Discovering new mTOR inhibitors for cancer treatment through virtual screening methods and in vitro assays

Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors

Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP ( https://kipp.idruglab.cn ) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing.

FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction

Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especially for drug design and discovery. In this study, we advanced a novel deep learning architecture, termed FP-GNN (fingerprints and graph neural networks), which combined and simultaneously learned information from molecular graphs and fingerprints for molecular property prediction. To evaluate the FP-GNN model, we conducted experiments on 13 public datasets, an unbiased LIT-PCBA dataset and 14 phenotypic screening datasets for breast cell lines. Extensive evaluation results showed that compared to advanced deep learning and conventional machine learning algorithms, the FP-GNN algorithm achieved state-of-the-art performance on these datasets. In addition, we analyzed the influence of different molecular fingerprints, and the effects of molecular graphs and molecular fingerprints on the performance of the FP-GNN model. Analysis of the anti-noise ability and interpretation ability also indicated that FP-GNN was competitive in real-world situations. Collectively, FP-GNN algorithm can assist chemists, biologists and pharmacists in predicting and discovering better molecules with desired functions or properties.

Ligand- and structure-based identification of novel CDK9 inhibitors for the potential treatment of leukemia

Cyclin-dependent kinase 9 (CDK9) plays a vital role in controlling cell transcription and has been an attractive target for cancer treatment. Herein, ten predictive models derived from 1330 unique molecules against CDK9 were constructed based on molecular fingerprints and graphs using two conventional machine learning and four deep learning methods. The evaluation results showed that FP-GNN deep learning architecture performed best for CDK9 inhibitors prediction with the highest BA and F1 values of 0.681 and 0.912 for testing set. We then performed virtual screening to identify new CDK9 inhibitors by incorporating the optimal established predictive model and molecular docking. Five compounds were identified to show broad anticancer activity against various cancer cell lines through bioassays. For example, C9 exhibited antiproliferative activities against HeLa, MOLM-13 and MDA-MB-231 with IC₅₀ values of 2.53, 3.92 and 11.65 μM. Kinase inhibition assay results demonstrated that these compounds displayed submicromolar (214 ∼ 504 nM) inhibitory activities against CDK9. Further cellular mechanism evaluation revealed that C9 suppressed the activity of CDK9 and interfered with the expression of Mcl-1 and cleaved PARP in MOLM-13 cells, resulting in the induction of cellular apoptosis. In addition, C9 displayed a good stability in rat liver microsomes, artificial gastrointestinal fluid and plasm. An online platform (called DEEPCDK9Pred) was developed based on the FP-GNN models to predict or design new CDK9 inhibitors. Collectively, our findings demonstrated that FP-GNN algorithm can achieve accurate prediction of CDK9 inhibitors and the subsequent discovery of C9 as a new potential CDK9 inhibitor deserves further structural modification for the treatment of leukemia.

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors

PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.

Combined In Silico and In Vitro Evidence Supporting an Aurora A Kinase Inhibitory Role of the Anti-Viral Drug Rilpivirine and an Anti-Proliferative Influence on Cancer Cells

The global burden of cancer necessitates rapid and ongoing development of effective cancer therapies. One promising approach in this context is the repurposing of existing non-cancer drugs for cancer indications. A key to this approach is selecting the cellular targets against which to identify novel repurposed drugs for pre-clinical analysis. Protein kinases are highly sought-after anticancer drug targets since dysregulation of kinases is the hallmark of cancer. To identify potential kinase-targeted drug candidates from the existing portfolio of non-cancer therapeutics, we used combined in silico and in vitro approaches, including ligand-based 3D screening followed by biochemical and cellular assessments. This strategy revealed that the anti-viral drug rilpivirine is an Aurora A kinase inhibitor. In view of previous findings implicating Aurora A kinase in abnormal cell cycle regulation, we also examined the influence of rilpivirine on the growth of T47D breast cancer cells. Herein, we detail the identification of rilpivirine as an Aurora A kinase inhibitor, its molecular basis of inhibitory activity towards this kinase, and its Aurora A-mediated anticancer mechanisms in T47D cells. Our results illustrate the value of integrated in silico and in vitro screening strategies in identifying repurposed drug candidates and provide a scientific basis for further exploring the potential anticancer properties of the anti-viral drug rilpivirine.

