Theory and applications of covalent docking in drug discovery: merits and pitfalls

Novel coumarin-piperazine-2(5H)-furanone hybrids as potential anti-lung cancer agents: Synthesis, biological evaluation and molecular docking studies

Novel coumarin-piperazine-2(5H)-furanone hybrids 5a-l were efficiently synthesized by introducing a furanone scaffold into coumarin using piperazine as a linker. The cytotoxicity of all hybrids 5a-l were evaluated by MTT assay on human lung cancer A549 cells and normal human lung fibroblast WI-38 cells with cytarabine (CAR) as a positive control. Hybrid 5l (IC50 = 11.28 μM) was the most toxic to A549 cells, 18-fold more toxic than the reference CAR (IC50 = 202.57 μM). Moreover, hybrid 5l (IC50 = 411.93 μM) was less toxic to WI-38 cells, with a much higher selectivity (5l, SI ≈ 37, WI-38/A549) than CAR (SI ≈ 2). Structure-activity relationship analysis showed that both the cytotoxicity against A549 cells and selectivity (WI-38/A549) were greatly improved when the bornyl group was incorporated in the hybrids (5c, 5f, 5i and 5l). Further, hybrid 5l was more toxic and selective against four types of human lung cancer cells (A549, Calu-1, PC-9 and H460; IC50 = 5.72-45.46 μM; SI ≈ 9-72) than three other types of human cancer cells (SK-BR-3, 786-O and SK-OV-3, IC50 = 39.07-130.82 μM; SI ≈ 0-2), showing remarkable specificity. In particular, hybrid 5l (IC50 = 5.72 μM) showed the highest cytotoxicity against H460 cells with the highest selectivity of up to 72 (WI-38/H460). Flow cytometric analysis showed that hybrid 5l induced apoptosis in H460 cells in a concentration-dependent manner. Molecular docking studies revealed a high binding affinity of hybrid 5l with CDK2 protein. Hybrid 5l is expected to be a leading candidate for anti-lung cancer agents.

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides

Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.

Computational investigation of turmeric phytochemicals targeting PTR1 enzyme of Leishmania species

In this study, we used in silico techniques to identify available parasite treatments, representing a promising therapeutic avenue. Building upon our computational initiatives aimed at discovering natural inhibitors for various target enzymes from parasites causing neglected tropical diseases (NTDs), we present novel findings on three turmeric-derived phytochemicals as inhibitors of Leishmania pteridine reductase I (PTR1) through in silico methodologies. PTR1, a crucial enzyme in the unique folate metabolism of trypanosomatid parasites, holds established therapeutic significance. Employing MOE software, a molecular docking analysis assesses the efficacy of turmeric phytochemicals against Leishmania PTR1. Validation of the docking protocol is confirmed with an RMSD value of 2. Post-docking, compounds displaying notable interactions with critical residues and binding affinities ranging between -6 and -8 kcal/mol are selected for interaction pattern exploration. Testing twelve turmeric phytochemicals, including curcumin, zingiberene, curcumol, curcumenol, eugenol, bisdemethoxycurcumin, tetrahydrocurcumin, tryethylcurcumin, turmerones, turmerin, demethoxycurcumin, and turmeronols, revealed binding affinities ranging from -5.5 to -8 kcal/mol. Notably, curcumin, demethoxycurcumin, and bisdemethoxycurcumin exhibit binding affinities within -6.5 to -8 kcal/mol and establish substantial interactions with catalytic residues. These phytochemicals hold promise as lead structures for rational drug design targeting Leishmania spp. PTR in future applications. This work underscores the potential of these identified phytochemicals in the development of more effective inhibitors, demonstrating their relevance in addressing neglected tropical diseases caused by parasites.

Targeted degrader technologies as prospective SARS-CoV-2 therapies

COVID-19 remains a severe public health threat despite the WHO declaring an end to the public health emergency in May 2023. Continual development of SARS-CoV-2 variants with resistance to vaccine-induced or natural immunity necessitates constant vigilance as well as new vaccines and therapeutics. Targeted protein degradation (TPD) remains relatively untapped in antiviral drug discovery and holds the promise of attenuating viral resistance development. From a unique structural design perspective, this review covers antiviral degrader merits and challenges by highlighting key coronavirus protein targets and their co-crystal structures, specifically illustrating how TPD strategies can refine existing SARS-CoV-2 3CL protease inhibitors to potentially produce superior protease-degrading agents.

The expanding repertoire of covalent warheads for drug discovery

The reactive functionalities of drugs that engage in covalent interactions with the enzyme/receptor residue in either a reversible or an irreversible manner are called 'warheads'. Covalent warheads that were previously neglected because of safety concerns have recently gained center stage as a result of their various advantages over noncovalent drugs, including increased selectivity, increased residence time, and higher potency. With the approval of several covalent inhibitors over the past decade, research in this area has accelerated. Various strategies are being continuously developed to tune the characteristics of warheads to improve their potency and mitigate toxicity. Here, we review research progress in warhead discovery over the past 5 years to provide valuable insights for future drug discovery.

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.

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.

Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation

Covalent drug design is an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium, while irreversible covalent drugs increase the drug-target interaction duration by forming a covalent bond with targeted residues and thus may offer a more effective therapeutic approach. To facilitate the design of this class of ligands, computational methods can be used to help identify reactive nucleophilic residues, frequently cysteines, on a target protein for covalent binding, to test various warhead groups for their potential reactivities, and to predict noncovalent contributions to binding that can facilitate drug-target interactions that are important for binding specificity. To further aid covalent drug design, we extended a functional group mapping approach based on explicit solvent all-atom molecular simulations (SILCS: site identification by ligand competitive saturation) that intrinsically considers protein flexibility, functional group, and protein desolvation along with functional group-protein interactions. Through docking of a library of representative warhead fragments using SILCS-Monte Carlo (SILCS-MC), reactive cysteines can be correctly identified for proteins being tested. Furthermore, a machine learning model was trained to quantify the effectiveness of various warhead groups for proteins using metrics from SILCS-MC as well as experimental model compound warhead reactivity data. The ability to rank covalent molecular binders with similar warheads using SILCS ligand grid free energy (LGFE) ranking was also tested for several proteins. Based on these tools, an integrated SILCS-based workflow was developed, named SILCS-Covalent, which can both qualitatively and quantitatively inform covalent drug discovery.

