Modeling covalent-modifier drugs

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design

Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.

Reversible Covalent Inhibition─Desired Covalent Adduct Formation by Mass Action

Covalent inhibition has seen a resurgence in the last several years. Although long-plagued by concerns of off-target effects due to nonspecific reactions leading to covalent adducts, there has been success in developing covalent inhibitors, especially within the field of anticancer therapy. Covalent inhibitors can have an advantage over noncovalent inhibitors since the formation of a covalent adduct may serve as an additional mode of selectivity due to the intrinsic reactivity of the target protein that is absent in many other proteins. Unfortunately, many covalent inhibitors form irreversible adducts with off-target proteins, which can lead to considerable side-effects. By designing the inhibitor to form reversible covalent adducts, one can leverage competing on/off kinetics in complex formation by taking advantage of the law of mass action. Although covalent adducts do form with off-target proteins, the reversible nature of inhibition prevents accumulation of the off-target adduct, thus limiting side-effects. In this perspective, we outline important characteristics of reversible covalent inhibitors, including examples and a guide for inhibitor development.

Molecular Insights into the Impact of Mutations on the Binding Affinity of Targeted Covalent Inhibitors of BTK

Targeted covalent inhibitors (TCIs) have witnessed a significant resurgence in recent years, particularly in the kinase drug discovery field for treating diverse clinical indications. The inhibition of Bruton's tyrosine kinase (BTK) for treating B-cell cancers is a classic example where TCIs such as ibrutinib have had breakthroughs in targeted therapy. However, selectivity remains challenging, and the emergence of resistance mutations is a critical concern for clinical efficacy. Computational methods that can accurately predict the impact of mutations on inhibitor binding affinity could prove helpful in informing targeted approaches─providing insights into drug resistance mechanisms. In addition, such systems could help guide the systematic evaluation and impact of mutations in disease models for optimal experimental design. Here, we have employed in silico physics-based methods to understand the effects of mutations on the binding affinity and conformational dynamics of select TCIs of BTK. The TCIs studied include ibrutinib, acalabrutinib, and zanubrutinib─all of which are FDA-approved drugs for treating multiple forms of leukemia and lymphoma. Our results offer useful molecular insights into the structural determinants, thermodynamics, and conformational energies that impact ligand binding for this biological target of clinical relevance.

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.

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.

Impact of the Recognition Part of Dipeptidyl Nitroalkene Compounds on the Inhibition Mechanism of Cysteine Proteases Cruzain and Cathepsin L

Cysteine proteases (CPs) are an important class of enzymes, many of which are responsible for several human diseases. For instance, cruzain of protozoan parasite Trypanosoma cruzi is responsible for the Chagas disease, while the role of human cathepsin L is associated with some cancers or is a potential target for the treatment of COVID-19. However, despite paramount work carried out during the past years, the compounds that have been proposed so far show limited inhibitory action against these enzymes. We present a study of proposed covalent inhibitors of these two CPs, cruzain and cathepsin L, based on the design, synthesis, kinetic measurements, and QM/MM computational simulations on dipeptidyl nitroalkene compounds. The experimentally determined inhibition data, together with the analysis and the predicted inhibition constants derived from the free energy landscape of the full inhibition process, allowed describing the impact of the recognition part of these compounds and, in particular, the modifications on the P2 site. The designed compounds and, in particular, the one with a bulky group (Trp) at the P2 site show promising in vitro inhibition activities against cruzain and cathepsin L for use as a starting lead compound in the development of drugs with medical applications for the treatment of human diseases and future designs.

Benchmarking In Silico Tools for Cysteine pKa Prediction

Accurate estimation of the pK_a's of cysteine residues in proteins could inform targeted approaches in hit discovery. The pK_a of a targetable cysteine residue in a disease-related protein is an important physiochemical parameter in covalent drug discovery, as it influences the fraction of nucleophilic thiolate amenable to chemical protein modification. Traditional structure-based in silico tools are limited in their predictive accuracy of cysteine pK_a's relative to other titratable residues. Additionally, there are limited comprehensive benchmark assessments for cysteine pK_a predictive tools. This raises the need for extensive assessment and evaluation of methods for cysteine pK_a prediction. Here, we report the performance of several computational pK_a methods, including single-structure and ensemble-based approaches, on a diverse test set of experimental cysteine pK_a's retrieved from the PKAD database. The dataset consisted of 16 wildtype and 10 mutant proteins with experimentally measured cysteine pK_a values. Our results highlight that these methods are varied in their overall predictive accuracies. Among the test set of wildtype proteins evaluated, the best method (MOE) yielded a mean absolute error of 2.3 pK units, highlighting the need for improvement of existing pK_a methods for accurate cysteine pK_a estimation. Given the limited accuracy of these methods, further development is needed before these approaches can be routinely employed to drive design decisions in early drug discovery efforts.

Nitriles: an attractive approach to the development of covalent inhibitors

Nitriles have broad applications in medicinal chemistry, with more than 60 small molecule drugs on the market containing the cyano functional group. In addition to the well-known noncovalent interactions that nitriles can perform with macromolecular targets, they are also known to improve drug candidates' pharmacokinetic profiles. Moreover, the cyano group can be used as an electrophilic warhead to covalently bind an inhibitor to a target of interest, forming a covalent adduct, a strategy that can present benefits over noncovalent inhibitors. This approach has gained much notoriety in recent years, mainly with diabetes and COVID-19-approved drugs. Nevertheless, the application of nitriles in covalent ligands is not restricted to it being the reactive center, as it can also be employed to convert irreversible inhibitors into reversible ones, a promising strategy for kinase inhibition and protein degradation. In this review, we introduce and discuss the roles of the cyano group in covalent inhibitors, how to tune its reactivity and the possibility of achieving selectivity only by replacing the warhead. Finally, we provide an overview of nitrile-based covalent compounds in approved drugs and inhibitors recently described in the literature.