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells

Breast cancer (BC) has surpassed lung cancer as the most frequently occurring cancer, and it is the leading cause of cancer-related death in women. Therefore, there is an urgent need to discover or design new drug candidates for BC treatment. In this study, we first collected a series of structurally diverse datasets consisting of 33,757 active and 21,152 inactive compounds for 13 breast cancer cell lines and one normal breast cell line commonly used in in vitro antiproliferative assays. Predictive models were then developed using five conventional machine learning algorithms, including naïve Bayesian, support vector machine, k-Nearest Neighbors, random forest, and extreme gradient boosting, as well as five deep learning algorithms, including deep neural networks, graph convolutional networks, graph attention network, message passing neural networks, and Attentive FP. A total of 476 single models and 112 fusion models were constructed based on three types of molecular representations including molecular descriptors, fingerprints, and graphs. The evaluation results demonstrate that the best model for each BC cell subtype can achieve high predictive accuracy for the test sets with AUC values of 0.689–0.993. Moreover, important structural fragments related to BC cell inhibition were identified and interpreted. To facilitate the use of the model, an online webserver called ChemBC (http://chembc.idruglab.cn/) and its local version software (https://github.com/idruglab/ChemBC) were developed to predict whether compounds have potential inhibitory activity against BC cells.

Design, synthesis and biological evaluation of novel hybrids targeting mTOR and HDACs for potential treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major contributor to global cancer incidence and mortality. Many pathways are involved in the development of HCC and various proteins including mTOR and HDACs have been identified as potential drug targets for HCC treatment. In the present study, two series of novel hybrid molecules targeting mTOR and HDACs were designed and synthesized based on parent inhibitors (MLN0128 and PP121 for mTOR, SAHA for HDACs) by using a fusion-type molecular hybridization strategy. In vitro antiproliferative assays demonstrated that these novel hybrids with suitable linker lengths exhibited broad cytotoxicity against various cancer cell lines, with significant activity against HepG2 cells. Notably, DI06, an MLN0128-based hybrid, exhibited antiproliferative activity against HepG2 cells with an IC₅₀ value of 1.61 μM, which was comparable to those of both parent drugs (MLN0128, IC₅₀ = 2.13 μM and SAHA, IC₅₀ = 2.26 μM). In vitro enzyme inhibition assays indicated that DI06, DI07 and DI17 (PP121-based hybrid) exhibited nanomolar inhibitory activity against mTOR kinase and HDACs (e.g., HDAC1, HDAC2, HDAC3, HADC6 and HADC8). Cellular studies and western blot analyses uncovered that in HepG2 cells, DI06 and DI17 induced cell apoptosis by targeting mTOR and HDACs, blocked the cell cycle at the G0/G1 phase and suppressed cell migration. The potential binding modes of the hybrids (DI06 and DI17) with mTOR and HDACs were investigated by molecular docking. DI06 displayed better stability in rat liver microsomes than DI07 and DI17. Collectively, DI06 as a novel mTOR and HDACs inhibitor presented here warrants further investigation as a potential treatment of HCC.

Rational design and synthesis of modified natural peptides from Boana pulchella (anura) as acetylcholinesterase inhibitors and antioxidants

The use of acetylcholinesterase (AChE) inhibitors, antioxidants or multitarget compounds are among the main strategies against Alzheimer's disease (AD). Between AChE inhibitors, those targeting the peripheral anionic site (PAS) are of special interest. Here, we describe the rational design and synthesis of peptide analogs of a natural PAS-targeting sequence that we recently discovered, aiming at increasing its activity against AChE. We also tested their radical scavenging and metal chelating properties. Our design strategy was based on the position-specific, computer-aided insertion of aromatic residues. The analog named as W3 showed a 30-fold higher inhibitory activity than the original sequence and an improved antioxidant activity. W3 is the most potent modified natural peptide against Electrophorus electricus AChE ever reported with an IC₅₀ of 10.42 μM (± 1.02). In addition, it showed a radical scavenging activity of 47.00% ± 3.11 at 50 μM and 93.47% ± 1.53 at 400 μM. Since peptides are receiving increasing interest as drugs, we propose the W3 analog as an attractive sequence for the development of new peptide-based multitarget drugs for AD. Besides, this work sheds light on the importance of the aromatic residues in the modulation of AChE activity and their effect on the radical scavenging activity of a peptide.

