Dynamic and multi-pharmacophore modeling for designing polo-box domain inhibitors

Computational insights into allosteric inhibition of focal adhesion kinase: A combined pharmacophore modeling and molecular dynamics approach

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase that modulates integrin and growth factor signaling pathways and is implicated in cancer cell migration, proliferation, and survival. Over the past decade various, FAK kinase, FERM, and FAT domain inhibitors have been reported and a few kinase domain inhibitors are under clinical consideration. However, few of them were identified as multikinase inhibitors. In kinase drug design selectivity is always a point of concern, to improve selectivity allosteric inhibitor development is the best choice. The current research utilized a pharmacophore modeling (PM) approach to identify novel allosteric inhibitors of FAK. The all-available allosteric inhibitor bound 3D structures with PDB ids 4EBV, 4EBW, and 4I4F were utilized for the pharmacophore modeling. The validated PM models were utilized to map a database of 770,550 compounds prepared from ZINC, EXIMED, SPECS, ASINEX, and InterBioScreen, aiming to identify potential allosteric inhibitors. The obtained compounds from screening step were forwarded to molecular docking (MD) for the prediction of binding orientation inside the allosteric site and the results were evaluated with the known FAK allosteric inhibitor (REF). Finally, 14 FAK-inhibitor complexes were selected from the docking study and were studied under molecular dynamics simulations (MDS) for 500 ns. The complexes were ranked according to binding free energy (BFE) and those demonstrated higher affinity for allosteric site of FAK than REF inhibitors were selected. The selected complexes were further analyzed for intermolecular interactions and finally, three potential allosteric inhibitor candidates for the inhibition of FAK protein were identified. We believe that identified scaffolds may help in drug development against FAK as an anticancer agent.

Identification of thrombopoiesis inducer based on a hybrid deep neural network model

Thrombocytopenia is a common haematological problem worldwide. Currently, there are no relatively safe and effective agents for the treatment of thrombocytopenia. To address this challenge, we propose a computational method that enables the discovery of novel drug candidates with haematopoietic activities. Based on different types of molecular representations, three deep learning (DL) algorithms, namely recurrent neural networks (RNNs), deep neural networks (DNNs), and hybrid neural networks (RNNs+DNNs), were used to develop classification models to distinguish between active and inactive compounds. The evaluation results illustrated that the hybrid DL model exhibited the best prediction performance, with an accuracy of 97.8 % and Matthews correlation coefficient of 0.958 on the test dataset. Subsequently, we performed drug discovery screening based on the hybrid DL model and identified a compound from the FDA-approved drug library that was structurally divergent from conventional drugs and showed a potential therapeutic action against thrombocytopenia. The novel drug candidate wedelolactone significantly promoted megakaryocyte differentiation in vitro and increased platelet levels and megakaryocyte differentiation in irradiated mice with no systemic toxicity. Overall, our work demonstrates how artificial intelligence can be used to discover novel drugs against thrombocytopenia.

Identification of Activated Cdc42-Associated Kinase Inhibitors as Potential Anticancer Agents Using Pharmacoinformatic Approaches

BACKGROUND

Activated Cdc42-associated kinase (ACK1) is essential for numerous cellular functions, such as growth, proliferation, and migration. ACK1 signaling occurs through multiple receptor tyrosine kinases; therefore, its inhibition can provide effective antiproliferative effects against multiple human cancers. A number of ACK1-specific inhibitors were designed and discovered in the previous decade, but none have reached the clinic. Potent and selective ACK1 inhibitors are urgently needed.

METHODS

In the present investigation, the pharmacophore model (PM) was rationally built utilizing two distinct inhibitors coupled with ACK1 crystal structures. The generated PM was utilized to screen the drug-like database generated from the four chemical databases. The binding mode of pharmacophore-mapped compounds was predicted using a molecular docking (MD) study. The selected hit-protein complexes from MD were studied under all-atom molecular dynamics simulations (MDS) for 500 ns. The obtained trajectories were ranked using binding free energy calculations (ΔG kJ/mol) and Gibb's free energy landscape.

