Identification of CK2 inhibitors with new scaffolds by a hybrid virtual screening approach based on Bayesian model; pharmacophore hypothesis and molecular docking

Machine Learning Models for the Classification of CK2 Natural Products Inhibitors with Molecular Fingerprint Descriptors

Casein kinase 2 (CK2) is considered an important target for anti-cancer drugs. Given the structural diversity and broad spectrum of pharmaceutical activities of natural products, numerous studies have been performed to prove them as valuable sources of drugs. However, there has been little study relevant to identifying structural factors responsible for their inhibitory activity against CK2 with machine learning methods. In this study, classification studies were conducted on 115 natural products as CK2 inhibitors. Seven machine learning methods along with six molecular fingerprints were employed to develop qualitative classification models. The performances of all models were evaluated by cross-validation and test set. By taking predictive accuracy(CA), the area under receiver operating characteristic (AUC), and (MCC)as three performance indicators, the optimal models with high reliability and predictive ability were obtained, including the Extended Fingerprint-Logistic Regression model (CA = 0.859, AUC = 0.826, MCC = 0.520) for training test andPubChem fingerprint along with the artificial neural model (CA = 0.826, AUC = 0.933, MCC = 0.628) for test set. Meanwhile, the privileged substructures responsible for their inhibitory activity against CK2 were also identified through a combination of frequency analysis and information gain. The results are expected to provide useful information for the further utilization of natural products and the discovery of novel CK2 inhibitors.

Chemical Manipulation of Abscisic Acid Signaling: A New Approach to Abiotic and Biotic Stress Management in Agriculture

The phytohormone abscisic acid (ABA) is the best-known stress signaling molecule in plants. ABA protects sessile land plants from biotic and abiotic stresses. The conserved pyrabactin resistance/pyrabactin resistance-like/regulatory component of ABA receptors (PYR/PYL/RCAR) perceives ABA and triggers a cascade of signaling events. A thorough knowledge of the sequential steps of ABA signaling will be necessary for the development of chemicals that control plant stress responses. The core components of the ABA signaling pathway have been identified with adequate characterization. The information available concerning ABA biosynthesis, transport, perception, and metabolism has enabled detailed functional studies on how the protective ability of ABA in plants might be modified to increase plant resistance to stress. Some of the significant contributions to chemical manipulation include ABA biosynthesis inhibitors, and ABA receptor agonists and antagonists. Chemical manipulation of key control points in ABA signaling is important for abiotic and biotic stress management in agriculture. However, a comprehensive review of the current knowledge of chemical manipulation of ABA signaling is lacking. Here, a thorough analysis of recent reports on small-molecule modulation of ABA signaling is provided. The challenges and prospects in the chemical manipulation of ABA signaling for the development of ABA-based agrochemicals are also discussed.

Different molecular enumeration influences in deep learning: an example using aqueous solubility

Aqueous solubility is the key property driving many chemical and biological phenomena and impacts experimental and computational attempts to assess those phenomena. Accurate prediction of solubility is essential and challenging, even with modern computational algorithms. Fingerprint-based, feature-based and molecular graph-based representations have all been used with different deep learning methods for aqueous solubility prediction. It has been clearly demonstrated that different molecular representations impact the model prediction and explainability. In this work, we reviewed different representations and also focused on using graph and line notations for modeling. In general, one canonical chemical structure is used to represent one molecule when computing its properties. We carefully examined the commonly used simplified molecular-input line-entry specification (SMILES) notation representing a single molecule and proposed to use the full enumerations in SMILES to achieve better accuracy. A convolutional neural network (CNN) was used. The full enumeration of SMILES can improve the presentation of a molecule and describe the molecule with all possible angles. This CNN model can be very robust when dealing with large datasets since no additional explicit chemistry knowledge is necessary to predict the solubility. Also, traditionally it is hard to use a neural network to explain the contribution of chemical substructures to a single property. We demonstrated the use of attention in the decoding network to detect the part of a molecule that is relevant to solubility, which can be used to explain the contribution from the CNN.

Pharmacophore development, drug-likeness analysis, molecular docking, and molecular dynamics simulations for identification of new CK2 inhibitors

Protein kinase 2 (CK2), an essential serine/threonine casein kinase, is considered an interesting target for cancer treatments. Different molecular modeling approaches such as pharmacophore modeling, molecular docking, and molecular dynamics simulations have been used to develop new CK2 inhibitors. This study presents a pharmacophore model that was generated by combining and merging the structure-based and ligand-based pharmacophore features and validated using receiver operating characteristic (ROC). Based on validation results revealing good predictive ability, this pharmacophore model was used as a three-dimensional query in a virtual screening simulation. Several compounds with different chemical scaffolds were retrieved as hits, which were further analyzed and refined using several molecular property filters. The obtained compounds were then filtered and compared to the crystallographic ligand on the basis of their predicted docking energies, binding mode, and interactions with CK2 active site residues. This step resulted in a compound with a high pharmacophore fit value and better docking energy. Molecular dynamics simulation indicated stable binding of the predicted compound to CK2 protein, characterized by root mean square deviation (RMSD) and root mean square fluctuation (RMSF) and hydrogen bond. Graphical abstract.

Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor of Mycobacterium tuberculosis

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), kills 1.6 million people annually. To bridge the gap between structure- and cell-based drug discovery strategies, we are pioneering a computer-aided discovery paradigm that merges structure-based virtual screening with ligand-based, machine learning methods trained with cell-based data. This approach successfully identified N-(3-methoxyphenyl)-7-nitrobenzo[c][1,2,5]oxadiazol-4-amine (JSF-2164) as an inhibitor of purified InhA with whole-cell efficacy versus in vitro cultured M. tuberculosis. When the intrabacterial drug metabolism (IBDM) platform was leveraged, mechanistic studies demonstrated that JSF-2164 underwent a rapid F₄₂₀H₂-dependent biotransformation within M. tuberculosis to afford intrabacterial nitric oxide and two amines, identified as JSF-3616 and JSF-3617. Thus, metabolism of JSF-2164 obscured the InhA inhibition phenotype within cultured M. tuberculosis. This study demonstrates a new docking/Bayesian computational strategy to combine cell- and target-based drug screening and the need to probe intrabacterial metabolism when clarifying the antitubercular mechanism of action.

Identification of novel CDK2 inhibitors by a multistage virtual screening method based on SVM, pharmacophore and docking model

Cyclin-dependent kinase 2 (CDK2) is the family of Ser/Thr protein kinases that has emerged as a highly selective with low toxic cancer therapy target. A multistage virtual screening method combined by SVM, protein-ligand interaction fingerprints (PLIF) pharmacophore and docking was utilised for screening the CDK2 inhibitors. The evaluation of the validation set indicated that this method can be used to screen large chemical databases because it has a high hit-rate and enrichment factor (80.1% and 332.83 respectively). Six compounds were screened out from NCI, Enamine and Pubchem database. After molecular dynamics and binding free energy calculation, two compounds had great potential as novel CDK2 inhibitors and they also showed selective inhibition against CDK2 in the kinase activity assay.

Design, synthesis, and biological evaluation of polo-like kinase 1/eukaryotic elongation factor 2 kinase (PLK1/EEF2K) dual inhibitors for regulating breast cancer cells apoptosis and autophagy

Both PLK1 and EEF2K are serine⁄threonine kinases that play important roles in the proliferation and programmed cell death of various types of cancer. They are highly expressed in breast cancer tissues. Based on the multiple-complexes generated pharmacophore models of PLK1 and homology models of EEF2K, the integrated virtual screening is performed to discover novel PLK1/EEF2K dual inhibitors. The top ten hit compounds are selected and tested in vitro, and five of them display PLK1 and EEF2K inhibition in vitro. Based on the docking modes of the most potent hit compound, a series of derivatives are synthesized, characterized and biological assayed on the PLK1, EEF2K as well as breast cancer cell proliferation models. Compound 18i with satisfied inhibitory potency are shifted to molecular mechanism studies contained molecular dynamics simulations, cell cycles, apoptosis and autophagy assays. Our results suggested that these novel PLK1/EEF2K dual inhibitors can be used as lead compounds for further development breast cancer chemotherapy.

Identification of potent inhibitors of DNA methyltransferase 1 (DNMT1) through a pharmacophore-based virtual screening approach

DNA methylation is an epigenetic change that results in the addition of a methyl group at the carbon-5 position of cytosine residues. DNA methyltransferase (DNMT) inhibitors can suppress tumour growth and have significant therapeutic value. However, the established inhibitors are limited in their application due to their substantial cytotoxicity. Additionally, the standard drugs for DNMT inhibition are non-selective cytosine analogues with considerable cytotoxic side-effects. In the present study, we have designed a workflow by integrating various ligand-based and structure-based approaches to discover new agents active against DNMT1. We have derived a pharmacophore model with the help of available DNMT1 inhibitors. Utilising this model, we performed the virtual screening of Maybridge chemical library and the identified hits were then subsequently filtered based on the Naïve Bayesian classification model. The molecules that have returned from this classification model were subjected to ensemble based docking. We have selected 10 molecules for the biological assay by inspecting the interactions portrayed by these molecules. Three out of the ten tested compounds have shown DNMT1 inhibitory activity. These compounds were also found to demonstrate potential inhibition of cellular proliferation in human breast cancer MDA-MB-231 cells. In the present study, we have utilized a multi-step virtual screening protocol to identify inhibitors of DNMT1, which offers a starting point to develop more potent DNMT1 inhibitors as anti-cancer agents.