Hayatine inhibits amino acid-induced mTORC1 activation as a novel mTOR-Rag A/C interaction disruptor

Abnormal activation of the mechanistic target of rapamycin (mTOR) signaling is commonly observed in many cancers and attracts extensive attention as an oncology drug discovery target, which is encouraged by the success of rapamycin and its analogs (rapalogs) in treatment of mTORC1-hyperactive cancers in both pre-clinic models and clinical trials. However, rapamycin and existing rapalogs have typically short-lasting partial responses due to drug resistance, thereby triggering our interest to investigate a potential mTORC1 inhibitor that is mechanistically different from rapamycin. Here, we report that hayatine, a derivative from Cissampelos, can serve as a potential mTORC1 inhibitor selected from a natural compound library. The unique properties owned by hayatine such as downregulation of mTORC1 activities, induction of mTORC1's translocation to lysosomes followed by autophagy, and suppression on cancer cell growth, strongly emphasize its role as a potential mTORC1 inhibitor. Mechanistically, we found that hayatine disrupts the interaction between mTORC1 complex and its lysosomal adaptor RagA/C by binding to the hydrophobic loop of RagC, leading to mTORC1 inhibition that holds great promise to overcome rapamycin resistance. Taken together, our data shed light on an innovative strategy using structural interruption-based mTORC1 inhibitors for cancer treatment.

Mapping of mTOR drug targets: Featured platforms for anti-cancer drug discovery

The mammalian/mechanistic target of rapamycin (mTOR) is a regulatory protein kinase involved in cell growth and proliferation. mTOR is usually assembled in two different complexes with different regulatory mechanisms, mTOR complex 1 (mTORC1) and mTORC2, which are involved in different functions such as cell proliferation and cytoskeleton assembly, respectively. In cancer cells, mTOR is hyperactivated in response to metabolic alterations and/or oncogenic signals to overcome the stressful microenvironments. Therefore, recent research progress for mTOR inhibition involves a variety of compounds that have been developed to disturb the metabolic processes of cancer cells through mTOR inhibition. In addition to competitive or allosteric inhibition, a new inhibition strategy that emerged mTOR complexes destabilization has recently been a concern. Here, we review the history of mTOR and its inhibition, along with the timeline of the mTOR inhibitors. We also introduce prospective drug targets to inhibit mTOR by disrupting the complexation of the components with peptides and small molecules.

Exploring Molecular Descriptors and Fingerprints to Predict mTOR Kinase Inhibitors using Machine Learning Techniques

Mammalian Target of Rapamycin (mTOR) is a Ser/Thr protein kinase, and its role is integral to the autophagy pathway in cancer. Targeting mTOR for therapeutic interventions in cancer through autophagy pathway is challenging due to the dual roles of autophagy in tumor progression. The architecture of mTOR reveals two complexes - mTORC1 and mTORC2, each having multiple protein subunits. mTOR kinase inhibitors target the structurally and functionally similar catalytic subunits of both mTORC1 and mTORC2. In this paper, we have explored two different categories of molecular features - descriptors and fingerprints for developing predictive models using machine learning techniques. Random Forest variable importance measures and autoencoders are used to identify molecular descriptors and fingerprints, respectively. We have built various predictive models using identified features and their combination for predicting mTOR kinase inhibitors. Finally, the best model based on the Mathew correlation co-efficient value over the validation dataset is selected for screening kinase SARfari bioactivity dataset. In this study, we have identified twenty best performing descriptors for predicting mTOR kinase inhibitors. To the best of our knowledge, it is the first study on integrating traditional machine learning and deep learning-based approaches for feature extraction to predict mTOR kinase inhibitors.

