Structure-based virtual screening approach for discovery of covalently bound ligands

Boronic acid inhibitors of penicillin-binding protein 1b: serine and lysine labelling agents

Penicillin-binding proteins (PBPs) contribute to bacterial cell wall biosynthesis and are targets of antibacterial agents. Here, we investigated PBP1b inhibition by boronic acid derivatives. Chemical starting points were identified by structure-based virtual screening and aliphatic boronic acids were selected for further investigations. Structure-activity relationship studies focusing on the branching of the boron-connecting carbon and quantum mechanical/molecular mechanical simulations showed that reaction barrier free energies are compatible with fast reversible covalent binding and small or missing reaction free energies limit the inhibitory activity of the investigated boronic acid derivatives. Therefore, covalent labelling of the lysine residue of the catalytic dyad was also investigated. Compounds with a carbonyl warhead and an appropriately positioned boronic acid moiety were shown to inhibit and covalently label PBP1b. Reversible covalent labelling of the catalytic lysine by imine formation and the stabilisation of the imine by dative N-B bond is a new strategy for PBP1b inhibition.

Discovery of Potent Covalent CRM1 Inhibitors Via a Customized Structure-Based Virtual Screening Pipeline and Bioassays

CRM1 (chromosomal region maintenance 1, also referred to as exportin 1 or XPO1) plays a crucial role in maintaining the appropriate nuclear levels of tumor suppressor proteins (TSPs), growth regulatory proteins (GRPs), and antiapoptotic proteins, thereby contributing significantly to their anticancer effects. Dysregulation of CRM1-mediated nuclear transport, observed in a range of cancers such as colon cancer as well as autoimmune diseases, highlights its significance in various disease processes. In this paper, we employed a customized structure-based virtual screening campaign to search for novel covalent CRM1 inhibitors and purchased 50 potentially active compounds for in vitro bioassays. Among these candidates, AN-988 displayed a notably higher binding affinity (KD = 615 nM) toward CRM1, as determined by the biolayer interferometry (BLI) assay. Furthermore, AN-988 exhibited a strong suppression of colorectal cancer cell proliferation and remarkable anti-inflammatory effects. Notably, AN-988 induced cell apoptosis and cell cycle arrest in a time- and dose-dependent manner by effectively inhibiting the translocation of FOXO3a from the nucleus to the cytosol, thereby preserving the activity of FOXO3a. Collectively, our study identified AN-988 as a promising CRM1 inhibitor, underscoring its potential as a preclinical colon cancer therapy candidate.

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed

Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.

Gene network analysis combined with preclinical studies to identify and elucidate the mechanism of action of novel irreversible Keap1 inhibitor for Parkinson's disease

The cysteine residues of Keap1 such as C151, C273, and C288 are critical for its repressor activity on Nrf2. However, to date, no molecules have been identified to covalently modify all three cysteine residues for Nrf2 activation. Hence, in this study, our goal is to discover new Keap1 covalent inhibitors that can undergo a Michael addition with all three cysteine residues. The Keap1's intervening region was modeled using Modeller v10.4. Covalent docking and binding free energy were calculated using CovDock. Molecular dynamics (MD) was performed using Desmond. Various in-vitro assays were carried out to confirm the neuroprotective effects of the hit molecule in 6-OHDA-treated SH-SY5Y cells. Further, the best hit was evaluated in vivo for its ability to improve rotenone-induced postural instability and cognitive impairment in male rats. Finally, network pharmacology was used to summarize the complete molecular mechanism of the hit molecule. Chalcone and plumbagin were found to form the necessary covalent bonds with all three cysteine residues. However, MD analysis indicated that the binding of plumbagin is more stable than chalcone. Plumbagin displayed neuroprotective effects in 6-OHDA-treated SH-SY5Y cells at concentrations 0.01 and 0.1 μM. Plumbagin at 0.1 µM had positive effects on reactive oxygen species formation and glutathione levels. Plumbagin also improved postural instability and cognitive impairment in rotenone-treated male rats. Our network analysis indicated that plumbagin could also improve dopamine signaling. Additionally, plumbagin could exhibit anti-oxidant and anti-inflammatory activity through the activation of Nrf2. Cumulatively, our study suggests that plumbagin is a novel Keap1 covalent inhibitor for Nrf2-mediated neuroprotection in PD.

Computational Investigation of the Covalent Inhibition Mechanism of Bruton's Tyrosine Kinase by Ibrutinib

Covalent inhibitors represent a promising class of therapeutic compounds. Nonetheless, rationally designing covalent inhibitors to achieve a right balance between selectivity and reactivity remains extremely challenging. To better understand the covalent binding mechanism, a computational study is carried out using the irreversible covalent inhibitor of Bruton tyrosine kinase (BTK) ibrutinib as an example. A multi-μs classical molecular dynamics trajectory of the unlinked inhibitor is generated to explore the fluctuations of the compound associated with the kinase binding pocket. Then, the reaction pathway leading to the formation of the covalent bond with the cysteine residue at position 481 via a Michael addition is determined using the string method in collective variables on the basis of hybrid quantum mechanical-molecular mechanical (QM/MM) simulations. The reaction pathway shows a strong correlation between the covalent bond formation and the protonation/deprotonation events taking place sequentially in the covalent inhibition reaction, consistent with a 3-step reaction with transient thiolate and enolates intermediate states. Two possible atomistic mechanisms affecting deprotonation/protonation events from the thiolate to the enolate intermediate were observed: a highly correlated direct pathway involving proton transfer to the Cα of the acrylamide warhead from the cysteine involving one or a few water molecules and a more indirect pathway involving a long-lived enolate intermediate state following the escape of the proton to the bulk solution. The results are compared with experiments by simulating the long-time kinetics of the reaction using kinetic modeling.

Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network

Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead compounds. However, it is difficult for covalent docking to screen covalent compounds on a large scale because covalent docking requires determination of the covalent reaction type of the compound. Here, we propose to use deep learning of a lateral interactions spiking neural network to construct a covalent lead compound screening model to quickly screen covalent lead compounds. We used the 3CL protease (3CL Pro) of SARS-CoV-2 as the screen target and constructed two classification models based on LISNN to predict the covalent binding and inhibitory activity of compounds. The two classification models were trained on the covalent complex data set targeting cysteine (Cys) and the compound inhibitory activity data set targeting 3CL Pro, respected, with good prediction accuracy (ACC > 0.9). We then screened the screening compound library with 6 covalent binding screening models and 12 inhibitory activity screening models. We tested the inhibitory activity of the 32 compounds, and the best compound inhibited SARS-CoV-2 3CL Pro with an IC50 value of 369.5 nM. Further assay implied that dithiothreitol can affect the inhibitory activity of the compound to 3CL Pro, indicating that the compound may covalently bind 3CL Pro. The selectivity test showed that the compound had good target selectivity to 3CL Pro over cathepsin L. These correlation assays can prove the rationality of the covalent lead compound screening model. Finally, covalent docking was performed to demonstrate the binding conformation of the compound with 3CL Pro. The source code can be obtained from the GitHub repository (https://github.com/guzh970630/Screen_Covalent_Compound_by_LISNN).

Discovery of novel PARP-1 inhibitors using tandem in silico studies: integrated docking, e-pharmacophore, deep learning based de novo and molecular dynamics simulation approach

Cancer accounts for the majority of deaths worldwide, and the increasing incidence of breast cancer is a matter of grave concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as an attractive target for the treatment of breast cancer as it has an important role in DNA repair. The focus of the study was to identify novel PARP-1 inhibitors using a blend of tandem structure-based screening (Docking and e-pharmacophore-based screening) and artificial intelligence (deep learning)-based de novo approaches. The scrutiny of compounds having good binding characteristics for PARP-1 was carried out using a tandem mode of screening along with parameters such as binding energy and ADME analysis. The efforts afforded compound Vab1 (PubChem ID 129142036), which was chosen as a seed for obtaining novel compounds through a trained artificial intelligence (AI)-based model. Resultant compounds were assessed for PARP-1 inhibition; binding affinity prediction and interaction pattern analysis were carried out using the extra precision (XP) mode of docking. Two best hits, Vab1-b and Vab1-g, exhibiting good dock scores and suitable interactions, were subjected to 100 nanoseconds (ns) of molecular dynamics simulation in the active site of PARP-1 and compared with the reference Protein-Ligand Complex. The stable nature of PARP-1 upon binding to these compounds was revealed through MD simulation.Communicated by Ramaswamy H. Sarma.