Recent Advances in Covalent Drug Discovery

In spite of the increasing number of biologics license applications, the development of covalent inhibitors is still a growing field within drug discovery. The successful approval of some covalent protein kinase inhibitors, such as ibrutinib (BTK covalent inhibitor) and dacomitinib (EGFR covalent inhibitor), and the very recent discovery of covalent inhibitors for viral proteases, such as boceprevir, narlaprevir, and nirmatrelvir, represent a new milestone in covalent drug development. Generally, the formation of covalent bonds that target proteins can offer drugs diverse advantages in terms of target selectivity, drug resistance, and administration concentration. The most important factor for covalent inhibitors is the electrophile (warhead), which dictates selectivity, reactivity, and the type of protein binding (i.e., reversible or irreversible) and can be modified/optimized through rational designs. Furthermore, covalent inhibitors are becoming more and more common in proteolysis, targeting chimeras (PROTACs) for degrading proteins, including those that are currently considered to be 'undruggable'. The aim of this review is to highlight the current state of covalent inhibitor development, including a short historical overview and some examples of applications of PROTAC technologies and treatment of the SARS-CoV-2 virus.

Technologies for Direct Detection of Covalent Protein-Drug Adducts

In the past two decades, drug candidates with a covalent binding mode have gained the interest of medicinal chemists, as several covalent anticancer drugs have successfully reached the clinic. As a covalent binding mode changes the relevant parameters to rank inhibitor potency and investigate structure-activity relationship (SAR), it is important to gather experimental evidence on the existence of a covalent protein-drug adduct. In this work, we review established methods and technologies for the direct detection of a covalent protein-drug adduct, illustrated with examples from (recent) drug development endeavors. These technologies include subjecting covalent drug candidates to mass spectrometric (MS) analysis, protein crystallography, or monitoring intrinsic spectroscopic properties of the ligand upon covalent adduct formation. Alternatively, chemical modification of the covalent ligand is required to detect covalent adducts by NMR analysis or activity-based protein profiling (ABPP). Some techniques are more informative than others and can also elucidate the modified amino acid residue or bond layout. We will discuss the compatibility of these techniques with reversible covalent binding modes and the possibilities to evaluate reversibility or obtain kinetic parameters. Finally, we expand upon current challenges and future applications. Overall, these analytical techniques present an integral part of covalent drug development in this exciting new era of drug discovery.

Quantum Alchemy Based Bonding Trends and Their Link to Hammett's Equation and Pauling's Electronegativity Model

We present an intuitive and general analytical approximation estimating the energy of covalent single and double bonds between participating atoms in terms of their respective nuclear charges with just three parameters, [E_AB ≈ a - bZ_AZ_B + c(Z_A^7/3 + Z_B^7/3) ]. The functional form of our expression models an alchemical atomic energy decomposition between participating atoms A and B. After calibration, reasonably accurate bond dissociation energy estimates are obtained for hydrogen-saturated diatomics composed of p-block elements coming from the same row 2 ≤ n ≤ 4 in the periodic table. Corresponding changes in bond dissociation energies due to substitution of atom B by C can be obtained via simple formulas. While being of different functional form and origin, our model is as simple and accurate as Pauling's well-known electronegativity model. Analysis indicates that the model's response in covalent bonding to variation in nuclear charge is near-linear, which is consistent with Hammett's equation.

Anastrozole-mediated modulation of mitochondrial activity by inhibition of mitochondrial permeability transition pore opening: an initial perspective

The mitochondrial permeability transition pore (mtPTP) plays a vital role in altering the structure and function of mitochondria. Cyclophilin D (CypD) is a mitochondrial protein that regulates mtPTP function and a known drug target for therapeutic studies involving mitochondria. While the effect of aromatase inhibition on the mtPTP has been studied previously, the effect of anastrozole on the mtPTP has not been completely elucidated. The role of anastrozole in modulating the mtPTP was evaluated by docking, molecular dynamics and network-guided studies using human CypD data. The peripheral blood mononuclear cells (PBMCs) of patients with mitochondrial disorders and healthy controls were treated with anastrozole and evaluated for mitochondrial permeability transition pore (mtPTP) function and apoptosis using a flow cytometer. Spectrophotometry was employed for estimating total ATP levels. The anastrozole-CypD complex is more stable than cyclosporin A (CsA)-CypD. Anastrozole performed better than cyclosporine in inhibiting mtPTP. Additional effects included inducing mitochondrial membrane depolarization and a reduction in mitochondrial swelling and superoxide generation, intrinsic caspase-3 activity and cellular apoptosis, along with an increase in ATP levels. Anastrozole may serve as a potential therapeutic agent for mitochondrial disorders and ameliorate the clinical phenotype by regulating the activity of mtPTP. However, further studies are required to substantiate our preliminary findings.Communicated by Ramaswamy H. Sarma.

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.

Venetoclax: a promising repurposed drug against SARS-CoV-2 main protease

Global health care emergency caused by a new coronavirus (severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2) demands urgent need to repurpose the approved pharmaceutical drugs. Main protease, M^pro of SARS-CoV-2 draws significant attention as a drug target. Herein, we have screened FDA approved organosulfur drugs (till 2016) and our laboratory synthesized organosulfur and organoselenium compounds (L1-L306) against M^pro-apo using docking followed by classical MD simulations. Additionally, a series of compounds (L307-L364) were chosen from previous experimental studies, which were reported to exhibit inhibitory potentials towards M^pro. We found several organosulfur drugs, particularly Venetoclax (FDA approved organosulfur drug for Leukemia) to be a high-affinity binders to the M^pro of SARS-CoV-2. The results reveal that organosulfur compounds including Venetoclax preferentially bind (non-covalently) to the non-catalytic pocket of the protein located in the dimer interface. We found that the ligand binding is primarily favoured by ligand-protein van der Waals interaction and penalized by desolvation effect. Interestingly, Venetoclax binding alters the local flexibility of M^pro and exerts pronounced effect in the C-terminal as well as two loop regions (Loop-A and Loop-B) that play important roles in catalysis. These findings highlighted the importance of drug repurposing and explored the non-catalytic pockets of M^pro in combating COVID-19 infection in addition to the importance of catalytic binding pocket of the protein.Communicated by Ramaswamy H. Sarma.

