Quantum Chemical Methods for Modeling Covalent Modification of Biological Thiols

QM/MM Simulations of Afatinib-EGFR Addition: The Role of β-Dimethylaminomethyl Substitution

Acrylamides are the most commonly used warheads of targeted covalent inhibitors (TCIs) directed at cysteines; however, the reaction mechanisms of acrylamides in proteins remain controversial, particularly for those involving protonated or unreactive cysteines. Using the combined semiempirical quantum mechanics (QM)/molecular mechanics (MM) free energy simulations, we investigated the reaction between afatinib, the first TCI drug for cancer treatment, and Cys797 in the EGFR kinase. Afatinib contains a β-dimethylaminomethyl (β-DMAM) substitution which has been shown to enhance the intrinsic reactivity and potency against EGFR for related inhibitors. Two hypothesized reaction mechanisms were tested. Our data suggest that Cys797 becomes deprotonated in the presence of afatinib, and the reaction proceeds via a classical Michael addition mechanism, with Asp800 stabilizing the ion-pair reactant state β-DMAM+/C797- and the transition state of the nucleophilic attack. Our work elucidates an important structure-activity relationship of acrylamides in proteins.

QM/MM simulations of EFGR with afatinib reveal the role of the β-dimethylaminomethyl substitution

Acrylamides are the most commonly used warheads of targeted covalent inhibitors (TCIs) directed at cysteines; however, the reaction mechanisms of acrylamides in proteins remain controversial, particularly for those involving protonated or unreactive cysteines. Using the combined semiempirical quantum mechanics (QM)/molecular mechanics (MM) free energy simulations, we investigated the reaction between afatinib, the first TCI drug for cancer treatment, and Cys797 in the EGFR kinase. Afatinib contains a β-dimethylaminomethyl (β-DMAM) substitution which has been shown to enhance the intrinsic reactivity and potency against EGFR for related inhibitors. Two hypothesized reaction mechanisms were tested. Our data suggest that Cys797 becomes deprotonated in the presence of afatinib and the reaction proceeds via a classical Michael addition mechanism, with Asp800 stabilizing the ion-pair reactant state β-DMAM+/C797- and the transition state of the nucleophilic attack. Our work elucidates an important structure-activity relationship of acrylamides in proteins.

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.

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.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

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.

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.

A mechanistic study of thiol addition to N-acryloylpiperidine

Nucleophilic cysteine (Cys) residues are present in many enzyme active sites and represent the target of many different irreversible enzyme inhibitors. Given its fine balance between aqueous stability and thiolate reactivity, the acrylamide group is a particularly popular warhead pharmacophore among inhibitors designed for biological and therapeutic application. The acrylamide group is well known to undergo thiol addition, but the precise mechanism of this addition reaction has not been studied in as much detail. In this work we have focussed on the reaction of N-acryloylpiperidine (AcrPip), which represents a motif found in many targeted covalent inhibitor drugs. Using a precise HPLC-based assay, we measured the second order rate constants for the reaction of AcrPip with a panel of thiols possessing different pK_a values. This allowed construction of a Brønsted-type plot that reveals the relative insensitivity of the reaction to the nucleophilicity of the thiolate. By studying temperature effects, we were able to construct an Eyring plot from which the enthalpy and entropy of activation were calculated. Ionic strength and solvent kinetic isotope effects were also studied, informing on charge dispersal and proton transfer in the transition state. DFT calculations were also performed, providing information on the potential structure of the activated complex. Taken together, these data strongly support one cohesive addition mechanism that is the microscopic reverse of the E1cb elimination, and highly relevant to the intrinsic thiol selectivity of AcrPip inhibitors and their subsequent design.

Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces

We have used molecular dynamics (MD) simulations with hybrid quantum mechanics/molecular mechanics (QM/MM) potentials to investigate the reaction mechanism for covalent inhibition of cathepsin K and assess the reversibility of inhibition. The computed free energy profiles suggest that a nucleophilic attack by the catalytic cysteine on the inhibitor warhead and proton transfer from the catalytic histidine occur in a concerted manner. The results indicate that the reaction is more strongly exergonic for the alkyne-based inhibitors, which bind irreversibly to cathepsin K, than for the nitrile-based inhibitor odanacatib, which binds reversibly. Gas-phase energies were also calculated for the addition of methanethiol to structural prototypes for a number of warheads of interest in cysteine protease inhibitor design in order to assess electrophilicity. The approaches presented in this study are particularly applicable to assessment of novel warheads, and computed transition state geometries can be incorporated into molecular models for covalent docking.

Testing Affordable Strategies for the Computational Study of Reactivity in Cysteine Proteases: The Case of SARS-CoV-2 3CL Protease Inhibition

Cysteine proteases are an important target for the development of inhibitors that could be used as drugs to regulate the activity of these kinds of enzymes involved in many diseases, including COVID-19. For this reason, it is important to have methodological tools that allow a detailed study of their activity and inhibition, combining computational efficiency and accuracy. We here explore the performance of different quantum mechanics/molecular mechanics methods to explore the inhibition reaction mechanism of the SARS-CoV-2 3CL protease with a hydroxymethyl ketone derivative. We selected two density functional theory (DFT) functionals (B3LYP and M06-2X), two semiempirical Hamiltonians (AM1d and PM6), and two tight-binding DFT methods (DFTB3 and GFN2-xTB) to explore the free energy landscape associated with this reaction. We show that it is possible to obtain an accurate description combining molecular dynamics simulations performed using tight-binding DFT methods and single-point energy corrections at a higher QM description. The use of a computational strategy that provides reliable results at a reasonable computational cost could assist the in silico screening of possible candidates during the design of new drugs directed against cysteine proteases.

A mechanistic study of thiol addition to N-phenylacrylamide

Experimental data from a Brønsted-type plot, a solvent kinetic isotope effect, a pH-rate plot and temperature studies are all consistent with rate-limiting nucleophilic attack of thiolate followed by rapid protonation of the enolate adduct.

Chemical reactivity prediction: current methods and different application areas

The ability to predict chemical reactivity of a molecule is highly desirable in drug discovery, both ex vivo (synthetic route planning, formulation, stability) and in vivo: metabolic reactions determine pharmacodynamics, pharmacokinetics and potential toxic effects, and early assessment of liabilities is vital to reduce attrition rates in later stages of development. Quantum mechanics offer a precise description of the interactions between electrons and orbitals in the breaking and forming of new bonds. Modern algorithms and faster computers have allowed the study of more complex systems in a punctual and accurate fashion, and answers for chemical questions around stability and reactivity can now be provided. Through machine learning, predictive models can be built out of descriptors derived from quantum mechanics and cheminformatics, even in the absence of experimental data to train on. In this article, current progress on computational reactivity prediction is reviewed: applications to problems in drug design, such as modelling of metabolism and covalent inhibition, are highlighted and unmet challenges are posed.

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.

Reactivities of the Front Pocket N-Terminal Cap Cysteines in Human Kinases

The front pocket (FP) N-terminal cap (Ncap) cysteine is the most popular site of covalent modification in kinases. A long-standing hypothesis associates the Ncap position with cysteine hyper-reactivity; however, traditional computational predictions suggest that the FP Ncap cysteines are predominantly unreactive. Here we applied the state-of-the-art continuous constant pH molecular dynamics (CpHMD) to test the Ncap hypothesis. Simulations found that the Ncap cysteines of BTK/BMX/TEC/ITK/TXK, JAK3, and MKK7 are reactive to varying degrees; however, those of BLK and EGFR/ERBB2/ERBB4 possessing a Ncap+3 aspartate are unreactive. Analysis suggested that hydrogen bonding and electrostatic interactions drive the reactivity, and their absence renders the Ncap cysteine unreactive. To further test the Ncap hypothesis, we examined the FP Ncap+2 cysteines in JNK1/JNK2/JNK3 and CASK. Our work offers a systematic understanding of the cysteine structure-reactivity relationship and illustrates the use of CpHMD to differentiate cysteines toward the design of targeted covalent inhibitors with reduced chemical reactivities.