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

Discovery of pyrimidoindol and benzylpyrrolyl inhibitors targeting SARS-CoV-2 main protease (Mpro) through pharmacophore modelling, covalent docking, and biological evaluation

The main protease (Mpro) enzyme has an imperative function in disease progression and the life cycle of the SARS-CoV-2 virus. Although the orally active drug nirmatrelvir (co-administered with ritonavir as paxlovid) has been approved for emergency use as the frontline antiviral agent, there are a number of limitations that necessitate the discovery of new drug scaffolds, such as poor pharmacokinetics and susceptibility to proteolytic degradation due to its peptidomimetic nature. This study utilized a novel virtual screening workflow that combines pharmacophore modelling, multiple-receptor covalent docking, and biological evaluation in order to find new Mpro inhibitors. After filtering and analysing ∼66,000 ligands from three different electrophilic libraries, 29 compounds were shortlisted for experimental testing, and two of them exhibited ≥20% inhibition at 100 μM. Our top candidate, GF04, is a benzylpyrrolyl compound that exhibited the highest inhibition activity of 38.3%, with a relatively small size (<350 Da) and leadlike character. Interestingly, our approach also identified another hit, DR07, a pyrimidoindol with a non-peptide character, and a molecular weight of 438.9 Da, reporting an inhibition of 26.3%. The established approach detailed in this study, in conjunction with the discovered inhibitors, has the capacity to yield novel perspectives for devising covalent inhibitors targeting the COVID-19 Mpro enzyme and other comparable targets.

Discovery of potent DNMT1 inhibitors against sickle cell disease using structural-based virtual screening, MM-GBSA and molecular dynamics simulation-based approaches

Sickle cell disease (SCD) is an autosomal recessive genetic disorder affecting millions of people worldwide. A reversible and selective DNMT1 inhibitor, GSK3482364, has been known to decrease the overall methylation activity of DNMT1, resulting in the increase of HbF levels and percentage of HbF-expressing erythrocytes in an in vitro and in vivo model. In this study, a structure-based virtual screening was done with GSK3685032, a co-crystalized ligand of DNMT1 (PDB ID: 6X9K) with an IC50 value of 0.036 μM and identified 3988 compounds from three databases (ChEMBL, PubChem and Drug Bank). Using this screening method, we identified around 15 compounds with XP docking scores greater than -8 kcal/mol. Further, prime MM-GBSA calculations have been performed and found compound SCHEMBL19716714 with the highest binding free energy of -83.31 kcal/mol. Finally, four compounds were identified based on glide energy and ΔG bind scores that have the most binding with DG7, DG19, DG20 bases and Lys1535, His1507, Trp1510, Ser1230, which were required for the target enzyme inhibition. Furthermore, molecular dynamics simulation studies of top ligands validate the stability of the docked complexes by examining root mean square deviations, root mean square fluctuations, solvent accessible surface area, and radius of gyration graphs from simulation trajectories. These findings suggest that the top four hit compounds may be capable of inhibiting DNMT1 and that additional in vitro and in vivo studies will be essential to prove the clinical effectiveness of the selected lead compounds.Communicated by Ramaswamy H. Sarma.

Covalent Targeting of Splicing in T Cells

Despite significant interest in therapeutic targeting of splicing, few chemical probes are available for the proteins involved in splicing. Here, we show that elaborated stereoisomeric acrylamide chemical probe EV96 and its analogues lead to a selective T cell state-dependent loss of interleukin 2-inducible T cell kinase (ITK) by targeting one of the core splicing factors SF3B1. Mechanistic investigations suggest that the state-dependency stems from a combination of differential protein turnover rates and availability of functional mRNA pools that can be depleted due to extensive alternative splicing. We further introduce a comprehensive list of proteins involved in splicing and leverage both cysteine- and protein-directed activity-based protein profiling (ABPP) data with electrophilic scout fragments to demonstrate covalent ligandability for many classes of splicing factors and splicing regulators in primary human T cells. Taken together, our findings show how chemical perturbation of splicing can lead to immune state-dependent changes in protein expression and provide evidence for the broad potential to target splicing factors with covalent chemistry.

A covalent fragment-based strategy targeting a novel cysteine to inhibit activity of mutant EGFR kinase

Several generations of ATP-competitive anti-cancer drugs that inhibit the activity of the intracellular kinase domain of the epidermal growth factor receptor (EGFR) have been developed over the past twenty years. The first-generation of drugs such as gefitinib bind reversibly and were followed by a second-generation such as dacomitinib that harbor an acrylamide moiety that forms a covalent bond with C797 in the ATP binding pocket. Resistance emerges through mutation of the T790 gatekeeper residue to methionine, which introduces steric hindrance to drug binding and increases the Km for ATP. A third generation of drugs, such as osimertinib were developed which were effective against T790M EGFR in which an acrylamide moiety forms a covalent bond with C797, although resistance has emerged by mutation to S797. A fragment-based screen to identify new starting points for an EGFR inhibitor serendipitously identified a fragment that reacted with C775, a previously unexploited residue in the ATP binding pocket for a covalent inhibitor to target. A number of acrylamide containing fragments were identified that selectively reacted with C775. One of these acrylamides was optimized to a highly selective inhibitor with sub-1 μM activity, that is active against T790M, C797S mutant EGFR independent of ATP concentration, providing a potential new strategy for pan-EGFR mutant inhibition.

Bisabosqual A: A novel asparagine synthetase inhibitor suppressing the proliferation and migration of human non-small cell lung cancer A549 cells

Asparagine synthetase (ASNS) is a crucial enzyme for the de novo biosynthesis of endogenous asparagine (Asn), and ASNS shows the positive relationship with the growth of several solid tumors. Most of ASNS inhibitors are analogs of transition-state in ASNS reaction, but their low cell permeability hinders their anticancer activity. Therefore, novel ASNS inhibitors with a new pharmacophore urgently need to be developed. In this study, we established and applied a system for in vitro screening of ASNS inhibitors, and found a promising unique bisabolane-type meroterpenoid molecule, bisabosqual A (Bis A), able to covalently modify K556 site of ASNS protein. Bis A targeted ASNS to suppress cell proliferation of human non-small cell lung cancer A549 cells and exhibited a synergistic effect with L-asparaginase (L-ASNase). Mechanistically, Bis A promoted oxidative stress and apoptosis, while inhibiting autophagy, cell migration and epithelial-mesenchymal transition (EMT), impeding cancer cell development. Moreover, Bis A induced negative feedback pathways containing the GCN2-eIF2α-ATF4, PI3K-AKT-mTORC1 and RAF-MEK-ERK axes, but combination treatment of Bis A and rapamycin/torin-1 overcame the potential drug resistance triggered by mTOR pathways. Our study demonstrates that ASNS inhibition is promising for cancer chemotherapy, and Bis A is a potential lead ASNS inhibitor for anticancer development.

