Covalent docking of large libraries for the discovery of chemical probes

Emerging and Re-emerging Warheads for Targeted Covalent Inhibitors: An Update

Covalent inhibitors and other types of covalent modalities have seen a revival in the past two decades, with a variety of new targeted covalent drugs having been approved in recent years. A key feature of such molecules is an intrinsically reactive group, typically a weak electrophile, which enables the irreversible or reversible formation of a covalent bond with a specific amino acid of the target protein. This reactive group, often called the "warhead", is a critical determinant of the ligand's activity, selectivity, and general biological properties. In 2019, we summarized emerging and re-emerging warhead chemistries to target cysteine and other amino acids (Gehringer, M.; Laufer, S. A. J. Med. Chem. 2019, 62, 5673-5724; DOI: 10.1021/acs.jmedchem.8b01153). Since then, the field has rapidly evolved. Here we discuss the progress on covalent warheads made since our last Perspective and their application in medicinal chemistry and chemical biology.

Fluoromethylketone-Fragment Conjugates Designed as Covalent Modifiers of EcDsbA are Atypical Substrates

Disulfide bond protein A (DsbA) is an oxidoreductase enzyme that catalyzes the formation of disulfide bonds in Gram-negative bacteria. In Escherichia coli, DsbA (EcDsbA) is essential for bacterial virulence, thus inhibitors have the potential to act as antivirulence agents. A fragment-based screen was conducted against EcDsbA and herein we describe the development of a series of compounds based on a phenylthiophene hit identified from the screen. A novel thiol reactive and "clickable" ethynylfluoromethylketone was designed for reaction with azide-functionalized fragments to enable rapid and versatile attachment to a range of fragments. The resulting fluoromethylketone conjugates showed selectivity for reaction with the active site thiol of EcDsbA, however unexpectedly, turnover of the covalent adduct was observed. A mechanism for this turnover was investigated and proposed which may have wider ramifications for covalent reactions with dithiol-disulfide oxidoreducatases.

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.

Compromise in Docking Power of Liganded Crystal Structures of Mpro SARS-CoV-2 Surpasses 90% Success Rate

Herein, we present the capacity of three different molecular docking programs (AutoDock, AutoDock Vina, and PLANTS) to identify and reproduce the binding modes of ligands present in 247 covalent and 169 noncovalent complex crystal structures of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). The compromise in docking power is evaluated with respect to their ability to generate poses similar to the crystal structure binding mode (heavy atoms' root-mean-square deviation < 2 Å) and their ability to recognize the native binding mode with an included compensation for the scoring function error. Noncovalently bound inhibitors are best modeled by AutoDock Vina (90.6% success rate in the active site), while the most relevant results for covalently bound inhibitors are produced by PLANTS (93.0%). AutoDock shows acceptable performance for both types of ligands, 81.1 and 76.4% for noncovalent and covalent complexes, respectively. All three programs manifest worse performance when reproducing surface-bound ligands. Comparison with other works illustrates the importance of crystal structure processing (12% of noncovalent and 26% of covalent ligands had to be manually corrected), proper sampling protocol settings, and inclusion of root-mean-square deviation (RMSD)/scoring function error compensations in crystal structure pose identification. Results are analyzed with respect to a clustering scheme of the noncovalently bound ligands and the chemical reaction type of the covalent ligand bound to the Cys145 residue. A comparison of screening power based on the docking scores of noncovalent ligands from the crystal structures with a "Directory of Useful Decoys, Enhanced" set of known decoys (6562 compounds) and ZINC15 in vivo subset (60,394 compounds) is provided. Ligand and protein input files are provided for future benchmarking purposes.

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.

Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

Xeno Amino Acids: A Look into Biochemistry as We Do Not Know It

Would another origin of life resemble Earth's biochemical use of amino acids? Here, we review current knowledge at three levels: (1) Could other classes of chemical structure serve as building blocks for biopolymer structure and catalysis? Amino acids now seem both readily available to, and a plausible chemical attractor for, life as we do not know it. Amino acids thus remain important and tractable targets for astrobiological research. (2) If amino acids are used, would we expect the same L-alpha-structural subclass used by life? Despite numerous ideas, it is not clear why life favors L-enantiomers. It seems clearer, however, why life on Earth uses the shortest possible (alpha-) amino acid backbone, and why each carries only one side chain. However, assertions that other backbones are physicochemically impossible have relaxed into arguments that they are disadvantageous. (3) Would we expect a similar set of side chains to those within the genetic code? Many plausible alternatives exist. Furthermore, evidence exists for both evolutionary advantage and physicochemical constraint as explanatory factors for those encoded by life. Overall, as focus shifts from amino acids as a chemical class to specific side chains used by post-LUCA biology, the probable role of physicochemical constraint diminishes relative to that of biological evolution. Exciting opportunities now present themselves for laboratory work and computing to explore how changing the amino acid alphabet alters the universe of protein folds. Near-term milestones include: (a) expanding evidence about amino acids as attractors within chemical evolution; (b) extending characterization of other backbones relative to biological proteins; and (c) merging computing and laboratory explorations of structures and functions unlocked by xeno peptides.

Covalent targeting of non-cysteine residues in PI4KIIIβ

The synthesis and characterisation of fluorosulfate covalent inhibitors of the lipid kinase PI4KIIIβ is described. The conserved lysine residue located within the ATP binding site was targeted, and optimised compounds based upon reversible inhibitors with good activity and physicochemical profile showed strong reversible interactions and slow onset times for the covalent inhibition, resulting in an excellent selectivity profile for the lipid kinase target. X-Ray crystallography demonstrated a distal tyrosine residue could also be targeted using a fluorosulfate strategy. Combination of this knowledge showed that a dual covalent inhibitor could be developed which reveals potential in addressing the challenges associated with drug resistant mutations.

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

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

Non-β Lactam Inhibitors of the Serine β-Lactamase blaCTX-M15 in Drug-Resistant Salmonella typhi

Antibiotic resistance by bacterial pathogens against widely used β-lactam drugs is a major concern to public health worldwide, resulting in high healthcare cost. The present study aimed to extend previous research by investigating the potential activity of reported compounds against the S. typhi β-lactamase protein. 74 compounds from computational screening reported in our previous study against β-lactamase CMY-10 were subjected to docking studies against blaCTX-M15. Site-Identification by Ligand Competitive Saturation (SILCS)-Monte Carlo (SILCS-MC) was applied to the top two ligands selected from molecular docking studies to predict and refine their conformations for binding conformations against blaCTX-M15. The SILCS-MC method predicted affinities of -8.6 and -10.7 kcal/mol for Top1 and Top2, respectively, indicating low micromolar binding to the blaCTX-M15 active site. MD simulations initiated from SILCS-MC docked orientations were carried out to better characterize the dynamics and stability of the complexes. Important interactions anchoring the ligand within the active site include pi-pi stacked, amide-pi, and pi-alkyl interactions. Simulations of the Top2-blaCTX-M15 complex exhibited stability associated with a wide range of hydrogen-bond and aromatic interactions between the protein and the ligand. Experimental β-lactamase (BL) activity assays showed that Top1 has 0.1 u/mg BL activity, and Top2 has a BL activity of 0.038 u/mg with a minimum inhibitory concentration of 1 mg/mL. The inhibitors proposed in this study are non-β-lactam-based β-lactamase inhibitors that exhibit the potential to be used in combination with β-lactam antibiotics against multidrug-resistant clinical isolates. Thus, Top1 and Top2 represent lead compounds that increase the efficacy of β-lactam antibiotics with a low dose concentration.