Bioinformatic Analysis of Two TOR (Target of Rapamycin)-Like Proteins Encoded by Entamoeba histolytica Revealed Structural Similarities with Functional Homologs

The target of rapamycin (TOR), also known as FKBP-rapamycin associated protein (FRAP), is a protein kinase belonging to the PIKK (phosphatidylinositol 3-kinase (PI3K)-related kinases) family. TOR kinases are involved in several signaling pathways that control cell growth and proliferation. Entamoeba histolytica, the protozoan parasite that causes human amoebiasis, contains two genes encoding TOR-like proteins: EhFRAP and EhTOR2. To assess their potential as drug targets to control the cell proliferation of E. histolytica, we studied the structural features of EhFRAP and EhTOR2 using a biocomputational approach. The overall results confirmed that both TOR amoebic homologs share structural similarities with functional TOR kinases, and show inherent abilities to form TORC complexes and participate in protein-protein interaction networks. To our knowledge, this study represents the first in silico characterization of the structure-function relationships of EhFRAP and EhTOR2.

A comprehensive comparative assessment of 3D molecular similarity tools in ligand-based virtual screening

Three-dimensional (3D) molecular similarity, one major ligand-based virtual screening (VS) method, has been widely used in the drug discovery process. A variety of 3D molecular similarity tools have been developed in recent decades. In this study, we assessed a panel of 15 3D molecular similarity programs against the DUD-E and LIT-PCBA datasets, including commercial ROCS and Phase, in terms of screening power and scaffold-hopping power. The results revealed that (1) SHAFTS, LS-align, Phase Shape_Pharm and LIGSIFT showed the best VS capability in terms of screening power. Some 3D similarity tools available to academia can yield relatively better VS performance than commercial ROCS and Phase software. (2) Current 3D similarity VS tools exhibit a considerable ability to capture actives with new chemotypes in terms of scaffold hopping. (3) Multiple conformers relative to single conformations will generally improve VS performance for most 3D similarity tools, with marginal improvement observed in area under the receiving operator characteristic curve values, enrichment factor in the top 1% and hit rate in the top 1% values showed larger improvement. Moreover, redundancy and complementarity analyses of hit lists from different query seeds and different 3D similarity VS tools showed that the combination of different query seeds and/or different 3D similarity tools in VS campaigns retrieved more (and more diverse) active molecules. These findings provide useful information for guiding choices of the optimal 3D molecular similarity tools for VS practices and designing possible combination strategies to discover more diverse active compounds.

The Protective Effects of a Combination of an Arginine Silicate Complex and Magnesium Biotinate Against UV-Induced Skin Damage in Rats

The purpose of this study was to observe the effects of a novel combination of inositol-stabilized arginine silicate complex (ASI) and magnesium biotinate (MgB) on the prevention of skin damage after UVB exposure in rats. Forty-nine Sprague-Dawley rats were randomized into one of the following groups: (1) NC, normal control, (2) SC, shaved control, (3) UVB (exposed to UVB radiation), (4) ASI+MgB-L (Low Dose), (5) ASI+MgB-H (High Dose), (6) ASI+MgB-L+MgB cream, (7) ASI+MgB-H+MgB cream. The results showed that ASI+MgB treatment alleviated the macroscopic and histopathological damages in the skin of rats caused by UVB exposure. Skin elasticity evaluation showed a similar trend. ASI+MgB increased serum Mg, Fe, Zn, Cu, Si, biotin, and arginine concentrations and skin hydroxyproline and biotinidase levels while decreasing skin elastase activity (p < 0.05) and malondialdehyde (MDA) concentration (p < 0.001). Moreover, ASI+MgB treatment increased skin levels of biotin-dependent carboxylases (ACC1, ACC2, PC, PCC, MCC) and decreased mammalian target of rapamycin (mTOR) pathways and matrix metalloproteinase protein levels by the regulation of the activator protein 1 (AP-1), and mitogen activated protein kinases (MAPKs) signaling pathways. In addition, ASI+MgB caused lower levels of inflammatory factors, including TNF-α, NFκB, IL-6, IL-8, and COX-2 in the skin samples (p < 0.05). The levels of Bax and caspase-3 were increased, while anti-apoptotic protein Bcl-2 was decreased by UVB exposure, which was reversed by ASI+MgB treatment. These results show that treatment with ASI and MgB protects against skin damage by improving skin appearance, elasticity, inflammation, apoptosis, and overall health.

