mpro) is the critical cysteine protease in coronavirus viral replication. Tea polyphenols are effective M^pro inhibitors. Therefore, we aim to isolate and synthesize more novel tea polyphenols from Zhenghedabai (ZHDB) white tea methanol-water (MW) extracts that might inhibit COVID-19. Through molecular networking, 33 compounds were identified and divided into 5 clusters. Further, natural products molecular network (MN) analysis showed that MN1 has new phenylpropanoid-substituted ester-catechin (PSEC), and MN5 has the important basic compound type hydroxycinnamoylcatechins (HCCs). Thus, a new PSEC (1, PSEC636) was isolated, which can be further detected in 14 green tea samples. A series of HCCs were synthesized (2-6), including three new acetylated HCCs (3-5). Then we used surface plasmon resonance (SPR) to analyze the equilibrium dissociation constants (K_D) for the interaction of 12 catechins and M^pro. The K_D values of PSEC636 (1), EGC-C (2), and EC-CDA (3) were 2.25, 2.81, and 2.44 μM, respectively. Moreover, compounds 1, 2, and 3 showed the potential M^pro inhibition with IC₅₀ 5.95 ± 0.17, 9.09 ± 0.22, and 23.10 ± 0.69 μM, respectively. Further, we used induced fit docking (IFD), binding pose metadynamics (BPMD), and molecular dynamics (MD) to explore the stable binding pose of M^pro-1, showing that 1 could tightly bond with the amino acid residues THR²⁶, HIS⁴¹, CYS⁴⁴, TYR⁵⁴, GLU¹⁶⁶, and ASP¹⁸⁷. The computer modeling studies reveal that the ester, acetyl, and pyrogallol groups could improve inhibitory activity. Our research suggests that these catechins are effective M^pro inhibitors, and might be developed as therapeutics against COVID-19.

Structure-Based Discovery of a Novel Class of Small-Molecule Pure Antagonists of Integrin αVβ3

Inhibitors of integrin αVβ3 have therapeutic promise for a variety of diseases. Most αVβ3-targeting small molecules patterned after the RGD motif are partial agonists because they induce a high-affinity, ligand-binding conformation and prime the receptor to bind the ligand without an activating stimulus, in part via a charge-charge interaction between their aspartic acid carboxyl group and the metal ion in the metal-ion-dependent adhesion site (MIDAS). Building upon our previous studies on the related integrin αIIbβ3, we searched for pure αVβ3 antagonists that lack this typical aspartic acid carboxyl group and instead engage through direct binding to one of the coordinating residues of the MIDAS metal ion, specifically β3 E220. By in silico screening of two large chemical libraries for compounds interacting with β3 E220, we indeed discovered a novel molecule that does not contain an acidic carboxyl group and does not induce the high-affinity, ligand-binding state of the receptor. Functional and structural characterization of a chemically optimized version of this compound led to the discovery of a novel small-molecule pure αVβ3 antagonist that (i) does not prime the receptor to bind the ligand and does not induce hybrid domain swing-out or receptor extension as judged by antibody binding and negative-stain electron microscopy, (ii) binds at the RGD-binding site as predicted by metadynamics rescoring of induced-fit docking poses and confirmed by a cryo-electron microscopy structure of the compound-bound integrin, and (iii) coordinates the MIDAS metal ion via a quinoline moiety instead of an acidic carboxyl group.

