Conformational analysis of the DFG-out kinase motif and biochemical profiling of structurally validated type II inhibitors

Targeting cyclin-dependent kinases: From pocket specificity to drug selectivity

The development of selective modulators of cyclin-dependent kinases (CDKs), a kinase family with numerous members and functional variations, is a significant preclinical challenge. Recent advancements in crystallography have revealed subtle differences in the highly conserved CDK pockets. Exploiting these differences has proven to be an effective strategy for achieving excellent drug selectivity. While previous reports briefly discussed the structural features that lead to selectivity in individual CDK members, attaining inhibitor selectivity requires consideration of not only the specific structures of the target CDK but also the features of off-target members. In this review, we summarize the structure-activity relationships (SARs) that influence selectivity in CDK drug development and analyze the pocket features that lead to selectivity using molecular-protein binding models. In addition, in recent years, novel CDK modulators have been developed, providing more avenues for achieving selectivity. These cases were also included. We hope that these efforts will assist in the development of novel CDK drugs.

Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches - tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory folded conformation, due to intrinsic sequence effects. Here we investigate the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a thermodynamic cycle involving many (n = 108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation DFG-out Activation Loop Folded, is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes in trans and additional modes of regulation in cis for TKs.

Inhibitors and PROTACs of CDK2: challenges and opportunities

INTRODUCTION

Abundant evidence suggests that the overexpression of CDK2-cyclin A/E complex disrupts normal cell cycle regulation, leading to uncontrolled proliferation of cancer cells. Thus, CDK2 has become a promising therapeutic target for cancer treatment. In recent years, insights into the structures of the CDK2 catalytic site and allosteric pockets have provided notable opportunities for developing more effective clinical candidates of CDK2 inhibitors.

AREA COVERED

This article reviews the latest CDK2 inhibitors that have entered clinical trials and discusses the design and discovery of the most promising new preclinical CDK2 inhibitors in recent years. Additionally, it summarizes the development of allosteric CDK2 inhibitors and CDK2-targeting PROTACs. The review encompasses strategies for inhibitor and PROTAC design, structure-activity relationships, as well as in vitro and in vivo biological assessments.

EXPERT OPINION

Despite considerable effort, no CDK2 inhibitor has yet received FDA approval for marketing due to poor selectivity and observed toxicity in clinical settings. Future research must prioritize the optimization of the selectivity, potency, and pharmacokinetics of CDK2 inhibitors and PROTACs. Moreover, exploring combination therapies incorporating CDK2 inhibitors with other targeted agents, or the design of multi-target inhibitors, presents significant promise for advancing cancer treatment strategies.

A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.

Insights into the structural and functional activities of forgotten Kinases: PCTAIREs CDKs

In cells, signal transduction heavily relies on the intricate regulation of protein kinases, which provide the fundamental framework for modulating most signaling pathways. Dysregulation of kinase activity has been implicated in numerous pathological conditions, particularly in cancer. The druggable nature of most kinases positions them into a focal point during the process of drug development. However, a significant challenge persists, as the role and biological function of nearly one third of human kinases remains largely unknown.Within this diverse landscape, cyclin-dependent kinases (CDKs) emerge as an intriguing molecular subgroup. In human, this kinase family encompasses 21 members, involved in several key biological processes. Remarkably, 13 of these CDKs belong to the category of understudied kinases, and only 5 having undergone broad investigation to date. This knowledge gap underscores the pressing need to delve into the study of these kinases, starting with a comprehensive review of the less-explored ones.Here, we will focus on the PCTAIRE subfamily of CDKs, which includes CDK16, CDK17, and CDK18, arguably among the most understudied CDKs members. To contextualize PCTAIREs within the spectrum of human pathophysiology, we conducted an exhaustive review of the existing literature and examined available databases. This approach resulted in an articulate depiction of these PCTAIREs, encompassing their expression patterns, 3D configurations, mechanisms of activation, and potential functions in normal tissues and in cancer.We propose that this effort offers the possibility of identifying promising areas of future research that extend from basic research to potential clinical and therapeutic applications.

Overcoming Secondary Mutations of Type II Kinase Inhibitors

Type II kinase inhibitors bind in the "DFG-out" kinase conformation and are generally considered to be more potent and selective than type I inhibitors, which target a DFG-in conformation. Nine type II inhibitors are currently clinically approved, with more undergoing clinical development. Resistance-conferring secondary mutations emerged with the first series of type II inhibitors, most commonly at residues within the kinase activation loop and at the "gatekeeper" position. Recently, new inhibitors have been developed to overcome such mutations; however, mutations activating other pathways (and/or other targets) have subsequently emerged on occasion. Here, we systematically summarize the secondary mutations that confer resistance to type II inhibitors, the structural basis for resistance, newer inhibitors designed to overcome resistance, as well as the challenges and opportunities for the development of new inhibitors to overcome secondary kinase domain mutations.

Virtual screening of natural products to enhance melanogenosis

Natural products have long been an important source of inspiration for medicinal chemistry and drug discovery. In the cosmetic field, they remain the major elements of the composition and serve as marketing asset. Recent research showed the implication of salt-inducible kinases on the melanin production in skin via MITF regulation. Finding new potent modulators on such target could open the way to several cosmetic applications to attenuate visible signs of photoaging and improve the tan without sun. Since virtual screening can be a powerful tool for detecting hit compounds in the early stages of a drug discovery process, we applied this method on salt-inducible kinase 2 to discover potential interesting compounds. Here, we present the different steps from the construction of a database of natural products, to the validation of a docking protocol and the results of the virtual screening. Hits from the screening were tested in vitro to confirm their efficiency and results are discussed.

Discovery of Ureido-Substituted 4-Phenylthiazole Derivatives as IGF1R Inhibitors with Potent Antiproliferative Properties

The existing kinase inhibitors for hepatocellular carcinoma (HCC) have conferred survival benefits but are hampered by adverse effects and drug resistance, necessitating the development of novel agents targeting distinct pathways. To discover potent new anti-HCC compounds, we leveraged scaffold hopping from Sorafenib and introduced morpholine/piperidine moieties to develop ureido-substituted 4-phenylthiazole analogs with optimized physicochemical properties and binding interactions. Notably, compound 27 exhibited potent cytotoxicity against HepG2 cells (IC50 = 0.62 ± 0.34 μM), significantly exceeding Sorafenib (IC50 = 1.62 ± 0.27 μM). Mechanistic investigations revealed that compound 27 potently inhibited HCC cell migration and colony formation, and it induced G2/M arrest and early-stage apoptosis. Kinase profiling revealed IGF1R as a key target, which compound 27 potently inhibited (76.84% at 10 μM). Molecular modeling substantiated compound 27's strong binding to IGF1R via multiple hydrogen bonds. Computational predictions indicate favorable drug-like properties for compound 27. These findings provide a promising drug candidate for the treatment of HCC patients.