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

AKT, is a serine/threonine protein kinase comprising three isoforms-namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT' inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.

Marine-Derived Natural Products as ATP-Competitive mTOR Kinase Inhibitors for Cancer Therapeutics

The mammalian target of rapamycin (mTOR) is a serine/threonine kinase portraying a quintessential role in cellular proliferation and survival. Aberrations in the mTOR signaling pathway have been reported in numerous cancers including thyroid, lung, gastric and ovarian cancer, thus making it a therapeutic target. To attain this objective, an in silico investigation was designed, employing a pharmacophore modeling approach. A structure-based pharmacophore (SBP) model exploiting the key features of a selective mTOR inhibitor, Torkinib directed at the ATP-binding pocket was generated. A Marine Natural Products (MNP) library was screened using SBP model as a query. The retrieved compounds after consequent drug-likeness filtration were subjected to molecular docking with mTOR, thus revealing four MNPs with better scores than Torkinib. Successive refinement via molecular dynamics simulations demonstrated that the hits formed crucial interactions with key residues of the pocket. Furthermore, the four identified hits exhibited good binding free energy scores through MM-PBSA calculations and the subsequent in silico toxicity assessments displayed three hits deemed essentially non-carcinogenic and non-mutagenic. The hits presented in this investigation could act as potent ATP-competitive mTOR inhibitors, representing a platform for the future discovery of drugs from marine natural origin.

Ligand-Based Virtual Screening, Molecular Docking, Molecular Dynamics, and MM-PBSA Calculations towards the Identification of Potential Novel Ricin Inhibitors

Ricin is a toxin found in the castor seeds and listed as a chemical weapon by the Chemical Weapons Convention (CWC) due to its high toxicity combined with the easiness of obtention and lack of available antidotes. The relatively frequent episodes of usage or attempting to use ricin in terrorist attacks reinforce the urge to develop an antidote for this toxin. In this sense, we selected in this work the current RTA (ricin catalytic subunit) inhibitor with the best experimental performance, as a reference molecule for virtual screening in the PubChem database. The selected molecules were then evaluated through docking studies, followed by drug-likeness investigation, molecular dynamics simulations and Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) calculations. In every step, the selection of molecules was mainly based on their ability to occupy both the active and secondary sites of RTA, which are located right next to each other, but are not simultaneously occupied by the current RTA inhibitors. Results show that the three PubChem compounds 18309602, 18498053, and 136023163 presented better overall results than the reference molecule itself, showing up as new hits for the RTA inhibition, and encouraging further experimental evaluation.

Discovering novel P38α inhibitors for the treatment of prostate cancer through virtual screening methods

Aim: P38α plays a crucial role in the development of castration-resistant prostate cancer. Discovering novel inhibitors of P38α offers potential for the development of new anticancer drugs. Methods & results: Compounds from the Chemdiv and Enamine virtual libraries were filtered to construct the P38α inhibitor-like library. A total of 58 new P38α inhibitors were discovered via virtual screening; these included three compounds (compound 1, 5, 9) with kinase IC₅₀ of below 10 μM. In vitro, these three compounds have the potential to suppress the viabilities of prostate cancer cell lines, however, only compound 9 can inhibit the proliferation and migration of prostate cancer cells. Conclusion: The potent compounds discovered in this study demonstrate anticancer functions by targeting the P38α mitogen-activated protein kinases signaling pathway and are worthy of further investigation.

Discovery, biological evaluation, structure-activity relationships and mechanism of action of pyrazolo[3,4-b]pyridin-6-one derivatives as a new class of anticancer agents

