In Silico Identification of a Novel Hinge-Binding Scaffold for Kinase Inhibitor Discovery

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 μM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.

Systematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual Screening

In recent years, large-scale structure-based virtual screening has attracted increasing levels of interest for identification of novel compounds corresponding to potential drug targets. It is critical to understand the strengths and weaknesses of docking algorithms to increase the success rate in practical applications. Here, we systematically investigated the docking successes and failures of two representative docking programs: UCSF DOCK 3.7 and AutoDock Vina. DOCK 3.7 performed better in early enrichment on the Directory of Useful Decoys: Enhanced (DUD-E) data set, although both docking methods were roughly comparable in overall enrichment performance. DOCK 3.7 also showed superior computational efficiency. Intriguingly, the Vina scoring function showed a bias toward compounds with higher molecular weights. Both the tested docking approaches yielded incorrectly predicted ligand binding poses caused by the limitations of torsion sampling. Based on a careful analysis of docking results from six representative cases, we propose the reasons underlying docking failures; furthermore, we provide a few solutions, representing practical guidance for large-scale virtual screening campaigns and future docking algorithm development.

Repositioning of Etravirine as a Potential CK1ε Inhibitor by Virtual Screening

CK1ε is a key regulator of WNT/β-catenin and other pathways that are linked to tumor progression; thus, CK1ε is considered a target for the development of antineoplastic therapies. In this study, we performed a virtual screening to search for potential CK1ε inhibitors. First, we characterized the dynamic noncovalent interactions profiles for a set of reported CK1ε inhibitors to generate a pharmacophore model, which was used to identify new potential inhibitors among FDA-approved drugs. We found that etravirine and abacavir, two drugs that are approved for HIV infections, can be repurposed as CK1ε inhibitors. The interaction of these drugs with CK1ε was further examined by molecular docking and molecular dynamics. Etravirine and abacavir formed stable complexes with the target, emulating the binding behavior of known inhibitors. However, only etravirine showed high theoretical binding affinity to CK1ε. Our findings provide a new pharmacophore for targeting CK1ε and implicate etravirine as a CK1ε inhibitor and antineoplastic agent.

Structure-based virtual screening and biological evaluation of novel small-molecule BTK inhibitors

Bruton tyrosine kinase (BTK) is linked to multiple signalling pathways that regulate cellular survival, activation, and proliferation. A covalent BTK inhibitor has shown favourable outcomes for treating B cell malignant leukaemia. However, covalent inhibitors require a high reactive warhead that may contribute to unexpected toxicity, poor selectivity, or reduced effectiveness in solid tumours. Herein, we report the identification of a novel noncovalent BTK inhibitor. The binding interactions (i.e. interactions from known BTK inhibitors) for the BTK binding site were identified and incorporated into a structure-based virtual screening (SBVS). Top-rank compounds were selected and testing revealed a BTK inhibitor with >50% inhibition at 10 µM concentration. Examining analogues revealed further BTK inhibitors. When tested across solid tumour cell lines, one inhibitor showed favourable inhibitory activity, suggesting its potential for targeting BTK malignant tumours. This inhibitor could serve as a basis for developing an effective BTK inhibitor targeting solid cancers.

Identification of a dual TAOK1 and MAP4K5 inhibitor using a structure-based virtual screening approach

The STE20 kinase family is a complex signalling cascade that regulates cytoskeletal organisation and modulates the stress response. This signalling cascade includes various kinase mediators, such as TAOK1 and MAP4K5. The dysregulation of the STE20 kinase pathway is linked with cancer malignancy. A small-molecule inhibitor targeting the STE20 kinase pathway has therapeutic potential. In this study, a structure-based virtual screening (SBVS) approach was used to identify potential dual TAOK1 and MAP4K5 inhibitors. Enzymatic assays confirmed three potential dual inhibitors (>50% inhibition) from our virtual screening, and analysis of the TAOK1 and MAP4K5 binding sites indicated common interactions for dual inhibition. Compound 1 revealed potent inhibition of colorectal and lung cancer cell lines. Furthermore, compound 1 arrested cancer cells in the G0/G1 phase, which suggests the induction of apoptosis. Altogether, we show that the STE20 signalling mediators TAOK1 and MAP4K5 are promising targets for drug research.