Role of CSF1R 550th-tryptophan in kusunokinin and CSF1R inhibitor binding and ligand-induced structural effect

Binding affinity is an important factor in drug design to improve drug-target selectivity and specificity. In this study, in silico techniques based on molecular docking followed by molecular dynamics (MD) simulations were utilized to identify the key residue(s) for CSF1R binding affinity among 14 pan-tyrosine kinase inhibitors and 15 CSF1R-specific inhibitors. We found tryptophan at position 550 (W550) on the CSF1R binding site interacted with the inhibitors' aromatic ring in a π-π way that made the ligands better at binding. Upon W550-Alanine substitution (W550A), the binding affinity of trans-(-)-kusunokinin and imatinib to CSF1R was significantly decreased. However, in terms of structural features, W550 did not significantly affect overall CSF1R structure, but provided destabilizing effect upon mutation. The W550A also did not either cause ligand to change its binding site or conformational changes due to ligand binding. As a result of our findings, the π-π interaction with W550's aromatic ring could be still the choice for increasing binding affinity to CSF1R. Nevertheless, our study showed that the increasing binding to W550 of the design ligand may not ensure CSF1R specificity and inhibition since W550-ligand bound state did not induce significantly conformational change into inactive state.

Natural Compounds for Bone Remodeling: A Computational and Experimental Approach Targeting Bone Metabolism-Related Proteins

Osteoporosis, characterized by reduced bone density and increased fracture risk, affects over 200 million people worldwide, predominantly older adults and postmenopausal women. The disruption of the balance between bone-forming osteoblasts and bone-resorbing osteoclasts underlies osteoporosis pathophysiology. Standard treatment includes lifestyle modifications, calcium and vitamin D supplementation and specific drugs that either inhibit osteoclasts or stimulate osteoblasts. However, these treatments have limitations, including side effects and compliance issues. Natural products have emerged as potential osteoporosis therapeutics, but their mechanisms of action remain poorly understood. In this study, we investigate the efficacy of natural compounds in modulating molecular targets relevant to osteoporosis, focusing on the Mitogen-Activated Protein Kinase (MAPK) pathway and the gut microbiome's influence on bone homeostasis. Using an in silico and in vitro methodology, we have identified quercetin as a promising candidate in modulating MAPK activity, offering a potential therapeutic perspective for osteoporosis treatment.

Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches - tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory "folded" conformation, due to intrinsic sequence effects. Here we investigated the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a novel thermodynamic cycle involving many (n=108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation "DFG-out Activation Loop Folded", is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes in trans and additional modes of regulation in cis for TKs.

Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches - tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory "folded" conformation, due to intrinsic sequence effects. Here we investigated the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a novel thermodynamic cycle involving many (n=108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation "DFG-out Activation Loop Folded", is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes in trans and additional modes of regulation in cis for TKs.

Discovery of Potent Multikinase Type-II Inhibitors Targeting CDK5 in the DFG-out Inactive State with Promising Potential against Glioblastoma

Kinases have proven valuable targets in successful cancer drug discovery projects, but not yet for malignant brain tumors where type-II inhibition of cyclin-dependent kinase 5 (CDK5) stabilizing the DFG-out inactive state has potential for design of selective and clinically efficient drug candidates. In the absence of crystallographic evidence for a CDK5 DFG-out inactive state protein-ligand complex, for the first time, a model was designed using metadynamics/molecular dynamics simulations. Glide docking of the ZINC15 biogenic database identified [pyrimidin-2-yl]amino-furo[3,2-b]-furyl-urea/amide hit chemical scaffolds. For four selected analogues (4, 27, 36, and 42), potent effects on glioblastoma cell viability in U87-MG, T98G, and U251-MG cell lines and patient-derived cultures were generally observed (IC50s ∼ 10-40 μM at 72 h). Selectivity profiling against 11 homologous kinases revealed multikinase inhibition (CDK2, CDK5, CDK9, and GSK-3α/β), most potent for GSK-3α in the nanomolar range (IC50s ∼ 0.23-0.98 μM). These compounds may therefore have diverse anticancer mechanisms of action and are of considerable interest for lead optimization.

Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling

Mapping the ensemble of protein conformations that contribute to function and can be targeted by small molecule drugs remains an outstanding challenge. Here, we explore the use of variational autoencoders for reducing the challenge of dimensionality in the protein structure ensemble generation problem. We convert high-dimensional protein structural data into a continuous, low-dimensional representation, carry out a search in this space guided by a structure quality metric, and then use RoseTTAFold guided by the sampled structural information to generate 3D structures. We use this approach to generate ensembles for the cancer relevant protein K-Ras, train the VAE on a subset of the available K-Ras crystal structures and MD simulation snapshots, and assess the extent of sampling close to crystal structures withheld from training. We find that our latent space sampling procedure rapidly generates ensembles with high structural quality and is able to sample within 1 Å of held-out crystal structures, with a consistency higher than that of MD simulation or AlphaFold2 prediction. The sampled structures sufficiently recapitulate the cryptic pockets in the held-out K-Ras structures to allow for small molecule docking.

Analysis of the binding pattern of NIK inhibitors by computational simulation

NF-kappaB-Inducing Kinase (NIK) is a key kinase in the activation of the NF-κB non-classical signalling pathway, which has been shown to be over-activated in patients with inflammatory diseases, immune disorders and malignancies and solid tumours inducing activation of the NF-κB non-classical signalling pathway. The design of ATP-competitive small molecule inhibitors against NIK has been a hot topic in the last decade, and many efficient NIK inhibitors have been identified. In this work, I aim to unravel the mechanism of NIK inhibition by different representative NIK type I 1/2 kinase inhibitors, using ADME, molecular docking, molecular dynamics simulation, MM-PBSA analysis and 3D-QSAR analysis. This work contributes to the understanding of the efficiency of NIK inhibitor binding by revealing the basis of the efficiency of NIK inhibitors, the difference in binding modes between different inhibitors and the overall effect on NIK.Communicated by Ramaswamy H. Sarma.

Targeting EphA2: a promising strategy to overcome chemoresistance and drug resistance in cancer

Erythropoietin-producing hepatocellular A2 (EphA2) is a vital member of the Eph tyrosine kinase receptor family and has been associated with developmental processes. However, it is often overexpressed in tumors and correlates with cancer progression and worse prognosis due to the activation of its noncanonical signaling pathway. Throughout cancer treatment, the emergence of drug-resistant tumor cells is relatively common. Since the early 2000s, researchers have focused on understanding the role of EphA2 in promoting drug resistance in different types of cancer, as well as finding efficient and secure EphA2 inhibitors. In this review, the current knowledge regarding induced resistance by EphA2 in cancer treatment is summarized, and the types of cancer that lead to the most cancer-related deaths are highlighted. Some EphA2 inhibitors were also investigated. Regardless of whether the cancer treatment has reached a drug-resistance stage in EphA2-overexpressing tumors, once EphA2 is involved in cancer progression and aggressiveness, targeting EphA2 is a promising therapeutic strategy, especially in combination with other target-drugs for synergistic effect. For that reason, monoclonal antibodies against EphA2 and inhibitors of this receptor should be investigated for efficacy and drug toxicity.