We have recently reported computational models for prediction of cell-based anticancer activity using machine learning methods. Herein, we have developed an integrated strategy to discover new anticancer agents using a cascade of the established screening models. Application of this strategy identified 17 compounds with antitumor activity. Among these compounds, h2 (containing a pyrazolo[3,4-b]pyridin-6-one scaffold) exhibited anticancer activity against six tumor cell lines, including MDA-MB-231, HeLa, MCF-7, HepG2, CNE2 and HCT116, with IC50 values of 13.37, 13.04, 15.45, 7.05, 9.30 and 8.93 μM. Subsequently, a total of 61 h2 analogues were obtained by similarity searching and tested for their anticancer activities. I2 was identified as a novel anticancer agent having activity against MDA-MB-231, HeLa, MCF-7, HepG2, CNE2 and HCT116 tumor cell lines with IC50 values of 3.30, 5.04, 5.08, 3.71, 2.99 and 5.72 μM. I2 also showed potent cytotoxicity against adriamycin-resistant human breast and hepatocarcinoma cells. Further investigation revealed that I2 inhibited the microtubule polymerization by binding to the colchicine site, resulting in inhibition of cell migration, cell cycle arrest in the G2/M phase and apoptosis of cancer cells. Finally, molecular docking and molecular dynamics provided insights into the binding interactions of I2 with tubulin. This study identified I2 as a novel starting point for further development of anticancer agents that target tubulin.

Turning to Computer-aided Drug Design in the Treatment of Diffuse Large B-cell Lymphoma: Has it been Helpful?

INTRODUCTION

Amidst the numerous effective therapeutic options available for the treatment of Diffuse Large B-cell Lymphoma (DLBCL), about 30-40% of patients treated with first-line chemoimmunotherapy still experience a relapse or refractory DLBCL. This has necessitated a continuous search for new therapeutic agents to augment the existing therapeutic arsenal.

METHODS

The dawn of Computer-Aided Drug Design (CADD) in the drug discovery process has accounted for persistency in the application of computational approaches either alone or in combinatorial strategies with experimental methods towards the identification of potential hit compounds with high therapeutic efficacy in abrogating DLBCL.

RESULTS

This review showcases the interventions of structure-based and ligand-based computational approaches which have led to the identification of numerous small molecule inhibitors against implicated targets in DLBCL therapy, even though many of these potential inhibitors are piled-up awaiting further experimental validation and exploration.

CONCLUSION

We conclude that a successful and a conscious amalgamation of CADD and experimental approaches could pave the way for the discovery of the next generation potential leads in DLBCL therapy with improved activities and minimal toxicities.

Identifying a novel anticancer agent with microtubule-stabilizing effects through computational cell-based bioactivity prediction models and bioassays

G03 is a novel anticancer agent with unusual microtubule-stabilizing effects.

Searching for potential mTOR inhibitors: Ligand-based drug design, docking and molecular dynamics studies of rapamycin binding site

The PI3K/Akt/mTOR pathway is an important intracellular signaling pathway in cell cycle regulation and its dysregulation is associated with various types of diseases. mTOR (mechanistic or mammalian target of rapamycin) is the main enzyme that performs intermediate control of the signaling pathway through a phosphotransfer process. The classical inhibition of the mTOR pathway is effected by rapamycin and its analogous blocking allosterically the catalytic phosphorylation site, avoiding the deleterious side effects induced by ATP-competitive inhibitors. We employed ligand-based drug design strategies such as pharmacophore searching and analysis, molecular docking, absorption, distribution, metabolism, excretion and toxicity (ADMETox) properties filtering, and molecular dynamics to select potential molecules to become non-ATP competitive inhibitors of the mTOR complex. According to our findings, we propose eight novel potential mTOR inhibitors with similar or better properties than the classic inhibitor complex, rapamycin.

Identification of Potent Chloride Intracellular Channel Protein 1 Inhibitors from Traditional Chinese Medicine through Structure-Based Virtual Screening and Molecular Dynamics Analysis

Chloride intracellular channel 1 (CLIC1) is involved in the development of most aggressive human tumors, including gastric, colon, lung, liver, and glioblastoma cancers. It has become an attractive new therapeutic target for several types of cancer. In this work, we aim to identify natural products as potent CLIC1 inhibitors from Traditional Chinese Medicine (TCM) database using structure-based virtual screening and molecular dynamics (MD) simulation. First, structure-based docking was employed to screen the refined TCM database and the top 500 TCM compounds were obtained and reranked by X-Score. Then, 30 potent hits were achieved from the top 500 TCM compounds using cluster and ligand-protein interaction analysis. Finally, MD simulation was employed to validate the stability of interactions between each hit and CLIC1 protein from docking simulation, and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) analysis was used to refine the virtual hits. Six TCM compounds with top MM-GBSA scores and ideal-binding models were confirmed as the final hits. Our study provides information about the interaction between TCM compounds and CLIC1 protein, which may be helpful for further experimental investigations. In addition, the top 6 natural products structural scaffolds could serve as building blocks in designing drug-like molecules for CLIC1 inhibition.