Discovery of 1-Amino-1H-imidazole-5-carboxamide Derivatives as Highly Selective, Covalent Bruton's Tyrosine Kinase (BTK) Inhibitors

Bruton's tyrosine kinase (BTK) inhibitors suppressing the aberrant activation of BTK have led to a paradigm shift in the therapy of B-cell malignancies. However, there is an urgent need to discover more selective covalent BTK inhibitors owing to the off-target adverse effects of the approved inhibitor, ibrutinib. Herein, we disclose the discovery and preliminary activity studies of novel BTK inhibitors carrying 1-amino-1H-imidazole-5-carboxamide as a hinge binder. The most potent BTK inhibitor 26 demonstrates impressive selectivity, favorable pharmacokinetic properties, and robust antitumor efficacy in vivo, which indicates its potential as a novel therapeutic option for B-cell lymphomas. Importantly, to the best of our knowledge, this is the first example of a 1-amino-1H-imidazole-5-carboxamide scaffold used as the hinge binder of kinase inhibitors, which will largely expand the chemical diversity of kinase inhibitors.

New horizons in drug discovery of lymphocyte-specific protein tyrosine kinase (Lck) inhibitors: a decade review (2011-2021) focussing on structure-activity relationship (SAR) and docking insights

Lymphocyte-specific protein tyrosine kinase (Lck), a non-receptor Src family kinase, has a vital role in various cellular processes such as cell cycle control, cell adhesion, motility, proliferation, and differentiation. Lck is reported as a key factor regulating the functions of T-cell including the initiation of TCR signalling, T-cell development, in addition to T-cell homeostasis. Alteration in expression and activity of Lck results in numerous disorders such as cancer, asthma, diabetes, rheumatoid arthritis, atherosclerosis, and neuronal diseases. Accordingly, Lck has emerged as a novel target against different diseases. Herein, we amass the research efforts in literature and pharmaceutical patents during the last decade to develop new Lck inhibitors. Additionally, structure-activity relationship studies (SAR) and docking models of these new inhibitors within the active site of Lck were demonstrated offering deep insights into their different binding modes in a step towards the identification of more potent, selective, and safe Lck inhibitors.

In silico molecular target prediction unveils mebendazole as a potent MAPK14 inhibitor

The concept of polypharmacology involves the interaction of drug molecules with multiple molecular targets. It provides a unique opportunity for the repurposing of already-approved drugs to target key factors involved in human diseases. Herein, we used an in silico target prediction algorithm to investigate the mechanism of action of mebendazole, an antihelminthic drug, currently repurposed in the treatment of brain tumors. First, we confirmed that mebendazole decreased the viability of glioblastoma cells in vitro (IC₅₀ values ranging from 288 nm to 2.1 µm). Our in silico approach unveiled 21 putative molecular targets for mebendazole, including 12 proteins significantly upregulated at the gene level in glioblastoma as compared to normal brain tissue (fold change > 1.5; P < 0.0001). Validation experiments were performed on three major kinases involved in cancer biology: ABL1, MAPK1/ERK2, and MAPK14/p38α. Mebendazole could inhibit the activity of these kinases in vitro in a dose-dependent manner, with a high potency against MAPK14 (IC₅₀  = 104 ± 46 nm). Its direct binding to MAPK14 was further validated in vitro, and inhibition of MAPK14 kinase activity was confirmed in live glioblastoma cells. Consistent with biophysical data, molecular modeling suggested that mebendazole was able to bind to the catalytic site of MAPK14. Finally, gene silencing demonstrated that MAPK14 is involved in glioblastoma tumor spheroid growth and response to mebendazole treatment. This study thus highlighted the role of MAPK14 in the anticancer mechanism of action of mebendazole and provides further rationale for the pharmacological targeting of MAPK14 in brain tumors. It also opens new avenues for the development of novel MAPK14/p38α inhibitors to treat human diseases.

MovableType Software for Fast Free Energy-Based Virtual Screening: Protocol Development, Deployment, Validation, and Assessment

For decades, the complicated energy surfaces found in macromolecular protein:ligand structures, which require large amounts of computational time and resources for energy state sampling, have been an inherent obstacle to fast, routine free energy estimation in industrial drug discovery efforts. Beginning in 2013, the Merz research group addressed this cost with the introduction of a novel sampling methodology termed “Movable Type” (MT). Using numerical integration methods, the MT method reduces the computational expense for energy state sampling by independently calculating each atomic partition function from an initial molecular conformation in order to estimate the molecular free energy using ensembles of the atomic partition functions. In this work, we report a software package, the DivCon Discovery Suite with the MovableType module from QuantumBio Inc., that performs this MT free energy estimation protocol in a fast, fully encapsulated manner. We discuss the computational procedures and improvements to the original work, and we detail the corresponding settings for this software package. Finally, we introduce two validation benchmarks to evaluate the overall robustness of the method against a broad range of protein:ligand structural cases. With these publicly available benchmarks, we show that the method can use a variety of input types and parameters and exhibits comparable predictability whether the method is presented with “expensive” X-ray structures or “inexpensively docked” theoretical models. We also explore some next steps for the method. The MovableType software is available at http://www.quantumbioinc.com/