A comprehensive review on role of Aurora kinase inhibitors (AKIs) in cancer therapeutics

Aurora kinases (AURKs) are a family of serine /threonine protein kinases that have a crucial role in cell cycle process mainly in the event of chromosomal segregation, centrosome maturation and cytokinesis. The family consists of three members including Aurora kinase A (AURK-A), Aurora kinase B (AURK-B) and Aurora kinase C (AURK-C). All AURKs contain a conserved kinase domain for their activity but differ in their cellular localization and functions. AURK-A and AURK-B are expressed mainly in somatic cells while the expression of AURK-C is limited to germ cells. AURK-A promotes G2 to M transition of cell cycle by controlling centrosome maturation and mitotic spindle assembly. AURK-B and AURK-C form the chromosome passenger complex (CPC) that ensures proper chromosomal alignments and segregation. Aberrant expression of AURK-A and AURK-B has been detected in several solid tumours and malignancies. Hence, they have become an attractive therapeutic target against cancer. The first part of this review focuses on AURKs structure, functions, subcellular localization, and their role in tumorigenesis. The review also highlights the functional and clinical impact of selective as well as pan kinase inhibitors. Currently, >60 compounds that target AURKs are in preclinical and clinical studies. The drawbacks of existing inhibitors like selectivity, drug resistance and toxicity have also been addressed. Since, majority of inhibitors are Aurora kinase inhibitor (AKI) type-1 that bind to the active (DFGin and Cin) conformation of the kinase, this information may be utilized to design highly selective kinase inhibitors that can be combined with other therapeutic agents for better clinical outcomes.

Identification of new 5-(2,6-dichlorophenyl)-3-oxo-2,3-dihydro-5H-thiazolo[3,2-a]pyrimidine-7-carboxylic acids as p38α MAPK inhibitors: Design, synthesis, antitumor evaluation, molecular docking and in silico studies

In pursuit of discovering novel scaffolds that demonstrate potential inhibitory activity against p38α MAPK and possess strong antitumor effects, we herein report the design and synthesis of new series of 17 final target 5-(2,6-dichlorophenyl)-3-oxo-2,3-dihydro-5H-thiazolo[3,2-a]pyrimidine-7-carboxylic acids (4-20). Chemical characterization of the compounds was performed using FT-IR, NMR, elemental analyses and mass spectra of some representative examples. With many compounds showing potential inhibitory activity against p38α MAPK, two derivatives, 8 and 9, demonstrated the highest activity (>70 % inhibition) among the series. Derivative 9 displayed IC50 value nearly 2.5 folds more potent than 8. As anticipated, they both showed explicit interactions inside the kinase active site with the key binding amino acid residues. Screening both compounds for cytotoxic effects, they exhibited strong antitumor activities against lung (A549), breast (MCF-7 and MDA MB-231), colon (HCT-116) and liver (Hep-G2) cancers more potent than reference 5-FU. Their noticeable strong antitumor activity pointed out to the possibility of an augmented DNA binding mechanism of antitumor action besides their kinase inhibition. Both 8 and 9 exhibited strong ctDNA damaging effects in nanomolar range. Further mechanistic antitumor studies revealed ability of compounds 8 and 9 to arrest cell cycle in MCF-7 cells at S phase, while in HCT-116 treated cells at G0-G1 and G2/M phases. They also displayed apoptotic induction effects in both MCF-7 and HCT-116 with total cell deaths more than control untreated cells in reference to 5-FU. Finally, the compounds were tested for their anti-migratory potential utilizing wound healing assay. They induced a significant decrease in wound closure percentage after 24 h treatment in the examined cancer cells when compared to untreated control MCF-7 and HCT-116 cells better than 5-FU. In silico computation of physicochemical parameters revealed the drug-like properties of 8 and 9 with no violation to Lipinski's rule of five as well as their tolerable ADMET parameters, thus suggesting their utilization as potential future drug leads amenable for further optimization and development.

Conformation selection by ATP-competitive inhibitors and allosteric communication in ERK2

Activation of the extracellular signal-regulated kinase-2 (ERK2) by phosphorylation has been shown to involve changes in protein dynamics, as determined by hydrogen-deuterium exchange mass spectrometry (HDX-MS) and NMR relaxation dispersion measurements. These can be described by a global exchange between two conformational states of the active kinase, named 'L' and 'R,' where R is associated with a catalytically productive ATP-binding mode. An ATP-competitive ERK1/2 inhibitor, Vertex-11e, has properties of conformation selection for the R-state, revealing movements of the activation loop that are allosterically coupled to the kinase active site. However, the features of inhibitors important for R-state selection are unknown. Here, we survey a panel of ATP-competitive ERK inhibitors using HDX-MS and NMR and identify 14 new molecules with properties of R-state selection. They reveal effects propagated to distal regions in the P+1 and helix αF segments surrounding the activation loop, as well as helix αL16. Crystal structures of inhibitor complexes with ERK2 reveal systematic shifts in the Gly loop and helix αC, mediated by a Tyr-Tyr ring stacking interaction and the conserved Lys-Glu salt bridge. The findings suggest a model for the R-state involving small movements in the N-lobe that promote compactness within the kinase active site and alter mobility surrounding the activation loop. Such properties of conformation selection might be exploited to modulate the protein docking interface used by ERK substrates and effectors.

Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.

KIT/PDGFRA inhibitors for the treatment of gastrointestinal stromal tumors: getting to the gist of the problem

INTRODUCTION

Approximately 90% of gastrointestinal stromal tumors (GISTs) are driven by activating mutations in receptor tyrosine-kinases KIT or PDGFRA. Despite the outstanding results of first-line imatinib in advanced GIST, resistance ultimately occurs mainly through secondary mutations in KIT/PDGFRA. Other tyrosine-kinase inhibitors (TKIs) with a broader spectrum of activity against these mutations are approved after imatinib failure. However, response rates and progression-free survival are drastically lower compared to imatinib. Notably, imatinib also triggers early tolerance adaptation mechanisms, which precede the occurrence of secondary mutations.

AREAS COVERED

In this review, we outline the current landscape of KIT inhibitors, discuss the novel agents, and present additional biological pathways that may be therapeutically exploitable.

EXPERT OPINION

The development of broad-spectrum and highly selective TKIs able to induce a sustained KIT/PDGFRA inhibition is the pillar of preclinical and clinical investigation in GIST. However, it is now recognized that the situation is more intricate, with various factors interacting with KIT and PDGFRA, playing a crucial role in the response and resistance to treatments. Future strategies in the management of advanced GIST should integrate driver inhibition with the blockade of other molecules to enhance cell death and establish enduring responses in patients.