RESULTS

Our results indicate that the three hit compounds displayed higher binding affinity toward ACK1 when compared with the known multi-kinase inhibitor dasatinib. The inter-molecular interactions of Hit1 and Hit3 reveal that compounds form desirable hydrogen bond interactions with gatekeeper T205, hinge region A208, and DFG motif D270. As a result, we anticipate that the proposed scaffolds might help in the design of promising selective ACK1 inhibitors.

Development of antidiabetic drugs from benzamide derivatives as glucokinase activator: A computational approach

Hyperglycemia is a condition known for the impairment of insulin secretion and is responsible for diabetes mellitus. Various small molecule inhibitors have been discovered as glucokinase activators. Recent studies on benzamide derivatives showed their importance in the treatment of diabetes as glucokinase activator. The present manuscript showed a computation study on benzamide derivatives to help in the production of potent glucokinase activators. In the present study, pharmacophore development, 3D-QSAR, and docking studies were performed on benzamide derivatives to find out the important features required for the development of a potential glucokinase activator. The generated pharmacophore hypothesis ADRR_1 consisted of essential features required for the activity. The resultant statistical data showed high significant values with R² > 0.99; 0.98 for the training set and Q² > 0.52; 0.71 for test set based on atom-based and field-based models, respectively. The potent compound 15b of the series showed a good docking score via binding with different amino acid residues such as (NH…ARG63), (SO₂…ARG250, THR65), and π-π staking with (phenyl……TYR214). The virtual screening study used 3563 compounds from ZINC database and screened hit compound ZINC08974524, binds with similar amino acids as shown by compound 15b and crystal ligand with docking scores SP (-11.17 kcal/mol) and XP (-8.43 kcal/mol). Compounds were further evaluated by ADME and MMGBSA parameters. Ligands and ZINC hits showed no violation of Lipinski rules. All the screened compounds showed good synthetic accessibility. The present study may be used by researchers for the development of novel benzamide derivatives as glucokinase activator.

Potential inhibitors of methionine aminopeptidase type II identified via structure-based pharmacophore modeling

Methionine aminopeptidase (MetAP2) is a metal-containing enzyme that removes initiator methionine from the N-terminus of a newly synthesized protein. Inhibition of the enzyme is crucial in diminishing cancer growth and metastasis. Fumagillin-a natural irreversible inhibitor of MetAP2-and its derivatives are used as potent MetAP2 inhibitors. However, because of their adverse effects, none of them has progressed to clinical studies. In search for potential reversible inhibitors, we built structure-based pharmacophore models using the crystal structure of MetAP2 complexed with fumagillin (PDB ID: 1BOA). The pharmacophore models were validated using Gunner-Henry scoring method. The best pharmacophore consisting of 1 H-bond donor, 1 H-bond acceptor, and 3 hydrophobic features was used to conduct pharmacophore-based virtual screening of ZINC15 database against MetAP2. The top 10 compounds with pharmacophore fit values > 3.00 were selected for further analysis. These compounds were subjected to absorption, distribution, metabolism, elimination, and toxicity (ADMET) prediction and found to have druglike properties. Furthermore, molecular docking calculations was performed on these hits using AutoDock4 to predict their binding mode and binding energy. Three diverse compounds: ZINC000014903160, ZINC000040174591, and ZINC000409110720 with respective binding energy/docking scores of - 9.22, - 9.21, and -817 kcal/mol, were submitted to 100 ns (MD) simulations using Nanoscale MD (NAMD) software. The compounds showed stable binding mode over time. Therefore, they may serve as a scaffold for further computational and experimental optimization toward the design of more potent and safer MetAP2 inhibitors.