Pseudo-irreversible butyrylcholinesterase inhibitors: Structure-activity relationships, computational and crystallographic study of the N-dialkyl O-arylcarbamate warhead

Alongside reversible butyrylcholinesterase inhibitors, a plethora of covalent butyrylcholinesterase inhibitors have been reported in the literature, typically pseudo-irreversible carbamates. For these latter, however, most cases lack full confirmation of their covalent mode of action. Additionally, the available reports regarding the structure-activity relationships of the O-arylcarbamate warhead are incomplete. Therefore, a follow-up on a series of pseudo-irreversible covalent carbamate human butyrylcholinesterase inhibitors and the structure-activity relationships of the N-dialkyl O-arylcarbamate warhead are presented in this study. The covalent mechanism of binding was tested by IC₅₀ time-dependency profiles, and sequentially and increasingly confirmed by kinetic analysis, whole protein LC-MS, and crystallographic analysis. Computational studies provided valuable insights into steric constraints and identified problematic, bulky carbamate warheads that cannot reach and carbamoylate the catalytic Ser198. Quantum mechanical calculations provided further evidence that steric effects appear to be a key factor in determining the covalent binding behaviour of these carbamate cholinesterase inhibitors and their duration of action. Additionally, the introduction of a clickable terminal alkyne moiety into one of the carbamate N-substituents and in situ derivatisation with azide-containing fluorophore enabled fluorescent labelling of plasma human butyrylcholinesterase. This proof-of-concept study highlights the potential of this novel approach and for these compounds to be further developed as clickable molecular probes for investigating tissue localisation and activity of cholinesterases.

In silico screening and covalent binding of phytochemicals of Ocimum sanctum against SARS-CoV-2 (COVID 19) main protease

Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has compelled the scientific community to search for an effective drug that can cure or a vaccine that can prevent the disease. Alternatively, symptomatic treatment and traditional immunity boosters are prescribed. Holy Tulsi (Ocimum sanctum) has been known as an ancient remedy for cure of common cold and respiratory ailment. Several reports have come on virtual screening of phytochemicals including those of Tulsi against various enzymes of the virus. We undertook in silico analysis of the ethanol extracted phytochemicals of Tulsi as inhibitors of SARS-CoV-2 (2019-nCoV) main protease with an approach to look into the possibility of covalent ligand binding with the catalytic residue Cys145, which makes the report unique. The results suggest that the flavonoids and polyphenolic compounds of Tulsi, have potential to covalently bind to the catalytic residue Cys145 of main protease and irreversibly inhibit the viral enzyme. Luteolin-7-O-glucuronide is specially considered for its optimum properties, namely, low toxicity (LD₅₀ 5000 mg/kg body weight), high drug-likeness score (0.71), the active site binding free energy (ΔG_bind) -19.19 kcal/mol by GBSA method and covalent binding energy -24.23 kcal/mol. Further experimental validations are required to establish the theoretical findings.Communicated by Ramaswamy H. Sarma.

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.

Targeting SARS-CoV-2 Main Protease for Treatment of COVID-19: Covalent Inhibitors Structure-Activity Relationship Insights and Evolution Perspectives

The viral main protease is one of the most attractive targets among all key enzymes involved in the SARS-CoV-2 life cycle. Covalent inhibition of the cysteine145 of SARS-CoV-2 MPRO with selective antiviral drugs will arrest the replication process of the virus without affecting human catalytic pathways. In this Perspective, we analyzed the in silico, in vitro, and in vivo data of the most representative examples of covalent SARS-CoV-2 MPRO inhibitors reported in the literature to date. In particular, the studied molecules were classified into eight different categories according to their reactive electrophilic warheads, highlighting the differences between their reversible/irreversible mechanism of inhibition. Furthermore, the analyses of the most recurrent pharmacophoric moieties and stereochemistry of chiral carbons were reported. The analyses of noncovalent and covalent in silico protocols, provided in this Perspective, would be useful for the scientific community to discover new and more efficient covalent SARS-CoV-2 MPRO inhibitors.

Estimating the binding energetics of reversible covalent inhibitors of the SARS-CoV-2 main protease: an in silico study

The main protease (M^pro) of the SARS-CoV-2 virus is an attractive therapeutic target for developing antivirals to combat COVID-19. M^pro is essential for the replication cycle of the SARS-CoV-2 virus, so inhibiting M^pro blocks a vital piece of the cell replication machinery of the virus. A promising strategy to disrupt the viral replication cycle is to design inhibitors that bind to the active site cysteine (Cys145) of the M^pro. Cysteine targeted covalent inhibitors are gaining traction in drug discovery owing to the benefits of improved potency and extended drug-target engagement. An interesting aspect of these inhibitors is that they can be chemically tuned to form a covalent, but reversible bond, with their targets of interest. Several small-molecule cysteine-targeting covalent inhibitors of the M^pro have been discovered-some of which are currently undergoing evaluation in early phase human clinical trials. Understanding the binding energetics of these inhibitors could provide new insights to facilitate the design of potential drug candidates against COVID-19. Motivated by this, we employed rigorous absolute binding free energy calculations and hybrid quantum mechanical/molecular mechanical (QM/MM) calculations to estimate the energetics of binding of some promising reversible covalent inhibitors of the M^pro. We find that the inclusion of enhanced sampling techniques such as replica-exchange algorithm in binding free energy calculations can improve the convergence of predicted non-covalent binding free energy estimates of inhibitors binding to the M^pro target. In addition, our results indicate that binding free energy calculations coupled with multiscale simulations can be a useful approach to employ in ranking covalent inhibitors to their targets. This approach may be valuable in prioritizing and refining covalent inhibitor compounds for lead discovery efforts against COVID-19 and other coronavirus infections.