An electrophilic fragment screening for the development of small molecules targeting caspase-2

Recent Alzheimer's research has shown increasing interest in the caspase-2 (Casp2) enzyme. However, the available Casp2 inhibitors, which have been pentapeptides or peptidomimetics, face challenges for use as CNS drugs. In this study, we successfully screened a 1920-compound chloroacetamide-based, electrophilic fragment library from Enamine. Our two-point dose screen identified 64 Casp2 hits, which were further evaluated in a ten-point dose-response study to assess selectivity over Casp3. We discovered compounds with inhibition values in the single-digit micromolar and sub-micromolar range, as well as up to 32-fold selectivity for Casp2 over Casp3. Target engagement analysis confirmed the covalent-irreversible binding of the selected fragments to Cys320 at the active site of Casp2. Overall, our findings lay a strong foundation for the future development of small-molecule Casp2 inhibitors.

Reactive Docking: A Computational Method for High-Throughput Virtual Screenings of Reactive Species

We describe the formalization of the reactive docking protocol, a method developed to model and predict reactions between small molecules and biological macromolecules. The method has been successfully used in a number of applications already, including recapitulating large proteomics data sets, performing structure-reactivity target optimizations, and prospective virtual screenings. By modeling a near-attack conformation-like state, no QM calculations are required to model the ligand and receptor geometries. Here, we present its generalization using a large data set containing more than 400 ligand-target complexes, 8 nucleophilic modifiable residue types, and more than 30 warheads. The method correctly predicts the modified residue in ∼85% of complexes and shows enrichments comparable to standard focused virtual screenings in ranking ligands. This performance supports this approach for the docking and screening of reactive ligands in virtual chemoproteomics and drug design campaigns.

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

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

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

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

Large library docking for novel SARS-CoV-2 main protease non-covalent and covalent inhibitors

Antiviral therapeutics to treat SARS-CoV-2 are needed to diminish the morbidity of the ongoing COVID-19 pandemic. A well-precedented drug target is the main viral protease (MPro ), which is targeted by an approved drug and by several investigational drugs. Emerging viral resistance has made new inhibitor chemotypes more pressing. Adopting a structure-based approach, we docked 1.2 billion non-covalent lead-like molecules and a new library of 6.5 million electrophiles against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC50 of 29 and 20 μM, respectively. Several series were optimized, resulting in low micromolar inhibitors. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. While the new chemotypes may aid further optimization of MPro inhibitors for SARS-CoV-2, the modest success rate also reveals weaknesses in our approach for challenging targets like MPro versus other targets where it has been more successful, and versus other structure-based techniques against MPro itself.

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

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

Isoform-Selective Fluorescent Labeling of 14-3-3σ by Acrylamide-Containing Fusicoccins

The 14-3-3 family of proteins is central to the regulation of signaling pathways driven by serine/threonine kinases. In humans, 14-3-3 consists of seven highly conserved isoforms, yet the function of each isoform remains to be fully elucidated. Synthetic agents capable of isoform-specific fluorescent labeling of 14-3-3 would provide a useful tool for studying in depth the biological roles of isoforms. In this study, the 14-3-3σ isoform was evaluated, which possesses a unique Cys38, and a natural product-based fluorescent labeling agent was designed by introducing an acrylamide group and a fluorescent dye to fusicoccin (FC). In vitro evaluation demonstrated that 12-hydroxy 1 and 2 exhibit 14-3-3σ selective labeling activity over 14-3-3ζ in the presence of a mode-3 phospholigand. Furthermore, 2 was shown to label 14-3-3σ in cell lysate in the presence of a C-terminal mode-3 phosphopeptide derived from ERα, with no apparent nonspecific labeling. These results indicate that 2 is capable of selective fluorescent detection of 14-3-3σ upon binding to mode-3 phospholigand under biologically relevant conditions.

Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors

Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔG_bind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R² ≥ 0.5) with experimental values of ΔG_bind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

An under-explored target for SARS-CoV-2 is the S-adenosyl methionine (SAM)-dependent methyltransferase Nsp14, which methylates the N7-guanosine of viral RNA at the 5'-end, allowing the virus to evade host immune response. We sought new Nsp14 inhibitors with three large library docking strategies. First, up to 1.1 billion lead-like molecules were docked against the enzyme's SAM site, leading to three inhibitors with IC50 values from 6 to 50 μM. Second, docking a library of 16 million fragments revealed 9 new inhibitors with IC50 values from 12 to 341 μM. Third, docking a library of 25 million electrophiles to covalently modify Cys387 revealed 7 inhibitors with IC50 values from 3.5 to 39 μM. Overall, 32 inhibitors encompassing 11 chemotypes had IC50 values < 50 μM and 5 inhibitors in 4 chemotypes had IC50 values < 10 μM. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing starting points for future optimization.