Identification of inhibitors of the polo-box domain of polo-like kinase 1 from natural and semisynthetic compounds

PLK1 has an important role in the regulation of cell cycle and represents an important target for cancer treatment. This enzyme belongs to the Polo-like kinases family, which is characterized by a regulatory domain named Polo-box domain (PBD). Rather than regular kinase inhibitors, this domain provides high selectivity to PLK1. Here, we report on four novel PLK1 PBD inhibitors identified by cytotoxicity screening and fluorescence polarization assay of a chemical library of natural and semisynthetic compounds. These compounds revealed two- to three-fold higher selectivity to the PDB of PLK1 than to those of the related family members, PLK2 and PLK3. These four substances inhibited tumor cell growth of sensitive CCRF-CEM and multidrug-resistant CEM/ADR5000 leukemia cells. The tested compounds increased the apoptotic cell fraction, which indicates apoptosis as a major mechanism of cell death. Cell cycle analysis showed compound (5) arrested the cell cycle of CCRF-CEM cells in the G2/M phase, while the other three molecules ((compound (3), compound (4), and compound (6)) exerted pronounced cytotoxicity with an increase of cells in the sub-G1 population. Molecular docking was performed for the understanding of ligand-protein interaction, the tested candidates showed strong binding affinity to PLK1 PBD. In conclusion, we identified four new chemical scaffolds that may serve as lead compounds for the development of selective PLK1 inhibitors in the future.

Molecular and enzoinformatics perspectives of targeting Polo-like kinase 1 in cancer therapy

Cancer is a disease that has been the focus of scientific research and discovery and continues to remain so. Polo-like kinases (PLKs) are basically serine/threonine kinase enzymes that control cell cycle from yeast to humans. PLK-1 stands for 'Polo-like kinase-1'. It is the most investigated protein among PLKs. It is crucial for intracellular processes, hence a 'hot' anticancer drug-target. Accelerating innovations in Enzoinformatics and associated molecular visualization tools have made it possible to literally perform a 'molecular level walk' traversing through and observing the minutest contours of the active site of relevant enzymes. PLK-1 as a protein consists of a kinase domain at the protein N-terminal and a Polo Box Domain (PBD) at the C-terminal connected by a short inter-domain linking region. PBD has two Polo-Boxes. PBD of PLK-1 gives the impression of "a small clamp sandwiched between two clips", where the two Polo Boxes are the 'clips' and the 'phosphopeptide' is the small 'clamp'. Broadly, two major sites of PLK-1 can be potential targets: one is the adenosine-5'-triphosphate (ATP)-binding site in the kinase domain and the other is PBD (more preferred due to specificity). Targeting PLK-1 RNA and the interaction of PLK-1 with a key binding partner can also be approached. However, the list of potent small molecule inhibitors targeting the PBD site of PLK-1 is still not long enough and needs due input from the scientific community. Recently, eminent scientists have proposed targeting the 'Y'-shaped pocket of PLK-1-PBD and encouraged design of ligands that should be able to concurrently bind to two or more modules of the 'Y' pocket. Hence, it is suggested that during molecular interaction analyses, particular focus should be kept on the moiety in each ligand/drug candidate which directly interacts with the amino acid residue(s) that belong(s) to one of the three binding modules which together create this Y-shaped cavity. This obviously includes (but it is not limited to) the 'shallow cleft'-forming residues i.e. Trp414, H538 and K540, as significance of these binding residues has been consistently highlighted by many studies. The present article attempts to give a concise yet critically updated overview of targeting PLK-1 for cancer therapy.

