Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors

Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1

Sphingosine kinases (SphKs) are a group of important enzymes that circulate at low micromolar concentrations in mammals and have received considerable attention due to the roles they play in a broad array of biological processes including apoptosis, mutagenesis, lymphocyte migration, radio- and chemo-sensitization, and angiogenesis. In the present study, we constructed three classification models by four machine learning (ML) algorithms including naive bayes (NB), support vector machine (SVM), logistic regression, and random forest from 395 compounds. The generated ML models were validated by fivefold cross validation. Five different scaffold hit fragments resulted from SVM model-based virtual screening and docking results indicate that all the five fragments exhibit common hydrogen bond interaction a catalytic residue of SphK1. Further, molecular dynamics (MD) simulations and binding free energy calculation had been carried out with the identified five fragment leads and three cocrystal inhibitors. The best 15 fragments were selected. Molecular dynamics (MD) simulations showed that among these compounds, 7 compounds have favorable binding energy compared with cocrystal inhibitors. Hence, the study showed that the present lead fragments could act as potential inhibitors against therapeutic target of cancers and neurodegenerative disorders.

Discovery of Potential Inhibitors of CDK1 by Integrating Pharmacophore-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation Studies, and Evaluation of Their Inhibitory Activity

The ability of CDK1 to compensate for the absence of other cell cycle CDKs poses a great challenge to treat cancers that overexpress these proteins. Despite several studies focusing on the area, there are no FDA-approved drugs selectively targeting CDK1. Here, the study aimed to develop potential CDK1 selective inhibitors through drug repurposing and leveraging the structural insights provided by the hit molecules generated. Approximately 280,000 compounds from DrugBank, Selleckchem, Otava and an in-house library were screened initially based on fit values using 3D QSAR pharmacophores built for CDK1 and subsequently through Lipinski, ADMET, and TOPKAT filters. 10,310 hits were investigated for docking into the binding site of CDK1 determined using the crystal structure of human CDK1 in complex with NU6102. The best 55 hits with better docking scores were further analyzed, and 12 hits were selected for 100 ns MD simulations followed by binding energy calculations using the MM-PBSA method. Finally, 10 hit molecules were tested in an in vitro CDK1 Kinase inhibition assay. Out of these, 3 hits showed significant CDK1 inhibitory potential with IC50 < 5 μM. These results indicate these compounds can be used to develop subtype-selective CDK1 inhibitors with better efficacy and reduced toxicities in the future.

Molecular modeling strategy for detailing the primary mechanism of action of copanlisib to PI3K: combined ligand-based and target-based approach

Since dysregulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3K has emerged as an attractive target for drug development. Although copanlisib is the first pan-PI3K inhibitor to be approved for clinical use, the precise mechanism by which it acts on PI3K has not been fully elucidated. To reveal the binding mechanisms and structure-activity relationship between PI3K and copanlisib, a comprehensive modeling approach that combines 3D-quantitative structure-activity relationship (3D-QSAR), pharmacophore model, and molecular dynamics (MD) simulation was utilized. Initially, the structure-activity relationship of copanlisib and its derivatives were explored by constructing a 3D-QSAR. Then, the key chemical characteristics were identified by building common feature pharmacophore models. Finally, MD simulations were performed to elucidate the important interactions between copanlisib and different PI3K subtypes, and highlight the key residues for tight-binding inhibitors. The present study uncovered the principal mechanism of copanlisib's action on PI3K at the theoretical level, and these findings might provide guidance for the rational design of pan-PI3K inhibitors.Communicated by Ramaswamy H. Sarma.

Identification of novel NLRP3 inhibitors as therapeutic options for epilepsy by machine learning-based virtual screening, molecular docking and biomolecular simulation studies