Advanced approaches of developing targeted covalent drugs

In recent years, the development of targeted covalent inhibitors has gained popularity around the world. Specific groups (electrophilic warheads) form irreversible bonds with the side chain of nucleophilic amino acid residues, thus changing the function of biological targets such as proteins. Since the first targeted covalent inhibitor was disclosed in the 1990s, great efforts have been made to develop covalent ligands from known reversible leads or drugs by addition of tolerated electrophilic warheads. However, high reactivity and "off-target" toxicity remain challenging issues. This review covers the concept of targeted covalent inhibition to diseases, discusses traditional and interdisciplinary strategies of cysteine-focused covalent drug discovery, and exhibits newly disclosed electrophilic warheads majorly targeting the cysteine residue. Successful applications to address the challenges of designing effective covalent drugs are also introduced.

CovBinderInPDB: A Structure-Based Covalent Binder Database

Covalent inhibition has emerged as a promising orthogonal approach for drug discovery, despite the significant challenge in achieving target specificity. To facilitate the structure-based rational design of target-specific covalent modulators, we developed an integrated computational protocol to curate covalent binders from the RCSB Protein Data Bank (PDB). Starting from the macromolecular crystallographic information files (mmCIF) in the PDB archive, covalent bond records, which indicate the side chain modification of amino acid residue by a covalent binder, were collected and cleaned. Then, residue-binder adducts, which are products of chemical reactions between targeted residues and covalent binders, were recovered with the help of the Chemical Component Dictionary in PDB. Finally, several strategies were employed to curate the pre-reaction forms of covalent binders from the adducts. Our curated CovBinderInPDB database contains 7375 covalent modifications in which 2189 unique covalent binders target nine types of amino acid residues (Cys, Lys, Ser, Asp, Glu, His, Met, Thr, and Tyr) from 3555 complex structures of 1170 unique protein chains. This database would set a solid foundation for developing and benchmarking computational strategies for covalent modulator design and is freely accessible at https://yzhang.hpc.nyu.edu/CovBinderInPDB.

Reactivity of Covalent Fragments and Their Role in Fragment Based Drug Discovery

Fragment based drug discovery has long been used for the identification of new ligands and interest in targeted covalent inhibitors has continued to grow in recent years, with high profile drugs such as osimertinib and sotorasib gaining FDA approval. It is therefore unsurprising that covalent fragment-based approaches have become popular and have recently led to the identification of novel targets and binding sites, as well as ligands for targets previously thought to be 'undruggable'. Understanding the properties of such covalent fragments is important, and characterizing and/or predicting reactivity can be highly useful. This review aims to discuss the requirements for an electrophilic fragment library and the importance of differing warhead reactivity. Successful case studies from the world of drug discovery are then be examined.

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.

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.

Antioxidant and anti-aging effects of Warburgia salutaris bark aqueous extract: Evidences from in silico, in vitro and in vivo studies

ETHNOPHARMACOLOGICAL RELEVANCE

The genus Warburgia (family Canellaceae) is widely distributed over Afrotropical and Neotropical realms. Traditionally, W. salutaris (G. Bertol.) Chiov., and other Warburgia species are used as anti-inflammatory, antimalarial, antimicrobial, and for wound healing, and treating several skin complaints as well. Specifically, different extracts from W. salutaris were reported to possess diuretic, anti-inflammatory, and cytotoxic effects.

AIM OF THE STUDY

This work aimed to investigate the phytochemical composition of an aqueous extract from W. salutaris bark, and evaluate its antioxidant and anti-aging activities in silico, in vitro, and in vivo.

MATERIALS AND METHODS

HPLC-PDA-MS/MS was used to investigate the phytochemical components of the extract. The antioxidant potential of the extract was evaluated in vitro using DPPH and FRAP assays. The Caenorhabditis elegans nematodes model was adopted to investigate the antioxidant and the anti-aging effects in vivo by determining the worms' survival rate, level of ROS, HSP16 expression, and nuclear translocation of the transcription factor DAF16. Molecular operating environment (MOE) software was utilized for in silico molecular docking of the extract's components into different enzymes involved in the aging process. Anti-collagenase, anti-elastase, anti-tyrosinase, and anti-hyaluronidase assays were used to evaluate the anti-aging effects in vitro.

RESULTS

HPLC-MS analysis furnished 30 compounds, among them catechin, 11α-hydroxy muzigadiolide, mukaadial, pereniporin B, and 11α-hydroxycinnamosmolide. The major components of the extract showed appropriate fitting in the binding site of the target enzymes adopted in the study with considerable minimum free binding energy relative to the standard inhibitors. The extract showed substantial in vitro antioxidant activity in DPPH and FRAP assays and in vitro anti-aging assays against collagenase, elastase, tyrosinase, and hyaluronidase with comparable IC₅₀ values to the reference standards. Moreover, it attenuated oxidative stress in vivo as it significantly increased the survival rate of ROS stressed C. elegans worms, decreased intracellular ROS, decreased the juglone-induced HSP16 expression and enhanced the nuclear localization of DAF16 in a dose-dependent manner.

CONCLUSION

Our results support the traditional use of W. salutaris to counteract inflammation and oxidative stress associated with several pathological conditions. In addition, W. salutaris bark extract can be considered as a substantial source for bioactive metabolites with strong potential as anti-aging and antioxidant agents.