Structural characterization of dicyanopyridine containing DNMT1-selective, non-nucleoside inhibitors

DNMT1 maintains the parental DNA methylation pattern on newly replicated hemimethylated DNA. The failure of this maintenance process causes aberrant DNA methylation that affects transcription and contributes to the development and progression of cancers such as acute myeloid leukemia. Here, we structurally characterized a set of newly discovered DNMT1-selective, reversible, non-nucleoside inhibitors that bear a core 3,5-dicyanopyridine moiety, as exemplified by GSK3735967, to better understand their mechanism of inhibition. All of the dicyanopydridine-containing inhibitors examined intercalate into the hemimethylated DNA between two CpG base pairs through the DNA minor groove, resulting in conformational movement of the DNMT1 active-site loop. In addition, GSK3735967 introduces two new binding sites, where it interacts with and stabilizes the displaced DNMT1 active-site loop and it occupies an open aromatic cage in which trimethylated histone H4 lysine 20 is expected to bind. Our work represents a substantial step in generating potent, selective, and non-nucleoside inhibitors of DNMT1.

B-nor-methylene Colchicinoid PT-100 Selectively Induces Apoptosis in Multidrug-Resistant Human Cancer Cells via an Intrinsic Pathway in a Caspase-Independent Manner

Colchicine, the main active alkaloid from Colchicum autumnale L., is a potent tubulin binder and represents an interesting lead structure for the development of potential anticancer chemotherapeutics. We report on the synthesis and investigation of potentially reactive colchicinoids and their surprising biological activities. In particular, the previously undescribed colchicinoid PT-100, a B-ring contracted 6-exo-methylene colchicinoid, exhibits extraordinarily high antiproliferative and apoptosis-inducing effects on various types of cancer cell lines like acute lymphoblastic leukemia (Nalm6), acute myeloid leukemia (HL-60), Burkitt-like lymphoma (BJAB), human melanoma (MelHO), and human breast adenocarcinoma (MCF7) cells at low nanomolar concentrations. Apoptosis induction proved to be especially high in multidrug-resistant Nalm6-derived cancer cell lines, while healthy human leukocytes and hepatocytes were not affected by the concentration range studied. Furthermore, caspase-independent initiation of apoptosis via an intrinsic pathway was observed. PT-100 also shows strong synergistic effects in combination with vincristine on BJAB and Nalm6 cells. Cocrystallization of PT-100 with tubulin dimers revealed its (noncovalent) binding to the colchicine-binding site of β-tubulin at the interface to the α-subunit. A pronounced effect of PT-100 on the cytoskeleton morphology was shown by fluorescence microscopy. While the reactivity of PT-100 as a weak Michael acceptor toward thiols was chemically proven, it remains unclear whether this contributes to the remarkable biological properties of this unusual colchicinoid.

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.

Modeling the Binding and Conformational Energetics of a Targeted Covalent Inhibitor to Bruton's Tyrosine Kinase

Targeted covalent inhibitors (TCIs) bind to their targets in both covalent and noncovalent modes, providing exceptionally high affinity and selectivity. These inhibitors have been effectively employed as inhibitors of protein kinases, with Taunton and coworkers (Nat. Chem. Biol. 2015, 11, 525-531) reporting a notable example of a TCI with a cyanoacrylamide warhead that forms a covalent thioether linkage to an active-site cysteine (Cys481) of Bruton's tyrosine kinase (BTK). The specific mechanism of the binding and the relative importance of the covalent and noncovalent interactions is difficult to determine experimentally, and established simulation methods for calculating the absolute binding affinity of an inhibitor cannot describe the covalent bond-forming steps. Here, an integrated approach using alchemical free-energy perturbation and QM/MM molecular dynamics methods was employed to model the complete Gibbs energy profile for the covalent inhibition of BTK by a cyanoacrylamide TCI. These calculations provide a rigorous and complete absolute Gibbs energy profile of the covalent modification binding process. Following a classic thiol-Michael addition mechanism, the target cysteine is deprotonated to form a nucleophilic thiolate, which then undergoes a facile conjugate addition to the electrophilic functional group to form a bond with the noncovalently bound ligand. This model predicts that the formation of the covalent linkage is highly exergonic relative to the noncovalent binding alone. Nevertheless, noncovalent interactions between the ligand and individual amino acid residues in the binding pocket of the enzyme are also essential for ligand binding, particularly van der Waals dispersion forces, which have a larger contribution to the binding energy than the covalent component in absolute terms. This model also shows that the mechanism of covalent modification of a protein occurs through a complex series of steps and that entropy, conformational flexibility, noncovalent interactions, and the formation of covalent linkage are all significant factors in the ultimate binding affinity of a covalent drug to its target.

Computer-aided design of 1,4-naphthoquinone-based inhibitors targeting cruzain and rhodesain cysteine proteases

Chagas disease and Human African Trypanosomiasis (HAT) are caused by Trypanosoma cruzi and T. brucei parasites, respectively. Cruzain (CRZ) and Rhodesain (RhD) are cysteine proteases that share 70% of identity and play vital functions in these parasites. These macromolecules represent promising targets for designing new inhibitors. In this context, 26 CRZ and 5 RhD 3D-structures were evaluated by molecular redocking to identify the most accurate one to be utilized as a target. Posteriorly, a virtual screening of a library containing 120 small natural and nature-based compounds was performed on both of them. In total, 14 naphthoquinone-based analogs were identified, synthesized, and biologically evaluated. In total, five compounds were active against RhD, being three of them also active on CRZ. A derivative of 1,4-naphthoquinonepyridin-2-ylsulfonamide was found to be the most active molecule, exhibiting IC₅₀ values of 6.3 and 1.8 µM for CRZ and RhD, respectively. Dynamic simulations at 100 ns demonstrated good stability and do not alter the targets' structures. MM-PBSA calculations revealed that it presents a higher affinity for RhD (-25.3 Kcal mol^-1) than CRZ, in which van der Waals interactions were more relevant. A mechanistic hypothesis (via C3-Michael-addition reaction) involving a covalent mode of inhibition for this compound towards RhD was investigated by covalent molecular docking and DFT B3LYP/6-31 + G* calculations, exhibiting a low activation energy (ΔG^‡) and providing a stable product (ΔG), with values of 7.78 and - 39.72 Kcal mol^-1, respectively; similar to data found in the literature. Nevertheless, a reversibility assay by dilution revealed that JN-11 is a time-dependent and reversible inhibitor. Finally, this study applies modern computer-aided techniques to identify promising inhibitors from a well-known chemical class of natural products. Then, this work could inspire other future studies in the field, being useful for designing potent naphthoquinones as RhD inhibitors.