The NOD-Like Receptor Protein-3 (NLRP3) inflammasome is a key therapeutic target for the treatment of epilepsy and has been reported to regulate inflammation in several neurological diseases. In this study, a machine learning-based virtual screening strategy has investigated candidate active compounds that inhibit the NLRP3 inflammasome. As machine learning-based virtual screening has the potential to accurately predict protein-ligand binding and reduce false positives outcomes compared to traditional virtual screening. Briefly, classification models were created using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) machine learning methods. To determine the most crucial features of a molecule's activity, feature selection was carried out. By utilizing 10-fold cross-validation, the created models were analyzed. Among the generated models, the RF model obtained the best results as compared to others. Therefore, the RF model was used as a screening tool against the large chemical databases. Molecular operating environment (MOE) and PyRx software's were applied for molecular docking. Also, using the Amber Tools program, molecular dynamics (MD) simulation of potent inhibitors was carried out. The results showed that the KNN, SVM, and RF accuracy was 0.911 %, 0.906 %, and 0.946 %, respectively. Moreover, the model has shown sensitivity of 0.82 %, 0.78 %, and 0.86 % and specificity of 0.95 %, 0.96 %, and 0.98 % respectively. By applying the model to the ZINC and South African databases, we identified 98 and 39 compounds, respectively, potentially possessing anti-NLRP3 activity. Also, a molecular docking analysis produced ten ZINC and seven South African compounds that has comparable binding affinities to the reference drug. Moreover, MD analysis of the two complexes revealed that the two compounds (ZINC000009601348 and SANC00225) form stable complexes with varying amounts of binding energy. The in-silico studies indicate that both compounds most likely display their inhibitory effect by inhibiting the NLRP3 protein.

Recent advances in the development of P2Y14R inhibitors: a patent and literature review (2018-present)

INTRODUCTION

The P2Y14 receptor (P2Y14R), a member of the G protein-coupled receptor family, is activated by extracellular nucleotides. Due to its involvement in inflammatory, immunological and other associated processes, P2Y14R has emerged as a promising therapeutic target. Despite lacking a determined three-dimensional crystal structure, the homology modeling technique based on closely related P2Y receptors' crystallography has been extensively utilized for developing active compounds targeting P2Y14R. Recent discoveries have unveiled numerous highly effective and subtype-specific P2Y14R inhibitors. This study presents an overview of the latest advancements in P2Y14R inhibitors.

AREAS COVERED

This review presents an overview of the advancements in P2Y14R inhibitor research over the past five years, encompassing new patents, journal articles, and highlighting the therapeutic prospects inherent in these compounds.

EXPERT OPINION

The recent revelation of the vast potential of P2Y14R inhibitors has led to the development of novel compounds that exhibit promising capabilities for the treatment of sterile inflammation of the kidney, potentially diabetes, and asthma. Despite being a relatively nascent class of compounds, certain members have already exhibited their capacity to surmount specific challenges posed by conventional P2Y14R inhibitors. Targeting P2Y14R through small molecules may present a promising therapeutic strategy for effectively managing diverse inflammatory diseases.

Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

New Advances in the Pharmacology and Toxicology of Lithium: A Neurobiologically Oriented Overview

Over the last six decades, lithium has been considered the gold standard treatment for the long-term management of bipolar disorder due to its efficacy in preventing both manic and depressive episodes as well as suicidal behaviors. Nevertheless, despite numerous observed effects on various cellular pathways and biologic systems, the precise mechanism through which lithium stabilizes mood remains elusive. Furthermore, there is recent support for the therapeutic potential of lithium in other brain diseases. This review offers a comprehensive examination of contemporary understanding and predominant theories concerning the diverse mechanisms underlying lithium's effects. These findings are based on investigations utilizing cellular and animal models of neurodegenerative and psychiatric disorders. Recent studies have provided additional support for the significance of glycogen synthase kinase-3 (GSK3) inhibition as a crucial mechanism. Furthermore, research has shed more light on the interconnections between GSK3-mediated neuroprotective, antioxidant, and neuroplasticity processes. Moreover, recent advancements in animal and human models have provided valuable insights into how lithium-induced modifications at the homeostatic synaptic plasticity level may play a pivotal role in its clinical effectiveness. We focused on findings from translational studies suggesting that lithium may interface with microRNA expression. Finally, we are exploring the repurposing potential of lithium beyond bipolar disorder. These recent findings on the therapeutic mechanisms of lithium have provided important clues toward developing predictive models of response to lithium treatment and identifying new biologic targets. SIGNIFICANCE STATEMENT: Lithium is the drug of choice for the treatment of bipolar disorder, but its mechanism of action in stabilizing mood remains elusive. This review presents the latest evidence on lithium's various mechanisms of action. Recent evidence has strengthened glycogen synthase kinase-3 (GSK3) inhibition, changes at the level of homeostatic synaptic plasticity, and regulation of microRNA expression as key mechanisms, providing an intriguing perspective that may help bridge the mechanistic gap between molecular functions and its clinical efficacy as a mood stabilizer.