Beyond Amyloid: A Machine Learning-Driven Approach Reveals Properties of Potent GSK-3β Inhibitors Targeting Neurofibrillary Tangles

Current treatments for Alzheimer's disease (AD) focus on slowing memory and cognitive decline, but none offer curative outcomes. This study aims to explore and curate the common properties of active, drug-like molecules that modulate glycogen synthase kinase 3β (GSK-3β), a well-documented kinase with increased activity in tau hyperphosphorylation and neurofibrillary tangles-hallmarks of AD pathology. Leveraging quantitative structure-activity relationship (QSAR) data from the PubChem and ChEMBL databases, we employed seven machine learning models: logistic regression (LogR), k-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), neural networks (NNs), and ensemble majority voting. Our goal was to correctly predict active and inactive compounds that inhibit GSK-3β activity and identify their key properties. Among the six individual models, the NN demonstrated the highest performance with a 79% AUC-ROC on unbalanced external validation data, while the SVM model was superior in accurately classifying the compounds. The SVM and RF models surpassed NN in terms of Kappa values, and the ensemble majority voting model demonstrated slightly better accuracy to the NN on the external validation data. Feature importance analysis revealed that hydrogen bonds, phenol groups, and specific electronic characteristics are important features of molecular descriptors that positively correlate with active GSK-3β inhibition. Conversely, structural features like imidazole rings, sulfides, and methoxy groups showed a negative correlation. Our study highlights the significance of structural, electronic, and physicochemical descriptors in screening active candidates against GSK-3β. These predictive features could prove useful in therapeutic strategies to understand the important properties of GSK-3β candidate inhibitors that may potentially benefit non-amyloid-based AD treatments targeting neurofibrillary tangles.

Deciphering the molecular choreography of Janus kinase 2 inhibition via Gaussian accelerated molecular dynamics simulations: a dynamic odyssey

The Janus kinases (JAK) are crucial targets in drug development for several diseases. However, accounting for the impact of possible structural rearrangements on the binding of different kinase inhibitors is complicated by the extensive conformational variability of their catalytic kinase domain (KD). The dynamic KD contains mainly four prominent mobile structural motifs: the phosphate-binding loop (P-loop), the αC-helix within the N-lobe, the Asp-Phe-Gly (DFG) motif, and the activation loop (A-loop) within the C-lobe. These distinct structural orientations imply a complex signal transmission path for regulating the A-loop's flexibility and conformational preference for optimal JAK function. Nevertheless, the precise dynamical features of the JAK induced by different types of inhibitors still remain elusive. We performed comparative, microsecond-long, Gaussian accelerated molecular dynamics simulations in triplicate of three phosphorylated JAK2 systems: the KD alone, type-I ATP-competitive inhibitor (CI) bound KD in the catalytically active DFG-in conformation, and the type-II inhibitor (AI) bound KD in the catalytically inactive DFG-out conformation. Our results indicate significant conformational variations observed in the A-loop and αC helix motions upon inhibitor binding. Our studies also reveal that the DFG-out inactive conformation is characterized by the closed A-loop rearrangement, open catalytic cleft of N and C-lobe, the outward movement of the αC helix, and open P-loop states. Moreover, the outward positioning of the αC helix impacts the hallmark salt bridge formation between Lys882 and Glu898 in an inactive conformation. Finally, we compared their ligand binding poses and free energy by the MM/PBSA approach. The free energy calculations suggested that the AI's binding affinity is higher than CI against JAK2 due to an increased favorable contribution from the total non-polar interactions and the involvement of the αC helix. Overall, our study provides the structural and energetic insights crucial for developing more promising type I/II JAK2 inhibitors for treating JAK-related diseases.

An imidazo[1,2-a]pyridine-pyridine derivative potently inhibits FLT3-ITD and FLT3-ITD secondary mutants, including gilteritinib-resistant FLT3-ITD/F691L

FLT3 activating mutations are detected in approximately 30 % of newly diagnosed acute myeloid leukemia (AML) cases, most commonly consisting of internal tandem duplication (ITD) mutations in the juxtamembrane region. Recently, several FLT3 inhibitors have demonstrated clinical activity and three are currently approved - midostaurin, quizartinib, and gilteritinib. Midostaurin is a first-generation FLT3 inhibitor with minimal activity as monotherapy. Midostaurin lacks selectivity and is only approved by the USFDA for use in combination with other chemotherapy agents. The second-generation inhibitors quizartinib and gilteritinib display improved specificity and selectivity, and have been approved for use as monotherapy. However, their clinical efficacies are limited in part due to the emergence of drug-resistant FLT3 secondary mutations in the tyrosine kinase domain at positions D835 and F691. Therefore, in order to overcome drug resistance and further improve outcomes, new compounds targeting FLT3-ITD with secondary mutants are urgently needed. In this study, through the structural modification of a reported compound Ling-5e, we identified compound 24 as a FLT3 inhibitor that is equally potent against FLT3-ITD and the clinically relevant mutants FLT3-ITD/D835Y, and FLT3-ITD/F691L. Its inhibitory effects were demonstrated in both cell viability assays and western blots analyses. When tested against cell lines lacking activating mutations in FLT3, no non-specific cytotoxicity effect was observed. Interestingly, molecular docking results showed that compound 24 may adopt different binding conformations with FLT3-F691L compared to FLT3, which may explain its retained activity against FLT3-ITD/F691L. In summary, compound 24 has inhibition potency on FLT3 comparable to gilteritinib, but a more balanced inhibition on FLT3 secondary mutations, especially FLT3-ITD/F691L which is gilteritinib resistant. Compound 24 may serve as a promising lead for the drug development of either primary or relapsed AML with FLT3 secondary mutations.

Structural basis and selectivity of sulfatinib binding to FGFR and CSF-1R

Acquired drug resistance poses a challenge for single-target FGFR inhibitors, leading to the development of dual- or multi-target FGFR inhibitors. Sulfatinib is a multi-target kinase inhibitor for treating neuroendocrine tumors, selectively targeting FGFR1/CSF-1R. To elucidate the molecular mechanisms behind its binding and kinase selectivity, we determined the crystal structures of sulfatinib with FGFR1/CSF-1R. The results reveal common structural features and distinct conformational adaptability of sulfatinib in response to FGFR1/CSF-1R binding. Further biochemical and structural analyses disclose sensitivity of sulfatinib to FGFR/CSF-1R gatekeeper mutations. The insensitivity of sulfatinib to FGFR gatekeeper mutations highlights the indispensable interactions with the hydrophobic pocket for FGFR selectivity, whereas the rotatory flexibility may enable sulfatinib to overcome CSF-1RT663I. This study not only sheds light on the structural basis governing sulfatinib's FGFR/CSF-1R inhibition, but also provides valuable insights into the rational design of dual- or multi-target FGFR inhibitors with selectivity for CSF-1R and sensitivity to gatekeeper mutations.

The Treatment of a New Entity in Advanced Non-small Cell Lung Cancer: MET Exon 14 Skipping Mutation

BACKGROUND

MET (MET Proto-Oncogene, Receptor Tyrosine Kinase) exon 14 skipping mutation represents one of the most common MET alterations, accounting for approximately 1-3% of all mutations in advanced lung adenocarcinomas. While until 2020 no specific treatment was available for this subset of patients, as of today, three MET Tyrosine Kinase Inhibitors (TKIs) are currently approved in this setting, namely capmatinib, tepotinib and savolitinib.

OBJECTIVE

This article aims to provide an extensive overview of the current therapeutic standard of care for exon 14 skipped advanced Non-small Cell Lung Cancer (NSCLC) patients, alongside with mentions of the main future challenges and opportunities.

CONCLUSION

FDA-approved MET-TKIs currently represent the best option for treating exon 14 skipped advanced NSCLC patients, thanks to their excellent efficacy profile, alongside their manageable safety and tolerability. However, we currently lack specific agents to treat patients progressing on capmatinib or tepotinib, due to a limited understanding of the mechanisms underlying both on- and off-target resistance. In this respect, on-target mutations presently constitute the most explored ones from a mechanistic point of view, and type II MET-TKIs are currently under investigation as the most promising agents capable of overcoming the acquired resistance.