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.

An update on the discovery and development of reversible covalent inhibitors

Small molecule drugs that covalently bind irreversibly to their target proteins have several advantages over conventional reversible inhibitors. They include increased duration of action, less-frequent drug dosing, reduced pharmacokinetic sensitivity, and the potential to target intractable shallow binding sites. Despite these advantages, the key challenges of irreversible covalent drugs are their potential for off-target toxicities and immunogenicity risks. Incorporating reversibility into covalent drugs would lead to less off-target toxicity by forming reversible adducts with off-target proteins and thus reducing the risk of idiosyncratic toxicities caused by the permanent modification of proteins, which leads to higher levels of potential haptens. Herein, we systematically review electrophilic warheads employed during the development of reversible covalent drugs. We hope the structural insights of electrophilic warheads would provide helpful information to medicinal chemists and aid in designing covalent drugs with better on-target selectivity and improved safety.

Graphical Abstract

Technologies for Direct Detection of Covalent Protein—Drug Adducts

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

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Reversible Dual-Covalent Molecular Locking of the 14-3-3/ERRγ Protein-Protein Interaction as a Molecular Glue Drug Discovery Approach

Molecules that stabilize protein-protein interactions (PPIs) are invaluable as tool compounds for biophysics and (structural) biology, and as starting points for molecular glue drug discovery. However, identifying initial starting points for PPI stabilizing matter is highly challenging, and chemical optimization is labor-intensive. Inspired by chemical crosslinking and reversible covalent fragment-based drug discovery, we developed an approach that we term "molecular locks" to rapidly access molecular glue-like tool compounds. These dual-covalent small molecules reversibly react with a nucleophilic amino acid on each of the partner proteins to dynamically crosslink the protein complex. The PPI between the hub protein 14-3-3 and estrogen-related receptor γ (ERRγ) was used as a pharmacologically relevant case study. Based on a focused library of dual-reactive small molecules, a molecular glue tool compound was rapidly developed. Biochemical assays and X-ray crystallographic studies validated the ternary covalent complex formation and overall PPI stabilization via dynamic covalent crosslinking. The molecular lock approach is highly selective for the specific 14-3-3/ERRγ complex, over other 14-3-3 complexes. This selectivity is driven by the interplay of molecular reactivity and molecular recognition of the composite PPI binding interface. The long lifetime of the dual-covalent locks enabled the selective stabilization of the 14-3-3/ERRγ complex even in the presence of several other competing 14-3-3 clients with higher intrinsic binding affinities. The molecular lock approach enables systematic, selective, and potent stabilization of protein complexes to support molecular glue drug discovery.

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

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

SiteRadar: Utilizing Graph Machine Learning for Precise Mapping of Protein-Ligand-Binding Sites

Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based drug design. Although multiple techniques have been proposed, including those using machine learning algorithms, the existing solutions do not provide significant advantages over nonmachine learning approaches and there is still a big room for improvement. The low ability to identify protein-ligand-binding sites makes available approaches inapplicable to automated drug design. Here, we present SiteRadar, a new algorithm for mapping cavities that are likely to bind a small-molecule ligand. SiteRadar shows higher accuracy in binding site identification compared with FPocket and PUResNet. SiteRadar demonstrates an ability to detect up to 74% of true ligand-binding sites according to the top N + 2 metric and usually covers approximately 80% of ligand atoms. Therefore, SiteRadar can be regarded as a promising solution for implementation into algorithms for automated drug design.