In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches

Single-point mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene are the most common cause of human cancer. In humans, oncogenic KRAS mutations are responsible for about 30% of lung, pancreatic, and colon cancers. One of the predominant mutant KRAS G12D variants is responsible for pancreatic cancer and is an attractive drug target. At the time of writing, no Food and Drug Administration (FDA) approved drugs are available for the KRAS G12D mutant. So, there is a need to develop an effective drug for KRAS G12D. The process of finding new drugs is expensive and time-consuming. On the other hand, in silico drug designing methodologies are cost-effective and less time-consuming. Herein, we employed machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) for the identification of new inhibitors against the KRAS G12D mutant. A total of 82 hits were predicted as active against the KRAS G12D mutant. The active hits were docked into the active site of the KRAS G12D mutant. Furthermore, to evaluate the stability of the compounds with a good docking score, the top two complexes and the standard complex (MRTX-1133) were subjected to 200 ns MD simulation. The top two hits revealed high stability as compared to the standard compound. The binding energy of the top two hits was good as compared to the standard compound. Our identified hits have the potential to inhibit the KRAS G12D mutation and can help combat cancer. To the best of our knowledge, this is the first study in which machine-learning-based virtual screening, molecular docking, and molecular dynamics simulation were carried out for the identification of new promising inhibitors for the KRAS G12D mutant.

Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking

Extensive research has accumulated which suggests that phosphatidylinositol 3-kinase delta (PI3Kδ) is closely related to the occurrence and development of various human diseases, making PI3Kδ a highly promising drug target. However, PI3Kδ exhibits high homology with other members of the PI3K family, which poses significant challenges to the development of PI3Kδ inhibitors. Therefore, in the present study, a hybrid virtual screening (VS) approach based on a ligand-based pharmacophore model and multicomplex-based molecular docking was developed to find novel PI3Kδ inhibitors. 13 crystal structures of the human PI3Kδ-inhibitor complex were collected to establish models. The inhibitors were extracted from the crystal structures to generate the common feature pharmacophore. The crystallographic protein structures were used to construct a naïve Bayesian classification model that integrates molecular docking based on multiple PI3Kδ conformations. Subsequently, three VS protocols involving sequential or parallel molecular docking and pharmacophore approaches were employed. External predictions demonstrated that the protocol combining molecular docking and pharmacophore resulted in a significant improvement in the enrichment of active PI3Kδ inhibitors. Finally, the optimal VS method was utilized for virtual screening against a large chemical database, and some potential hit compounds were identified. We hope that the developed VS strategy will provide valuable guidance for the discovery of novel PI3Kδ inhibitors.

Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target

Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12-14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).

Deep Learning with Geometry-Enhanced Molecular Representation for Augmentation of Large-Scale Docking-Based Virtual Screening

Structure-based virtual screening has been a crucial tool in drug discovery for decades. However, as the chemical space expands, the existing structure-based virtual screening techniques based on molecular docking and scoring struggle to handle billion-entry ultralarge libraries due to the high computational cost. To address this challenge, people have resorted to machine learning techniques to enhance structure-based virtual screening for efficiently exploring the vast chemical space. In those cases, compounds are usually treated as sequential strings or two-dimensional topology graphs, limiting their ability to incorporate three-dimensional structural information for downstream tasks. We herein propose a novel deep learning protocol, GEM-Screen, which utilizes the geometry-enhanced molecular representation of the compounds docking to a specific target and is trained on docking scores of a small fraction of a library through an active learning strategy to approximate the docking outcome for yet nontraining entries. This protocol is applied to virtual screening campaigns against the AmpC and D4 targets, demonstrating that GEM-Screen enriches more than 90% of the hit scaffolds for AmpC in the top 4% of model predictions and more than 80% of the hit scaffolds for D4 in the same top-ranking size of library. GEM-Screen can be used in conjunction with traditional docking programs for docking of only the top-ranked compounds to avoid the exhaustive docking of the whole library, thus allowing for discovering top-scoring compounds from billion-entry libraries in a rapid yet accurate fashion.