Molecular dynamics articulated multilevel virtual screening protocol to discover novel dual PPAR α/γ agonists for anti-diabetic and metabolic applications

PPARα and PPARγ are isoforms of the nuclear receptor superfamily which regulate glucose and lipid metabolism. Activation of PPARα and PPARγ receptors by exogenous ligands could transactivate the expression of PPARα and PPARγ-dependent genes, and thereby, metabolic pathways get triggered, which are helpful to ameliorate treatment for the type 2 diabetes mellitus, and related metabolic complications. Herein, by understanding the structural requirements for ligands to activate PPARα and PPARγ proteins, we developed a multilevel in silico-based virtual screening protocol to identify novel chemical scaffolds and further design and synthesize two distinct series of glitazone derivatives with advantages over the classical PPARα and PPARγ agonists. Moreover, the synthesized compounds were biologically evaluated for PPARα and PPARγ transactivation potency from nuclear extracts of 3T3-L1 cell. Furthermore, glucose uptake assay on L6 cells confirmed the potency of the synthesized compounds toward glucose regulation. Percentage lipid-lowering potency was also assessed through triglyceride estimate from 3T3-L1 cell extracts. Results suggested the ligand binding mode was in orthosteric fashion as similar to classical agonists. Thus molecular docking and molecular dynamics (MD) simulation experiments were executed to validate our hypothesis on mode of ligands binding and protein complex stability. Altogether, the present study developed a newer protocol for virtual screening and enables to design of novel glitazones for activation of PPARα and PPARγ-mediated pathways. Accordingly, present approach will offer benefit as a therapeutic strategy against type 2 diabetes mellitus and associated metabolic complications.

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

Identification of prospective covalent inhibitors for SARS-CoV-2 main protease using structure-based approach

The rapid global spread of SARS-CoV-2 has recently caused havoc and forced the world into a state of the pandemic causing respiratory, gastrointestinal, hepatic, and neurologic diseases. It persistently, through mutation, develops into new variants of the virus that have appeared over time. As main protease (Mpro) is involved in proteolysis of two overlapping polyproteins pp1a and pp1ab to produce 16 non-structural proteins having a paramount factor in the virus replication that have a cysteine-histidine catalytic dyad. A computational approach, guiding a covalent docking as it offers higher potency, long duration of action and decreased drug resistance advantages over the conventional docking of the ligands on a catalytic dyad, is applied for SARS-CoV-2 main protease (Mpro) in this manuscript to divulge better molecules. Mpro active site contains Cys145 residue which act as a nucleophile and can donate its electron to an electrophilic molecule by interacting covalently. Furthermore, the ligand-protein complexes are allowed to simulate their dynamic studies to look into their time-based interaction stability and also, a parallel study of ADME properties for the hit molecules is also performed. Important insights from the studies revealed that the interactions are persistent and molecules may be considered for further optimization in clinical investigation.Communicated by Ramaswamy H. Sarma.

DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein-Ligand Binding Affinity Prediction

Accurate prediction of the protein-ligand binding affinity (PLBA) with an affordable cost is one of the ultimate goals in the field of structure-based drug design (SBDD), as well as a great challenge in the computational and theoretical chemistry. Herein, we have systematically addressed the complicated solvation and desolvation effects on the PLBA brought by the difference of the explicit water in the protein cavity before and after ligands bind to the protein-binding site. Based on the new solvation model, a nonfitting method at the first-principles level for the PLBA prediction was developed by taking the bridging and displaced water (BDW) molecules into account simultaneously. The newly developed method, DOX_BDW, was validated against a total of 765 noncovalent and covalent protein-ligand binding pairs, including the CASF2016 core set, Cov_2022 covalent binding testing set, and six testing sets for the hit and lead compound optimization (HLO) simulation. In all of the testing sets, the DOX_BDW method was able to produce PLBA predictions that were strongly correlated with the corresponding experimental data (R = 0.66-0.85). The overall performance of DOX_BDW is better than the current empirical scoring functions that are heavily parameterized. DOX_BDW is particularly outstanding for the covalent binding situation, implying the need for considering an electronic structure in covalent drug design. Furthermore, the method is especially recommended to be used in the HLO scenario of SBDD, where hundreds of similar derivatives need to be screened and refined. The computational cost of DOX_BDW is affordable, and its accuracy is remarkable.

Pharmacophoric analogs of sotorasib-entrapped KRAS G12C in its inactive GDP-bound conformation: covalent docking and molecular dynamics investigations

For decades, KRAS G12C was considered an undruggable target. However, in recent times, a covalent inhibitor known as sotorasib was discovered and approved for the treatment of patients with KRAS G12C-driven cancers. Ever since the discovery of this drug, several preclinical efforts have focused on identifying novel therapeutic candidates that could act as covalent binders of KRAS G12C. Despite these intensive efforts, only a few KRAS G12C inhibitors have entered clinical trials. Hence, this highlights the need to develop effective drug candidates that could be used in the treatment of KRAS G12C-driven cancers. Herein, we embarked on a virtual screening campaign that involves the identification of pharmacophores of sotorasib that could act as covalent arsenals against the KRAS G12C target. To our knowledge, this is the first computational study that involves the compilation of sotorasib pharmacophores from an online chemical database against KRAS G12C. After this library of chemical entities was compiled, we conducted a covalent docking-based virtual screening that revealed three promising drug candidates (CID_146235944, CID_160070181, and CID_140956845) binding covalently to the crucial nucleophilic side chain of Cys12 and interact with the residues that form the cryptic allosteric pocket of KRAS G12C in its inactive GDP-bound conformation. Subsequently, ADMET profiling portrayed the covalent inhibitors as lead-like candidates, while 100 ns molecular dynamics was used to substantiate their stability. Although our overall computational study has shown the promising potential of the lead-like candidates in impeding oncogenic RAS signaling, more experimental efforts are needed to validate and establish their preclinical relevance. Implication of KRAS G12C in cancer and computational approach towards impeding the KRAS G12C RAS signaling.

Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

The continuous approval of covalent drugs in recent years for the treatment of diseases has led to an increased search for covalent agents by medicinal chemists and computational scientists worldwide. In the computational parlance, molecular docking which is a popular tool to investigate the interaction of a ligand and a protein target, does not account for the formation of covalent bond, and the increasing application of these conventional programs to covalent targets in early drug discovery practice is a matter of utmost concern. Thus, in this comprehensive review, we sought to educate the docking community about the realization of covalent docking and the existence of suitable programs to make their future virtual-screening events on covalent targets worthwhile and scientifically rational. More interestingly, we went beyond the classical description of the functionality of covalent-docking programs down to selecting the 'best' program to consult with during a virtual-screening campaign based on receptor class and covalent warhead chemistry. In addition, we made a highlight on how covalent docking could be achieved using random conventional docking software. And lastly, we raised an alert on the growing erroneous molecular docking practices with covalent targets. Our aim is to guide scientists in the rational docking pursuit when dealing with covalent targets, as this will reduce false-positive results and also increase the reliability of their work for translational research.

Sensitization of GSH synthesis by curcumin curtails acrolein-induced alveolar epithelial apoptosis via Keap1 cysteine conjugation: A randomized controlled trial and experimental animal model of pneumonitis

INTRODUCTION

Epidemiological studies have reported an association between exposures to ambient air pollution and respiratory diseases, including chronic obstructive pulmonary disease (COPD). Pneumonitis is a critical driving factor of COPD and exposure to air pollutants (e.g., acrolein) is associated with increased incidence of pneumonitis.

OBJECTIVES

Currently available anti-inflammatory therapies provide little benefit against respiratory diseases. To this end, we investigated the preventive role of curcumin against air pollutant-associated pneumonitis and its underlying mechanism.

METHODS

A total of 40 subjects was recruited from Chengdu, China which is among the top three cities in terms of respiratory mortality related to air pollution. The participants were randomly provided either placebo or curcumin supplements for 2 weeks and blood samples were collected at the baseline and at the end of the intervention to monitor systemic markers. In our follow up mechanistic study, C57BL/6 mice (n = 40) were randomly allocated into 4 groups: Control group (saline + no acrolein), Curcumin only group (curcumin + no acrolein), Acrolein only group (saline + acrolein), and Acrolein + Curcumin group (curcumin + acrolein). Curcumin was orally administered at 100 mg/kg body weight once a day for 10 days, and then the mice were subjected to nasal instillation of acrolein (5 mg/kg body weight). Twelve hours after single acrolein exposure, all mice were euthanized.