Fragment-based drug discovery-the importance of high-quality molecule libraries

Fragment-based drug discovery (FBDD) is now established as a complementary approach to high-throughput screening (HTS). Contrary to HTS, where large libraries of drug-like molecules are screened, FBDD screens involve smaller and less complex molecules which, despite a low affinity to protein targets, display more 'atom-efficient' binding interactions than larger molecules. Fragment hits can, therefore, serve as a more efficient start point for subsequent optimisation, particularly for hard-to-drug targets. Since the number of possible molecules increases exponentially with molecular size, small fragment libraries allow for a proportionately greater coverage of their respective 'chemical space' compared with larger HTS libraries comprising larger molecules. However, good library design is essential to ensure optimal chemical and pharmacophore diversity, molecular complexity, and physicochemical characteristics. In this review, we describe our views on fragment library design, and on what constitutes a good fragment from a medicinal and computational chemistry perspective. We highlight emerging chemical and computational technologies in FBDD and discuss strategies for optimising fragment hits. The impact of novel FBDD approaches is already being felt, with the recent approval of the covalent KRASG12C inhibitor sotorasib highlighting the utility of FBDD against targets that were long considered undruggable.

Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics

Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.

Free Energy Calculations Using the Movable Type Method with Molecular Dynamics Driven Protein–Ligand Sampling

Fast and accurate biomolecular free energy estimation has been a significant interest for decades, and with recent advances in computer hardware, interest in new method development in this field has even grown. Thorough configurational state sampling using molecular dynamics (MD) simulations has long been applied to the estimation of the free energy change corresponding to the receptor-ligand complexing process. However, performing large-scale simulation is still a computational burden for the high-throughput hit screening. Among molecular modeling tools, docking and scoring methods are widely used during the early stages of the drug discovery process in that they can rapidly generate discrete receptor-ligand binding modes and their individual binding affinities. Unfortunately, the lack of thorough conformational sampling in docking and scoring protocols leads to difficulty discovering global minimum binding modes on a complicated energy landscape. The Movable Type (MT) method is a novel absolute binding free energy approach which has demonstrated itself to be robust across a wide range of targets and ligands. Traditionally, the MT method is used with protein-ligand binding modes generated with rigid-receptor or flexible-receptor (induced fit) docking protocols; however, these protocols are by their nature less likely to be effective with more highly flexible targets or with those situations in which binding involves multiple step pathways. In these situations, more thorough samplings are required to better explain the free energy of binding. Therefore, to explore the prediction capability and computational efficiency of the MT method when using more thorough protein-ligand conformational sampling protocols, in the present work, we introduced a series of binding mode modeling protocols ranging from conventional docking routines to single-trajectory conventional molecular dynamics (cMD) and parallel Monte Carlo molecular dynamics (MCMD). Through validation against several structurally and mechanistically diverse protein-ligand test sets, we explore the performance of the MT method as a virtual screening tool to work with the docking protocols and as an MD simulation-based binding free energy tool.

A high-performance genetically encoded fluorescent indicator for in vivo cAMP imaging

cAMP is a key second messenger that regulates diverse cellular functions including neural plasticity. However, the spatiotemporal dynamics of intracellular cAMP in intact organisms are largely unknown due to low sensitivity and/or brightness of current genetically encoded fluorescent cAMP indicators. Here, we report the development of the new circularly permuted GFP (cpGFP)-based cAMP indicator G-Flamp1, which exhibits a large fluorescence increase (a maximum ΔF/F0 of 1100% in HEK293T cells), decent brightness, appropriate affinity (a Kd of 2.17 μM) and fast response kinetics (an association and dissociation half-time of 0.20 and 0.087 s, respectively). Furthermore, the crystal structure of the cAMP-bound G-Flamp1 reveals one linker connecting the cAMP-binding domain to cpGFP adopts a distorted β-strand conformation that may serve as a fluorescence modulation switch. We demonstrate that G-Flamp1 enables sensitive monitoring of endogenous cAMP signals in brain regions that are implicated in learning and motor control in living organisms such as fruit flies and mice.