Development of Kinase-Centric Drugs: A Computational Perspective

Kinases are prominent drug targets in the pharmaceutical and research community due to their involvement in signal transduction, physiological responses, and upon dysregulation, in diseases such as cancer, neurological and autoimmune disorders. Several FDA-approved small-molecule drugs have been developed to combat human diseases since Gleevec was approved for the treatment of chronic myelogenous leukemia. Kinases were considered "undruggable" in the beginning. Several FDA-approved small-molecule drugs have become available in recent years. Most of these drugs target ATP-binding sites, but a few target allosteric sites. Among kinases that belong to the same family, the catalytic domain shows high structural and sequence conservation. Inhibitors of ATP-binding sites can cause off-target binding. Because members of the same family have similar sequences and structural patterns, often complex relationships between kinases and inhibitors are observed. To design and develop drugs with desired selectivity, it is essential to understand the target selectivity for kinase inhibitors. To create new inhibitors with the desired selectivity, several experimental methods have been designed to profile the kinase selectivity of small molecules. Experimental approaches are often expensive, laborious, time-consuming, and limited by the available kinases. Researchers have used computational methodologies to address these limitations in the design and development of effective therapeutics. Many computational methods have been developed over the last few decades, either to complement experimental findings or to forecast kinase inhibitor activity and selectivity. The purpose of this review is to provide insight into recent advances in theoretical/computational approaches for the design of new kinase inhibitors with the desired selectivity and optimization of existing inhibitors.

Developing new PI3Kγ inhibitors by combining pharmacophore modeling, molecular dynamic simulation, molecular docking, fragment-based drug design, and virtual screening

Since dysregulation of the phosphatidylinositol 3-kinase gamma (PI3Kγ) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3Kγ has emerged as an attractive target for drug development. IPI-549 is the only selective PI3Kγ inhibitor that has advanced to clinical trials, thus, IPI-549 could serve as a promising template for designing novel PI3Kγ inhibitors. In this present study, a modeling strategy consisting of common feature pharmacophore modeling, receptor-ligand pharmacophore modeling, and molecular dynamics simulation was utilized to identify the key pharmacodynamic characteristic elements of the target compound and the key residue information of the PI3Kγ interaction with the inhibitors. Then, 10 molecules were designed based on the structure-activity relationships, and some of them exhibited satisfactory predicted binding affinities to PI3Kγ. Finally, a hierarchical multistage virtual screening method, involving the developed common feature and receptor-ligand pharmacophore model and molecular docking, was constructed for screening the potential PI3Kγ inhibitors. Overall, we hope these findings would provide some guidance for the development of novel PI3Kγ inhibitors.

TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa

Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance. Pseudomonas aeruginosa is a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibit Pseudomonas aeruginosa by interfering with its ability to form biofilms has become a promising strategy in drug discovery. Identifying molecules able to interfere with biofilm formation is difficult, but further developing these molecules by rationally improving their activity is particularly challenging, as it requires knowledge of the specific protein target that is inhibited. This work describes the development of a machine learning multitechnique consensus workflow to predict the protein targets of molecules with confirmed inhibitory activity against biofilm formation by Pseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation by Pseudomonas aeruginosa. The experimentally confirmed inhibitors available on ChEMBL, together with chemical descriptors, were used as the input features for a combination of nine different classification models, yielding a consensus method to predict the most likely target of a ligand. The implemented algorithm is freely available at https://github.com/BioSIM-Research-Group/TargIDe under licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.

Development of machine learning models for the screening of potential HSP90 inhibitors

Heat shock protein 90 (Hsp90) is a molecular chaperone playing a significant role in the folding of client proteins. This cellular protein is linked to the progression of several cancer types, including breast cancer, lung cancer, and gastrointestinal stromal tumors. Several oncogenic kinases are Hsp90 clients and their activity depends on this molecular chaperone. This makes HSP90 a prominent therapeutic target for cancer treatment. Studies have confirmed the inhibition of HSP90 as a striking therapeutic treatment for cancer management. In this study, we have utilized machine learning and different in silico approaches to screen the KCB database to identify the potential HSP90 inhibitors. Further evaluation of these inhibitors on various cancer cell lines showed favorable inhibitory activity. These inhibitors could serve as a basis for future development of effective HSP90 inhibitors.

Multi-Level High-Throughput Screening for Discovery of Ligands That Inhibit Insulin Aggregation

