Automated computational screening of the thiol reactivity of substituted alkenes

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.

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

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

Quantum Descriptors for Predicting and Understanding the Structure-Activity Relationships of Michael Acceptor Warheads

Predictive modeling and understanding of chemical warhead reactivities have the potential to accelerate targeted covalent drug discovery. Recently, the carbanion formation free energies as well as other ground-state electronic properties from density functional theory (DFT) calculations have been proposed as predictors of glutathione reactivities of Michael acceptors; however, no clear consensus exists. By profiling the thiol-Michael reactions of a diverse set of singly- and doubly-activated olefins, including several model warheads related to afatinib, here we reexamined the question of whether low-cost electronic properties can be used as predictors of reaction barriers. The electronic properties related to the carbanion intermediate were found to be strong predictors, e.g., the change in the Cβ charge accompanying carbanion formation. The least expensive reactant-only properties, the electrophilicity index, and the Cβ charge also show strong rank correlations, suggesting their utility as quantum descriptors. A second objective of the work is to clarify the effect of the β-dimethylaminomethyl (DMAM) substitution, which is incorporated in the warheads of several FDA-approved covalent drugs. Our data suggest that the β-DMAM substitution is cationic at neutral pH in solution and promotes acrylamide's intrinsic reactivity by enhancing the charge accumulation at Cα upon carbanion formation. In contrast, the inductive effect of the β-trimethylaminomethyl substitution is diminished due to steric hindrance. Together, these results reconcile the current views of the intrinsic reactivities of acrylamides and contribute to large-scale predictive modeling and an understanding of the structure-activity relationships of Michael acceptors for rational TCI design.

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.

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.

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.

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.

A Fast Ab Initio Predictor Tool for Covalent Reactivity Estimation of Acrylamides

Thanks to their unique mode of action, covalent drugs represent an exceptional opportunity for drug design. After binding to a biologically relevant target system, covalent compounds form a reversible or irreversible covalent bond with a nucleophilic amino acid. Due to the inherently large binding energy of a covalent bond, covalent binders exhibit higher potencies and thus allow potentially lower drug dosages. However, a proper balancing of compound reactivity is key for the design of covalent binders, to achieve high levels of target inhibition while minimizing promiscuous covalent binding to nontarget proteins. In this work, we demonstrated the possibility to apply the electrophilicity index concept to estimate covalent compound reactivity. We tested this approach on acrylamides, one of the most prominent classes of covalent warheads. Our study clearly demonstrated that, for compounds with molecular weight (MW) below 250 Da, the electrophilicity index can be directly used to estimate compound reactivity. On the other hand, for leadlike molecules (MW > 250 Da) we implemented a new truncation algorithm that has to be applied before reactivity calculations. This algorithm can ensure the localization of HOMO/LUMO orbitals on the compound warhead and thus a correct estimation of its reactivity. Our results also indicate that caution should be used when employing the electrophilicity index to estimate the reactivity of nonterminal acrylamides. The nonparametric nature of this method and its reasonable computational cost make it a suitable tool to support covalent drug design.

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.

The hydration structure of methylthiolate from QM/MM molecular dynamics

Thiols are widely present in biological systems, most notably as the side chain of cysteine amino acids in proteins. Thiols can be deprotonated to form a thiolate which affords a diverse range of enzymatic activity and modes for chemical modification of proteins. Parameters for modeling thiolates using molecular mechanical force fields have not yet been validated, in part due to the lack of structural data on thiolate solvation. Here, the CHARMM36 and Amber models for thiolates in aqueous solutions are assessed using free energy perturbation and hybrid quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations. The hydration structure of methylthiolate was calculated from 1 ns of QM/MM MD (PBE0-D3/def2-TZVP//TIP3P), which shows that the water-S^- distances are approximately 2 Å with a coordination number near 6. The CHARMM thiolate parameters predict a thiolate S radius close to the QM/MM value and predict a hydration Gibbs energy of -329.2 kJ/mol, close to the experimental value of -318 kJ/mol. The cysteine thiolate model in the Amber force field underestimates the thiolate radius by 0.2 Å and overestimates the thiolate hydration energy by 119 kJ/mol because it uses the same Lennard-Jones parameters for thiolates as for thiols. A recent Drude polarizable model for methylthiolate with optimized thiolate parameters also performs well. SAPT2+ [Symmetry Adapted Perturbation Theory (SAPT)] analysis indicates that exchange repulsion is larger for the methylthiolate, consistent with it having a more diffuse electron density distribution in comparison with the parent thiol. These data demonstrate that it is important to define distinct non-bonded parameters for the protonated/deprotonated states of amino acid side chains in molecular mechanical force fields.