RESULTS

Curcumin supplementation, with no noticeable adverse responses, reduced circulating pro-inflammatory cytokines in association with clinical pneumonitis as positive predictive while improving those of anti-inflammatory cytokines. In the pre-clinical study, curcumin reduced pneumonitis manifestations by suppression of intrinsic and extrinsic apoptotic signaling, which is attributed to enhanced redox sensing of Nrf2 and thus sensitized synthesis and restoration of GSH, at least in part, through curcumin-Keap1 conjugation.

CONCLUSIONS

Our study collectively suggests that curcumin could provide an effective preventive measure against air pollutant-enhanced pneumonitis and thus COPD.

Targeting caspase-2 interactions with tau in Alzheimer's disease and related dementias

Targeting amyloid-β plaques and tau tangles has failed to provide effective treatments for Alzheimer's disease and related dementias (ADRD). A more fruitful pathway to ADRD therapeutics may be the development of therapies that target common signaling pathways that disrupt synaptic connections and impede communication between neurons. In this review, we present our characterization of a signaling pathway common to several neurological diseases featuring dementia including Alzheimer's disease, frontotemporal dementia, Lewy body dementia, and Huntington's disease. This signaling pathway features the cleavage of tau by caspase-2 (Casp2) yielding Δtau314 (Casp2/tau/Δtau314). Through a not yet fully delineated mechanism, Δtau314 catalyzes the mislocalization and accumulation of tau to dendritic spines leading to the internalization of AMPA receptors and the concomitant weakening of synaptic transmission. Here, we review the accumulated evidence supporting Casp2 as a druggable target and its importance in ADRD. Additionally, we provide a brief overview of our initial medicinal chemistry explorations aimed at the preparation of novel, brain penetrant Casp2 inhibitors. We anticipate that this review will spark broader interest in Casp2 as a target for restoring synaptic dysfunction in ADRD.

SCARdock: A Web Server and Manually Curated Resource for Discovering Covalent Ligands

BACKGROUND

Covalent drugs have been intentionally discarded historically due to the concern of off-target side effects, but the past decade has seen a fast resurgence of the discovery of covalent drugs. Compared to noncovalent ligands, covalent ligands might have better biochemical efficiency, lower patient burden, less dosing frequency, less drug resistance, and improved target specificity.

RESULTS

Previously, we proposed the steric-clashes alleviating receptor (SCAR) strategy for screening and repurposing covalent inhibitors. To help the discovery of covalent ligands targeting protein targets, we have developed a web server dedicated to providing the SCARdock protocol to general users. Along with this server, we presented three high-quality data sets for the discovery of covalent ligands: a manually curated data set containing 954 high-quality complex structures of covalent ligands and proteins, a manually curated data set of 68 experimentally confirmed covalent warheads targeting 11 different residues, and a prefiltered, classified, and ready-to-use data set of 690,018 entries of purchasable virtual compounds containing these experimentally verified warheads.

CONCLUSIONS

The SCARdock server and the accompanied data sets would be of great value to the discovery of covalent ligands and are available freely at http://www.liugroup.site/scardock/ or https://scardock.com.

An approach combining deep learning and molecule docking for drug discovery of cathepsin L

OBJECTIVES

Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders and COVID-19. However, there are still no clinically available CTSL inhibitors. Our objective is to develop an approach for the discovery of potential reversible covalent CTSL inhibitors.

METHODS

The authors combined Chemprop, a deep learning-based strategy, and the Schrödinger CovDock algorithm to identify potential CTSL inhibitors. First, they used Chemprop to train a deep learning model capable of predicting whether a molecule would inhibit the activity of CTSL and performed predictions on ZINC20 in-stock librarie (~9.2 million molecules). Then, they selected the top-200 predicted molecules and performed the Schrödinger covalent docking algorithm to explore the binding patterns to CTSL (PDB: 5MQY). The authors then calculated the binding energies using Prime MM/GBSA and examined the stability between the best two molecules and CTSL using 100ns molecular dynamics simulations.

RESULTS

The authors found five molecules that showed better docking results than the well-known cathepsin inhibitor odanacatib. Notably, two of these molecules, ZINC-35287427 and ZINC-1857528743, showed better docking results with CTSL compared to other cathepsins.

CONCLUSION

Our approach enables drug discovery from large-scale databases with little computational consumption, which will save the cost and time required for drug discovery.

In Vitro Study of Biological Activity of Tanacetum vulgare Extracts

Tanacetum vulgare is an herbaceous plant widely used in folk medicine. It is rich in phenolic acids and flavonoids, which have pharmacological and medicinal properties, such as anthelmintic, antispasmodic, tonic, antidiabetic, diuretic, and antihypertensive. This study aimed to confirm the presence of biologically active substances in Tanacetum vulgare and to determine the pharmacological spectrum of biological activity of Tanacetum vulgare extract components. When preparing Tanacetum vulgare extracts, the highest yield was observed when using the maceration method with a mixture of solvents methanol + trifluoroacetic acid (22.65 ± 0.68%). The biologically active substances in Tanacetum vulgare extract samples were determined using high-performance liquid chromatography. Biologically active substances such as luteolin-7-glucoside (550.80 mg/kg), chlorogenic acid (5945.40 mg/kg), and rosmarinic acid (661.31 mg/kg) were identified. Their structures were determined. The experiments have confirmed the antioxidant and antibacterial activities. Secondary metabolites of Tanacetum vulgare extracts have been found to have previously unknown biological activity types; experimental confirmation of their existence will advance phytochemical research and lead to the development of new drugs.

Structure-based virtual screening of novel natural products as chalcone derivatives against SARS-CoV-2 Mpro

Coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2, has spread quickly around the world, causing a global pandemic. It has infected more than 500 million people as of April 28, 2022. Much research has been reported to stop the virus from spreading, but there are currently no approved medicines to treat COVID-19. In this work, a dataset of 142 natural products collected from various medicinal plants was used to perform structure-based virtual screening (SBVS) through the combined application of molecular docking and molecular dynamics (MD) simulation methods. First, the dataset of compounds was optimized using the density functional theory (DFT) approach. The optimized compounds were then submitted to the first screening, which was done by the pKCM web server to look for drug-likeness and the PyRx to look for binding affinity. Among the 142 natural substances, 10 compounds were selected for docking validation. Compounds that interact with CYS145 and LEU141, the essential catalytic residues, as well as compounds with binding affinities less than -8.0 kcal/mol, are considered promising anti-SARS-CoV-2 drug candidates. The top-ranked compounds were then evaluated by MD simulations and MM-GBSA method. These results could help researchers come up with new natural compounds that could be used to treat SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

HCovDock: an efficient docking method for modeling covalent protein-ligand interactions

Covalent inhibitors have received extensive attentions in the past few decades because of their long residence time, high binding efficiency and strong selectivity. Therefore, it is valuable to develop computational tools like molecular docking for modeling of covalent protein-ligand interactions or screening of potential covalent drugs. Meeting the needs, we have proposed HCovDock, an efficient docking algorithm for covalent protein-ligand interactions by integrating a ligand sampling method of incremental construction and a scoring function with covalent bond-based energy. Tested on a benchmark containing 207 diverse protein-ligand complexes, HCovDock exhibits a significantly better performance than seven other state-of-the-art covalent docking programs (AutoDock, Cov_DOX, CovDock, FITTED, GOLD, ICM-Pro and MOE). With the criterion of ligand root-mean-squared distance < 2.0 Å, HCovDock obtains a high success rate of 70.5% and 93.2% in reproducing experimentally observed structures for top 1 and top 10 predictions. In addition, HCovDock is also validated in virtual screening against 10 receptors of three proteins. HCovDock is computationally efficient and the average running time for docking a ligand is only 5 min with as fast as 1 sec for ligands with one rotatable bond and about 18 min for ligands with 23 rotational bonds. HCovDock can be freely assessed at http://huanglab.phys.hust.edu.cn/hcovdock/.