An Anti-Cancer Drug Candidate OSI-027 and its Analog as Inhibitors of mTOR: Computational Insights Into the Inhibitory Mechanisms

The mammalian target of rapamycin (mTOR) is a serine-threonine kinase, which regulates cellular metabolism and growth, and is a validated therapeutic target in various cancers. Recently, OSI-027, a selective ATP competitive inhibitor of mTOR, has been developed. The OSI-027 is an orally bioavailable compound whose anti-cancer activities were observed in various cancer cell lines and tumor xenograft models. The current study is the first attempt to explore the binding mode and the molecular-interactions of OSI-027 with mTOR using molecular docking and (un)binding simulation approaches. The study identified various interacting residues and their extent of involvement in binding was emphasized using different methods. The (un)binding simulation analyses provided snapshots of various phases in OSI-027 binding and identified residues important for binding but away from the catalytic site. Further, to explore a better binder for mTOR among OSI-027 analogs, the virtual screening led to propose an OSI-027 analog with CID: 73294902 as a better inhibitor than the OSI-027 and the native ligand PI-103. The binding mode of the proposed compound is compared with those of OSI-027 and other native inhibitors. The comparison of (un)binding simulation phases of proposed compound with that of OSI-027 revealed that both, bound to the same catalytic site, follow different (un)binding path. Thus, the current study presents computational insights into the OSI-027 mediated inhibition of mTOR kinase and proposed an OSI-027 analog as better mTOR inhibitor, and thus, a good drug for further research in experimental laboratories. J. Cell. Biochem. 118: 4558-4567, 2017. © 2017 Wiley Periodicals, Inc.

Far-infrared suppresses skin photoaging in ultraviolet B-exposed fibroblasts and hairless mice

Ultraviolet (UV) induces skin photoaging, which is characterized by thickening, wrinkling, pigmentation, and dryness. Collagen, which is one of the main building blocks of human skin, is regulated by collagen synthesis and collagen breakdown. Autophagy was found to block the epidermal hyperproliferative response to UVB and may play a crucial role in preventing skin photoaging. In the present study, we investigated whether far-infrared (FIR) therapy can inhibit skin photoaging via UVB irradiation in NIH 3T3 mouse embryonic fibroblasts and SKH-1 hairless mice. We found that FIR treatment significantly increased procollagen type I through the induction of the TGF-β/Smad axis. Furthermore, UVB significantly enhanced the expression of matrix metalloproteinase-1 (MMP-1) and MMP-9. FIR inhibited UVB-induced MMP-1 and MMP-9. Treatment with FIR reversed UVB-decreased type I collagen. In addition, FIR induced autophagy by inhibiting the Akt/mTOR signaling pathway. In UVB-induced skin photoaging in a hairless mouse model, FIR treatment resulted in decreased skin thickness in UVB irradiated mice and inhibited the degradation of collagen fibers. Moreover, FIR can increase procollagen type I via the inhibition of MMP-9 and induction of TGF-β in skin tissues. Therefore, our study provides evidence for the beneficial effects of FIR exposure in a model of skin photoaging.

EGAR, A Food Protein-Derived Tetrapeptide, Reduces Seizure Activity in Pentylenetetrazole-Induced Epilepsy Models Through α-Amino-3-Hydroxy-5-Methyl-4-Isoxazole Propionate Receptors