Selective Covalent Targeting of Pyruvate Kinase M2 Using Arsenous Warheads

There is growing interest in covalent targeted inhibitors in drug discovery against previously "undruggable" sites and targets. These molecules typically feature an electrophilic warhead that reacts with nucleophilic groups of protein residues, most notably the thiol group of cysteines. One main challenge in the field is to develop versatile utilizable warheads. Here, we characterize the unique features of novel arsenous warheads for reaction with thiol species in a reversible manner and further demonstrate that organoarsenic probes can be chemically tuned toward specific molecular targets by developing selective and potent inhibitors of pyruvate kinase M2 (PKM2). We show that compound 24 is a covalent and allosteric inhibitor of PKM2 and its orally bioavailable prodrug 25 exerts efficacious inhibition of PKM2-dependent tumor growth in vitro and in vivo. Our results introduce 25 and its derivatives as useful pharmacological tools and provide a general road map for targeting the protein cysteinome using arsenous warheads.

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.

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

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

Profiling Sulfur(VI) Fluorides as Reactive Functionalities for Chemical Biology Tools and Expansion of the Ligandable Proteome

Here, we report a comprehensive profiling of sulfur(VI) fluorides (S^VI-Fs) as reactive groups for chemical biology applications. S^VI-Fs are reactive functionalities that modify lysine, tyrosine, histidine, and serine sidechains. A panel of S^VI-Fs were studied with respect to hydrolytic stability and reactivity with nucleophilic amino acid sidechains. The use of S^VI-Fs to covalently modify carbonic anhydrase II (CAII) and a range of kinases was then investigated. Finally, the S^VI-F panel was used in live cell chemoproteomic workflows, identifying novel protein targets based on the type of S^VI-F used. This work highlights how S^VI-F reactivity can be used as a tool to expand the liganded proteome.

BIreactive: Expanding the Scope of Reactivity Predictions to Propynamides

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

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.

Advanced approaches of developing targeted covalent drugs

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

CovBinderInPDB: A Structure-Based Covalent Binder Database

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

Advances in covalent drug discovery

Covalent drugs have been used to treat diseases for more than a century, but tools that facilitate the rational design of covalent drugs have emerged more recently. The purposeful addition of reactive functional groups to existing ligands can enable potent and selective inhibition of target proteins, as demonstrated by the covalent epidermal growth factor receptor (EGFR) and Bruton's tyrosine kinase (BTK) inhibitors used to treat various cancers. Moreover, the identification of covalent ligands through 'electrophile-first' approaches has also led to the discovery of covalent drugs, such as covalent inhibitors for KRAS(G12C) and SARS-CoV-2 main protease. In particular, the discovery of KRAS(G12C) inhibitors validates the use of covalent screening technologies, which have become more powerful and widespread over the past decade. Chemoproteomics platforms have emerged to complement covalent ligand screening and assist in ligand discovery, selectivity profiling and target identification. This Review showcases covalent drug discovery milestones with emphasis on the lessons learned from these programmes and how an evolving toolbox of covalent drug discovery techniques facilitates success in this field.

Structure-guided design and characterization of a clickable, covalent PARP16 inhibitor

PARP16-the sole ER-resident PARP family member-is gaining attention as a potential therapeutic target for cancer treatment. Nevertheless, the precise function of the catalytic activity of PARP16 is poorly understood. This is primarily due to the lack of inhibitors that are selective for PARP16 over other PARP family members. Herein, we describe a structure-guided strategy for generating a selective PARP16 inhibitor by incorporating two selectivity determinants into a phthalazinone pan-PARP inhibitor scaffold: (i) an acrylamide-based inhibitor (DB008) designed to covalently react with a non-conserved cysteine (Cys169, human numbering) in the NAD+ binding pocket of PARP16 and (ii) a dual-purpose ethynyl group designed to bind in a unique hydrophobic cavity adjacent to the NAD+ binding pocket as well as serve as a click handle. DB008 exhibits good selectivity for PARP16 versus other PARP family members. Copper-catalyzed azide-alkyne cycloaddition (CuAAC) confirmed that covalent labeling of PARP16 by DB008 in cells is dependent on Cys169. DB008 exhibits excellent proteome-wide selectivity at concentrations required to achieve saturable labeling of endogenous PARP16. In-cell competition labeling experiments using DB008 provided a facile strategy for evaluating putative PARP16 inhibitors. Lastly, we found that PARP16 is sequestered into a detergent-insoluble fraction under prolonged amino acid starvation, and surprisingly, treatment with PARP16 inhibitors prevented this effect. These results suggest that the catalytic activity of PARP16 regulates its solubility in response to nutrient stress.