Conformation Selection by ATP-competitive Inhibitors and Allosteric Communication in ERK2

Activation of the extracellular signal regulated kinase-2 (ERK2) by phosphorylation has been shown to involve changes in protein dynamics, as determined by hydrogen-deuterium exchange mass spectrometry (HDX-MS) and NMR relaxation dispersion measurements. These can be described by a global exchange between two conformational states of the active kinase, named "L" and "R", where R is associated with a catalytically productive ATP-binding mode. An ATP-competitive ERK1/2 inhibitor, Vertex-11e, has properties of conformation selection for the R-state, revealing movements of the activation loop that are allosterically coupled to the kinase active site. However, the features of inhibitors important for R-state selection are unknown. Here we survey a panel of ATP-competitive ERK inhibitors using HDX-MS and NMR and identify 14 new molecules with properties of R-state selection. They reveal effects propagated to distal regions in the P+1 and helix αF segments surrounding the activation loop, as well as helix αL16. Crystal structures of inhibitor complexes with ERK2 reveal systematic shifts in the Gly loop and helix αC, mediated by a Tyr-Tyr ring stacking interaction and the conserved Lys-Glu salt bridge. The findings suggest a model for the R-state involving small movements in the N-lobe that promote compactness within the kinase active site and alter mobility surrounding the activation loop. Such properties of conformation selection might be exploited to modulate the protein docking interface used by ERK substrates and effectors.

Crystal Structures Reveal Hidden Domain Mechanics in Protein Kinase A (PKA)

Cyclic-AMP-dependent protein kinase A (PKA) is a critical enzyme involved in various signaling pathways that plays a crucial role in regulating cellular processes including metabolism, gene transcription, cell proliferation, and differentiation. In this study, the mechanisms of allostery in PKA were investigated by analyzing the vast repertoire of crystal structures available in the RCSB database. From existing structures of murine and human PKA, we elucidated the conformational ensembles and protein dynamics that are altered in a ligand-dependent manner. Distance metrics to analyze conformations of the G-loop were proposed to delineate different states of PKA and were compared to existing structural metrics. Furthermore, ligand-dependent flexibility was investigated through normalized B'-factors to better understand the inherent dynamics in PKA. The presented study provides a contemporary approach to traditional methods in engaging the use of crystal structures for understanding protein dynamics. Importantly, our studies provide a deeper understanding into the conformational ensemble of PKA as the enzyme progresses through its catalytic cycle. These studies provide insights into kinase regulation that can be applied to both PKA individually and protein kinases as a class.

Genetic complementation screening and molecular docking give new insight on phosphorylation-dependent Mastl kinase activation

Mastl is a mitotic kinase that is essential for error-free chromosome segregation. It is an atypical member of AGC kinase family, possessing a unique non-conserved middle region. The mechanism of Mastl activation has been studied extensively in vitro. Phosphorylation of several residues were identified to be crucial for activation. These sites correspond to T193 and T206 in the activation loop and S861 in the C-terminal tail of mouse Mastl. To date, the significance of these phosphosites was not confirmed in intact mammalian cells. Here, we utilize a genetic complementation approach to determine the essentials of mammalian Mastl kinase activation. We used tamoxifen-inducible conditional knockout mouse embryonic fibroblasts to delete endogenous Mastl and screened various mutants for their ability to complement its loss. S861A mutant was able to complement endogenous Mastl loss. In parallel, we performed computational molecular docking studies to evaluate the significance of this residue for kinase activation. Our in-depth sequence and structure analysis revealed that Mastl pS861 does not belong to a conformational state, where the phosphoresidue contributes to C-tail docking. C-tail of Mastl is relatively short and it lacks a hydrophobic (HF) motif that would otherwise help its anchoring over N-lobe, required for the final steps of kinase activation. Our results show that phosphorylation of Mastl C-tail turn motif (S861) is dispensable for kinase function in cellulo.Communicated by Ramaswamy H. Sarma.

Exploring the Druggable Conformational Space of Protein Kinases Using AI-Generated Structures

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs, which enable kinases to adopt various conformational states. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the kinase conformation(s) they bind. However, the limited availability of experimentally determined structural data for kinases in inactive states restricts drug discovery efforts for this major protein family. Modern AI-based structural modeling methods hold potential for exploring the previously experimentally uncharted druggable conformational space for kinases. Here, we first evaluated the currently explored conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) (1) and ESMFold (2), two prominent AI-based structure prediction methods. We then investigated AF2's ability to predict kinase structures in different conformations at various multiple sequence alignment (MSA) depths, based on this parameter's ability to explore conformational diversity. Our results showed a bias within the PDB and predicted structural models generated by AF2 and ESMFold toward structures of kinases in the active state over alternative conformations, particularly those conformations controlled by the DFG motif. Finally, we demonstrate that predicting kinase structures using AF2 at lower MSA depths allows the exploration of the space of these alternative conformations, including identifying previously unobserved conformations for 398 kinases. The results of our analysis of structural modeling by AF2 create a new avenue for the pursuit of new therapeutic agents against a notoriously difficult-to-target family of proteins.

Significance Statement

Greater abundance of kinase structural data in inactive conformations, currently lacking in structural databases, would improve our understanding of how protein kinases function and expand drug discovery and development for this family of therapeutic targets. Modern approaches utilizing artificial intelligence and machine learning have potential for efficiently capturing novel protein conformations. We provide evidence for a bias within AlphaFold2 and ESMFold to predict structures of kinases in their active states, similar to their overrepresentation in the PDB. We show that lowering the AlphaFold2 algorithm's multiple sequence alignment depth can help explore kinase conformational space more broadly. It can also enable the prediction of hundreds of kinase structures in novel conformations, many of whose models are likely viable for drug discovery.

Evaluating the ability of end-point methods to predict the binding affinity tendency of protein kinase inhibitors

Because of the high economic cost of exploring the experimental impact of mutations occurring in kinase proteins, computational approaches have been employed as alternative methods for evaluating the structural and energetic aspects of kinase mutations. Among the main computational methods used to explore the affinity linked to kinase mutations are docking procedures and molecular dynamics (MD) simulations combined with end-point methods or alchemical methods. Although it is known that end-point methods are not able to reproduce experimental binding free energy (ΔG) values, it is also true that they are able to discriminate between a better or a worse ligand through the estimation of ΔG. In this contribution, we selected ten wild-type and mutant cocrystallized EGFR-inhibitor complexes containing experimental binding affinities to evaluate whether MMGBSA or MMPBSA approaches can predict the differences in affinity between the wild type and mutants forming a complex with a similar inhibitor. Our results show that a long MD simulation (the last 50 ns of a 100 ns-long MD simulation) using the MMGBSA method without considering the entropic components reproduced the experimental affinity tendency with a Pearson correlation coefficient of 0.779 and an R2 value of 0.606. On the other hand, the correlation between theoretical and experimental ΔΔG values indicates that the MMGBSA and MMPBSA methods are helpful for obtaining a good correlation using a short rather than a long simulation period.

