Summary LIM kinases, LIMK1 and LIMK2, play a crucial role in cytoskeleton dynamics. They are involved in many physiological processes but also in several pathologies such as cancer, neuronal diseases and neurofibromatosis. Although LIM kinases appear as promising therapeutic targets, they remain undruggable. A better understanding of their activity and regulation is thus required to better design efficient targeted therapies. Here, we have shown the impact of a single amino acid on LIMK activity on cofilin, their main substrate in actin filament remodelling. We demonstrated that Y632 and Y630, for LIMK1 and LIMK2 respectively, mediate LIMK dimerization, resulting in their transphosphorylation. This process seems to be a prerequisite for their canonical phosphorylation on their respective T508 and T505 residues within the activation loop. These Tyrosine are not phosphorylated, their aromatic nature is rather critical to ensure proper LIMK activity on cofilin. These results bring new insights into LIMK molecular features.

Background/Objectives: Protein kinases represent major therapeutic targets because dysregulation of their phosphorylation activity is associated with several diseases, including cancer, diabetes, and inflammatory disorders. Thus, many researchers in the pharmaceutical filed are making significant effort to design potent new protein kinase inhibitors (PKIs) as potential drugs. In this context, we aimed to identify new alternatives by exploiting the chemical space defined by ligands of the nucleotide-binding protein family for the discovery of novel protein kinase inhibitors. Protein kinases bind the nucleotide adenosine triphosphate (ATP) and belong to the nucleotide-binding protein group. Methods: All ligands of the nucleotide-binding protein family, excluding known kinase inhibitors, that were identified in the ChEMBL database were used in a structure-based virtual screening approach. From this set, we aimed to identify novel nucleotide-binding protein inhibitors (NBPIs) as novel kinase inhibitors. A total of 19,709 NBPI compounds that were dissimilar to known PKIs were docked on five protein kinases, and the 200 best scoring docking poses were retained for potential purchase. Results: Only 25 compounds were commercially available in stock and were evaluated experimentally on a panel of 10 diverse protein kinases. Three NBPI compounds, one of which had originally been identified as active against the ATP-binding cassette transporter ABCG2, were identified as Haspin kinase inhibitors with micromolar activity. Conclusions: This study presents an efficient computational approach to identifying novel kinase inhibitors from a database of ligands of the nucleotide-binding protein family, and the protocol could be applied to other protein target families.

There are currently no antiviral drugs available to treat or prevent life-threatening human non-poliovirus enterovirus infections, such as those caused by CV-B3, EV-A71 or EV-D68. Our aim is to develop novel inhibitors that target the non-structural ATPase/Helicase 2C protein, which is involved in the RNA replication process that is essential for enterovirus replication, among other functions. In this study, we describe the optimization of (S)-fluoxetine, a promising hit identified through drug repurposing that binds to an allosteric site on the CV-B3 2C ATPase domain. Our optimization process was guided by rational design, X-ray crystallographic structures, computational docking, and validation by enzyme and cell-based assays, leading to several new inhibitors, among which compound 53 (CV-B3 EC50 = 0.5 μM and EV-D68 EC50 = 0.4 μM), a novel anti-enterovirus with higher selectivity indexes than (S)-fluoxetine.

CLK1 is one of the four human isoforms of the cdc2-like (CLK) kinases that has been suggested as a therapeutic target in diverse diseases based on its important role regulating mRNA splicing. For example, CLKs and closely related kinases such as DYRK1A have been targeted in Alzheimer’s disease and other diseases in which splice site selection contributes to the disease development. Here we have developed an efficient in silico fragment-based ligand design approach to identify novel CLK1 inhibitors with excellent ligand efficiency based on an imidazo[2,1-b][1,3,4]thiadiazole fragment. More than one million docking poses were generated from 26,225 unique virtual compounds, and after applying several filtering steps, 11 compounds were selected, synthesized and their CLK1 inhibition and cellular potency were evaluated. Gratifyingly, inhibitor potencies were in excellent agreement with predicted values and crystallographic data of an inhibitor bound to CLK1 confirmed the unusual binding mode of the compounds.