We have developed a multi-level virtual screening protocol to identify lead molecules from the FDA inactives database that can inhibit insulin aggregation. The method is based on the presence of structural and interaction specificity in non-native aggregation pathway protein-protein interactions. Some key challenges specific to the present problem, when compared with native protein association, include structural heterogeneity of the protein species involved, multiple association pathways, and relatively higher probability of conformational rearrangement of the association complex. In this multi-step method, the inactives database was first screened using the dominant pharmacophore features of previously identified molecules shown to significantly inhibit insulin aggregation nucleation by binding to its aggregation-prone conformers. We then performed ensemble docking of several low-energy ligand conformations on these aggregation-prone conformers followed by molecular dynamics simulations and binding affinity calculations on a subset of docked complexes to identify a final set of five potential lead molecules to inhibit insulin aggregation nucleation. Their effect on aggregation inhibition was extensively investigated by incubating insulin under aggregation-prone aqueous buffer conditions (low pH, high temperature). Aggregation kinetics were characterized using size exclusion chromatography and Thioflavin T fluorescence assay, and the secondary structure was determined using circular dichroism spectroscopy. Riboflavin provided the best aggregation inhibition, with 85% native monomer retention after 48 h incubation under aggregation-prone conditions, whereas the no-ligand formulation showed complete monomer loss after 36 h. Further, insulin incubated with two of the screened inactives (aspartame, riboflavin) had the characteristic α-helical dip in CD spectra, while the no-ligand formulation showed a change to β-sheet rich conformations.

Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation

c-Jun N-terminal kinase 1 (JNK1) is currently considered a critical therapeutic target for type-2 diabetes. In recent years, there has been a great interest in naturopathic molecules, and the discovery of active ingredients from natural products for specific targets has received increasing attention. Based on the above background, this research aims to combine emerging Artificial Intelligence technologies with traditional Computer-Aided Drug Design methods to find natural products with JNK1 inhibitory activity. First, we constructed three machine learning models (Support Vector Machine, Random Forest, and Artificial Neural Network) and performed model fusion based on Voting and Stacking strategies. The integrated models with better performance (AUC of 0.906 and 0.908, respectively) were then employed for the virtual screening of 4112 natural products in the ZINC database. After further drug-likeness filtering, we calculated the binding free energy of 22 screened compounds using molecular docking and performed a consensus analysis of the two methodologies. Subsequently, we identified the three most promising candidates (Lariciresinol, Tricin, and 4′-Demethylepipodophyllotoxin) according to the obtained probability values and relevant reports, while their binding characteristics were preliminarily explored by molecular dynamics simulations. Finally, we performed in vitro biological validation of these three compounds, and the results showed that Tricin exhibited an acceptable inhibitory activity against JNK1 (IC₅₀ = 17.68 μM). This natural product can be used as a template molecule for the design of novel JNK1 inhibitors.

Combining structure-based pharmacophore modeling and machine learning for the identification of novel BTK inhibitors

Bruton's tyrosine kinase (BTK) is a critical enzyme which is involved in multiple signaling pathways that regulate cellular survival, activation, and proliferation, making it a major cancer therapeutic target. We applied the novel integrated structure-based pharmacophore modeling, machine learning, and other in silico studies to screen the Korean chemical database (KCB) to identify the potential BTK inhibitors (BTKi). Further evaluation of these inhibitors on three different human cancer cell lines showed significant cell growth inhibitory activity. Among the 13 compounds shortlisted, four demonstrated consistent cell inhibition activity among breast, gastric, and lung cancer cells (IC50 below 3 μM). The selected compounds also showed significant kinase inhibition activity (IC50 below 5 μM). The current study suggests the potential of these inhibitors for targeting BTK malignant tumors.

Molecular docking study of GSK-3β interaction with nomilin, kihadanin B, and related limonoids and triterpenes with a furyl-δ-lactone core

Glycogen synthase kinase-3β (GSK-3β) is a target enzyme considered for the treatment of multiple human diseases, from neurodegenerative pathologies to viral infections and cancers. Numerous inhibitors of GSK-3β have been discovered but thus far only a few have reached clinical trials and only one drug, tideglusib (1), has been registered. Natural products targeting GSK-3β have been identified, including the two anticancer limonoids obacunone (5) and gedunin (4), both presenting a furyl-δ-lactone core. To help identifying novel GSK-3β ligands, we have performed a molecular docking study with 15 complementary natural products bearing a furyl-δ-lactone unit (such as limonin (6) and kihadanins A (8) and B (9)) or a closely related structure (such as cedrelone (10) and nimbolide (11)). The formation of GSK-3β-binding complexes for those natural products was compared to reference GSK-3β ATP-competitive inhibitors LY2090314 (3) and AR-A014418 (2). Our in silico analysis led to the identification of two new GSK-3β-binding natural products: kihadanin B (9) and nomilin (7). The latter surpassed the reference compounds in terms of calculated empirical energy of interaction (ΔE). Nomilin (7) can possibly bind to the active site of GSK-3β, notably via the furyl-δ-lactone core and its 1-acetyl group, implicated in the protein interaction. Compound structure-binding relationships are discussed. The study should help the discovery of novel natural products targeting GSK-3β.