Tuning Proton Transfer Thermodynamics in SARS-CoV-2 Main Protease: Implications for Catalysis and Inhibitor Design

The catalytic reaction in SARS-CoV-2 main protease is activated by a proton transfer (PT) from Cys145 to His41. The same PT is likely also required for the covalent binding of some inhibitors. Here we use a multiscale computational approach to investigate the PT thermodynamics in the apo enzyme and in complex with two potent inhibitors, N3 and the α-ketoamide 13b. We show that with the inhibitors the free energy cost to reach the charge-separated state of the active-site dyad is lower, with N3 inducing the most significant reduction. We also show that a few key sites (including specific water molecules) significantly enhance or reduce the thermodynamic feasibility of the PT reaction, with selective desolvation of the active site playing a crucial role. The approach presented is a cost-effective procedure to identify the enzyme regions that control the activation of the catalytic reaction and is thus also useful to guide the design of inhibitors.

Covalent and non-covalent binding free energy calculations for peptidomimetic inhibitors of SARS-CoV-2 main protease

COVID-19, the disease caused by the newly discovered coronavirus-SARS-CoV-2, has created a global health, social, and economic crisis. As of mid-January 2021, there are over 90 million confirmed cases and more than 2 million reported deaths due to COVID-19. Currently, there are very limited therapeutics for the treatment or prevention of COVID-19. For this reason, it is important to find drug targets that will lead to the development of safe and effective therapeutics against the disease. The main protease (M^pro) of the virus is an attractive target for the development of effective antiviral therapeutics because it is required for proteolytic cleavage of viral polyproteins. Furthermore, the M^pro has no human homologues, so drugs designed to bind to this target directly have less risk for off-target effects. Recently, several high-resolution crystallographic structures of the M^pro in complex with inhibitors have been determined-to guide drug development and to spur efforts in structure-based drug design. One of the primary objectives of modern structure-based drug design is the accurate prediction of receptor-ligand binding affinities for rational drug design and discovery. Here, we perform rigorous alchemical absolute binding free energy calculations and QM/MM calculations to give insight into the total binding energy of two recently crystallized inhibitors of SARS-CoV-2 M^pro, namely, N3 and α-ketoamide 13b. The total binding energy consists of both covalent and non-covalent binding components since both compounds are covalent inhibitors of the M^pro. Our results indicate that the covalent and non-covalent binding free energy contributions of both inhibitors to the M^pro target differ significantly. The N3 inhibitor has more favourable non-covalent interactions, particularly hydrogen bonding, in the binding site of the M^pro than the α-ketoamide inhibitor. Also, the Gibbs energy of reaction for the M^pro-N3 covalent adduct is greater than the Gibbs reaction energy for the M^pro-α-ketoamide covalent adduct. These differences in the covalent and non-covalent binding free energy contributions for both inhibitors could be a plausible explanation for their in vitro differences in antiviral activity. Our findings are consistent with the reversible and irreversible character of both inhibitors as reported by experiment and highlight the importance of both covalent and non-covalent binding free energy contributions to the absolute binding affinity of a covalent inhibitor towards its target. This information could prove useful in the rational design, discovery, and evaluation of potent SARS-CoV-2 M^pro inhibitors for targeted antiviral therapy.

Mechanism of covalent binding of ibrutinib to Bruton's tyrosine kinase revealed by QM/MM calculations

Ibrutinib is the first covalent inhibitor of Bruton's tyrosine kinase (BTK) to be used in the treatment of B-cell cancers. Understanding the mechanism of covalent inhibition will aid in the design of safer and more selective covalent inhibitors that target BTK. The mechanism of covalent inhibition in BTK has been uncertain because there is no appropriate residue nearby that can act as a base to deprotonate the cysteine thiol prior to covalent bond formation. We investigate several mechanisms of covalent modification of C481 in BTK by ibrutinib using combined quantum mechanics/molecular mechanics (QM/MM) molecular dynamics reaction simulations. The lowest energy pathway involves direct proton transfer from C481 to the acrylamide warhead in ibrutinib, followed by covalent bond formation to form an enol intermediate. There is a subsequent rate-limiting keto-enol tautomerisation step (ΔG ^‡ = 10.5 kcal mol^-1) to reach the inactivated BTK/ibrutinib complex. Our results represent the first mechanistic study of BTK inactivation by ibrutinib to consider multiple mechanistic pathways. These findings should aid in the design of covalent drugs that target BTK and other similar targets.

Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease

The main protease (M^pro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics. Recently, many high-resolution apo and inhibitor-bound structures of M^pro, a cysteine protease, have been determined, facilitating structure-based drug design. M^pro plays a central role in the viral life cycle by catalyzing the cleavage of SARS-CoV-2 polyproteins. In addition to the catalytic dyad His41–Cys145, M^pro contains multiple histidines including His163, His164, and His172. The protonation states of these histidines and the catalytic nucleophile Cys145 have been debated in previous studies of SARS-CoV M^pro, but have yet to be investigated for SARS-CoV-2. In this work we have used molecular dynamics simulations to determine the structural stability of SARS-CoV-2 M^pro as a function of the protonation assignments for these residues. We simulated both the apo and inhibitor-bound enzyme and found that the conformational stability of the binding site, bound inhibitors, and the hydrogen bond networks of M^pro are highly sensitive to these assignments. Additionally, the two inhibitors studied, the peptidomimetic N3 and an α-ketoamide, display distinct His41/His164 protonation-state-dependent stabilities. While the apo and the N3-bound systems favored N_δ (HD) and N_ϵ (HE) protonation of His41 and His164, respectively, the α-ketoamide was not stably bound in this state. Our results illustrate the importance of using appropriate histidine protonation states to accurately model the structure and dynamics of SARS-CoV-2 M^pro in both the apo and inhibitor-bound states, a necessary prerequisite for drug-design efforts.

The main protease (M^pro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics.

Drug design targeting active posttranslational modification protein isoforms

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.

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.

Mechanism of inhibition of SARS-CoV-2 Mpro by peptidyl Michael acceptor explained by QM/MM simulations and design of new derivatives with tunable chemical reactivity

The SARS-CoV-2 main protease (M^pro) is essential for replication of the virus responsible for the COVID-19 pandemic, and one of the main targets for drug design. Here, we simulate the inhibition process of SARS-CoV-2 M^pro with a known Michael acceptor (peptidyl) inhibitor, N3. The free energy landscape for the mechanism of the formation of the covalent enzyme-inhibitor product is computed with QM/MM molecular dynamics methods. The simulations show a two-step mechanism, and give structures and calculated barriers in good agreement with experiment. Using these results and information from our previous investigation on the proteolysis reaction of SARS-CoV-2 M^pro, we design two new, synthetically accessible N3-analogues as potential inhibitors, in which the recognition and warhead motifs are modified. QM/MM modelling of the mechanism of inhibition of M^pro by these novel compounds indicates that both may be promising candidates as drug leads against COVID-19, one as an irreversible inhibitor and one as a potential reversible inhibitor.

Tuning Proton Transfer Thermodynamics in SARS-Cov-2 Main Protease: Implications for Catalysis and Inhibitor Design

In this comutational work a hybrid quantum mechanics/molecular mechanics approach, the MD-PMM approach, is used to investigate the proton transfer reaction the activates the catalytic activity of SARS-CoV-2 main protease. The proton transfer thermodynamics is investigated for the apo ensyme (i.e., without any bound substrate or inhibitor) and in the presence of a inhibitor, N3, which was previously shown to covalently bind SARS-CoV-2 main protease.

An electrophilic warhead library for mapping the reactivity and accessibility of tractable cysteines in protein kinases

Targeted covalent inhibitors represent a viable strategy to block protein kinases involved in different disease pathologies. Although a number of computational protocols have been published for identifying druggable cysteines, experimental approaches are limited for mapping the reactivity and accessibility of these residues. Here, we present a ligand based approach using a toolbox of fragment-sized molecules with identical scaffold but equipped with diverse covalent warheads. Our library represents a unique opportunity for the efficient integration of warhead-optimization and target-validation into the covalent drug development process. Screening this probe kit against multiple kinases could experimentally characterize the accessibility and reactivity of the targeted cysteines and helped to identify suitable warheads for designed covalent inhibitors. The usefulness of this approach has been confirmed retrospectively on Janus kinase 3 (JAK3). Furthermore, representing a prospective validation, we identified Maternal embryonic leucine zipper kinase (MELK), as a tractable covalent target. Covalently labelling and biochemical inhibition of MELK would suggest an alternative covalent strategy for MELK inhibitor programs.

Predicting Reactive Cysteines with Implicit-Solvent-Based Continuous Constant pH Molecular Dynamics in Amber

Cysteines existing in the deprotonated thiolate form or having a tendency to become deprotonated are important players in enzymatic and cellular redox functions and frequently exploited in covalent drug design; however, most computational studies assume cysteines as protonated. Thus, developing an efficient tool that can make accurate and reliable predictions of cysteine protonation states is timely needed. We recently implemented a generalized Born (GB) based continuous constant pH molecular dynamics (CpHMD) method in Amber for protein pKa calculations on CPUs and GPUs. Here we benchmark the performance of GB-CpHMD for predictions of cysteine pKa's and reactivities using a data set of 24 proteins with both down- and upshifted cysteine pKa's. We found that 10 ns single-pH or 4 ns replica-exchange CpHMD titrations gave root-mean-square errors of 1.2-1.3 and correlation coefficients of 0.8-0.9 with respect to experiment. The accuracy of predicting thiolates or reactive cysteines at physiological pH with single-pH titrations is 86 or 81% with a precision of 100 or 90%, respectively. This performance well surpasses the traditional structure-based methods, particularly a widely used empirical pKa tool that gives an accuracy less than 50%. We discuss simulation convergence, dependence on starting structures, common determinants of the pKa downshifts and upshifts, and the origin of the discrepancies from the structure-based calculations. Our work suggests that CpHMD titrations can be performed on a desktop computer equipped with a single GPU card to predict cysteine protonation states for a variety of applications, from understanding biological functions to covalent drug design.