A selective inhibitor of the Polo-box domain of Polo-like kinase 1 identified by virtual screening

Polo-like kinase 1 (PLK1), a member of the Polo-like kinase family, plays an important regulatory role in mitosis and cell cycle progression. PLK1 overexpression is correlated with tumourigenesis and poor prognosis in cancer patients. Therefore, the identification of novel compounds that inhibit PLK1 would provide attractive therapeutic approaches. Although some PLK1 kinase inhibitors have been developed, their application has been limited by off-target effects. PLK1 contains a regulatory domain named the Polo-box domain (PBD), which is characteristic only for the Polo-like kinase family. This domain represents an alternative therapeutic target with higher selectivity for PLK1. In this study, we applied in silico virtual drug screening, fluorescence polarization and microscale thermophoresis to identify new scaffolds targeting the PBD of PLK1. One compound, 3-{[(1R,9S)-3-(naphthalen-2-yl)-6-oxo-7,11-diazatricyclo[7.3.1.02,7]trideca-2,4-dien-11-yl]methyl}benzonitrile (designated compound (1)), out of a total of 30,793 natural product derivatives, inhibited the PLK1 PBD with high selectivity (IC50: 17.9 ± 0.5 µM). This compound inhibited the growth of cultured leukaemia cells (CCRF-CEM and CEM/ADR5000) and arrested the cell cycle in the G2/M phase, which is characteristic for PLK1 inhibitors. Immunofluorescence analyses showed that treatment with compound (1) disrupted spindle formation due to the aberrant localization of PLK1 during the mitotic process, leading to G2/M arrest and ultimately cell death. In conclusion, compound (1) is a selective PLK1 inhibitor that inhibits cancer cell growth. It represents a chemical scaffold for the future synthesis of new selective PLK1 inhibitors for cancer therapy.

Pharmacophore-based virtual screening for identification of potential selective inhibitors of human histone deacetylase 6

Histone deacetylase (HDAC) 6 plays a role in oncogenic transformation and cancer metastasis via tubulin deacetylation, making it a critical target for anticancer drug design. However, lack of selectivity shown by many of the current HDAC6 inhibitors in clinical use and trials prompts the continuous search for selective inhibitors. Here, 10 pharmacophore hypotheses were developed based on the 3D common features of training set of 20 HDAC inhibitors in clinical use and trials. The hypotheses were validated using a test set of another 20 HDAC inhibitors along with 400 inactive (decoys) molecules based on Güner-Henry pharmacophore scoring method. Hypothesis 1 consisting of 1 H-bond donor, 1 H-bond acceptor and 2 hydrophobic features, was used to screen "DruglikeDiverse" database using Biovia Discovery Studio 4.5. The top 10 hit compounds were selected based on the pharmacophore fit values (>3.00). Their binding affinity against HDAC6 compared to class I HDACs (1, 2, 3 & 8) and a class IIa member (HDAC7), was calculated by molecular docking using AutoDock4. The stability of binding modes of 2 potential HDAC6-selective inhibitors (ENA501965 and IBS399024) was examined by 30 ns-molecular dynamics (MD) simulation using nanoscale MD (NAMD) software. Both ligands showed potential stability in HDAC6 active site over time. Therefore, these may provide additional scaffolds for further optimization towards the design of safe, potent and selective HDAC6 inhibitors.

The hunt for antimitotic agents: an overview of structure-based design strategies

INTRODUCTION

Structure-based drug discovery offers a rational approach for the design and development of novel anti-mitotic agents which target specific proteins involved in mitosis. This strategy has paved the way for development of a new generation of chemotypes which selectively interfere with the target proteins. The interference of these anti-mitotic targets implicated in diverse stages of mitotic cell cycle progression culminates in cancer cell apoptosis.

AREAS COVERED

This review covers the various mitotic inhibitors developed against validated mitotic checkpoint protein targets using structure-based design and optimization strategies. The protein-ligand interactions and the insights gained from these studies, culminating in the development of more potent and selective inhibitors, have been presented.

EXPERT OPINION

The advent of structure-based drug design coupled with advances in X-ray crystallography has revolutionized the discovery of candidate lead molecules. The structural insights gleaned from the co-complex protein-drug interactions have provided a new dimension in the design of anti-mitotic molecules to develop drugs with a higher selectivity and specificity profile. Targeting non-catalytic domains has provided an alternate approach to address cross-reactivity and broad selectivity among kinase inhibitors. The elucidation of structures of emerging mitotic drug targets has opened avenues for the design of inhibitors that target cancer.