Plasmodium falciparum FIKK 9.1 kinase modeling to screen and identify potent antimalarial agents from chemical library

The Plasmodium FIKK kinases are diverged from human kinases structurally. They harbour conserved ATP-binding domains that are non-homologous to other existing kinases. FIKK9.1 kinase is considered as an essential protein for parasite survival. It is localized in major organelles present in parasite and trafficked throughout the infected RBC. It is speculated that FIKK9.1 may phosphorylate several substrates in the parasite's proteome and contribute to parasite survival. Therefore, FIKK9.1 is an attractive target that may lead to a novel class of antimalarials. To identify specific FIKK9.1 kinase inhibitors, we virtually screened organic structural scaffolds from a library of 623 entries. The top hits were identified based on conformations and molecular interactions with the ATP biophore. The hits were also validated under in vitro conditions. In this study, we identified seven top hit organic compounds that may arrest the growth of parasites by inhibiting FIKK9.1 kinase. Evaluation of top hit compounds in antimalarial activity assay identifies that the highly substituted 1,3-selenazolidin-2-imine 1 and thiophene 2 are inhibiting parasite growth with an IC50 of 3.2 ± 0.27 μg/ml and 3.13 ± 0.16 μg/ml, respectively. These functionalized heterocyclic compounds 1 and 2 kills the malaria parasite with an IC50 of 2.68 ± 0.02 μg/ml and 3.08 ± 0.14 μg/ml, respectively. Isothermal titration calorimetry analysis indicate that ATP is binding to the FIKK9.1 kinase. The dissociation constant (Kd) is measured to be 27.8 ± 2.07 μM with a stoichiometry of n = 1. The heterocyclic scaffolds 1 and 2 were abolishing the binding of ATP into the binding pocket. They in-turn reduce the ability of FIKK9.1 kinase to phosphorylate its substrate. Our study found that compounds 1 and 2 are potent inhibitor of FIKK9.1 kinase and the inhibition of FIKK9.1 kinase using small molecules disturbs the parasite life cycle and leads to the death of parasites. This provides new insight in development of novel antimalarials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03677-x.

Design, synthesis, and docking studies of novel pyrazole-based scaffolds and their evaluation as VEGFR2 inhibitors in the treatment of prostate cancer

Since VEGFR-2 plays a crucial role in tumor growth, angiogenesis, and metastasis, it is a prospective target for cancer treatment. In this work, a series of 3-phenyl-4-(2-substituted phenylhydrazono)-1H-pyrazol-5(4H)-ones (3a-l) were synthesized and investigated for their cytotoxicity against the PC-3 human cancer cell line compared to Doxorubicin and Sorafenib as reference drugs. Two compounds 3a and 3i showed comparable cytotoxic activity with IC₅₀ values of 1.22 and 1.24 μM compared to the reference drugs (IC₅₀ = 0.932, 1.13 μM). Compound 3i was found to be the most effective VEGFR-2 inhibitor using in vitro testing of the synthesized compounds, with nearly 3-fold higher activity than Sorafenib (30 nM), with IC₅₀ 8.93 nM. Compound 3i significantly stimulated total apoptotic prostate cancer cell death 55.2-fold (34.26% compared to 0.62% for the control) arresting the cell cycle at the S-phase. The genes involved in apoptosis were also impacted, with proapoptotic genes being upregulated and antiapoptotic Bcl-2 being downregulated. These results were supported by docking studies of these two compounds within the active site of the VEGFR2 enzyme. Finally, in vivo, the study revealed the potentiality of compound 3i to inhibit tumor proliferation by 49.8% reducing the tumor weight from 234.6 mg in untreated mice to 83.2 mg. Therefore, 3i could be a promising anti-prostate cancer agent.

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.

AI-Assisted chemical probe discovery for the understudied Calcium-Calmodulin Dependent Kinase, PNCK

PNCK, or CAMK1b, is an understudied kinase of the calcium-calmodulin dependent kinase family which recently has been identified as a marker of cancer progression and survival in several large-scale multi-omics studies. The biology of PNCK and its relation to oncogenesis has also begun to be elucidated, with data suggesting various roles in DNA damage response, cell cycle control, apoptosis and HIF-1-alpha related pathways. To further explore PNCK as a clinical target, potent small-molecule molecular probes must be developed. Currently, there are no targeted small molecule inhibitors in pre-clinical or clinical studies for the CAMK family. Additionally, there exists no experimentally derived crystal structure for PNCK. We herein report a three-pronged chemical probe discovery campaign which utilized homology modeling, machine learning, virtual screening and molecular dynamics to identify small molecules with low-micromolar potency against PNCK activity from commercially available compound libraries. We report the discovery of a hit-series for the first targeted effort towards discovering PNCK inhibitors that will serve as the starting point for future medicinal chemistry efforts for hit-to-lead optimization of potent chemical probes.

The FGF/FGFR signalling mediated anti-cancer drug resistance and therapeutic intervention

ABSTRACT Fibroblast Growth Factor (FGF) ligands and their receptors are crucial factors driving chemoresistance in several malignancies, challenging the efficacy of currently available anti-cancer drugs. The Fibroblast growth factor/receptor (FGF/FGFR) signalling malfunctions in tumor cells, resulting in a range of molecular pathways that may impact its drug effectiveness. Deregulation of cell signalling is critical since it can enhance tumor growth and metastasis. Overexpression and mutation of FGF/FGFR induce regulatory changes in the signalling pathways. Chromosomal translocation facilitating FGFR fusion production aggravates drug resistance. Apoptosis is inhibited by FGFR-activated signalling pathways, reducing multiple anti-cancer medications' destructive impacts. Angiogenesis and epithelial-mesenchymal transition (EMT) are facilitated by FGFRs-dependent signalling, which correlates with drug resistance and enhances metastasis. Further, lysosome-mediated drug sequestration is another prominent method of resistance. Inhibition of FGF/FGFR by following a plethora of therapeutic approaches such as covalent and multitarget inhibitors, ligand traps, monoclonal antibodies, recombinant FGFs, combination therapy, and targeting lysosomes and micro RNAs would be helpful. As a result, FGF/FGFR suppression treatment options are evolving nowadays. To increase positive impacts, the processes underpinning the FGF/FGFR axis' role in developing drug resistance need to be clarified, emphasizing the need for more studies to develop novel therapeutic options to address this significant problem. Communicated by Ramaswamy H. Sarma.

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τ _b =0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τ _b =0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

Decoding the Conformational Selective Mechanism of FGFR Isoforms: A Comparative Molecular Dynamics Simulation

Fibroblast growth factor receptors (FGFRs) play critical roles in the regulation of cell growth, differentiation, and proliferation. Specifically, FGFR2 gene amplification has been implicated in gastric and breast cancer. Pan-FGFR inhibitors often cause large toxic side effects, and the highly conserved ATP-binding pocket in the FGFR1/2/3 isoforms poses an immense challenge in designing selective FGFR2 inhibitors. Recently, an indazole-based inhibitor has been discovered that can selectively target FGFR2. However, the detailed mechanism involved in selective inhibition remains to be clarified. To this end, we performed extensive molecular dynamics simulations of the apo and inhibitor-bound systems along with multiple analyses, including Markov state models, principal component analysis, a cross-correlation matrix, binding free energy calculation, and community network analysis. Our results indicated that inhibitor binding induced the phosphate-binding loop (P-loop) of FGFR2 to switch from the open to the closed conformation. This effect enhanced extensive hydrophobic FGFR2-inhibitor contacts, contributing to inhibitor selectivity. Moreover, the key conformational intermediate states, dynamics, and driving forces of this transformation were uncovered. Overall, these findings not only provided a structural basis for understanding the closed P-loop conformation for therapeutic potential but also shed light on the design of selective inhibitors for treating specific types of cancer.