Small-molecule covalent bond formation at tyrosine creates a binding site and inhibits activation of Ral GTPases

Ral (Ras-like) GTPases are directly activated by oncogenic Ras GTPases. Mutant K-Ras (G12C) has enabled the development of covalent K-Ras inhibitors currently in clinical trials. However, Ral, and the overwhelming majority of mutant oncogenic K-Ras, are devoid of a druggable pocket and lack an accessible cysteine for the development of a covalent inhibitor. Here, we report that covalent bond formation by an aryl sulfonyl fluoride electrophile at a tyrosine residue (Tyr-82) inhibits guanine exchange factor Rgl2-mediated nucleotide exchange of Ral GTPase. A high-resolution 1.18-Å X-ray cocrystal structure shows that the compound binds to a well-defined binding site in RalA as a result of a switch II loop conformational change. The structure, along with additional high-resolution crystal structures of several analogs in complex with RalA, confirm the importance of key hydrogen bond anchors between compound sulfone oxygen atoms and Ral backbone nitrogen atoms. Our discovery of a pocket with features found on known druggable sites and covalent modification of a bystander tyrosine residue present in Ral and Ras GTPases provide a strategy that could lead to therapeutic agent targeting oncogenic Ras mutants that are devoid of a cysteine nucleophile.

Assessment of the Cruzain Cysteine Protease Reversible and Irreversible Covalent Inhibition Mechanism

Reversible and irreversible covalent ligands are advanced cysteine protease inhibitors in the drug development pipeline. K777 is an irreversible inhibitor of cruzain, a necessary enzyme for the survival of the Trypanosoma cruzi (T. cruzi) parasite, the causative agent of Chagas disease. Despite their importance, irreversible covalent inhibitors are still often avoided due to the risk of adverse effects. Herein, we replaced the K777 vinyl sulfone group with a nitrile moiety to obtain a reversible covalent inhibitor (Neq0682) of cysteine protease. Then, we used advanced experimental and computational techniques to explore details of the inhibition mechanism of cruzain by reversible and irreversible inhibitors. The isothermal titration calorimetry (ITC) analysis shows that inhibition of cruzain by an irreversible inhibitor is thermodynamically more favorable than by a reversible one. The hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) and Molecular Dynamics (MD) simulations were used to explore the mechanism of the reaction inhibition of cruzain by K777 and Neq0682. The calculated free energy profiles show that the Cys25 nucleophilic attack and His162 proton transfer occur in a single step for a reversible inhibitor and two steps for an irreversible covalent inhibitor. The hybrid QM/MM calculated free energies for the inhibition reaction correspond to -26.7 and -5.9 kcal mol^-1 for K777 and Neq0682 at the MP2/MM level, respectively. These results indicate that the ΔG of the reaction is very negative for the process involving K777, consequently, the covalent adduct cannot revert to a noncovalent protein-ligand complex, and its binding tends to be irreversible. Overall, the present study provides insights into a covalent inhibition mechanism of cysteine proteases.

Polarisable force fields: what do they add in biomolecular simulations?

The quality of biomolecular simulations critically depends on the accuracy of the force field used to calculate the potential energy of the molecular configurations. Currently, most simulations employ non-polarisable force fields, which describe electrostatic interactions as the sum of Coulombic interactions between fixed atomic charges. Polarisation of these charge distributions is incorporated only in a mean-field manner. In the past decade, extensive efforts have been devoted to developing simple, efficient, and yet generally applicable polarisable force fields for biomolecular simulations. In this review, we summarise the latest developments in accounting for key biomolecular interactions with polarisable force fields and applications to address challenging biological questions. In the end, we provide an outlook for future development in polarisable force fields.

Evaluating QM/MM Free Energy Surfaces for Ranking Cysteine Protease Covalent Inhibitors

One tactic for cysteine protease inhibition is to form a covalent bond between an electrophilic atom of the inhibitor and the thiol of the catalytic cysteine. In this study, we evaluate the reaction free energy obtained from a hybrid quantum mechanical/molecular mechanical (QM/MM) free energy profile as a predictor of affinity for reversible, covalent inhibitors of rhodesain. We demonstrate that the reaction free energy calculated with the PM6/MM potential is in agreement with the experimental data and suggest that the free energy profile for covalent bond formation in a protein environment may be a useful tool for the inhibitor design.

Design and SAR Analysis of Covalent Inhibitors Driven by Hybrid QM/MM Simulations

Quantum mechanics/molecular mechanics (QM/MM) hybrid technique is emerging as a reliable computational method to investigate and characterize chemical reactions occurring in enzymes. From a drug discovery perspective, a thorough understanding of enzyme catalysis appears pivotal to assist the design of inhibitors able to covalently bind one of the residues belonging to the enzyme catalytic machinery. Thanks to the current advances in computer power, and the availability of more efficient algorithms for QM-based simulations, the use of QM/MM methodology is becoming a viable option in the field of covalent inhibitor design. In the present review, we summarized our experience in the field of QM/MM simulations applied to drug design problems which involved the optimization of agents working on two well-known drug targets, namely fatty acid amide hydrolase (FAAH) and epidermal growth factor receptor (EGFR). In this context, QM/MM simulations gave valuable information in terms of geometry (i.e., of transition states and metastable intermediates) and reaction energetics that allowed to correctly predict inhibitor binding orientation and substituent effect on enzyme inhibition. What is more, enzyme reaction modelling with QM/MM provided insights that were translated into the synthesis of new covalent inhibitor featured by a unique combination of intrinsic reactivity, on-target activity, and selectivity.

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

Predicting the affinity of halogenated reversible covalent inhibitors through relative binding free energy

Nitrile reversible covalent inhibitors of human cathepsin L (hCatL) bind covalently to the side chain of the catalytic Cys25 residue in the S1 pocket to form thioimidates. Predicting the binding of reversible covalent inhibitors is essential for their practical application in drug design. In this report, five nitrile-based inhibitors coded Neq0570, Neq0710, Neq0802, Neq0803 and Neq0804 had their hCatL inhibition constants, K_i, determined. These analogs of the prototypical Neq0570 are halogenated reversible covalent inhibitors of hCatL, which bear a halogen atom in the meta position of the P3 benzyl ring that can form a halogen bond with the Gly61 of the hCatL. To describe halogen bonding interaction in an inhibitor-hCatL complex, we applied an extra point (EP) of charge to represent the anisotropic distribution of charge on the iodine, bromine and chlorine atoms. Besides, we have used alchemical free energy calculations for evaluating the overall relative binding free energies of these inhibitors using a two-state binding model: noncovalent and covalent bond states. Our results show that free energy perturbation (FEP) can predict the hCatL binding affinities of halogenated reversible covalent inhibitors in close agreement with experiments.