BIreactive: Expanding the Scope of Reactivity Predictions to Propynamides

We present the first comprehensive study on the prediction of reactivity for propynamides. Covalent inhibitors like propynamides often show improved potency, selectivity, and unique pharmacologic properties compared to their non-covalent counterparts. In order to achieve this, it is essential to tune the reactivity of the warhead. This study shows how three different in silico methods can predict the in vitro properties of propynamides, a covalent warhead class integrated into approved drugs on the market. Whereas the electrophilicity index is only applicable to individual subclasses of substitutions, adduct formation and transition state energies have a good predictability for the in vitro reactivity with glutathione (GSH). In summary, the reported methods are well suited to estimate the reactivity of propynamides. With this knowledge, the fine tuning of the reactivity is possible which leads to a speed up of the design process of covalent drugs.

BC-11 is a covalent TMPRSS2 fragment inhibitor that impedes SARS-CoV-2 host cell entry

Host cell entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is facilitated via priming of its spike glycoprotein by the human transmembrane protease serine 2 (TMPRSS2). Although camostat and nafamostat are two highly potent covalent TMPRSS2 inhibitors, they nevertheless did not hold promise in COVID-19 clinical trials, presumably due to their short plasma half-lives. Herein, we report an integrative chemogenomics approach based on computational modeling and in vitro enzymatic assays, for repurposing serine-targeted covalent inhibitors. This led to the identification of BC-11 as a covalent TMPRSS2 inhibitor displaying a unique selectivity profile for serine proteases, ascribable to its boronic acid warhead. BC-11 showed modest inhibition of SARS-CoV-2 (omicron variant) spike pseudotyped particles in a cell-based entry assay, and a combination of BC-11 and AHN 1-055 (a spike glycoprotein inhibitor) demonstrated better viral entry inhibition than either compound alone. Given its low molecular weight and good activity against TMPRSS2, BC-11 qualifies as a good starting point for further structural optimizations.

Integration of molecular modelling and in vitro studies to inhibit LexA proteolysis

Introduction

As antibiotic resistance has become more prevalent, the social and economic impacts are increasingly pressing. Indeed, bacteria have developed the SOS response which facilitates the evolution of resistance under genotoxic stress. The transcriptional repressor, LexA, plays a key role in this response. Mutation of LexA to a non-cleavable form that prevents the induction of the SOS response sensitizes bacteria to antibiotics. Achieving the same inhibition of proteolysis with small molecules also increases antibiotic susceptibility and reduces drug resistance acquisition. The availability of multiple LexA crystal structures, and the unique Ser-119 and Lys-156 catalytic dyad in the protein enables the rational design of inhibitors.

Methods

We pursued a binary approach to inhibit proteolysis; we first investigated β-turn mimetics, and in the second approach we tested covalent warheads targeting the Ser-119 residue. We found that the cleavage site region (CSR) of the LexA protein is a classical Type II β-turn, and that published 1,2,3-triazole compounds mimic the β-turn. Generic covalent molecule libraries and a β-turn mimetic library were docked to the LexA C-terminal domain using molecular modelling methods in FlexX and CovDock respectively. The 133 highest-scoring molecules were screened for their ability to inhibit LexA cleavage under alkaline conditions. The top molecules were then tested using a RecA-mediated cleavage assay.

Results

The β-turn library screen did not produce any hit compounds that inhibited RecA-mediated cleavage. The covalent screen discovered an electrophilic serine warhead that can inhibit LexA proteolysis, reacting with Ser-119 via a nitrile moiety.

Discussion

This research presents a starting point for hit-to-lead optimisation, which could lead to inhibition of the SOS response and prevent the acquisition of antibiotic resistance.

In silico trials to design potent inhibitors against matrilysin (MMP-7)

The study deals with structure-based rational drug design against the chief zinc-rely endopeptidase called matrilysin (MMP-7) that is involved in inflammatory and metastasis process of several carcinomas. Hyperactivated matrilysin of human was targeted, because of its hydrolytic actions on extracellular matrix (ECM) protein components constitutes fibrillar collagens, gelatins, fibronectins and it also activates zymogen forms of vital matrix metalloproteinases (gelatinase A-MMP-2 and B-MMP-9) responsible for ECM destruction in many cancers. In the present work, e-pharmacophores were generated for the respective five co-crystal structures of human matrilysin by mapping ligand's pharmacophoric features. During the lead-optimization campaign, the five e-pharmacophores-based shape screening against an in-house library of >21 million compounds created a dataset of 5000 structural analogs. The subsequent three different docking strategies, including rigid-receptor docking, quantum-polarized-ligand docking, induced-fit docking and free energy binding calculations resulted four leads as novel and potent MMP-7 binders. These four leads were observed with good pharmacological features and good receiver operating characteristics curve metrics (ROC: 0.93) in post-docking evaluations against five existing co-crystal inhibitors and 1000 decoy molecules with MMP-7. Moreover, stability and dynamics behavior of matrilysin-lead1 complex and matrilysin-cocrystal ligand (TQJ) complex were analyzed in natural physiological milieu of 1000 ns or 1 µs molecular dynamics simulations. Lead1-MMP-7 complex was found with an average Cα root-mean-square deviation (RMSD) of 2.35 Å, average ligand root-mean-square fluctuations (RMSF) of 0.66 Å and the strong metallic interactions with E220, a key residue for proteolytic action thereby hinders ECM proteolysis that in turn can halt metastatic cancerous condition.Communicated by Ramaswamy H. Sarma.

Characterization of caspase-2 inhibitors based on specific sites of caspase-2-mediated proteolysis

Since the discovery of the caspase-2 (Casp2)-mediated ∆tau314 cleavage product and its associated impact on tauopathies such as Alzheimer's disease, the design of selective Casp2 inhibitors has become a focus in medicinal chemistry research. In the search for new lead structures with respect to Casp2 selectivity and drug-likeness, we have taken an approach by looking more closely at the specific sites of Casp2-mediated proteolysis. Using seven selected protein cleavage sequences, we synthesized a peptide series of 53 novel molecules and studied them using in vitro pharmacology, molecular modeling, and crystallography. Regarding Casp2 selectivity, AcITV(Dab)D-CHO (23) and AcITV(Dap)D-CHO (26) demonstrated the best selectivity (1-6-fold), although these trends were only moderate. However, some analogous tetrapeptides, most notably AcDKVD-CHO (45), showed significantly increased Casp3 selectivities (>100-fold). Tetra- and tripeptides display decreased or no Casp2 affinity, supporting the assumption that a motif of five amino acids is required for efficient Casp2 inhibition. Overall, the results provide a reasonable basis for the development of both selective Casp2 and Casp3 inhibitors.

Identification of 4-acrylamido-N-(pyridazin-3-yl)benzamide as anti-COVID-19 compound: a DFTB, molecular docking, and molecular dynamics study

Omicron is one of the variants of COVID-19 and continuing member of a pandemic. There are several types of vaccines that were developed around the globe to fight against the virus. However, the world is suffering to find suitable drug candidates for the virus. The main protease (Mpro) enzyme of the virus is the best target for finding drug molecules because of its involvement in viral infection and protein synthesis. ZINC-15 is a database of 750 million commercially available compounds. We find 125 compounds having two aromatic rings and amide groups for non-covalent interactions with active site amino acids and functional groups with the capability to bind -SH group of C145 of Mpro through covalent bonding by a nucleophilic addition reaction. The lead compound (Z144) was identified using molecular docking. The non-covalent interactions (NCI) calculations show the interactions between amino acids present in the active site of the protein and the lead molecules are attractive in nature. The density functional-based tight-binding (DFTB) study of the lead compound with amino acids in the active site indicates that Q190 and Q193 play a very critical role in stabilization. The Michael addition of the acrylamide group of the lead molecule at β-position is facile because the low energy lowest unoccupied molecular orbital (LUMO) is concentrated on the group. From molecular dynamics during 100 ns, it has come to light that strong non-covalent interactions are key for the stability of the lead inside the protein and such binding can fold the protein. The free energy for this interaction is -42.72 kcal mol-1 which was obtained from MM-GB/SA calculations.