Theoretical study of myriocin-binding mechanism targeting serine palmitoyltransferase

Sphingolipids (SLs) are vital for cells as forming membrane and transducing signals. The first step for de novo biosynthesis of SLs is catalyzed by the pyridoxal-5'-phosphate (PLP)-dependent enzyme serine palmitoyltransferase (SPT), which has been proven to be a promising drug target for treating various diseases. However, there are few SPT-specific inhibitors have been identified so far. Myriocin, a natural fungal product, is confirmed as the most potent inhibitor of SPT and has been widely used, but studies of its molecular mechanism are still underway. Besides, there is no intact co-crystal structure of SPT-binding myriocin until now. Aiming to uncover the interaction mechanism between SPT- and PLP-binding myriocin at the molecular level, a systematic computational strategy was performed in this present study. Firstly, covalent docking was implemented to preliminarily predict the binding pose SPT/PLP-myriocin aldimine and its structurally similar intermediate SPT/PLP-β-ketoacid aldimine. Secondly, two binding complexes were treated as initial structures to perform molecular dynamics simulations and binding free energy calculations. The calculated docking scores and predicted binding energies were consistent with the reported bioactivities. Finally, the binding mechanism of myriocin binding with SPT was meticulously described, and the key residues making favorable contributions were highlighted. Taken together, the current study could provide some important information and valuable guidance for further rational screening, design, and modification of potent specific SPT inhibitors.

Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening

Virtual screening is receiving renewed attention in drug discovery, but progress is hampered by challenges on two fronts: handling the ever-increasing sizes of libraries of drug-like compounds and separating true positives from false positives. Here, we developed a machine learning-enabled pipeline for large-scale virtual screening that promises breakthroughs on both fronts. By clustering compounds according to molecular properties and limited docking against a drug target, the full library was trimmed by 10-fold; the remaining compounds were then screened individually by docking; and finally, a dense neural network was trained to classify the hits into true and false positives. As illustration, we screened for inhibitors against RPN11, the deubiquitinase subunit of the proteasome, and a drug target for breast cancer.

Discovery of potential inhibitors targeting the kinase domain of polynucleotide kinase/phosphatase (PNKP): Homology modeling, virtual screening based on multiple conformations, and molecular dynamics simulation

In recent years, the level of interest has been increased in developing the DNA-repair inhibitors, to enhance the cytotoxic effects in the treatment of cancers. Polynucleotide kinase/phosphatase (PNKP) is a critical human DNA repair enzyme that repairs DNA strand breaks by catalyzing the restoration of 5'-phosphate and 3'-hydroxyl termini that are required for subsequent processing by DNA ligases and polymerases. PNKP is the only protein that repairs the 3'-hydroxyl group and 5'-phosphate group, which depicts PNKP as a potential therapeutic target. Besides, PNKP is the only DNA-repair enzyme that contains the 5'-kinase activity, therefore, targeting this kinase domain would motivate the development of novel PNKP-specific inhibitors. However, there are neither crystal structures of human PNKP nor the kinase inhibitors reported so far. Thus, in this present study, a sequential molecular docking-based virtual screening with multiple PNKP conformations integrating homology modeling, molecular dynamics simulation, and binding free energy calculation was developed to discover novel PNKP kinase inhibitors, and the top-scored molecule was finally submitted to molecular dynamics simulation to reveal the binding mechanism between the inhibitor and PNKP. Taken together, the current study could provide some guidance for the molecular docking based-virtual screening of novel PNKP kinase inhibitors.

A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ

Nowadays, more and more attention has been attracted to develop selective PI3Kγ inhibitors, but the unique structural features of PI3Kγ protein make it a very big challenge. In the present study, a virtual screening strategy based on machine learning with multiple PI3Kγ protein structures was developed to screen novel PI3Kγ inhibitors. First, six mainstream docking programs were chosen to evaluate their scoring power and screening power; CDOCKER and Glide show satisfactory reliability and accuracy against the PI3Kγ system. Next, virtual screening integrating multiple PI3Kγ protein structures was demonstrated to significantly improve the screening enrichment rate comparing to that with an individual protein structure. Last, a multi-conformational Naïve Bayesian Classification model with the optimal docking programs was constructed, and it performed a true capability in the screening of PI3Kγ inhibitors. Taken together, the current study could provide some guidance for the docking-based virtual screening to discover novel PI3Kγ inhibitors.