Limitations of Ligand-Only Approaches for Predicting the Reactivity of Covalent Inhibitors

Covalent inhibition has undergone a resurgence and is an important modern-day drug design and chemical biology approach. To avoid off-target interactions and to fine-tune reactivity, the ability to accurately predict reactivity is vitally important for the design and development of safer and more effective covalent drugs. Several ligand-only metrics have been proposed that promise quick and simple ways of determining covalent reactivity. In particular, we examine proton affinity and reaction energies calculated with the density functional B3LYP-D3/6-311+G**//B3LYP-D3/6-31G* method to assess the reactivity of a series of α,β-unsaturated carbonyl compounds that form covalent adducts with cysteine. We demonstrate that while these metrics correlate well with experiment for a diverse range of small reactive molecules these approaches fail for predicting the reactivity of drug-like compounds. We conclude that ligand-only metrics such as proton affinity and reaction energies do not capture determinants of reactivity in situ and fail to account for important factors such as conformation, solvation, and intermolecular interactions.

Quantum Chemical Methods for Modeling Covalent Modification of Biological Thiols

Targeted covalent inhibitor drugs require computational methods that go beyond simple molecular-mechanical force fields in order to model the chemical reactions that occur when they bind to their targets. Here, several semiempirical and density-functional theory (DFT) methods are assessed for their ability to describe the potential energy surface and reaction energies of the covalent modification of a thiol by an electrophile. Functionals such as PBE and B3LYP fail to predict a stable enolate intermediate. This is largely due to delocalization error, which spuriously stabilizes the prereaction complex, in which excess electron density is transferred from the thiolate to the electrophile. Functionals with a high-exact exchange component, range-separated DFT functionals, and variationally optimized exact exchange (i.e., the LC-B05minV functional) correct this issue to various degrees. The large gradient behavior of the exchange enhancement factor is also found to significantly affect the results, leading to the improved performance of PBE0. While ωB97X-D and M06-2X were reasonably accurate, no method provided quantitative accuracy for all three electrophiles, making this a very strenuous test of functional performance. Additionally, one drawback of M06-2X was that molecular dynamics (MD) simulations using this functional were only stable if a fine integration grid was used. The low-cost semiempirical methods, PM3, AM1, and PM7, provide a qualitatively correct description of the reaction mechanism, although the energetics is not quantitatively reliable. As a proof of concept, the potential of mean force for the addition of methylthiolate to methylvinyl ketone was calculated using quantum mechanical/molecular mechanical MD in an explicit polarizable aqueous solvent. © 2019 Wiley Periodicals, Inc.

Exploring the Lapse in Druggability: Sequence Analysis, Structural Dynamics and Binding Site Characterization of K-RasG12C Variant, a Feasible Oncotherapeutics Target

BACKGROUND

The difficulty in druggability of K-Ras variant has presented a challenge in the treatment of cancer diseases associated with its dysfunctionality. Despite the identification of different binding sites, limited information exists in the literature about their characteristics. Therefore, identification, crossvalidation and characterization of its druggable sites would aid the design of chemical compounds that will arrest its dysfunctionality related oncogenesis.

OBJECTIVE

This study entails the identification, cross-validation and characterization of K-Ras G12C variant's binding sites for potential druggability, coupled with the elucidation of alterations in 3D conformations and dynamics.

METHOD

Molecular dynamics simulation was carried out on the inactive, the active and the hyperactive K-RasG12Cvariant using the amber software package. The SiteMap software was employed in identifying and characterizing the druggable binding sites while the validation of the binding sites was carried out with the SiteHound and MetaPocket servers.

RESULTS

Four druggable binding sites were identified, validated and characterized based on physicochemical attributes such as size, volume, degree of enclosure or exposure, degree of contact, hydrophobic/hydrophilic character, hydrophobic/hydrophilic balance and hydrogen-bonding features. Conformational studies also revealed that the K-Ras variant exhibited notable structural instability, increased flexibility and a strongly anticorrelated movement compared to the inactive and active wildtype forms.

CONCLUSION

The attributes of the characterized druggable sites will be useful in designing site-specific K-Ras inhibitors for the treatment of K-Ras variant associated cancer diseases.

Exploiting Cysteine Residues of SLC Membrane Transporters as Targets for Drugs

The observation that cysteine is the top gainer amino acid during evolution attracted the attention of scientists dealing with protein chemistry. The thiol group of cysteine, indeed, is a potential site for several types of reactions with variable specificity and strength. This feature proved to be promising also in the field of membrane transporters that represent boundary proteins fundamental for cell homeostasis. These proteins are classified, according to the driving force for transport, in primary or secondary active transporters. Another frequently used classification is nowadays based on phylogenesis. Two major groups are identified that take into account both criteria: the ABC and the SLC transporters, the second being much more numerous. The cellular localization of the transporters makes them very attractive for drug design. Moreover, the presence of at least one cysteine residue in all the annotated SLC transporters, so far, highlights the possibility of using the thiol (SH) residue for covalent drug targeting. Even if a delay exists in this research field due to the scarce knowledge of structure/function relationships, the setup of novel experimental tools for studying SLC proteins of plasma and organelle membranes opens an important perspective in pharmacology.