Impact of Side-Chain Hydrophilicity on Packing, Swelling, and Ion Interactions in Oxy-Bithiophene Semiconductors

Exchanging hydrophobic alkyl-based side chains to hydrophilic glycol-based side chains is a widely adopted method for improving mixed-transport device performance, despite the impact on solid-state packing and polymer-electrolyte interactions being poorly understood. Presented here is a molecular dynamics (MD) force field for modeling alkoxylated and glycolated polythiophenes. The force field is validated against known packing motifs for their monomer crystals. MD simulations, coupled with X-ray diffraction (XRD), show that alkoxylated polythiophenes will pack with a "tilted stack" and straight interdigitating side chains, whilst their glycolated counterpart will pack with a "deflected stack" and an s-bend side-chain configuration. MD simulations reveal water penetration pathways into the alkoxylated and glycolated crystals-through the π-stack and through the lamellar stack respectively. Finally, the two distinct ways triethylene glycol polymers can bind to cations are revealed, showing the formation of a metastable single bound state, or an energetically deep double bound state, both with a strong side-chain length dependence. The minimum energy pathways for the formation of the chelates are identified, showing the physical process through which cations can bind to one or two side chains of a glycolated polythiophene, with consequences for ion transport in bithiophene semiconductors.

Dynamic recognition of naloxone, morphine and endomorphin1 in the same pocket of µ-opioid receptors

Morphine, the most widely used analgesic, relieves severe pain by activating the μ-opioid receptor (MOR), whereas naloxone, with only slight structural changes compared to morphine, exhibits inhibitory effect, and is used to treat opioid abuse. The mechanism by which the MOR distinguishes between the two is unclear. Molecular dynamics (MD) simulations on a 1-μs time scale and metadynamics-enhanced conformational sampling are used here to determine the different interactions of these two ligands with MOR: morphine adjusted its pose by continuously flipping deeper into the pocket, whereas naloxone failed to penetrate deeper because its allyl group conflicts with several residues of MOR. The endogenous peptide ligand endomorphin-1 (EM-1) underwent almost no significant conformational changes during the MD simulations. To validate these processes, we employed GIRK4S143T, a MOR-activated Gβγ-protein effector, in combination with mutagenesis and electrophysiological recordings. We verified the role of some key residues in the dynamic recognition of naloxone and morphine and identified the key residue I322, which leads to differential recognition of morphine and naloxone while assisting EM-1 in activating MOR. Reducing the side chain size of I322 (MORI322A) transformed naloxone from an inhibitor directly into an agonist of MOR, and I322A also significantly attenuated the potency of MOR on EM-1, confirming that binding deep in the pocket is critical for the agonistic effect of MOR. This finding reveals a dynamic mechanism for the response of MOR to different ligands and provides a basis for the discovery of new ligands for MOR at the atomic level.

Induced-Fit Docking Enables Accurate Free Energy Perturbation Calculations in Homology Models

Homology models have been used for virtual screening and to understand the binding mode of a known active compound; however, rarely have the models been shown to be of sufficient accuracy, comparable to crystal structures, to support free-energy perturbation (FEP) calculations. We demonstrate here that the use of an advanced induced-fit docking methodology reliably enables predictive FEP calculations on congeneric series across homology models ≥30% sequence identity. Furthermore, we show that retrospective FEP calculations on a congeneric series of drug-like ligands are sufficient to discriminate between predicted binding modes. Results are presented for a total of 29 homology models for 14 protein targets, showing FEP results comparable to those obtained using experimentally determined crystal structures for 86% of homology models with template structure sequence identities ranging from 30 to 50%. Implications for the use and validation of homology models in drug discovery projects are discussed.

Amelioration of high-fat diet (HFD) + CCl4 induced NASH/NAFLD in CF-1 mice by activation of SIRT-1 using cinnamoyl sulfonamide hydroxamate derivatives: in-silico molecular modelling and in-vivo prediction