Background/Objectives: Fragment-based approaches in the field of drug discovery and design have been widely developed and employed in both academia and industry. We present here an innovative in silico fragment-based drug design approach aimed at designing new inhibitors in the ATP-binding site of protein kinases. Methods: This tool, named Frags2Drugs (F2D), relies on a three-dimensional fragment library obtained from co-crystallized ligands. This library is stored in a graph-oriented database containing the required information to link fragments together. F2D builds every possible molecule that fits into the given cavity on a minute scale. Molecules are then filtered to keep those presenting the best predicted affinity. Several specific molecular filters can be applied, including protein kinase inhibitor-like filters. Results: We validated our method by reconstructing existing co-crystallized ligands and known kinase inhibitors. In this study, we provide several examples of its use to retrieve known or design new type I, type I1/2, type II, and macrocyclic inhibitors on several protein kinases. Conclusions: We have developed an in silico fragment-based ligand design tool able to identify novel kinase inhibitors by growing any scaffolds positioned in the ATP-binding site..

MOTIVATION:The field of protein-ligand binding affinity prediction continues to face significant challenges. While deep learning (DL) models can leverage 3D structural information of protein-ligand complexes, they perform well only on heavily biased test sets containing information leaked from training sets. This lack of generalization arises from the limited availability of training data and the models' inability to effectively learn from protein-ligand interactions. Since these interactions are inherently time-dependent, molecular dynamics (MD) simulations offer a potential solution by incorporating conformational sampling and providing interaction rich information. RESULTS:We have developed MDbind, a dataset comprising 63 000 simulations of protein-ligand interactions, along with novel neural networks capable of learning from these simulations to predict binding affinity. By utilizing MD as data augmentation, our models achieved state-of-the-art performance on the PDBbind v.2016 core set and an external test set, the free energy perturbation (FEP) dataset. Additionally, when trained on the full MD simulations, the models demonstrated less biased predictions. AVAILABILITY AND IMPLEMENTATION:The code for neural networks is available at https://github.com/ICOA-SBC/MD_DL_BA. The models, the results and the training/validation/test sets are available for download at https://zenodo.org/records/10390550. The MDbind trajectories are being transferred to the MDDB: https://mmb-dev.mddbr.eu/#/browse? option=mdbind.

LIM Kinases, LIMK1 and LIMK2, have become promising targets for the development of inhibitors with potential application for the treatment of several major diseases. LIMKs play crucial roles in cytoskeleton remodeling as downstream effectors of small G proteins of the Rho-GTPase family, and as major regulators of cofilin, an actin depolymerizing factor. In this article we describe the conception, synthesis, and biological evaluation of novel tetrahydropyridine pyrrolopyrimidine LIMK inhibitors. Homology models were first constructed to better understand the binding mode of our preliminary compounds and to explain differences in biological activity. A library of over 60 products was generated and in vitro enzymatic activities were measured in the mid to low nanomolar range. The most promising derivatives were then evaluated in cell on cofilin phosphorylation inhibition which led to the identification of 52 which showed excellent selectivity for LIMKs in a kinase selectivity panel. We also demonstrated that 52 affected the cell cytoskeleton by disturbing actin filaments. Cell migration studies with this derivative using three different cell lines displayed a significant effect on cell motility. Finally, the crystal structure of the kinase domain of LIMK2 complexed with 52 was solved, greatly improving our understanding of the interaction between 52 and LIMK2 active site. The reported data represent a basis for the development of more efficient LIMK inhibitors for future in vivo preclinical validation.

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.

The catalytically inactive caspase-8-homologous protein, c-FLIP, is a potent antiapoptotic protein highly expressed in various types of cancers. c-FLIP competes with caspase-8 for binding to the adaptor protein FADD (Fas-Associated Death Domain) following death receptors' (DRs) activation via the ligands of the TNF-R family. As a consequence, the extrinsic apoptotic signaling pathway involving DRs is inhibited. The inhibition of c-FLIP activity in tumor cells might enhance DR-mediated apoptosis and overcome immune and anticancer drug resistance. Based on an in silico approach, the aim of this work was to identify new small inhibitory molecules able to bind selectively to c-FLIP and block its anti-apoptotic activity. Using a homology 3D model of c-FLIP, an in silico screening of 1880 compounds from the NCI database (National Cancer Institute) was performed. Nine molecules were selected for in vitro assays, based on their binding affinity to c-FLIP and their high selectivity compared to caspase-8. These molecules selectively bind to the Death Effector Domain 2 (DED2) of c-FLIP. We have tested in vitro the inhibitory effect of these nine molecules using the human lung cancer cell line H1703, overexpressing c-FLIP. Our results showed that six of these newly identified compounds efficiently prevent FADD/c-FLIP interactions in a molecular pull-down assay, as well as in a DISC immunoprecipitation assay. The overexpression of c-FLIP in H1703 prevents TRAIL-mediated apoptosis; however, a combination of TRAIL with these selected molecules significantly restored TRAIL-induced cell death by rescuing caspase cleavage and activation. Altogether, our findings indicate that new inhibitory chemical molecules efficiently prevent c-FLIP recruitment into the DISC complex, thus restoring the caspase-8-dependent apoptotic cascade. These results pave the way to design new c-FLIP inhibitory molecules that may serve as anticancer agents in tumors overexpressing c-FLIP.