Fibroblast Growth Factor Receptors (FGFRs): Structures and Small Molecule Inhibitors

Fibroblast growth factor receptors (FGFRs) are a family of receptor tyrosine kinases expressed on the cell membrane that play crucial roles in both developmental and adult cells. Dysregulation of FGFRs has been implicated in a wide variety of cancers, such as urothelial carcinoma, hepatocellular carcinoma, ovarian cancer and lung adenocarcinoma. Due to their functional importance, FGFRs have been considered as promising drug targets for the therapy of various cancers. Multiple small molecule inhibitors targeting this family of kinases have been developed, and some of them are in clinical trials. Furthermore, the pan-FGFR inhibitor erdafitinib (JNJ-42756493) has recently been approved by the U.S. Food and Drug Administration (FDA) for the treatment of metastatic or unresectable urothelial carcinoma (mUC). This review summarizes the structure of FGFR, especially its kinase domain, and the development of small molecule FGFR inhibitors.

Ranking Reversible Covalent Drugs: From Free Energy Perturbation to Fragment Docking

Reversible covalent inhibitors have drawn increasing attention in drug design, as they are likely more potent than noncovalent inhibitors and less toxic than covalent inhibitors. Despite those advantages, the computational prediction of reversible covalent binding presents a formidable challenge because the binding process consists of multiple steps and quantum mechanics (QM) level calculation is needed to estimate the covalent binding free energy. It has been shown that the dissociation rates and the equilibrium dissociation constants vary significantly even with similar warheads, due to noncovalent interactions. We have previously used a simplistic two-state model for predicting the relative binding selectivity of reversible covalent inhibitors ( J. Am. Chem. Soc. 2017, 139 , 17945 ). Here we go beyond binding selectivity and demonstrate that it is possible to use free energy perturbation (FEP) molecular dynamics (MD) to calculate the overall reversible covalent binding using a specially designed thermodynamic cycle. We show that FEP can predict the varying binding free energies of the analogs sharing a common warhead. More importantly, our results revealed that the chemical modification away from warhead alters the binding affinity at both noncovalent and covalent binding states, and the computational prediction can be improved by considering the binding free energy of both states. Furthermore, we explored the possibility of using a more rapid computational method, site-identification by ligand competitive saturation (SILCS), to rank the same set of reversible covalent inhibitors. We found that the fragment docking to a set of precomputed fragment maps produces a reasonable ranking. In conclusion, two independent approaches provided consistent results that the covalent binding state is suitable for the initial ranking of the reversible covalent drug candidates. For lead-optimization, the FEP approach designed here can provide more rigorous and detailed information regarding how much the covalent and noncovalent binding states are contributing to the overall binding affinity, thus offering a new avenue for fine-tuning the noncovalent interactions for optimizing reversible covalent drugs.

Covalent Inhibition in Drug Discovery

Although covalent inhibitors have been used as therapeutics for more than a century, there has been general resistance in the pharmaceutical industry against their further development due to safety concerns. This inclination has recently been reverted after the development of a wide variety of covalent inhibitors to address human health conditions along with the US Food and Drug Administration (FDA) approval of several covalent therapeutics for use in humans. Along with this exciting resurrection of an old drug discovery concept, this review surveys enzymes that can be targeted by covalent inhibitors for the treatment of human diseases. We focus on protein kinases, RAS proteins, and a few other enzymes that have been studied extensively as targets for covalent inhibition, with the aim to address challenges in designing effective covalent drugs and to provide suggestions in the area that have yet to be explored.

Covalent binders in drug discovery

Covalent modulation of protein function can have multiple utilities including therapeutics, and probes to interrogate biology. While this field is still viewed with scepticism due to the potential for (idiosyncratic) toxicities, significant strides have been made in terms of understanding how to tune electrophilicity to selectively target specific residues. Progress has also been made in harnessing the potential of covalent binders to uncover novel biology and to provide an enhanced utility as payloads for Antibody Drug Conjugates. This perspective covers the tenets and applications of covalent binders.

Structure-Based Engineering of Irreversible Inhibitors against Histone Lysine Demethylase KDM5A

The active sites of hundreds of human α-ketoglutarate (αKG) and Fe(II)-dependent dioxygenases are exceedingly well preserved, which challenges the design of selective inhibitors. We identified a noncatalytic cysteine (Cys481 in KDM5A) near the active sites of KDM5 histone H3 lysine 4 demethylases, which is absent in other histone demethylase families, that could be explored for interaction with the cysteine-reactive electrophile acrylamide. We synthesized analogs of a thienopyridine-based inhibitor chemotype, namely, 2-((3-aminophenyl)(2-(piperidin-1-yl)ethoxy)methyl)thieno[3,2- b]pyridine-7-carboxylic acid (N70) and a derivative containing a (dimethylamino)but-2-enamido)phenyl moiety (N71) designed to form a covalent interaction with Cys481. We characterized the inhibitory and binding activities against KDM5A and determined the cocrystal structures of the catalytic domain of KDM5A in complex with N70 and N71. Whereas the noncovalent inhibitor N70 displayed αKG-competitive inhibition that could be reversed after dialysis, inhibition by N71 was dependent on enzyme concentration and persisted even after dialysis, consistent with covalent modification.

Covalent simulations of covalent/irreversible enzyme inhibition in drug discovery: a reliable technical protocol

Aim: Irreversible covalent inhibition of biological targets in disease pathogenesis is an emerging field in drug design. Computational techniques have assumed a critical role in understanding covalent enzyme inhibition. However, a gap currently exists with regards to the reliability and reproducibility of currently available protocols available in literature and open scientific forums. Methodology/results: Appropriate ligand and protein target are selected, docked covalently or noncovalently using respective docking tools. Both components are subjected to premolecular dynamic preparations. This was followed by parameterization of the ligand, protein and covalent complex, respectively. The production runs were initiated and the resulting trajectories are saved and analyzed. Conclusion: This protocol is reliable and reproducible, hence would advance the development of irreversible covalent inhibitors toward disease treatment.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.