Non-alcoholic fatty liver disease (NAFLD) is one of the major hepatic metabolic disorders that occurs because of the accumulation of lipids in hepatocytes in the form of free fatty acids (FFA) and triglycerides (TG) which become non-alcoholic steatohepatitis (NASH). NOTCH-1 receptors act as novel targets for the development of NAFLD/NASH, where overexpression of NOTCH-1 receptor alters the lipid metabolism in hepatocytes leading to NAFLD. SIRT-1 deacetylates the NOTCH-1 receptor and inhibits NAFLD. Hence, computer-aided drug design (CADD) was used to check the SIRT-1 activation ability of cinnamic sulfonyl hydroxamate derivatives (NMJ 1–8), resveratrol, and vorinostat. SIRT-1 (PDB ID: 5BTR) was docked with eight hydroxamate derivatives and vorinostat using Schrödinger software. Based on binding energy obtained (– 26.31 to – 47.34 kcal/mol), vorinostat, NMJ-2, NMJ-3, NMJ-5 were selected for induced-fit docking (IFD) and results were within – 750.70 to – 753.22 kcal/mol. Qikprop tool was used to analyse the pre pharmacokinetic parameters (ADME analysis) of all hydroxamate compounds. As observed in the molecular dynamic (MD) study, NMJ-2, NMJ-3 were showing acceptable results for activation of SIRT-1. Based on these predictions, in-vivo studies were conducted in CF1 mice, where NMJ-3 showed significant (p < 0.05) changes in lipid profile and anti-oxidant parameters (Catalase, SOD, GSH, nitrite, and LPO) and plasma insulin levels. NMJ-3 treatment also reduced inflammation, fibrosis, and necrosis in liver samples.

Application of molecular dynamics simulation in biomedicine

Molecular dynamics (MD) simulation has been widely used in the field of biomedicine to study the conformational transition of proteins caused by mutation or ligand binding/unbinding. It provides some perspectives those are difficult to find in traditional biochemical or pathological experiments, for example, detailed effects of mutations on protein structure and protein-protein/ligand interaction at the atomic level. In this review, a broad overview on conformation changes and drug discovery by MD simulation is given. We first discuss the preparation of protein structure for MD simulation, which is a key step that determines the accuracy of the simulation. Then, we summarize the applications of commonly used force fields and MD simulations in scientific research. Finally, enhanced sampling methods and common applications of these methods are introduced. In brief, MD simulation is a powerful tool and it can be used to guide experimental study. The combination of MD simulation and experimental techniques is an a priori means to solve the biomedical problems and give a deep understanding on the relationship between protein structure and function.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Computational Insights and In Vitro Validation of Antibacterial Potential of Shikimate Pathway-Derived Phenolic Acids as NorA Efflux Pump Inhibitors

The expression of the efflux pump systems is the most important mechanism of antibiotic resistance in bacteria, as it contributes to reduced concentration and the subsequent inactivity of administered antibiotics. NorA is one of the most studied antibacterial targets used as a model for efflux-mediated resistance. The present study evaluated shikimate pathway-derived phenolic acids against NorA (PDB ID: 1PW4) as a druggable target in antibacterial therapy using in silico modelling and in vitro methods. Of the 22 compounds evaluated, sinapic acid (−9.0 kcal/mol) and p-coumaric acid (−6.3 kcal/mol) had the best and most prominent affinity for NorA relative to ciprofloxacin, a reference standard (−4.9 kcal/mol). A further probe into the structural stability and flexibility of the resulting NorA-phenolic acids complexes through molecular dynamic simulations over a 100 ns period revealed p-coumaric acid as the best inhibitor of NorA relative to the reference standard. In addition, both phenolic acids formed H-bonds with TYR 76, a crucial residue implicated in NorA efflux pump inhibition. Furthermore, the phenolic acids demonstrated favourable drug likeliness and conformed to Lipinski’s rule of five for ADME properties. For the in vitro evaluation, the phenolic acids had MIC values in the range 31.2 to 62.5 μg/mL against S. aureus, and E. coli, and there was an overall reduction in MIC following their combination with ciprofloxacin. Taken together, the findings from both the in silico and in vitro evaluations in this study have demonstrated high affinity of p-coumaric acid towards NorA and could be suggestive of its exploration as a novel NorA efflux pump inhibitor.