A microscopic description of SARS-CoV-2 main protease inhibition with Michael acceptors. Strategies for improving inhibitor design

The irreversible inhibition of the main protease of SARS-CoV-2 by a Michael acceptor known as N3 has been investigated using multiscale methods. The noncovalent enzyme-inhibitor complex was simulated using classical molecular dynamics techniques and the pose of the inhibitor in the active site was compared to that of the natural substrate, a peptide containing the Gln-Ser scissile bond. The formation of the covalent enzyme-inhibitor complex was then simulated using hybrid QM/MM free energy methods. After binding, the reaction mechanism was found to be composed of two steps: (i) the activation of the catalytic dyad (Cys145 and His41) to form an ion pair and (ii) a Michael addition where the attack of the Sγ atom of Cys145 to the Cβ atom of the inhibitor precedes the water-mediated proton transfer from His41 to the Cα atom. The microscopic description of protease inhibition by N3 obtained from our simulations is strongly supported by the excellent agreement between the estimated activation free energy and the value derived from kinetic experiments. Comparison with the acylation reaction of a peptide substrate suggests that N3-based inhibitors could be improved by adding chemical modifications that could facilitate the formation of the catalytic dyad ion pair.

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.

Affinity and Selectivity Assessment of Covalent Inhibitors by Free Energy Calculations

Covalent inhibitors have been gaining increased attention in drug discovery due to their beneficial properties such as long residence time, high biochemical efficiency, and specificity. Optimization of covalent inhibitors is a complex task that involves parallel monitoring of the noncovalent recognition elements and the covalent reactivity of the molecules to avoid potential idiosyncratic side effects. This challenge calls for special design protocols, including a variety of computational chemistry methods. Covalent inhibition proceeds through multiple steps, and calculating free energy changes of the subsequent binding events along the overall binding process would help us to better control the design of drug candidates. Inspired by the recent success of free energy calculations on reversible binders, we developed a complex protocol to compute free energies related to the noncovalent and covalent binding steps with thermodynamic integration and hybrid quantum mechanical/molecular mechanical (QM/MM) potential of mean force (PMF) calculations, respectively. In optimization settings, we examined two therapeutically relevant proteins complexed with congeneric sets of irreversible cysteine targeting covalent inhibitors. In the selectivity paradigm, we studied the irreversible binding of covalent inhibitors to phylogenetically close targets by a mutational approach. The results of the calculations are in good agreement with the experimental free energy values derived from the inhibition and kinetic constants (K_i and k_inact) of the enzyme-inhibitor binding. The proposed method might be a powerful tool to predict the potency, selectivity, and binding mechanism of irreversible covalent inhibitors.

Systematic Study of the Glutathione Reactivity of N-Phenylacrylamides: 2. Effects of Acrylamide Substitution

A comprehensive understanding of structure-reactivity relationships is critical to the design and optimization of cysteine-targeted covalent inhibitors. Herein, we report glutathione (GSH) reaction rates for N-phenyl acrylamides with varied substitutions at the α- and β-positions of the acrylamide moiety. We find that the GSH reaction rates can generally be understood in terms of the electron donating or withdrawing ability of the substituent. When installed at the β-position, aminomethyl substituents with amine pK_a's > 7 accelerate, while those with pK_a's < 7 slow the rate of GSH addition at pH 7.4, relative to a hydrogen substituent. Although a computational model was able to only approximately capture experimental reactivity trends, our calculations do not support a frequently invoked mechanism of concerted amine/thiol proton transfer and C-S bond formation and instead suggest that protonated aminomethyl functions as an electron-withdrawing group to reduce the barrier for thiolate addition to the acrylamide.