DNA-Compatible Solid-Phase Combinatorial Synthesis of β-Cyanoacrylamides and Related Electrophiles

We demonstrate that the Knoevenagel condensation can be exploited in combinatorial synthesis on the solid phase. Condensation products from such reactions were structurally characterized, and their Michael reactivity with thiol and phosphine nucleophiles is described. Cyanoacrylamides were previously reported to react reversibly with thiols, and notably, we show that dilution into low pH buffer can trap covalent adducts, which are isolable via chromatography. Finally, we synthesized both traditional and DNA-encoded one-bead, one-compound libraries containing cyanoacrylamides as a source of cysteine-reactive reversibly covalent protein ligands.

Novel K-Ras G12C Switch-II Covalent Binders Destabilize Ras and Accelerate Nucleotide Exchange

The success of targeted covalent inhibitors in the global pharmaceutical industry has led to a resurgence of covalent drug discovery. However, covalent inhibitor design for flexible binding sites remains a difficult task due to a lack of methodological development. Here, we compared covalent docking to empirical electrophile screening against the highly dynamic target K-Ras^G12C. While the overall hit rate of both methods was comparable, we were able to rapidly progress a docking hit to a potent irreversible covalent binder that modifies the inactive, GDP-bound state of K-Ras^G12C. Hydrogen-deuterium exchange mass spectrometry was used to probe the protein dynamics of compound binding to the switch-II pocket and subsequent destabilization of the nucleotide-binding region. SOS-mediated nucleotide exchange assays showed that, contrary to prior switch-II pocket inhibitors, these new compounds appear to accelerate nucleotide exchange. This study highlights the efficiency of covalent docking as a tool for the discovery of chemically novel hits against challenging targets.

Covalent inhibitors design and discovery

In the history of therapeutics, covalent drugs occupy a very distinct category. While representing a significant fraction of the drugs on the market, very few have been deliberately designed to interact covalently with their biological target. In this review, the prevalence of covalent drugs will first be briefly covered, followed by an introduction to their mechanisms of action and more detailed discussions of their discovery and the development of safe and efficient covalent enzyme inhibitors. All stages of a drug discovery program will be covered, from target considerations to lead optimization, strategies to tune reactivity and computational methods. The goal of this article is to provide an overview of the field and to outline good practices that are needed for the proper assessment and development of covalent inhibitors as well as a good understanding of the potential and limitations of current computational methods for the design of covalent drugs.

Modeling covalent-modifier drugs

In this review, we present a summary of how computer modeling has been used in the development of covalent-modifier drugs. Covalent-modifier drugs bind by forming a chemical bond with their target. This covalent binding can improve the selectivity of the drug for a target with complementary reactivity and result in increased binding affinities due to the strength of the covalent bond formed. In some cases, this results in irreversible inhibition of the target, but some targeted covalent inhibitor (TCI) drugs bind covalently but reversibly. Computer modeling is widely used in drug discovery, but different computational methods must be used to model covalent modifiers because of the chemical bonds formed. Structural and bioinformatic analysis has identified sites of modification that could yield selectivity for a chosen target. Docking methods, which are used to rank binding poses of large sets of inhibitors, have been augmented to support the formation of protein-ligand bonds and are now capable of predicting the binding pose of covalent modifiers accurately. The pK_a's of amino acids can be calculated in order to assess their reactivity towards electrophiles. QM/MM methods have been used to model the reaction mechanisms of covalent modification. The continued development of these tools will allow computation to aid in the development of new covalent-modifier drugs. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.