Cleaning of Wastewater Using Crosslinked Poly(Acrylamide-co-Acrylic Acid) Hydrogels: Analysis of Rotatable Bonds, Binding Energy and Hydrogen Bonding

The discharge of untreated wastewater, often contaminated by harmful substances, such as industrially used dyes, can provoke environmental and health risks. Among various techniques, the adsorption of dyes, using three-dimensional (3D) networks consisting of hydrophilic polymers (hydrogels), represents a low-cost, clean, and efficient remediation method. Three industrially used dyes, Methylene Blue, Eosin, and Rose Bengal, were selected as models of pollutants. Poly(acrylamide) (poly(AM)) and poly(acrylamide-co-acrylic acid) (poly(AM-co-AA)) networks were chosen as adsorbent materials (hydrogels). These polymers were synthesized by crosslinking the photopolymerization of their respective monomer(s) in an aqueous medium under exposure to UV light. Experimental adsorption measurements revealed substantially higher dye uptakes for poly(AM-co-AA) compared to poly(AM) hydrogels. In this report, a theoretical model based on docking simulations was applied to analyze the conformation of polymers and pollutants in order to investigate some aspects of the adsorption process. In particular, hydrogen and halogen interactions were studied. The presence of strong hydrogen bonding plays a crucial role in the retention of dyes, whereas halogen bonding has a small or negligible effect on adsorption. An evaluation of binding energies allowed us to obtain information about the degree of affinity between polymers and dyes. The number of rotatable bonds in the copolymer exceeds those of poly(AM),meaning that poly(AM-co-AA) is revealed to be more suitable for obtaining a high retention rate for pollutants.

The important role of non-covalent drug-protein interactions in drug hypersensitivity reactions

Drug hypersensitivity reactions (DHR) are heterogeneous and unusual immune reactions with rather unique clinical presentations. Accumulating evidence indicates that certain non-covalent drug-protein interactions are able to elicit exclusively effector functions of antibody reactions or complete T-cell reactions which contribute substantially to DHR. Here, we discuss three key interactions; (a) mimicry: whereby soluble, non-covalent drug-protein complexes ("fake antigens") mimic covalent drug-protein adducts; (b) increased antibody affinity: for example, in quinine-type immune thrombocytopenia where the drug gets trapped between antibody and membrane-bound glycoprotein; and (c) p-i-stimulation: where naïve and memory T cells are activated by direct binding of drugs to the human leukocyte antigen and/or T-cell receptors. This transient drug-immune receptor interaction initiates a polyclonal T-cell response with mild-to-severe DHR symptoms. Notable complications arising from p-i DHR can include viral reactivations, autoimmunity, and multiple drug hypersensitivity. In conclusion, DHR is characterized by abnormal immune stimulation driven by non-covalent drug-protein interactions. This contrasts DHR from "normal" immunity, which relies on antigen-formation by covalent hapten-protein adducts and predominantly results in asymptomatic immunity.

Platinum Complexes as Inhibitors of DNA Repair Protein Ku70 and Topoisomerase IIα in Cancer Cells

Ku70 protein and topoisomerase IIα (Topo IIα) are promising targets of anticancer drugs, which play critical roles in DNA repair and replication processes. Three platinum(II) complexes, [PtCl(NH3)2(9-(pyridin-2-ylmethyl)-9H-carbazole)]NO3 (OPPC), [PtCl(NH3)2(9-(pyridin-3-ylmethyl)-9H-carbazole)]NO3 (MPPC),...

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

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

Predicting and Understanding Non-Covalent Interactions Using Novel Forms of Symmetry-Adapted Perturbation Theory

Although sometimes derided as "weak" interactions, non-covalent forces play a critical role in ligand binding and crystal packing and in determining the conformational landscape of flexible molecules. Symmetry-adapted perturbation theory (SAPT) provides a framework for accurate ab initio calculation of intermolecular interactions and furnishes a natural decomposition of the interaction energy into physically meaningful components: semiclassical electrostatics (rigorously obtained from monomer charge densities), Pauli or steric repulsion, induction (including both polarization and charge transfer), and dispersion. This decomposition helps to foster deeper understanding of non-covalent interactions and can be used to construct transferable, physics-based force fields. Separability of the SAPT interaction energy also provides the flexibility to construct composite methods, a feature that we exploit to improve the description of dispersion interactions. These are challenging to describe accurately because they arise from nonlocal electron correlation effects that appear for the first time at second order in perturbation theory but are not quantitatively described at that level.As with all quantum-chemical methods, a major limitation of SAPT is nonlinear scaling of the computational cost with respect to system size. This cost can be significantly mitigated using "SAPT0(KS)", which incorporates monomer electron correlation by means of Kohn-Sham (KS) molecular orbitals from density functional theory (DFT), as well as by an "extended" theory called XSAPT, developed by the authors. XSAPT generalizes traditional dimer SAPT to many-body systems, so that a ligand-protein interaction (for example) can be separated into contributions from individual amino acids, reducing the cost of the calculation below that of even supramolecular DFT while retaining the accuracy of high-level ab initio quantum chemistry.This Account provides an overview of the SAPT0(KS) approach and the XSAPT family of methods. Several low-cost variants are described that provide accuracy approaching that of the best ab initio benchmarks yet are affordable enough to tackle ligand-protein binding and sizable host-guest complexes. These variants include SAPT+aiD, which uses ab initio atom-atom dispersion potentials ("+aiD") in place of second-order SAPT dispersion, and also SAPT+MBD, which incorporates many-body dispersion (MBD) effects that are important in the description of nanoscale materials. Applications to drug binding highlight the size-extensive nature of dispersion, which is not a weak interaction in large systems. Other applications highlight how a physics-based analysis can sometimes upend conventional wisdom regarding intermolecular forces. In particular, careful reconsideration of π-π interactions makes clear that the quadrupolar electrostatics (or "Hunter-Sanders") model of π-π stacking should be replaced by a "van der Waals model" in which conformational preferences arise from a competition between dispersion and Pauli repulsion. Our analysis also suggests that molecular shape, rather than aromaticity per se, is the key factor driving strong stacking interactions. Looking forward, we anticipate that XSAPT-based methods can play a role in screening of drug candidates and in materials design.

Prerequisite Binding Modes Determine the Dynamics of Action of Covalent Agonists of Ion Channel TRPA1

Transient receptor potential ankyrin 1 (TRPA1) is a transmembrane protein channeling the influx of calcium ions. As a polymodal nocisensor, TRPA1 can be activated by thermal, mechanical stimuli and a wide range of chemically damaging molecules including small volatile environmental toxicants and endogenous algogenic lipids. After activation by such compounds, the ion channel opens up, its central pore widens allowing calcium influx into the cytosol inducing signal transduction pathways. Afterwards, the calcium influx desensitizes irritant evoked responses and results in an inactive state of the ion channel. Recent experimental determination of structures of apo and holo forms of TRPA1 opened the way towards the design of new agonists, which can activate the ion channel. The present study is aimed at the elucidation of binding dynamics of agonists using experimental structures of TRPA1-agonist complexes at the atomic level applying molecular docking and dynamics methods accounting for covalent and non-covalent interactions. Following a test of docking methods focused on the final, holo structures, prerequisite binding modes were detected involving the apo forms. It was shown how reversible interactions with prerequisite binding sites contribute to structural changes of TRPA1 leading to covalent bonding of agonists. The proposed dynamics of action allowed a mechanism-based forecast of new, druggable binding sites of potent agonists.