Modeling receptor flexibility in the structure-based design of KRASG12C inhibitors

KRAS has long been referred to as an ‘undruggable’ target due to its high affinity for its cognate ligands (GDP and GTP) and its lack of readily exploited allosteric binding pockets. Recent progress in the development of covalent inhibitors of KRAS^G12C has revealed that occupancy of an allosteric binding site located between the α3-helix and switch-II loop of KRAS^G12C—sometimes referred to as the ‘switch-II pocket’—holds great potential in the design of direct inhibitors of KRAS^G12C. In studying diverse switch-II pocket binders during the development of sotorasib (AMG 510), the first FDA-approved inhibitor of KRAS^G12C, we found the dramatic conformational flexibility of the switch-II pocket posing significant challenges toward the structure-based design of inhibitors. Here, we present our computational approaches for dealing with receptor flexibility in the prediction of ligand binding pose and binding affinity. For binding pose prediction, we modified the covalent docking program CovDock to allow for protein conformational mobility. This new docking approach, termed as FlexCovDock, improves success rates from 55 to 89% for binding pose prediction on a dataset of 10 cross-docking cases and has been prospectively validated across diverse ligand chemotypes. For binding affinity prediction, we found standard free energy perturbation (FEP) methods could not adequately handle the significant conformational change of the switch-II loop. We developed a new computational strategy to accelerate conformational transitions through the use of targeted protein mutations. Using this methodology, the mean unsigned error (MUE) of binding affinity prediction were reduced from 1.44 to 0.89 kcal/mol on a set of 14 compounds. These approaches were of significant use in facilitating the structure-based design of KRAS^G12C inhibitors and are anticipated to be of further use in the design of covalent (and noncovalent) inhibitors of other conformationally labile protein targets.

Covalent docking in CDOCKER

Targeted covalent inhibitors (TCIs) are considered to be an important component in the toolbox of drug discovery and about 30% of currently marketed drugs are TCIs. Although these drugs raise concerns about toxicity, their high potencies and prolonged effects result in less-frequent drug dosing and wide therapeutic margins for patients. This leads to increased interests in developing new computational methods to identify novel covalent inhibitors. The implementation of successful in silico docking algorithms have the potential to provide significant savings of time and money in the discovery of lead compounds. In this paper, we describe the implementation and testing of a covalent docking methodology in Rigid CDOCKER and the optimization of the corresponding physics-based scoring function with an additional customizable covalent bond grid potential which represents the free energy change of bond formation between the ligand and the receptor. We optimize the covalent bond grid potential for different common covalent bond formation reaction in TCIs. The average runtime for docking one covalent compound is 15 minutes which is comparable or faster than other well-established covalent docking methods. We demonstrate comparable top rank accuracy compared with other covalent docking algorithms using the pose prediction benchmark dataset for covalent docking algorithms developed by the Keserű group. Finally, we construct a retrospective virtual screening benchmark dataset containing 8 different receptor targets with different covalent bond formation reactions. To our knowledge, this is the largest dataset for benchmarking covalent docking methods. We show that our new covalent docking algorithm has the ability to identify lead compounds among a large chemical space. The largest AUC value is 0.909 for the target receptor CATK and the warhead chemistry of the covalent inhibitors is addition to the aldehyde functionality.

Protocetraric and Salazinic Acids as Potential Inhibitors of SARS-CoV-2 3CL Protease: Biochemical, Cytotoxic, and Computational Characterization of Depsidones as Slow-Binding Inactivators

The study investigated the inhibitory activity of protocetraric and salazinic acids against SARS-CoV-2 3CL^pro. The kinetic parameters were determined by microtiter plate-reading fluorimeter using a fluorogenic substrate. The cytotoxic activity was tested on murine Sertoli TM4 cells. In silico analysis was performed to ascertain the nature of the binding with the 3CL^pro. The compounds are slow-binding inactivators of 3CL^pro with a K_i of 3.95 μM and 3.77 μM for protocetraric and salazinic acid, respectively, and inhibitory efficiency k_inact/K_i at about 3 × 10⁻⁵ s⁻¹µM⁻¹. The mechanism of inhibition shows that both compounds act as competitive inhibitors with the formation of a stable covalent adduct. The viability assay on epithelial cells revealed that none of them shows cytotoxicity up to 80 μM, which is well below the K_i values. By molecular modelling, we predicted that the catalytic Cys145 makes a nucleophilic attack on the carbonyl carbon of the cyclic ester common to both inhibitors, forming a stably acyl-enzyme complex. The computational and kinetic analyses confirm the formation of a stable acyl-enzyme complex with 3CL^pro. The results obtained enrich the knowledge of the already numerous biological activities exhibited by lichen secondary metabolites, paving the way for developing promising scaffolds for the design of cysteine enzyme inhibitors.

Virtual Screening Based on Machine Learning Explores Mangrove Natural Products as KRASG12C Inhibitors

Mangrove secondary metabolites have many unique biological activities. We identified lead compounds among them that might target KRAS^G12C. KRAS is considered to be closely related to various cancers. A variety of novel small molecules that directly target KRAS are being developed, including covalent allosteric inhibitors for KRAS^G12C mutant, protein–protein interaction inhibitors that bind in the switch I/II pocket or the A59 site, and GTP-competitive inhibitors targeting the nucleotide-binding site. To identify a candidate pool of mangrove secondary metabolic natural products, we tested various machine learning algorithms and selected random forest as a model for predicting the targeting activity of compounds. Lead compounds were then subjected to virtual screening and covalent docking, integrated absorption, distribution, metabolism and excretion (ADME) testing, and structure-based pharmacophore model validation to select the most suitable compounds. Finally, we performed molecular dynamics simulations to verify the binding mode of the lead compound to KRAS^G12C. The lazypredict function package was initially used, and the Accuracy score and F1 score of the random forest algorithm exceeded 60%, which can be considered to carry a strong ability to distinguish the data. Four marine natural products were obtained through machine learning identification and covalent docking screening. Compound 44 and compound 14 were selected for further validation after ADME and toxicity studies, and pharmacophore analysis indicated that they had a favorable pharmacodynamic profile. Comparison with the positive control showed that they stabilized switch I and switch II, and like MRTX849, retained a novel binding mechanism at the molecular level. Molecular dynamics analysis showed that they maintained a stable conformation with the target protein, so compound 44 and compound 14 may be effective inhibitors of the G12C mutant. These findings reveal that the mangrove-derived secondary metabolite compound 44 and compound 14 might be potential therapeutic agents for KRAS^G12C.

Development of novel monoamine oxidase B (MAO-B) inhibitors by combined application of docking-based alignment, 3D-QSAR, ADMET prediction, molecular dynamics simulation, and MM_GBSA binding free energy

Unsaturated ketone derivatives are known as inhibitors of monoamine oxidase B (MAO-B), a potential drug target of Parkinson's disease. Here, docking-based alignment, 3 D-QSAR (three-dimensional quantitative structure-activity relationship) studies, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction, molecular dynamics (MD) simulation, and MM_GBSA binding free energy were performed on a novel series of MAO-B inhibitors. The objective is to predict new MAO-B inhibitors with high potency activity. The 3 D-QSAR models were created using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). Molecular docking findings indicated that compounds with strong inhibitory efficacy also had a high binding affinity. 3 D-QSAR studies showed the importance of steric, electrostatic, and H-bond acceptor fields on the inhibitory activity of MAO-B. Based on the appropriate 3 D-QSAR model, a new series of MAO-B inhibitors were predicted and their pharmacokinetic characteristics were evaluated using in silico ADMET prediction. All screened compounds show good oral bioavailability without any side effects. Moreover, the dynamic behavior and stability of the most active compounds were evaluated using MD simulations. The results showed that unsaturated ketone derivatives are stable and compact during the 100 ns of MD simulation. Finally, the binding free energy of complexes was determined using the MM_GBSA method; the findings indicated that the T1 compound is more stable (ΔG_binding = -409.506 KJ/mol) than the data set's highest active compound (ΔG_binding = -31.883 KJ/mol).Communicated by Ramaswamy H. Sarma.