Quercetin negatively regulates IL-1β production in Pseudomonas aeruginosa-infected human macrophages through the inhibition of MAPK/NLRP3 inflammasome pathways

Pseudomonas aeruginosa remains a leading cause of nosocomial and serious life-threatening infections, and contributes to increased mortality in immunocompromised individuals. P. aeruginosa infection triggers host immune response and often provokes potent inflammatory mediators, which do not necessarily eradicate the causative pathogen. On the other hand, it causes severe airway damage and eventually decreased lung function. Such unfavorable outcomes of inflammatory injury have necessitated the development of novel effective agents that can combat with P. aeruginosa-mediated inflammation. Herein, we investigated the potential of quercetin in regulating P. aeruginosa-induced inflammation, with particular emphasized on the interleukin-1β (IL-1β). Our results showed that quercetin exerted the potent inhibitory activity against the production of IL-1β in macrophages infected by live P. aeruginosa PAO1, without exhibiting cytotoxicity. According to our settings, such the potent inhibitory activity of quercetin was clearly demonstrated through its ability to efficiently inhibit IL-1β during P. aeruginosa infection, pre- or even post-infection. In addition, quercetin strongly suppressed MAPK signaling pathway by inhibiting phosphorylation of the p38 MAPK and JNK2, and molecular docking study supported well with this observation. Moreover, quercetin reduced the NLRP3 expression and inhibited the P. aeruginosa-mediated cleavage of caspase-1 as well as mature IL-1β. These results thus indicated that quercetin inhibition of P. aeruginosa-induced IL-1β production is mediated by suppressing the initial priming step and by inhibiting the NLRP3 inflammasome activation. Taken together, our findings demonstrated the promising regulatory activity of quercetin against IL-1β production in P. aeruginosa-infected macrophages, and indicated that quercetin has the potential to be effective as a novel therapeutic agent for treatment of P. aeruginosa-induced inflammation.

Cov_FB3D: A De Novo Covalent Drug Design Protocol Integrating the BA-SAMP Strategy and Machine-Learning-Based Synthetic Tractability Evaluation

De novo drug design actively seeks to use sets of chemical rules for the fast and efficient identification of structurally new chemotypes with the desired set of biological properties. Fragment-based de novo design tools have been successfully applied in the discovery of noncovalent inhibitors. Nevertheless, these tools are rarely applied in the field of covalent inhibitor design. Herein, we present a new protocol, called Cov_FB3D, which involves the in silico assembly of potential novel covalent inhibitors by identifying the active fragments in the covalently binding site of the target protein. In this protocol, we propose a BA-SAMP strategy, which combines the noncovalent moiety score with the X-Score as the molecular mechanism (MM) level, and the covalent candidate score with the PM7 as the QM level. The synthetic accessibility of each suggested compound could be further evaluated with machine-learning-based synthetic complexity evaluation (SCScore). An in-depth test of this protocol against the crystal structures of 15 covalent complexes consisting of BTK inhibitors, KRAS inhibitors, EGFR inhibitors, EphB1 inhibitors, MAGL inhibitors, and MAPK inhibitors revealed that most of these inhibitors could be de novo reproduced from the fragments by Cov_FB3D. The binding modes of most generated reference poses could accurately reproduce the known binding mode of most of the reference covalent adduct in the binding site (RMSD ≤ 2 Å). In particular, most of these inhibitors were ranked in the top 2%, using the BA-SAMP strategy. Notably, the novel human ALDOA inhibitor (T1) with potent inhibitory activity (0.34 ± 0.03 μM) and greater synthetic accessibility was successfully de novo designed by this protocol. The positive results confirm the abilities of Cov_FB3D protocol.

Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets

Predicting protein-ligand interactions using artificial intelligence (AI) models has attracted great interest in recent years. However, data-driven AI models unequivocally suffer from a lack of sufficiently large and unbiased datasets. Here, we systematically investigated the data biases on the PDBbind and DUD-E datasets. We examined the model performance of atomic convolutional neural network (ACNN) on the PDBbind core set and achieved a Pearson R² of 0.73 between experimental and predicted binding affinities. Strikingly, the ACNN models did not require learning the essential protein-ligand interactions in complex structures and achieved similar performance even on datasets containing only ligand structures or only protein structures, while data splitting based on similarity clustering (protein sequence or ligand scaffold) significantly reduced the model performance. We also identified the property and topology biases in the DUD-E dataset which led to the artificially increased enrichment performance of virtual screening. The property bias in DUD-E was reduced by enforcing the more stringent ligand property matching rules, while the topology bias still exists due to the use of molecular fingerprint similarity as a decoy selection criterion. Therefore, we believe that sufficiently large and unbiased datasets are desirable for training robust AI models to accurately predict protein-ligand interactions.

Advances in covalent kinase inhibitors

This comprehensive review details recent advances, challenges and innovations in covalent kinase inhibition within a 10 year period (2007–2018).

Modeling MEK4 Kinase Inhibitors through Perturbed Electrostatic Potential Charges

MEK4, mitogen-activated protein kinase kinase 4, is overexpressed and induces metastasis in advanced prostate cancer lesions. However, the value of MEK4 as an oncology target has not been pharmacologically validated because selective chemical probes targeting MEK4 have not been developed. With advances in both computer and biological high-throughput screening, selective chemical entities can be discovered. Structure-based quantitative structure-activity relationship (QSAR) modeling often fails to generate accurate models due to poor alignment of training sets containing highly diverse compounds. Here we describe a highly predictive, nonalignment based robust QSAR model based on a data set of strikingly diverse MEK4 inhibitors. We computed the electrostatic potential (ESP) charges using a density functional theory (DFT) formalism of the donor and acceptor atoms of the ligands and hinge residues. Novel descriptors were then generated from the perturbation of the charge densities of the donor and acceptor atoms and were used to model a diverse set of 84 compounds, from which we built a robust predictive model.

Dual Kit/Aur Inhibitors as Chemosensitizing Agents for the Treatment of Melanoma: Design, Synthesis, Docking Studies and Functional Investigation

Melanoma is the most serious form of skin cancer but its medication is still far from being safe and thoroughly effective. The search of novel therapeutic approaches represents therefore a health emergency to push through eagerly. In this study, we describe a novel class of dual c-Kit/Aur inhibitors, characterized by a 1,2,4-triazole core and developed by a structure-based optimization of a previously developed hit, and report the evidence of their significance as drug candidates for the treatment of melanoma. Compound 6a, merging the best inhibitory profile against the target kinases, showed anti-proliferative efficacy against the human melanoma cell lines A2058, expressing the BRAF V600D mutation, and WM266-4, expressing BRAF V600E. Significantly, it displayed also a highly synergistic profile when tested in combination with vemurafenib, thus proving its efficacy not only per se but even in a combination therapy, which is nowadays acknowledged as the cornerstone approach of the forthcoming tumour management.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

Synthesis and biological evaluation of 1-benzyl-N-(2-(phenylamino)pyridin-3-yl)-1H-1,2,3-triazole-4-carboxamides as antimitotic agents

A library of 1-benzyl-N-(2-(phenylamino)pyridin-3-yl)-1H-1,2,3-triazole-4-carboxamides (7a-al) have been designed, synthesized and screened for their anti-proliferative activity against some selected human cancer cell lines namely DU-145, A-549, MCF-7 and HeLa. Most of them have shown promising cytotoxicity against lung cancer cell line (A549), amongst them 7f was found to be the most potent anti-proliferative congener. Furthermore, 7f exhibited comparable tubulin polymerization inhibition (IC₅₀ value 2.04 µM) to the standard E7010 (IC₅₀ value 2.15 µM). Moreover, flow cytometric analysis revealed that this compound induced apoptosis via cell cycle arrest at G₂/M phase in A549 cells. Induction of apoptosis was further observed by examining the mitochondrial membrane potential and was also confirmed by Hoechst staining as well as Annexin V-FITC assays. Furthermore, molecular docking studies indicated that compound 7f binds to the colchicine binding site of the β-tubulin. Thus, 7f exhibits anti-proliferative properties by inhibiting the tubulin polymerization through the binding at the colchicine active site and by induction of apoptosis.