In silico detection of inhibitor potential of Passiflora compounds against SARS-Cov-2(Covid-19) main protease by using molecular docking and dynamic analyses

SARS-Cov-2(Covid-19) is a new strain of coronavirus and was firstly emerged in December 2019 in Wuhan, China. Now, there is no known specific treatment of Covid-19 available. COVID-19 main protease is a potential drug target and is firstly crystallised by Liu et al (2020). In the study, we investigated the drug potential of molecules that the components of an important medicinal plant Passiflora by using molecular docking, molecular dynamic and drug possibility properties of these molecules. Docking performances were done by Autodock. Chloroquine, hydroxychloroquine were used as standarts for comparison of tested ligands. The molecular docking results showed that the Luteolin, Lucenin, Olealonic acid, Isoorientin, Isochaphoside, Saponarin, Schaftoside etc. ligands was bound with COVID-19 main protease above -8,0 kcal/mol binding energy. Besides, ADME, drug-likeness features of compounds of Passiflora were investigated using the rules of Lipinski, Veber, and Ghose. According to the results obtained, it has been shown that compounds of Passiflora have the potential to be an effective drug in the COVID-19 pandemic. Further studies are needed to reveal the drug potential of these ligands. Our results will be a source for these studies.

Bispecific repurposed medicines targeting the viral and immunological arms of COVID-19

Effective agents to treat coronavirus infection are urgently required, not only to treat COVID-19, but to prepare for future outbreaks. Repurposed anti-virals such as remdesivir and human anti-inflammatories such as barcitinib have received emergency approval but their overall benefits remain unclear. Vaccines are the most promising prospect for COVID-19, but will need to be redeveloped for any future coronavirus outbreak. Protecting against future outbreaks requires the identification of targets that are conserved between coronavirus strains and amenable to drug discovery. Two such targets are the main protease (Mpro) and the papain-like protease (PLpro) which are essential for the coronavirus replication cycle. We describe the discovery of two non-antiviral therapeutic agents, the caspase-1 inhibitor SDZ 224015 and Tarloxotinib that target Mpro and PLpro, respectively. These were identified through extensive experimental screens of the drug repurposing ReFRAME library of 12,000 therapeutic agents. The caspase-1 inhibitor SDZ 224015, was found to be a potent irreversible inhibitor of Mpro (IC50 30 nM) while Tarloxotinib, a clinical stage epidermal growth factor receptor inhibitor, is a sub micromolar inhibitor of PLpro (IC50 300 nM, Ki 200 nM) and is the first reported PLpro inhibitor with drug-like properties. SDZ 224015 and Tarloxotinib have both undergone safety evaluation in humans and hence are candidates for COVID-19 clinical evaluation.

Computer-Aided Drug Design Applied to Secondary Metabolites as Anticancer Agents

Computer-Aided Drug Design (CADD) techniques have garnered a great deal of attention in academia and industry because of their great versatility, low costs, possibilities of cost reduction in in vitro screening and in the development of synthetic steps; these techniques are compared with highthroughput screening, in particular for candidate drugs. The secondary metabolism of plants and other organisms provide substantial amounts of new chemical structures, many of which have numerous biological and pharmacological properties for virtually every existing disease, including cancer. In oncology, compounds such as vimblastine, vincristine, taxol, podophyllotoxin, captothecin and cytarabine are examples of how important natural products enhance the cancer-fighting therapeutic arsenal. In this context, this review presents an update of Ligand-Based Drug Design and Structure-Based Drug Design techniques applied to flavonoids, alkaloids and coumarins in the search of new compounds or fragments that can be used in oncology. A systematical search using various databases was performed. The search was limited to articles published in the last 10 years. The great diversity of chemical structures (coumarin, flavonoids and alkaloids) with cancer properties, associated with infinite synthetic possibilities for obtaining analogous compounds, creates a huge chemical environment with potential to be explored, and creates a major difficulty, for screening studies to select compounds with more promising activity for a selected target. CADD techniques appear to be the least expensive and most efficient alternatives to perform virtual screening studies, aiming to selected compounds with better activity profiles and better "drugability".

Toward the Identification of Potential α-Ketoamide Covalent Inhibitors for SARS-CoV-2 Main Protease: Fragment-Based Drug Design and MM-PBSA Calculations

Since December 2019, the world has been facing the outbreak of the SARS-CoV-2 pandemic that has infected more than 149 million and killed 3.1 million people by 27 April 2021, according to WHO statistics. Safety measures and precautions taken by many countries seem insufficient, especially with no specific approved drugs against the virus. This has created an urgent need to fast track the development of new medication against the virus in order to alleviate the problem and meet public expectations. The SARS-CoV-2 3CL main protease (Mpro) is one of the most attractive targets in the virus life cycle, which is responsible for the processing of the viral polyprotein and is a key for the ribosomal translation of the SARS-CoV-2 genome. In this work, we targeted this enzyme through a structure-based drug design (SBDD) protocol, which aimed at the design of a new potential inhibitor for Mpro. The protocol involves three major steps: fragment-based drug design (FBDD), covalent docking and molecular dynamics (MD) simulation with the calculation of the designed molecule binding free energy at a high level of theory. The FBDD step identified five molecular fragments, which were linked via a suitable carbon linker, to construct our designed compound RMH148. The mode of binding and initial interactions between RMH148 and the enzyme active site was established in the second step of our protocol via covalent docking. The final step involved the use of MD simulations to test for the stability of the docked RMH148 into the Mpro active site and included precise calculations for potential interactions with active site residues and binding free energies. The results introduced RMH148 as a potential inhibitor for the SARS-CoV-2 Mpro enzyme, which was able to achieve various interactions with the enzyme and forms a highly stable complex at the active site even better than the co-crystalized reference.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Docking-based virtual screening studies aiming at the covalent inhibition of SARS-CoV-2 MPro by targeting the cysteine 145