A primary pathogeny of epilepsy is excessive activation of α-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptors (AMPARs). To find potential molecules to inhibit AMPARs, high-throughput screening was performed in a library of tetrapeptides in silico. Computational results suggest that some tetrapeptides bind stably to the AMPAR. We aligned these sequences of tetrapeptide candidates with those from in vitro digestion of the trout skin protein. Among salmon-derived products, Glu-Gly-Ala-Arg (EGAR) showed a high biological affinity toward AMPAR when tested in silico. Accordingly, natural EGAR was hypothesized to have anticonvulsant activity, and in vitro experiments showed that EGAR selectively inhibited AMPAR-mediated synaptic transmission without affecting the electrophysiological properties of hippocampal pyramidal neurons. In addition, EGAR reduced neuronal spiking in an in vitro seizure model. Moreover, the ability of EGAR to reduce seizures was evaluated in a rodent epilepsy model. Briefer and less severe seizures versus controls were shown after mice were treated with EGAR. In conclusion, the promising experimental results suggest that EGAR inhibitor against AMPARs may be a target for antiepilepsy pharmaceuticals. Epilepsy is a common brain disorder characterized by the occurrence of recurring, unprovoked seizures. Twenty to 30 % of persons with epilepsy do not achieve adequate seizure control with any drug. Here we provide a possibility in which a natural and edible tetrapeptide, EGAR, can act as an antiepileptic agent. We have combined computation with in vitro experiments to show how EGAR modulates epilepsy. We also used an animal model of epilepsy to prove that EGAR can inhibit seizures in vivo. This study suggests EGAR as a potential pharmaceutical for the treatment of epilepsy.

Active fragments-guided drug discovery and design of selective tropane alkaloids using ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry coupled with virtual calculation and biological evaluation

Tropane alkaloids (TAs), rich in the plant of Physochlaina infundibularis Kuang, which is named Huashanshen (HSS) in China, showed good effects on types of spasms. However, no data were collected to explore the relationship between the specificity for muscarinic receptor subtypes and the structures of these TAs. To address this issue, an extracted ion chromatogram (EIC) strategy using ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-Q/TOF-MS) based on the fragmentation behavior of the TA standards was established to rapidly capture the varied TAs from HSS. Based on the provided structural information of diagnostic ions or neutral loss, 29 TAs were efficiently profiled, especially some trace ingredients. In additional, via virtual validation combined with molecular dynamic simulation, approximately a dozen alkaloids were found with high selectivity for muscarinic receptors. In additional, N-acetyl convolicine was chosen for selectivity evaluation of M₂ or M₃ receptors through the use of a dual-luciferase reporter assay system at the cellular level and an ACh-induced constricted strip test in vitro. After summarizing the active fragments and the structure-activity relationship (SAR) information, a new modified TA that takes advantage of both the high affinity and high selectivity for M₃ receptors was proposed and evaluated successfully. This study provided an effective approach for the discovery and design of natural products based on highly selective drugs by UPLC-Q/TOF-MS coupled with virtual calculation and biological evaluation. Graphical Abstract Active fragments-guided strategy for selective inhibitors from HSS.

Diallyl disulfide induces apoptosis and autophagy via mTOR pathway in myeloid leukemic cell line

Leukemia is a hematological malignancy which is produced by uncontrolled proliferation of leukocyte precursors. Currently, alternative medicines, using herb extracts, have been developed for cancer treatment. In this study, the effect of diallyl disulfide (DADS) on the induction of apoptosis and autophagy was investigated in K562 and NB4 myeloid leukemia cells. Leukemia cells were treated with various concentrations of DADS for 24 and 48 h. The percentage of cell viability was measured using an MTT assay. The percentages of apoptosis and autophagy were analyzed by staining with annexin-FITC and anti-LC3 FITC-conjugated antibodies, respectively. Then, the stained cells were detected by flow cytometry. In addition, PP242, a mammalian target rapamycin (mTOR) inhibitor, was used to study the involvement of the mTOR pathway in DADS-induced apoptosis and autophagy. mTOR mRNA expression was measured by real-time PCR. The results showed that DADS decreased cell viability and increased the percentage of cell apoptosis in a dose- and time-dependent manner. mTOR expression was significantly decreased in DADS- and mTOR inhibitor-treated cells. The highest percentages of apoptosis and autophagy were shown in cells treated with 100 μg/ml DADS combined with 10 μM of the mTOR inhibitor. According to our results, DADS could induce apoptosis and autophagy via the mTOR pathway in both K562 and NB4 myeloid leukemia cell lines.