Identification of Novel Covalent XPO1 Inhibitors Based on a Hybrid Virtual Screening Strategy

Nuclear export protein 1 (XPO1), a member of the nuclear export protein-p (Karyopherin-P) superfamily, regulates the transport of “cargo” proteins. To facilitate this important process, which is essential for cellular homeostasis, XPO1 must first recognize and bind the cargo proteins. To inhibit this process, small molecule inhibitors have been designed that inhibit XPO1 activity through covalent binding. However, the scaffolds for these inhibitors are very limited. While virtual screening may be used to expand the diversity of the XPO1 inhibitor skeleton, enormous computational resources would be required to accomplish this using traditional screening methods. In the present study, we report the development of a hybrid virtual screening workflow and its application in XPO1 covalent inhibitor screening. After screening, several promising XPO1 covalent molecules were obtained. Of these, compound 8 performed well in both tumor cell proliferation assays and a nuclear export inhibition assay. In addition, molecular dynamics simulations were performed to provide information on the mode of interaction of compound 8 with XPO1. This research has identified a promising new scaffold for XPO1 inhibitors, and it demonstrates an effective and resource-saving workflow for identifying new covalent inhibitors.

Covalent Proximity Scanning of a Distal Cysteine to Target PI3Kα

Covalent protein kinase inhibitors exploit currently noncatalytic cysteines in the adenosine 5′-triphosphate (ATP)-binding site via electrophiles directly appended to a reversible-inhibitor scaffold. Here, we delineate a path to target solvent-exposed cysteines at a distance >10 Å from an ATP-site-directed core module and produce potent covalent phosphoinositide 3-kinase α (PI3Kα) inhibitors. First, reactive warheads are used to reach out to Cys862 on PI3Kα, and second, enones are replaced with druglike warheads while linkers are optimized. The systematic investigation of intrinsic warhead reactivity (k_chem), rate of covalent bond formation and proximity (k_inact and reaction space volume V_r), and integration of structure data, kinetic and structural modeling, led to the guided identification of high-quality, covalent chemical probes. A novel stochastic approach provided direct access to the calculation of overall reaction rates as a function of k_chem, k_inact, K_i, and V_r, which was validated with compounds with varied linker lengths. X-ray crystallography, protein mass spectrometry (MS), and NanoBRET assays confirmed covalent bond formation of the acrylamide warhead and Cys862. In rat liver microsomes, compounds 19 and 22 outperformed the rapidly metabolized CNX-1351, the only known PI3Kα irreversible inhibitor. Washout experiments in cancer cell lines with mutated, constitutively activated PI3Kα showed a long-lasting inhibition of PI3Kα. In SKOV3 cells, compounds 19 and 22 revealed PI3Kβ-dependent signaling, which was sensitive to TGX221. Compounds 19 and 22 thus qualify as specific chemical probes to explore PI3Kα-selective signaling branches. The proposed approach is generally suited to develop covalent tools targeting distal, unexplored Cys residues in biologically active enzymes.

Cov_DOX: A Method for Structure Prediction of Covalent Protein-Ligand Bindings

A handful of molecular docking tools have been extended to enable a covalent docking. However, all of them face the challenge brought by the covalent bond between proteins and ligands. Many covalent drug design scenarios still heavily rely on demanding crystallographic experiments for accurate binding structures. Aiming at filling the gap between covalent dockings and crystallographic experiments, we develop and validate a hybrid method, dubbed as Cov_DOX, in this work. Cov_DOX achieves an overall success rate of 81% with RMSD < 2 Å for the Top 1 pose prediction in the validation against a test set including 405 crystal structures for covalent protein-ligand complexes, covering various types of the warhead chemistry and receptors. Such accuracy is not far from the much more demanding crystallographic experiments, in sharp contrast to the performance of the covalent docking front runners (success rate: 40-60%).

Structure-Based Design and Biological Evaluation of Novel Caspase-2 Inhibitors Based on the Peptide AcVDVAD-CHO and the Caspase-2-Mediated Tau Cleavage Sequence YKPVD314

Alzheimer's disease (AD) was first described by Alois Alzheimer over 100 years ago, but there is still no overarching theory that can explain its cause in detail. There are also no effective therapies to treat either the cause or the associated symptoms of this devastating disease. A potential approach to better understand the pathogenesis of AD could be the development of selective caspase-2 (Casp2) probes, as we have shown that a Casp2-mediated cleavage product of tau (Δtau314) reversibly impairs cognitive and synaptic function in animal models of tauopathies. In this article, we map out the Casp2 binding site through the preparation and assay of a series of 35 pentapeptide inhibitors with the goal of gaining selectivity against caspase-3 (Casp3). We also employed computational docking methods to understand the key interactions in the binding pocket of Casp2 and the differences predicted for binding at Casp3. Moreover, we crystallographically characterized the binding of selected pentapeptides with Casp3. Furthermore, we engineered and expressed a series of recombinant tau mutants and investigated them in an in vitro cleavage assay. These studies resulted in simple peptidic inhibitors with nanomolar affinity, for example, AcVDV(Dab)D-CHO (24) with up to 27.7-fold selectivity against Casp3. Our findings provide a good basis for the future development of selective Casp2 probes and inhibitors that can serve as pharmacological tools in planned in vivo studies and as lead compounds for the design of bioavailable and more drug-like small molecules.

Mechanism-Based and Computational-Driven Covalent Drug Design

Covalent drugs offer higher efficacy and longer duration of action than their noncovalent counterparts. Significant advances in computational methods for modeling covalent drugs are poised to shift the paradigm of small molecule therapeutics within the next decade. This viewpoint discusses the advantages of a two-state model for ranking reversible and irreversible covalent ligands and of more complex models for dissecting reaction mechanisms. The relation between these models highlights the complexity and diversity of covalent drug binding and provides opportunities for mechanism-based rational design.

Inhibition of XPO-1 Mediated Nuclear Export through the Michael-Acceptor Character of Chalcones

The nuclear export receptor exportin-1 (XPO1, CRM1) mediates the nuclear export of proteins that contain a leucine-rich nuclear export signal (NES) towards the cytoplasm. XPO1 is considered a relevant target in different human diseases, particularly in hematological malignancies, tumor resistance, inflammation, neurodegeneration and viral infections. Thus, its pharmacological inhibition is of significant therapeutic interest. The best inhibitors described so far (leptomycin B and SINE compounds) interact with XPO1 through a covalent interaction with Cys528 located in the NES-binding cleft of XPO1. Based on the well-established feature of chalcone derivatives to react with thiol groups via hetero-Michael addition reactions, we have synthesized two series of chalcones. Their capacity to react with thiol groups was tested by incubation with GSH to afford the hetero-Michael adducts that evolved backwards to the initial chalcone through a retro-Michael reaction, supporting that the covalent interaction with thiols could be reversible. The chalcone derivatives were evaluated in antiproliferative assays against a panel of cancer cell lines and as XPO1 inhibitors, and a good correlation was observed with the results obtained in both assays. Moreover, no inhibition of the cargo export was observed when the two prototype chalcones 9 and 10 were tested against a XPO1-mutated Jurkat cell line (XPO1C528S), highlighting the importance of the Cys at the NES-binding cleft for inhibition. Finally, their interaction at the molecular level at the NES-binding cleft was studied by applying the computational tool CovDock.

Structure-based de novo drug design using 3D deep generative models

Deep generative models are attracting much attention in the field of de novo molecule design. Compared to traditional methods, deep generative models can be trained in a fully data-driven way with little requirement for expert knowledge. Although many models have been developed to generate 1D and 2D molecular structures, 3D molecule generation is less explored, and the direct design of drug-like molecules inside target binding sites remains challenging. In this work, we introduce DeepLigBuilder, a novel deep learning-based method for de novo drug design that generates 3D molecular structures in the binding sites of target proteins. We first developed Ligand Neural Network (L-Net), a novel graph generative model for the end-to-end design of chemically and conformationally valid 3D molecules with high drug-likeness. Then, we combined L-Net with Monte Carlo tree search to perform structure-based de novo drug design tasks. In the case study of inhibitor design for the main protease of SARS-CoV-2, DeepLigBuilder suggested a list of drug-like compounds with novel chemical structures, high predicted affinity, and similar binding features to those of known inhibitors. The current version of L-Net was trained on drug-like compounds from ChEMBL, which could be easily extended to other molecular datasets with desired properties based on users' demands and applied in functional molecule generation. Merging deep generative models with atomic-level interaction evaluation, DeepLigBuilder provides a state-of-the-art model for structure-based de novo drug design and lead optimization.