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 which has infected millions of people worldwide. The main protease of SARS-CoV-2 (M^Pro) has been recognized as a key target for the development of antiviral compounds. Taking advantage of the X-ray crystal complex with reversible covalent inhibitors interacting with the catalytic cysteine 145 (Cys145), we explored flexible docking studies to select alternative compounds able to target this residue as covalent inhibitors. First, docking studies of three known electrophilic compounds led to results consistent with co-crystallized data validating the method for SARS-CoV-2 M^Pro covalent inhibition. Then, libraries of soft electrophiles (overall 41 757 compounds) were submitted to docking-based virtual screening resulting in the identification of 17 molecules having their electrophilic group close to the Cys145 residue. We also investigated flexible docking studies of a focused approved covalent drugs library including 32 compounds with various electrophilic functional groups. Among them, the calculations resulted in the identification of four compounds, namely dimethylfumarate, fosfomycin, ibrutinib and saxagliptin, able first, to bind to the active site of the protein and second, to form a covalent bond with the catalytic cysteine.

Fragment-based covalent ligand discovery

Targeted covalent inhibitors have regained widespread attention in drug discovery and have emerged as powerful tools for basic biomedical research. Fueled by considerable improvements in mass spectrometry sensitivity and sample processing, chemoproteomic strategies have revealed thousands of proteins that can be covalently modified by reactive small molecules. Fragment-based drug discovery, which has traditionally been used in a target-centric fashion, is now being deployed on a proteome-wide scale thereby expanding its utility to both the discovery of novel covalent ligands and their cognate protein targets. This powerful approach is allowing ‘high-throughput’ serendipitous discovery of cryptic pockets leading to the identification of pharmacological modulators of proteins previously viewed as “undruggable”. The reactive fragment toolkit has been enabled by recent advances in the development of new chemistries that target residues other than cysteine including lysine and tyrosine. Here, we review the emerging area of covalent fragment-based ligand discovery, which integrates the benefits of covalent targeting and fragment-based medicinal chemistry. We discuss how the two strategies synergize to facilitate the efficient discovery of new pharmacological modulators of established and new therapeutic target proteins.

Covalent fragment-based ligand discovery greatly facilitates the discovery of useful fragments for drug discovery and helps unveil chemical-tractable biological targets in native biological systems.

Systemic In Silico Screening in Drug Discovery for Coronavirus Disease (COVID-19) with an Online Interactive Web Server

The emergence of the new coronavirus (nCoV-19) has impacted human health on a global scale, while the interaction between the virus and the host is the foundation of the disease. The viral genome codes a cluster of proteins, each with a unique function in the event of host invasion or viral development. Under the current adverse situation, we employ virtual screening tools in searching for drugs and natural products which have been already deposited in DrugBank in an attempt to accelerate the drug discovery process. This study provides an initial evaluation of current drug candidates from various reports using our systemic in silico drug screening based on structures of viral proteins and human ACE2 receptor. Additionally, we have built an interactive online platform (https://shennongproject.ai/) for browsing these results with the visual display of a small molecule docked on its potential target protein, without installing any specialized structural software. With continuous maintenance and incorporation of data from laboratory work, it may serve not only as the assessment tool for the new drug discovery but also an educational web site for the public.

10 years into the resurgence of covalent drugs

In the first decade of targeted covalent inhibition, scientists have successfully reversed the previous trend that had impeded the use of covalent inhibition in drug development. Successes in the clinic, mainly in the field of kinase inhibitors, are existing proof that safe covalent inhibitors can be designed and employed to develop effective treatments. The case of KRAS_G12C covalent inhibitors entering clinical trials in 2019 has been among the hottest topics discussed in drug discovery, raising expectations for the future of the field. In this perspective, an overview of the milestones hit with targeted covalent inhibitors, as well as the promise and the needs of current research, are presented. While recent results have confirmed the potential that was foreseen, many questions remain unexplored in this branch of precision medicine.

Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors

Targeted covalent inhibitors are currently showing great promise for systems that are normally difficult to target with small molecule therapies. This renewed interest has spurred the refinement of existing computational methods as well as the designof new ones, expanding the toolbox for discovery and optimization of selectiveand effective covalent inhibitors. Commonly applied approaches are covalentdocking methods that predict the conformation of the covalent complex with known residues. More recently, a new predictive method, reactive docking, was developed, building on the growing corpus of data generated by large proteomics experiments. This method was successfully used in several 'inverse drug discovery' programs that use high-throughput techniques to isolate effective compounds based on screening of entire compound libraries based on desired phenotypes.

Covalent Versus Non-covalent Enzyme Inhibition: Which Route Should We Take? A Justification of the Good and Bad from Molecular Modelling Perspective

The pace and efficiency of drug target strategies have been emanating debates among researchers in the field of drug development. Covalent inhibitors possess significant advantages over non-covalent inhibitors, such that covalent warheads can target rare residues of a particular target protein, thus leading to the development of highly selective inhibitors. However, toxicity can be a real challenge related to this class of therapeutics. From the challenges of irreversible drug toxicity to the declining reactivity of reversible drugs, herein we provide justifications from the computational point of view. It was evident that both classes had its merits; however, with the increase in drug resistance, covalent inhibition seemed more suitable. There also seems to be enhanced selectivity of the covalent systems, proving its use as a therapeutic regimen worldwide. We believe that this study will assist researchers in making informed decisions on which drug class to choose as lead compounds in the drug discovery pipeline.