Strategy toward Kinase-Selective Drug Discovery

Kinase drug selectivity is the ground challenge in cancer research. Due to the structurally similar kinase drug pockets, off-target inhibitor toxicity has been a major cause for clinical trial failures. The pockets are similar but not identical. Here, we describe a transformation invariant protocol to identify distinct geometric features in the drug pocket that can distinguish one kinase from all others. We integrate available experimental structures with the artificial intelligence-based structural kinome, performing a kinome-wide structural bioinformatic analysis to establish the structural principles of kinase drug selectivity. We generate the structural landscape from the experimental kinase-ligand complexes and propose a binary network that encapsulates the information. The results show that all kinases contain binary units that are shared by less than seven other kinases in the kinome. 331 kinases contain unique binary units that may distinguish them from all others. The structural features encoded by these binary units in the network represent the inhibitor-accessible geometric space that may capture the kinome-wide selectivity. Our proposed binary network with the unsupervised clustering can serve as a general structural bioinformatic protocol for extracting the distinguishing structural features for any protein from their families. We apply the binary network to epidermal growth factor receptor tyrosine kinase inhibitor selectivity by targeting the gate area and the AKT1 serine/threonine kinase selectivity by binding to the αC-helix region and the allosteric pocket. Finally, we develop the cross-platform software, KDS (Kinase Drug Selectivity), for customized visualization and analysis of the binary networks in the human kinome (https://github.com/CBIIT/KDS).

Allosteric Boost by TAB1 on the TAK1 Kinase Favorably Sculpts the Thermodynamic Landscape of Activation

The intricate mechanisms of allosteric regulation in kinases are of general interest to the scientific community for potential therapeutic implications. However, the diversity among kinases and their regulatory routes requires a case-by-case study to widen the repertoire of known mechanisms. The present study achieves this by understanding TAK1 kinase activation by TAB1 as a model phenomenon for the first time. Despite the known capacity of TAK1 to switch between its inactive ("DFG-out") and active-like ("DFG-in") conformations, the questionable role of TAB1 in offering an energetic favor to this has been addressed here using sequential combination of enhanced sampling methods like targeted molecular dynamics (TMD) and Gaussian accelerated molecular dynamics (GaMD). It reveals how a minimal domain of TAB1 sufficiently acts like a "catalytic gear" by favorably sculpting TAK1's thermodynamic landscape (potential of mean force in 2D) that accelerates "in"-"out" conformational switching of the conserved DFG motif. Standard molecular dynamics simulations (∼5 μs) reveal that TAB1 fascinatingly exploits the "lever-like" αF helix of TAK1 kinase domain to remotely propel the DFG motif via subtle helical "unfolding-folding" modifications within the kinase activation loop. The presence of two charged residues on terminal poles of αF helix imparts it, with this unique "lever-like" utility, and this turns out to be one important signature of co-evolution between TAK1 and TAB1. The entire mechanism of TAB1's impact transduction, which is found to be analogous to the moves in the popular "Chinese checker" game, gives a clear proof of the "dynamics-driven allostery" concept in kinases. The findings further benchmark TAK1's known autophosphorylation capacity. A novel insight into kinase allostery is thus provided, which potentiates investigation of similar capacities in other kinases.

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τ_b = 0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τ_b = 0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

Evolutionary divergence in the conformational landscapes of tyrosine vs serine/threonine kinases

Inactive conformations of protein kinase catalytic domains where the DFG motif has a "DFG-out" orientation and the activation loop is folded present a druggable binding pocket that is targeted by FDA-approved 'type-II inhibitors' in the treatment of cancers. Tyrosine kinases (TKs) typically show strong binding affinity with a wide spectrum of type-II inhibitors while serine/threonine kinases (STKs) usually bind more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. To investigate this, we use sequence covariation analysis with a Potts Hamiltonian statistical energy model to guide absolute binding free-energy molecular dynamics simulations of 74 protein-ligand complexes. Using the calculated binding free energies together with experimental values, we estimated free-energy costs for the large-scale (~17-20 Å) conformational change of the activation loop by an indirect approach, circumventing the very challenging problem of simulating the conformational change directly. We also used the Potts statistical potential to thread large sequence ensembles over active and inactive kinase states. The structure-based and sequence-based analyses are consistent; together they suggest TKs evolved to have free-energy penalties for the classical 'folded activation loop' DFG-out conformation relative to the active conformation, that is, on average, 4-6 kcal/mol smaller than the corresponding values for STKs. Potts statistical energy analysis suggests a molecular basis for this observation, wherein the activation loops of TKs are more weakly 'anchored' against the catalytic loop motif in the active conformation and form more stable substrate-mimicking interactions in the inactive conformation. These results provide insights into the molecular basis for the divergent functional properties of TKs and STKs, and have pharmacological implications for the target selectivity of type-II inhibitors.

Electronic and structural study of T315I mutated form in DFG-out conformation of BCR-ABL inhibitors

In this work, the four main drugs for the treatment of chronic myeloid leukemia were analyzed, being imatinib, dasatinib, nilotinib and ponatinib followed by four derivative molecules of nilotinib and ponatinib. For these derivative molecules, the fluorine atoms were replaced by hydrogen and chlorine atoms in order to shade light to the structural effects on this set of inhibitors. Electronic studies were performed at density functional theory level with the B3LYP functional and 6-311+G(d,p) basis set. The frontier molecular orbitals, gap HOMO-LUMO, and NBO were analyzed and compared to docking studies for mutant T315I tyrosine kinase protein structure code 3IK3, in the DFG-out conformation. Structural similarities were pointed out, such as the presence of groups common to all inhibitors and modifications raised up on new generations of imatinib-based inhibitors. One of them is the trifluoromethyl group present in nilotinib and later included in ponatinib, in addition to the 1-methylpiperazin-1-ium group that is present in imatinib and ponatinib. The frontier molecular orbitals of imatinib and ponatinib are contributing to the same amino acid residues, and the ineffectiveness of imatinib against the T315I mutation was discussed.Communicated by Ramaswamy H. Sarma.