Decomplexation as a rate limitation in the thiol-Michael addition of N-acrylamides

The thiol-Michael addition is a popular, selective, high-yield "click" reaction utilized for applications ranging from small-molecule synthesis to polymer or surface modification. Here, we combined experimental and quantum mechanical modeling approaches using density functional theory (DFT) to examine the thiol-Michael reaction of N-allyl-N-acrylamide monomers used to prepare sequence-defined oligothioetheramides (oligoTEAs). Experimentally, the reaction was evaluated with two fluorous tagged thiols and several monomers at room temperature (22 °C and 40 °C). Using the Eyring equation, the activation energies (enthalpies) were calculated, observing a wide range of energy barriers ranging from 28 kJ mol-1 to 108 kJ mol-1 within the same alkene class. Computationally, DFT coupled with the Nudged Elastic Band method was used to calculate the entire reaction coordinate of each monomer reaction using the B97-D3 functional and a hybrid implicit-explicit methanol solvation approach. The thiol-Michael reaction is traditionally rate-limited by the propagation or chain-transfer steps. However, our test case with N-acrylamides and fluorous thiols revealed experimental and computational data produced satisfactory agreement only when we considered a previously unconsidered step that we termed "product decomplexation", which occurs as the product physically dissociates from other co-reactants after chain transfer. Five monomers were investigated to support this finding, capturing a range of functional groups varying in alkyl chain length (methyl to hexyl) and aromaticity (benzyl and ethylenephenyl). Increased substrate alkyl chain length increased activation energy, explained by the inductive effect. Aromatic ring-stacking configurations significantly impacted the activation energy and contributed to improved molecular packing density. Hydrogen-bonding between reactants increased the activation energy emphasizing the rate-limitation of the product decomplexation. Our findings begin to describe a new structure-kinetic relationship for thiol-Michael acceptors to enable further design of reactive monomers for synthetic polymers and biomaterials.

Revealing the molecular mechanisms of proteolysis of SARS-CoV-2 Mpro by QM/MM computational methods

SARS-CoV-2 M^pro is one of the enzymes essential for the replication process of the virus responsible for the COVID-19 pandemic. This work is focused on exploring its proteolysis reaction by means of QM/MM methods. The resulting free energy landscape of the process provides valuable information on the species appearing along the reaction path and suggests that the mechanism of action of this enzyme, taking place in four steps, slightly differs from that of other cysteine proteases. Our predictions, which are in agreement with some recently published experimental data, can be used to guide the design of COVID-19 antiviral compounds with clinical potential.

The molecular mechanism of the proteolysis reaction catalyzed by SARS-CoV-2 M^pro, one of the enzymes essential for the replication process of the virus responsible for the COVID-19 pandemic, is described using computational QM/MM methods.

BIreactive: A Machine-Learning Model to Estimate Covalent Warhead Reactivity

In the past decade, the pharmaceutical industry has paid closer attention to covalent drugs. Differently from standard noncovalent drugs, these compounds can exhibit peculiar properties, such as higher potency or longer duration of target inhibition with a potentially lower dosage. These properties are mainly driven by the reactive functional group present in the compound, the so-called warhead that forms a covalent bond with a specific nucleophilic amino-acid on the target. In this work, we report the possibility to combine ab initio activation energies with machine-learning to estimate covalent compound intrinsic reactivity. The idea behind this approach is to have a precise estimation of the transition state barriers, and thus of the compound reactivity, but with the speed of a machine-learning algorithm. We call this method "BIreactive". Here, we demonstrate this approach on acrylamides and 2-chloroacetamides, two warhead classes that possess different reaction mechanisms. In combination with our recently implemented truncation algorithm, we also demonstrate the possibility to use BIreactive not only for fragments but also for lead-like molecules. The generic nature of this approach allows also the extension to several other warheads. The combination of these factors makes BIreactive a valuable tool for the covalent drug discovery process in a pharmaceutical context.

How does cross-conjugation influence thiol additions to enones? A computational study of thiol trapping by the naturally occurring divinyl ketones zerumbone and α-santonin

DFT calculations show how the kinetics and thermodynamics of thiol additions to enones are affected by incorporation of the enone into a cross-conjugated divinyl ketone moiety.