The Development of CK2 Inhibitors: From Traditional Pharmacology to in Silico Rational Drug Design

Casein kinase II (CK2) is an ubiquitous and pleiotropic serine/threonine protein kinase able to phosphorylate hundreds of substrates. Being implicated in several human diseases, from neurodegeneration to cancer, the biological roles of CK2 have been intensively studied. Upregulation of CK2 has been shown to be critical to tumor progression, making this kinase an attractive target for cancer therapy. Several CK2 inhibitors have been developed so far, the first being discovered by "trial and error testing". In the last decade, the development of in silico rational drug design has prompted the discovery, de novo design and optimization of several CK2 inhibitors, active in the low nanomolar range. The screening of big chemical libraries and the optimization of hit compounds by Structure Based Drug Design (SBDD) provide telling examples of a fruitful application of rational drug design to the development of CK2 inhibitors. Ligand Based Drug Design (LBDD) models have been also applied to CK2 drug discovery, however they were mainly focused on methodology improvements rather than being critical for de novo design and optimization. This manuscript provides detailed description of in silico methodologies whose applications to the design and development of CK2 inhibitors proved successful and promising.

Discriminating agonist and antagonist ligands of the nuclear receptors using 3D-pharmacophores

Nuclear receptors (NRs) constitute an important class of therapeutic targets. We evaluated the performance of 3D structure-based and ligand-based pharmacophore models in predicting the pharmacological profile of NRs ligands using the NRLiSt BDB database. We could generate selective pharmacophores for agonist and antagonist ligands and we found that the best performances were obtained by combining the structure-based and the ligand-based approaches. The combination of pharmacophores that were generated allowed to cover most of the chemical space of the NRLiSt BDB datasets. By screening the whole NRLiSt BDB on our 3D pharmacophores, we demonstrated their selectivity towards their dedicated NRs ligands. The 3D pharmacophores herein presented can thus be used as a predictor of the pharmacological activity of NRs ligands.Graphical AbstractUsing a combination of structure-based and ligand-based pharmacophores, agonist and antagonist ligands of the Nuclear Receptors included in the NRLiSt BDB database could be separated.

Multistage virtual screening and identification of novel HIV-1 protease inhibitors by integrating SVM, shape, pharmacophore and docking methods

The HIV-1 protease has proven to be a crucial component of the HIV replication machinery and a reliable target for anti-HIV drug discovery. In this study, we applied an optimized hierarchical multistage virtual screening method targeting HIV-1 protease. The method sequentially applied SVM (Support Vector Machine), shape similarity, pharmacophore modeling and molecular docking. Using a validation set (270 positives, 155,996 negatives), the multistage virtual screening method showed a high hit rate and high enrichment factor of 80.47% and 465.75, respectively. Furthermore, this approach was applied to screen the National Cancer Institute database (NCI), which contains 260,000 molecules. From the final hit list, 6 molecules were selected for further testing in an in vitro HIV-1 protease inhibitory assay, and 2 molecules (NSC111887 and NSC121217) showed inhibitory potency against HIV-1 protease, with IC50 values of 62 μM and 162 μM, respectively. With further chemical development, these 2 molecules could potentially serve as HIV-1 protease inhibitors.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

Identification and characterization of bi-thiazole-2,2'-diamines as kinase inhibitory scaffolds

Based on bioinformatics interrogation of the genome, >500 mammalian protein kinases can be clustered within seven different groups. Of these kinases, the mitogen-activated protein kinase (MAPK) family forms part of the CMGC group of serine/threonine kinases that includes extracellular signal regulated kinases (ERKs), cJun N-terminal kinases (JNKs), and p38 MAPKs. With the JNKs considered attractive targets in the treatment of pathologies including diabetes and stroke, efforts have been directed to the discovery of new JNK inhibitory molecules that can be further developed as new therapeutics. Capitalizing on our biochemical understanding of JNK, we performed in silico screens of commercially available chemical databases to identify JNK1-interacting compounds and tested their in vitro JNK inhibitory activity. With in vitro and cell culture studies, we showed that the compound, 4'-methyl-N(2)-3-pyridinyl-4,5'-bi-1,3-thiazole-2,2'-diamine (JNK Docking (JD) compound 123, but not the related compound (4'-methyl-N~2~-(6-methyl-2-pyridinyl)-4,5'-bi-1,3-thiazole-2,2'-diamine (JD124), inhibited JNK1 activity towards a range of substrates. Molecular docking, saturation transfer difference NMR experiments and enzyme kinetic analyses revealed both ATP- and substrate-competitive inhibition of JNK by JD123. In characterizing JD123 further, we noted its ATP-competitive inhibition of the related p38-γ MAPK, but not ERK1, ERK2, or p38-α, p38-β or p38-δ. Further screening of a broad panel of kinases using 10μM JD123, identified inhibition of kinases including protein kinase Bβ (PKBβ/Aktβ). Appropriately modified thiazole diamines, as typified by JD123, thus provide a new chemical scaffold for development of inhibitors for the JNK and p38-γ MAPKs as well as other kinases that are also potential therapeutic targets such as PKBβ/Aktβ.