Dysregulation of cyclin-dependent kinase 8 (CDK8) activity has been associated with many diseases, including colorectal and breast cancer. As usual in the CDK family, the activity of CDK8 is controlled by a regulatory protein called cyclin C (CycC). But, while human CDK family members are generally activated in two steps, that is, the binding of the cyclin to CDK and the phosphorylation of a residue in the CDK activation loop, CDK8 does not require the phosphorylation step to be active. Another peculiarity of CDK8 is its ability to be associated with CycC while adopting an inactive form. These specificities raise the question of the role of CycC in the complex CDK8–CycC, which appears to be more complex than the other members of the CDK family. Through molecular dynamics (MD) simulations and binding free energy calculations, we investigated the effect of CycC on the structure and dynamics of CDK8. In a second step, we particularly focused our investigation on the structural and molecular basis of the protein–protein interaction between the two partners by finely analyzing the energetic contribution of residues and simulating the transition between the active and the inactive form. We found that CycC has a stabilizing effect on CDK8, and we identified specific interaction hotspots within its interaction surface compared to other human CDK/Cyc pairs. Targeting these specific interaction hotspots could be a promising approach in terms of specificity to effectively disrupt the interaction between CDK8. The simulation of the conformational transition from the inactive to the active form of CDK8 suggests that the residue Glu99 of CycC is involved in the orientation of three conserved arginines of CDK8. Thus, this residue may assume the role of the missing phosphorylation step in the activation mechanism of CDK8. In a more general view, these results point to the importance of keeping the CycC in computational studies when studying the human CDK8 protein in both the active and the inactive form.

Background Atopic dermatitis has a marked economic impact and affects the quality of life. A cosmetic compound with an innovative strategy is proposed here as a small chemical neutraligand, GPN279 (previously identified as a theophylline derivative), that binds and potently neutralizes the TARC/CCL17 chemokine, activating the Th2 cell-expressed CCR4 receptor. Objective Our objective was to evaluate the safety and activity of topically applied GPN279 in mild-to-moderate atopic dermatitis patients in a randomized, double-blind, placebo-controlled, parallel group trial. Such cosmetic active ingredient targeting dry skin with an atopic tendency would open a parallel strategy to the pharmaceutical approach, in particular for mild to moderate subjects, as an alternative to reduce the evolution towards severe forms of atopy. Methods This 4-week trial included adults with mild-to-moderate atopic dermatitis, according to the SCORAD index. Patients were randomized into two groups treated by topical applications of either an emulsion containing 0.44% GPN279 in placebo on skin lesions or the placebo (4.56% glycerin). Clinical activity was evaluated with the SCORAD as the primary objective. As secondary objectives, POEM, erythema, skin moisturization, its barrier function (TEWL) and safety were evaluated. Results Twenty-one patients in each group completed the study. SCORAD was significantly improved in the GPN279 group vs. placebo. GPN279 also significantly improved POEM, induced a rapid and significant decrease of erythema, and improved skin moisture. GPN279 and placebo were well tolerated throughout the study. Conclusion A cosmetic cream comprising the CCL17 neutraligand GPN279 improved the skin barrier and physiology criteria in patients with mild-to-moderate atopic dermatitis.

Computational approaches are nowadays largely applied in drug discovery projects. Among these, molecular docking is the most used for hit identification against a drug target protein. However, many scientists in the field shed light on the lack of availability and reproducibility of the data obtained from such studies to the whole community. Consequently, sustaining and developing the efforts toward a large and fully transparent sharing of those data could be beneficial for all researchers in drug discovery. The purpose of this article is first to propose guidelines and recommendations on the appropriate way to conduct virtual screening experiments and second to depict the current state of sharing molecular docking data. In conclusion, we have explored and proposed several prospects to enhance data sharing from docking experiment that could be developed in the foreseeable future.

Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with deep learning (DL) algorithms playing a crucial role in predicting protein–ligand binding affinities. Despite advancements in neural network architectures, system representation, and training techniques, the performance of DL affinity prediction has reached a plateau, prompting the question of whether it is truly solved or if the current performance is overly optimistic and reliant on biased, easily predictable data. Like other DL-related problems, this issue seems to stem from the training and test sets used when building the models. In this work, we investigate the impact of several parameters related to the input data on the performance of neural network affinity prediction models. Notably, we identify the size of the binding pocket as a critical factor influencing the performance of our statistical models; furthermore, it is more important to train a model with as much data as possible than to restrict the training to only high-quality datasets. Finally, we also confirm the bias in the typically used current test sets. Therefore, several types of evaluation and benchmarking are required to understand models’ decision-making processes and accurately compare the performance of models.

The mutation V600E in B-Raf leads to mitogen activated protein kinase (MAPK) pathway activation, uncontrolled cell proliferation, and tumorigenesis. ATP competitive type I B-Raf inhibitors, such as vemurafenib (1) and PLX4720 (4) efficiently block the MAPK pathways in B-Raf mutant cells, however these inhibitors induce conformational changes in the wild type B-Raf (wtB-Raf) kinase domain leading to heterodimerization with C-Raf, causing paradoxical hyperactivation of the MAPK pathway. This unwanted activation may be avoided by another class of inhibitors (type II) which bind the kinase in the DFG-out conformation, such as AZ628 (3) preventing heterodimerization. Here we present a new B-Raf kinase domain inhibitor, based on a phenyl(1H-pyrrolo [2,3-b]pyridin-3-yl)methanone template, that represents a hybrid between 4 and 3. This novel inhibitor borrows the hinge binding region from 4 and the back pocket binding moiety from 3. We determined its binding mode, performed activity/selectivity studies, and molecular dynamics simulations in order to study the conformational effects induced by this inhibitor on wt and V600E mutant B-Raf kinase. We discovered that the inhibitor was active and selective for B-Raf, binds in a DFG-out/αC-helix-in conformation, and did not induce the aforementioned paradoxical hyperactivation in the MAPK pathway. We propose that this merging approach can be used to design a novel class of B-Raf inhibitors for translational studies.

Using a coiled-coil peptide dimer as a model system to explore furan reactivity, we describe novel cross-link partners of furan warheads for site-specific cross-linking. We demonstrate that replacement of weak interhelical ionic contacts with a furan moiety and its potential cross-link partner affords covalently connected coiled-coil motifs upon furan activation. We describe for the first time the reaction of the activated furan warhead with cysteine and tyrosine, besides the previously reported lysine, thus enhancing the versatility of the furan cross-link methodology by the possibility to target different amino acids. The present in vitro validation of “furan-armed” α -helices provides further grounds for exploiting furan technology in the development of furan-modified ligands/proteins to target proteins in a covalent way through various amino acid side chains.

A library of substituted indolo[2,3-c]quinolone-6-ones was developed as simplified Lamellarin isosters. Synthesis was achieved from indole after a four-step pathway sequence involving iodination, a Suzuki-Miyaura cross-coupling reaction, and a reduction/lactamization sequence. The inhibitory activity of the 22 novel derivatives was assessed on Haspin kinase. Two of them possessed an IC50 of 1 and 2 nM with selectivity towards a panel of 10 other kinases including the parent kinases DYRK1A and CLK1. The most selective compound exerted additionally a very interesting cell effect on the osteosarcoma U-2 OS cell line.

Drug-target residence time has emerged as a key selection factor in drug discovery since the binding duration of a drug molecule to its protein target can significantly impact its in vivo efficacy. The challenge in studying the residence time, in early drug discovery stages, lies in how to cost-effectively determine the residence time for the systematic assessment of compounds. Currently, there is still a lack of computational protocols to quickly estimate such a measure, particularly for large and flexible protein targets and drugs. Here, we report an efficient computational protocol, based on targeted molecular dynamics, to rank drug candidates by their residence time and to obtain insights into ligand-target dissociation mechanisms. The method was assessed on a dataset of 10 arylpyrazole inhibitors of CDK8, a large, flexible, and clinically important target, for which the experimental residence time of the inhibitors ranges from minutes to hours. The compounds were correctly ranked according to their estimated residence time scores compared to their experimental values. The analysis of protein-ligand interactions along the dissociation trajectories highlighted the favorable contribution of hydrophobic contacts to residence time and revealed key residues that strongly affect compound residence time.