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.

Fragment-based drug discovery-the importance of high-quality molecule libraries

Fragment-based drug discovery (FBDD) is now established as a complementary approach to high-throughput screening (HTS). Contrary to HTS, where large libraries of drug-like molecules are screened, FBDD screens involve smaller and less complex molecules which, despite a low affinity to protein targets, display more 'atom-efficient' binding interactions than larger molecules. Fragment hits can, therefore, serve as a more efficient start point for subsequent optimisation, particularly for hard-to-drug targets. Since the number of possible molecules increases exponentially with molecular size, small fragment libraries allow for a proportionately greater coverage of their respective 'chemical space' compared with larger HTS libraries comprising larger molecules. However, good library design is essential to ensure optimal chemical and pharmacophore diversity, molecular complexity, and physicochemical characteristics. In this review, we describe our views on fragment library design, and on what constitutes a good fragment from a medicinal and computational chemistry perspective. We highlight emerging chemical and computational technologies in FBDD and discuss strategies for optimising fragment hits. The impact of novel FBDD approaches is already being felt, with the recent approval of the covalent KRAS^G12C inhibitor sotorasib highlighting the utility of FBDD against targets that were long considered undruggable.

Systematic Exploration of Privileged Warheads for Covalent Kinase Drug Discovery

Kinase-targeted drug discovery for cancer therapy has advanced significantly in the last three decades. Currently, diverse kinase inhibitors or degraders have been reported, such as allosteric inhibitors, covalent inhibitors, macrocyclic inhibitors, and PROTAC degraders. Out of these, covalent kinase inhibitors (CKIs) have been attracting attention due to their enhanced selectivity and exceptionally strong affinity. Eight covalent kinase drugs have been FDA-approved thus far. Here, we review current developments in CKIs. We explore the characteristics of the CKIs: the features of nucleophilic amino acids and the preferences of electrophilic warheads. We provide systematic insights into privileged warheads for repurposing to other kinase targets. Finally, we discuss trends in CKI development across the whole proteome.

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

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

Covalent docking in CDOCKER

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

Design of Rational JAK3 Inhibitors Based on the Parent Core Structure of 1,7-Dihydro-Dipyrrolo [2,3-b:3',2'-e] Pyridine

JAK3 differs from other JAK family members in terms of tissue distribution and functional properties, making it a promising target for autoimmune disease treatment. However, due to the high homology of these family members, targeting JAK3 selectively is difficult. As a result, exploiting small changes or selectively boosting affinity within the ATP binding region to produce new tailored inhibitors of JAK3 is extremely beneficial. PubChem CID 137321159 was used as the lead inhibitor in this study to preserve the characteristic structure and to collocate it with the redesigned new parent core structure, from which a series of 1,7-dihydro-dipyrrolo [2,3-b:3′,2′-e] pyridine derivatives were obtained using the backbone growth method. From the proposed compounds, 14 inhibitors of JAK3 were found based on the docking scoring evaluation. The RMSD and MM/PBSA methods of molecular dynamics simulations were also used to confirm the stable nature of this series of complex systems, and the weak protein−ligand interactions during the dynamics were graphically evaluated and further investigated. The results demonstrated that the new parent core structure fully occupied the hydrophobic cavity, enhanced the interactions of residues LEU828, VAL836, LYS855, GLU903, LEU905 and LEU956, and maintained the structural stability. Apart from this, the results of the analysis show that the binding efficiency of the designed inhibitors of JAK3 is mainly achieved by electrostatic and VDW interactions and the order of the binding free energy with JAK3 is: 8 (−70.286 kJ/mol) > 11 (−64.523 kJ/mol) > 6 (−51.225 kJ/mol) > 17 (−42.822 kJ/mol) > 10 (−40.975 kJ/mol) > 19 (−39.754 kJ/mol). This study may provide a valuable reference for the discovery of novel JAK3 inhibitors for those patients with immune diseases.