In vitro anti-leishmanial activity of Prunus armeniaca fractions on Leishmania tropica and molecular docking studies

Prunus armeniaca (L.) is a member of the Rosaceae, subfamily Prunoideae, shows anticancer, antitubercular, antimutagenic, antimicrobial, antioxidant, and cardioprotective activities. Here we fractionated the leaves extract of this highly medicinally important plant for antileishmanial activity. In the current study, the leaves extract was fractionated and characterized using column and thin layer chromatography by n-hexane, ethyl acetate, and methanol solvents. Twelve fractions were isolated and subjected for evaluation of their cytotoxicity and in vitro antileishmanial activity against promastigotes and amastigotes of Leishmania tropica. Among all fractions used, the fraction (F7) exhibited the strongest antileishmanial activity. The bioactive fraction was further characterized by spectroscopy (FTIR, UV-Vis), and GC-MS analysis. The in silico docking was carried out to find the active site of PTR1. All derived fractions exhibited toxicity in the safety range IC₅₀ > 100 μg/ml. The fraction (F7) showed significantly the highest antipromastigotes activity with IC₅₀11.48 ± 0.82 μg/ml and antiamastigotes activity with IC₅₀ 21.03 ± 0.98 μg/ml compared with control i.e. 11.60 ± 0.70 and 22.03 ± 1.02 μg/ml respectively. The UV-Vis spectroscopic analysis revealed the presence of six absorption peaks and the FTIR spectrum revealed the presence of alkane, aldehyde, carboxylic acid, thiols, alkynes, and carbonyls compounds The GC-MS chromatogram exhibited the presence of nine compounds: (a) benzeneethanol, alpha, beta dimethyl, (b)carbazic acid, 3-(1 propylbutylidene)-, ethyl ester, (c)1, 2-benzenedicarboxylic acid, diisooctyl ester, (d)benzeneethanamine a-methyl, (e)2aminononadecane, (f)2-heptanamine-5-methyl, (g)cyclobutanol, (h)cyclopropyl carbine, and (i)nitric acid, nonyl ester. Among all compounds, the 1, 2-benzenedicarboxylic acid, diisooctyl ester bound well to the PTR1 receptor. Fraction (F7) showed acceptable results with no cytotoxicity. However, in vivo studies are required in the future.

How 'Protein-Docking' Translates into the New Emerging Field of Docking Small Molecules to Nucleic Acids?

In this review, we retraced the '40-year evolution' of molecular docking algorithms. Over the course of the years, their development allowed to progress from the so-called 'rigid-docking' searching methods to the more sophisticated 'semi-flexible' and 'flexible docking' algorithms. Together with the advancement of computing architecture and power, molecular docking's applications also exponentially increased, from a single-ligand binding calculation to large screening and polypharmacology profiles. Recently targeting nucleic acids with small molecules has emerged as a valuable therapeutic strategy especially for cancer treatment, along with bacterial and viral infections. For example, therapeutic intervention at the mRNA level allows to overcome the problematic of undruggable proteins without modifying the genome. Despite the promising therapeutic potential of nucleic acids, molecular docking programs have been optimized mostly for proteins. Here, we have analyzed literature data on nucleic acid to benchmark some of the widely used docking programs. Finally, the comparison between proteins and nucleic acid targets docking highlighted similarity and differences, which are intrinsically related to their chemical and structural nature.

Using integrated computational approaches to identify safe and rapid treatment for SARS-CoV-2

SARS-CoV-2 is a new generation of coronavirus, which was first determined in Wuhan, China, in December 2019. So far, however, there no effective treatment has been found to stop this new generation of coronavirus but discovering of the crystal structure of SARS-CoV-2 main protease (SARS-CoV-2 Mpro) may facilitate searching for new therapies for SARS-COV-2. The aim was to assess the effectiveness of available FDA approved drugs which can construct a covalent bond with Cys145 inside binding site SARS-CoV-2 main protease by using covalent docking screening. We conducted the covdock module MMGBSA module in the Schrodinger suite 2020-1, to examine the covalent bonding utilizing. Besides, we submitted the top three drugs to molecular dynamics simulations via Gromacs 2018.1. The covalent docking showed that saquinavir, ritonavir, remdesivir, delavirdine, cefuroxime axetil, oseltamivir and prevacid have the highest binding energies MMGBSA of –72.17, −72.02, −65.19, −57.65, −54.25, −51.8, and −51.14 kcal/mol, respectively. The 50 ns molecular dynamics simulation was conducted for saquinavir, ritonavir and remdesivir to evaluate the stability of these drugs inside the binding pocket of SARS-CoV-2 main protease. The current study provides a powerful in silico results, means for rapid screening of drugs as anti-protease medications and recommend that the above-mentioned drugs can be used in the treatment of SARS-CoV-2 in combined or sole therapy.

Communicated by Ramaswamy H. Sarma

The design and development of covalent protein-protein interaction inhibitors for cancer treatment

Protein-protein interactions (PPIs) are central to a variety of biological processes, and their dysfunction is implicated in the pathogenesis of a range of human diseases, including cancer. Hence, the inhibition of PPIs has attracted significant attention in drug discovery. Covalent inhibitors have been reported to achieve high efficiency through forming covalent bonds with cysteine or other nucleophilic residues in the target protein. Evidence suggests that there is a reduced risk for the development of drug resistance against covalent drugs, which is a major challenge in areas such as oncology and infectious diseases. Recent improvements in structural biology and chemical reactivity have enabled the design and development of potent and selective covalent PPI inhibitors. In this review, we will highlight the design and development of therapeutic agents targeting PPIs for cancer therapy.

Deciphering the selectivity of inhibitor MKC9989 towards residue K907 in IRE1α; a multiscale in silico approach

The selectivity of the ligand MKC9989, as inhibitor of the Inositol-Requiring Enzyme 1α (IRE1α) transmembrane kinase/ribonuclease protein, towards the residue K907 in the context of Schiff base formation, has been investigated by employing an array of in silico techniques including Multi-Conformation Continuum Electrostatics (MCCE) simulations, Quantum Mechanics/Molecular Mechanics (QM/MM) calculations, covalent docking, and Molecular Dynamics (MD) simulations. According to the MCCE results, K907 displays the lowest pK_a value among all 23 lysine residues in IRE1α. The MMCE simulations also indicate a critical interaction between K907 and D885 within the hydrophobic pocket which increases significantly at low protein dielectric constants. The QM/MM calculations reveal a spontaneous proton transfer from K907 to D885, consistent with the low pK_a value of K907. A Potential Energy Surface (PES) scan confirms the lack of energy barrier and transition state associated with this proton transfer reaction. Covalent docking and MD simulations verify that the protein pocket containing K907 can effectively stabilize the inhibitor by strong π–π and hydrogen bonding interactions. In addition, Radial Distribution Function (RDF) analysis shows that the imine group formed in the chemical reaction between MKC9989 and K907 is inaccessible to water molecules and thus the probability of imine hydrolysis is almost zero. The results of the current study explain the high selectivity of the MKC9989 inhibitor towards the K907 residue of IRE1α.