DeepLigBuilder, a novel deep generative model for structure-based de novo drug design, directly generates 3D structures of drug-like compounds in the target binding site.

Optimization of covalent docking for organophosphates interaction with Anopheles acetylcholinesterase

Organophosphates (OPs) used as potent insecticides for malaria vector control, covalently phosphorylate the catalytic serine residue of Anopheles gambiae AChE (AgAChE) in a reaction that liberates their leaving groups. In the recent 10-year insecticide use assessment, OPs were the most frequently used World Health Organization prequalified insecticides. Molecular modelling programs are best suited to display molecular interactions between ligands and the target proteins. The docking modes that generate ligand poses closer to the binding site show high accuracy in predicting the ligand binding mode. The implicit solvation approach such as molecular mechanics-generalized born surface area (MM-GBSA) is a more reliable method to predict ligand onformations and binding affinities. Apart from covalent docking studies being scarce, current molecular docking programs do not adequately possess the covalent docking reaction algorithm to display the molecular mechanism of OPs at the AgAChE catalytic site. This results into OP docking studies commonly being conducted through noncovalent pannels. The aim of this study was to establish the optimim covalent docking system for OPs through manual customization of Schrödinger's Glide covalent docking reaction algorithm. To achieve this, a newly customized covalent reaction algorithm was assessed on a set of ligands covering aromatic, non-aromatic and hydrophobic OPs and compared to the noncovalent docking results in terms of reliability based on the reported X-ray diffraction molecular interactions and crystal poses. The study established that by virtue of omitting the well-known OP hydrolysis, noncovalent mode suggested molecular interactions that were further from the catalytic triad and could not otherwise occur when the molecule is hydrolyzed as in the customized covalent docking mode. Moreover, the MM-GBSA concurred with the optimized covalent docking in eliminating such inaccurate molecular interactions. Additionally, the covalent docking mode confined the interactions and ligand poses to the catalytic site indicating relatively high accuracy and reliability. This study reports the optimized covalent docking panel that effectively confirmed the molecular mechanisms of OPs, as well as indentifying the corresponding amino acid residues required to stabilize the aromatic, non-aromatic and hydrophobic OPs at the AgAChE catalytic site in line with the reported X-ray diffraction studies. As such, the proposed manual customization of the Schrödinger's Glide covalent docking platform can be used to reliably predict molecular interactions between OPs and AgAChE target.

Novel, selective acrylamide linked quinazolines for the treatment of double mutant EGFR-L858R/T790M Non-Small-Cell lung cancer (NSCLC)

T790M mutation is the most common mechanism of acquired resistance to first-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). To overcome this resistance, 4-anilinoquinazoline-based irreversible inhibitors afatinib, dacomitinib has been developed. However, the clinical application of these irreversible inhibitors is limited due to its narrow selectivity against L858R/T790M mutant EGFR. In an attempt to develop potent and selective EGFR T790M inhibitors, we have designed and synthesized two series of novel acrylamide linked quinazolines. Among them, compounds 2i (IC₅₀ 0.171 µM) and 11h (IC₅₀ 0.159 µM) were identified as potent compounds, which displayed selective and potent anti-proliferative activity on gefitinib-resistant cell line NCI-H1975 as compared to the gefitinib and WZ4002 in cellular assay. Furthermore, a molecular dynamic simulation of 11h was carried out to assess the stability to form a complex with the L858R/T790M EGFR Kinase domain, which demonstrated that complex was stable for the 100 ns and form strong crucial covalent binding contacts with the thiol group of Cys797 residue. Finally, satisfactory in silico pharmacokinetics properties of 2i, 11h and 11i compounds were predicted. The synthesized compounds were also evaluated for in vitro cytotoxic activity/hepatotoxicity against HepG2 cell line through MTT assay. The results revealed that compounds exhibited lower cytotoxicity to HepG2 cells.

Pharmacoinformatics and molecular dynamics simulation studies reveal potential covalent and FDA-approved inhibitors of SARS-CoV-2 main protease 3CLpro

The SARS-CoV-2 was confirmed to cause the global pandemic of coronavirus disease 2019 (COVID-19). The 3-chymotrypsin-like protease (3CLpro), an essential enzyme for viral replication, is a valid target to combat SARS-CoV and MERS-CoV. In this work, we present a structure-based study to identify potential covalent inhibitors containing a variety of chemical warheads. The targeted Asinex Focused Covalent (AFCL) library was screened based on different reaction types and potential covalent inhibitors were identified. In addition, we screened FDA-approved protease inhibitors to find candidates to be repurposed against SARS-CoV-2 3CLpro. A number of compounds with significant covalent docking scores were identified. These compounds were able to establish a covalent bond (C-S) with the reactive thiol group of Cys145 and to form favorable interactions with residues lining the substrate-binding site. Moreover, paritaprevir and simeprevir from FDA-approved protease inhibitors were identified as potential inhibitors of SARS-CoV-2 3CLpro. The mechanism and dynamic stability of binding between the identified compounds and SARS-CoV-2 3CLpro were characterized by molecular dynamics (MD) simulations. The identified compounds are potential inhibitors worthy of further development as COVID-19 drugs. Importantly, the identified FDA-approved anti-hepatitis-C virus (HCV) drugs paritaprevir and simeprevir could be ready for clinical trials to treat infected patients and help curb COVID-19. Communicated by Ramaswamy H. Sarma.

An automatic pipeline for the design of irreversible derivatives identifies a potent SARS-CoV-2 Mpro inhibitor

Designing covalent inhibitors is increasingly important, although it remains challenging. Here, we present covalentizer, a computational pipeline for identifying irreversible inhibitors based on structures of targets with non-covalent binders. Through covalent docking of tailored focused libraries, we identify candidates that can bind covalently to a nearby cysteine while preserving the interactions of the original molecule. We found ∼11,000 cysteines proximal to a ligand across 8,386 complexes in the PDB. Of these, the protocol identified 1,553 structures with covalent predictions. In a prospective evaluation, five out of nine predicted covalent kinase inhibitors showed half-maximal inhibitory concentration (IC₅₀) values between 155 nM and 4.5 μM. Application against an existing SARS-CoV M^pro reversible inhibitor led to an acrylamide inhibitor series with low micromolar IC₅₀ values against SARS-CoV-2 M^pro. The docking was validated by 12 co-crystal structures. Together these examples hint at the vast number of covalent inhibitors accessible through our protocol.

Impact of amino acid substitutions on the behavior of a photoactivatable near infrared fluorescent protein PAiRFP1

A photoactivatable near-infrared fluorescent protein (NIR-FP) PAiRFP1 has been developed by 15 amino acid substitutions in its nonfluorescent template Agp2. In our previous communication, we investigated the role of three amino acids in PHY domain distal from BV molecule. The impact of the twelve amino acids in GAF domain, especially five residues near BV-binding pocket is unclear. In this paper, PCR based reverse mutagenesis, spectroscopic methods, molecular modelling and simulations have been employed to explore the roles of these substitutions during the molecular evolution of PAiRFP1. It was found that the residue L163 is important for protein folding in PAiRFP1. The residues F244 and C280 exerted remarkable effects on molar extinction coefficient, NIR fluorescence quantum yield, molecular brightness, fluorescence fold, and dark recovery rate. The residues F244 and V276 modulate the maximum absorption and emission peak position. The reverse mutant L168M exhibited a higher fluorescence fold than PAiRFP1. Additionally, the reverse mutants V203A, V294E, S218G and D127G possessed better spectral properties than PAiRFP1. This study is important for the rational design of a better BphP-based photoactivatable NIR-FPs.