Class C β-Lactamases: Molecular Characteristics

Class C β-lactamases or cephalosporinases can be classified into two functional groups (1, 1e) with considerable molecular variability (≤20% sequence identity). These enzymes are mostly encoded by chromosomal and inducible genes and are widespread among bacteria, including Proteobacteria in particular. Molecular identification is based principally on three catalytic motifs (⁶⁴SXSK, ¹⁵⁰YXN, ³¹⁵KTG), but more than 70 conserved amino-acid residues (≥90%) have been identified, many close to these catalytic motifs. Nevertheless, the identification of a tiny, phylogenetically distant cluster (including enzymes from the genera Legionella, Bradyrhizobium, and Parachlamydia) has raised questions about the possible existence of a C2 subclass of β-lactamases, previously identified as serine hydrolases. In a context of the clinical emergence of extended-spectrum AmpC β-lactamases (ESACs), the genetic modifications observed in vivo and in vitro (point mutations, insertions, or deletions) during the evolution of these enzymes have mostly involved the Ω- and H-10/R2-loops, which vary considerably between genera, and, in some cases, the conserved triplet ¹⁵⁰YXN. Furthermore, the conserved deletion of several amino-acid residues in opportunistic pathogenic species of Acinetobacter, such as A. baumannii, A. calcoaceticus, A. pittii and A. nosocomialis (deletion of residues 304-306), and in Hafnia alvei and H. paralvei (deletion of residues 289-290), provides support for the notion of natural ESACs. The emergence of higher levels of resistance to β-lactams, including carbapenems, and to inhibitors such as avibactam is a reality, as the enzymes responsible are subject to complex regulation encompassing several other genes (ampR, ampD, ampG, etc.). Combinations of resistance mechanisms may therefore be at work, including overproduction or change in permeability, with the loss of porins and/or activation of efflux systems.

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

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

Covalent Proximity Scanning of a Distal Cysteine to Target PI3Kα

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

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

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

Targeting key proteins involved in transcriptional regulation for cancer therapy: Current strategies and future prospective

The key proteins involved in transcriptional regulation play convergent roles in cellular homeostasis, and their dysfunction mediates aberrant gene expressions that underline the hallmarks of tumorigenesis. As tumor progression is dependent on such abnormal regulation of transcription, it is important to discover novel chemical entities as antitumor drugs that target key tumor-associated proteins involved in transcriptional regulation. Despite most key proteins (especially transcription factors) involved in transcriptional regulation are historically recognized as undruggable targets, multiple targeting approaches at diverse levels of transcriptional regulation, such as epigenetic intervention, inhibition of DNA-binding of transcriptional factors, and inhibition of the protein-protein interactions (PPIs), have been established in preclinically or clinically studies. In addition, several new approaches have recently been described, such as targeting proteasomal degradation and eliciting synthetic lethality. This review will emphasize on accentuating these developing therapeutic approaches and provide a thorough conspectus of the drug development to target key proteins involved in transcriptional regulation and their impact on future oncotherapy.

An in-membrane NMR spectroscopic approach probing native ligand-GPCR interaction

Conventional approaches to study ligand-receptor interactions using solution-state NMR often involve laborious sample preparation, isotopic labeling, and receptor reconstitution. Each of these steps remains challenging for membrane proteins such as G protein-coupled receptors (GPCRs). Here we introduce a combinational approach integrating NMR and homogenized membrane nano-discs preparation to characterize the ligand-GPCR interactions. The approach will have a great potential for drug screening as it benefits from minimal receptor preparation, minimizing non-specific binding. In addition, the approach maintains receptor structural heterogeneity essential for functional diversity, making it feasible for probing a more reliable ligand-GPCR interaction that is vital for faithful ligand discovery.