The high selectivity of inhibitor MKC9989 towards Lys907 of IRE1α is explained by the unique pK_a properties of the lysine.

Evaluating Iso-Mukaadial Acetate and Ursolic Acid Acetate as Plasmodium falciparum Hypoxanthine-Guanine-Xanthine Phosphoribosyltransferase Inhibitors

To date, Plasmodium falciparum is one of the most lethal strains of the malaria parasite. P. falciparum lacks the required enzymes to create its own purines via the de novo pathway, thereby making Plasmodium falciparum hypoxanthine-guanine-xanthine phosphoribosyltransferase (PfHGXPT) a crucial enzyme in the malaria life cycle. Recently, studies have described iso-mukaadial acetate and ursolic acid acetate as promising antimalarials. However, the mode of action is still unknown, thus, the current study sought to investigate the selective inhibitory and binding actions of iso-mukaadial acetate and ursolic acid acetate against recombinant PfHGXPT using in-silico and experimental approaches. Recombinant PfHGXPT protein was expressed using E. coli BL21 cells and homogeneously purified by affinity chromatography. Experimentally, iso-mukaadial acetate and ursolic acid acetate, respectively, demonstrated direct inhibitory activity towards PfHGXPT in a dose-dependent manner. The binding affinity of iso-mukaadial acetate and ursolic acid acetate on the PfHGXPT dissociation constant (KD), where it was found that 0.0833 µM and 2.8396 µM, respectively, are indicative of strong binding. The mode of action for the observed antimalarial activity was further established by a molecular docking study. The molecular docking and dynamics simulations show specific interactions and high affinity within the binding pocket of Plasmodium falciparum and human hypoxanthine-guanine phosphoribosyl transferases. The predicted in silico absorption, distribution, metabolism and excretion/toxicity (ADME/T) properties predicted that the iso-mukaadial acetate ligand may follow the criteria for orally active drugs. The theoretical calculation derived from ADME, molecular docking and dynamics provide in-depth information into the structural basis, specific bonding and non-bonding interactions governing the inhibition of malarial. Taken together, these findings provide a basis for the recommendation of iso-mukaadial acetate and ursolic acid acetate as high-affinity ligands and drug candidates against PfHGXPT.

From the Explored to the Unexplored: Computer-Tailored Drug Design Attempts in the Discovery of Selective Caspase Inhibitors

The pathophysiological roles of caspases have made them attractive targets in the treatment and amelioration of neurologic diseases. In normal conditions, the expression of caspases is regulated in the brain, while at the onset of neurodegeneration, such as in Alzheimer's disease, they are typically overexpressed. Till date, several therapeutic efforts that include the use of small endogenous binders have been put forward to curtail dysfunctionalities that drive aberrant death in neuronal cells. Caspases are highly homologous, both in structure and in sequence, which leaves us with the question: is it possible to specifically and individually target caspases, while multiple therapeutic attempts to achieve selective targeting have failed! Based on antecedent events, the use of Computer-Aided Drug Design (CADD) methods has significantly contributed to the design of small molecule inhibitors, especially with selective target ability and reduced off-target therapeutic effects. Interestingly, we found out that there still exists an enormous room for the integration of structure/ligand-based drug design techniques towards the development of highly specific reversible and irreversible caspase inhibitors. Therefore, in this review, we highlight drug discovery approaches that have been directed towards caspase inhibition in addition to an insightful focus on applicable CADD techniques for achieving selective targeting in caspase research.

Systematic Studies on the Protocol and Criteria for Selecting a Covalent Docking Tool

With the resurgence of drugs with covalent binding mechanisms, much attention has been paid to docking methods for the discovery of targeted covalent inhibitors. The existence of many available covalent docking tools has inspired development of a systematic and objective procedure and criteria with which to evaluate these programs. In order to find a tool appropriate to studies of a covalently binding system, protocols and criteria are proposed for protein–ligand covalent docking studies. This paper consists of three sections: (1) curating a standard data set to evaluate covalent docking tools objectively; (2) establishing criteria to measure the performance of a tool applied for docking ligands into a complex system; and (3) creating a protocol to evaluate and select covalent binding tools. The protocols were applied to evaluate four covalent docking tools (MOE, GOLD, CovDock, and ICM-Pro) and parameters affecting covalent docking performance were investigated.

Acaricidal activity, biochemical effects and molecular docking of some monoterpenes against two-spotted spider mite (Tetranychus urticae Koch)

Six natural monoterpenes (1,8-cineole, (-)-citronellal, limonene, α-pinene, pulegone and 4-terpineol) showed high acaricidal activity by fumigant and contact actions against adult females of the two-spotted spider mite, Tetranychus urticae Koch. The monoterpenes exhibited varying degrees of acaricidal potency using contact toxicity test after 24 and 48 h of treatment, where the LC₅₀ values were <160 and 45 mg/L, respectively. In fumigation test, of these six monoterpenes, pulegone exhibited the highest toxicity (LC₅₀ = 3.81 mg/L air), while (-)-citronellal had the lowest fumigant toxicity (LC₅₀ = 15.20 mg/L air). All compounds had high inhibitory effect on acetylcholinesterase (AChE) and gama amino butyric acid transaminase (GABA-T) activities. Pulegone was the most AChE inhibitor (IC₅₀ = 8.79 mg/L), while 4-terpineol revealed the lowest inhibitory effect (IC₅₀ = 32.82 mg/L). However, limonene caused the highest inhibition of GABA-T (IC₅₀ = 11.37 mg/L). The molecular docking studies revealed that the compounds displayed different binding interactions with the amino acid residues at the catalytic sites of AChE and GABA-T enzymes. Noncovalent interactions especially van der Waals, hydrogen bonding as well as hydrophobic was found between the compounds and the enzymes. A significant relationship was found between the docking score and the biological activity of monoterpenes compared to the standard acaricide pyridaben. In silico ADMET properties were also performed and displayed potential for the development of good acaricidal candidates.