Affinities and Kinetics Detection of Protein-Small Molecule Interactions with a Monolayer MoS2 -Based Optical Imaging Platform

Quantifying the binding kinetics and affinities of protein-small molecule interactions is critical for biomarker validation, drug discovery, and deep understanding of various biological processes at the molecular-scale. Novel approaches are demanded as most common label-free techniques are mass-sensitive, which are not suitable for the detection of small molecule interactions. Here, an optical imaging platform is developed to measure the binding kinetics of both protein-small molecules and protein-ions based on monolayer MoS₂ , an ultra-thin 2D material whose optical absorption is extremely sensitive to charge. A model is established to calibrate the optical response due to the charged analyte binding and it is applied to quantify the interactions between abl1 kinase and different small-molecule inhibitors. Such a presented method is capable of distinguishing different inhibitors binding to a wild or mutated kinase, which provides guidance for drug evaluation and drug mechanism exploration. The binding kinetics of calcium ions to calmodulin is also measured, further broadening the application field of the method. In addition, the imaging capability allows mapping the local binding kinetics of the molecular interactions with a high resolution, which reveals visible spatial variability and offers a promising tool for studying heterogeneous local interfacial interactions.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

Two-Front War on Cancer-Targeting TAM Receptors in Solid Tumour Therapy

Simple Summary

In recent years, many studies have shown the importance of TAM kinases in both normal and neoplastic cells. In this review, we present and discuss the role of the TAM family (AXL, MERTK, TYRO3) of receptor tyrosine kinases (RTKs) as a dual target in cancer, due to their intrinsic roles in tumour cell survival, migration, chemoresistance, and their immunosuppressive roles in the tumour microenvironment. This review presents the potential of TAMs as emerging therapeutic targets in cancer treatment, focusing on the distinct structures of TAM receptor tyrosine kinases. We analyse and compare different strategies of TAM inhibition, for a full perspective of current and future battlefields in the war with cancer.

Abstract

Receptor tyrosine kinases (RTKs) are transmembrane receptors that bind growth factors and cytokines and contain a regulated kinase activity within their cytoplasmic domain. RTKs play an important role in signal transduction in both normal and malignant cells, and their encoding genes belong to the most frequently affected genes in cancer cells. The TAM family proteins (TYRO3, AXL, and MERTK) are involved in diverse biological processes: immune regulation, clearance of apoptotic cells, platelet aggregation, cell proliferation, survival, and migration. Recent studies show that TAMs share overlapping functions in tumorigenesis and suppression of antitumour immunity. MERTK and AXL operate in innate immune cells to suppress inflammatory responses and promote an immunosuppressive tumour microenvironment, while AXL expression correlates with epithelial-to-mesenchymal transition, metastasis, and motility in tumours. Therefore, TAM RTKs represent a dual target in cancer due to their intrinsic roles in tumour cell survival, migration, chemoresistance, and their immunosuppressive roles in the tumour microenvironment (TME). In this review, we discuss the potential of TAMs as emerging therapeutic targets in cancer treatment. We critically assess and compare current approaches to target TAM RTKs in solid tumours and the development of new inhibitors for both extra- and intracellular domains of TAM receptor kinases.

Signaling Pathway and Small-Molecule Drug Discovery of FGFR: A Comprehensive Review

Targeted therapy is a groundbreaking innovation for cancer treatment. Among the receptor tyrosine kinases, the fibroblast growth factor receptors (FGFRs) garnered substantial attention as promising therapeutic targets due to their fundamental biological functions and frequently observed abnormality in tumors. In the past 2 decades, several generations of FGFR kinase inhibitors have been developed. This review starts by introducing the biological basis of FGF/FGFR signaling. It then gives a detailed description of different types of small-molecule FGFR inhibitors according to modes of action, followed by a systematic overview of small-molecule-based therapies of different modalities. It ends with our perspectives for the development of novel FGFR inhibitors.

AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens

Structure-based, virtual High-Throughput Screening (vHTS) methods for predicting ligand activity in drug discovery are important when there are no or relatively few known compounds that interact with a therapeutic target of interest. State-of-the-art computational vHTS necessarily relies on effective methods for pose sampling and docking and generating an accurate affinity score from the docked poses. However, proteins are dynamic; in vivo ligands bind to a conformational ensemble. In silico docking to the single conformation represented by a crystal structure can adversely affect the pose quality. Here, we introduce AtomNet PoseRanker (ANPR), a graph convolutional network trained to identify and rerank crystal-like ligand poses from a sampled ensemble of protein conformations and ligand poses. In contrast to conventional vHTS methods that incorporate receptor flexibility, a deep learning approach can internalize valid cognate and noncognate binding modes corresponding to distinct receptor conformations, thereby learning to infer and account for receptor flexibility even on single conformations. ANPR significantly enriched pose quality in docking to cognate and noncognate receptors of the PDBbind v2019 data set. Improved pose rankings that better represent experimentally observed ligand binding modes improve hit rates in vHTS campaigns and thereby advance computational drug discovery, especially for novel therapeutic targets or novel binding sites.

Homology Modelling, Docking-based Virtual Screening, ADME Properties, and Molecular Dynamics Simulation for Identification of Probable Type II Inhibitors of AXL Kinase

Background:

AXL kinase is an important member of the TAM family for kinases which is involved in most cancers. Considering its role in different cancers due to its pro-tumorigenic effects and its involvement in the resistance, it has gained importance recently. Majority of research carried out is on Type I inhibitors and limited studies have been carried out for Type II inhibitors. Taking this into consideration, we have attempted to build Homology models to identify the Type II inhibitors for the AXL kinase.

Methods:

Homology Models for DFG-out C-helix-in/out state were developed using SWISS Model, PRIMO, and Prime. These models were validated by different methods and further evaluated for stability by molecular dynamics simulation using Desmond software. Selected models PED1-EB and PEDI1-EB were used for the docking-based virtual screening of four compound libraries using Glide software. The hits identified were subjected to interaction analysis and shortlisted compounds were subjected to Prime MM-GBSA studies for energy calculation. These compounds were also docked in the DFG-in state to check for binding and elimination of any compounds that may not be Type II inhibitors. The Prime energies were calculated for these complexes as well and some compounds were eliminated. ADMET studies were carried out using Qikprop. Some selected compounds were subjected to molecular dynamics simulation using Desmond for evaluating the stability of the complexes.

Results:

Out of 78 models inclusive of both DFG-out C-helix-in and DFG-out C-helix-out, 5 models were identified after different types of evaluation as well as validation studies. 1 model representing each type (PED1-EB and PEDI1-EB) was selected for the screening studies. The screening studies resulted in the identification of 29 compounds from the screen on PED1-EB and 10 compounds from the screen on PEDI1-EB. Hydrogen bonding interactions with Pro621, Met623, and Asp690 were observed for these compounds primarily. In some compounds, hydrogen bonding with Leu542, Glu544, Lys567, and Asn677 as well as pi-pi stacking interactions with either Phe622 or Phe691 were also seen. 4 compounds identified from PED1-EB screen were subjected to molecular dynamics simulation and their interactions were found to be consistent during the simulation. 2 compounds identified from PEDI1-EB screen were also subjected to the simulation studies, however, their interactions with Asp690 were not observed for a significant time and in both cases differed from the docked pose.

Conclusion:

Multiple models of DFG-out conformations of AXL kinase were built, validated and used for virtual screening. Different compounds were identified in the virtual screening, which may possibly act as Type II inhibitors for AXL kinase. Some more experimental studies can be done to validate these findings in future. This study will play a guiding role in the further development of the newer Type II inhibitors of the AXL kinase for the probable treatment of cancer.