Virtual screening strategies: recent advances in the identification and design of anti-cancer agents

Computational Approaches: A New Frontier in Cancer Research

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

Identification of allosteric inhibitor against AKT1 through structure-based virtual screening

AKT (serine/threonine protein kinase) is a potential therapeutic target for many types of cancer as it plays a vital role in cancer progression. Many AKT inhibitors are already in practice under single and combinatorial therapy. However, most of these inhibitors are orthosteric / pan-AKT that are non-selective and non-specific to AKT kinase and their isoforms. Hence, researchers are searching for novel allosteric inhibitors that bind in the interface between pH and kinase domain. In this study, we performed structure-based virtual screening from the afroDB (a diverse natural compounds library) to find the potential inhibitor targeting the AKT1. These compounds were filtered through Lipinski, ADMET properties, combined with a molecular docking approach to obtain the 8 best compounds. Then we performed molecular dynamics simulation for apoprotein, AKT1 with 8 complexes, and AKT1 with the positive control (Miransertib). Molecular docking and simulation analysis revealed that Bianthracene III (hit 1), 10-acetonyl Knipholonecyclooxanthrone (hit 2), Abyssinoflavanone VII (hit 5) and 8-c-p-hydroxybenzyldiosmetin (hit 6) had a better binding affinity, stability, and compactness than the reference compound. Notably, hit 1, hit 2 and hit 5 had molecular features required for allosteric inhibition.

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.

A combination strategy of structure-based virtual screening, MM-GBSA, cross docking, molecular dynamics and metadynamics simulations used to investigate natural compounds as potent and specific inhibitors of tumor linked human carbonic anhydrase IX

Cancer remains a serious health concern representing one of the leading causes of deaths worldwide. The enzyme human carbonic anhydrase IX (hCA IX) is found to be over-expressed in many cancer types and its selective inhibition over its cytosolic off-target isoform, human carbonic anhydrase II (hCA II), represents a potential area of research in the development of novel anticancer compounds. This work is concerned with the use of various in silico tools for the identification of natural product based molecules that can selectively inhibit hCA IX over hCA II. MM-GBSA assisted structure-based virtual screening against hCA IX was performed for nearly 225,000 natural products imported from the ZINC15 database. The obtained hits were checked for their potency by considering acetazolamide, the bound inhibitor of hCA IX, as the reference molecule, and 121 molecules were identified as potent hCA IX inhibitors. After ensuring their potency, cross-docking, followed by MM-GBSA calculations of the hits with hCA II, was performed, and their selectivity was assessed by considering the hCA IX selective compound SLC-0111 as the reference molecule, and 50 natural products were identified as potent as well as selective hCA IX inhibitors. Molecules with the quinoline scaffold showed the highest selectivity, and their selectivity was attributed to the strong electrostatic interactions of the zinc binding group (ZBG) with the active site Zn(II) ion. Furthermore, the stability of the binding modes of the top hCA IX selective hits was ensured by performing molecular dynamics (MD) simulations, which clearly proved that one of the short-listed molecules is truly selective, as it does not interact with the active site Zn(II) ion of hCA II, but interacts strongly with this ion in hCA IX. Bonding pose metadynamics studies revealed that the ligand moves to a more stable binding site from the one predicted by the docking studies and shows stronger interaction with the protein and Zn(II) at this binding site. The ligand is not likely to have issues with bioavailability. As a result, this ligand can be taken for bioassay testing and subsequently used as a feasible therapeutic treatment for a variety of cancer types. Communicated by Ramaswamy H. Sarma.

The Research of New Inhibitors of Bacterial Methionine Aminopeptidase by Structure Based Virtual Screening Approach of ZINC DATABASE and In Vitro Validation

BACKGROUND

The great emergence of multi-resistant bacterial strains and the low renewal of antibiotics molecules are leading human and veterinary medicine to certain therapeutic impasses. Therefore, there is an urgent need to find new therapeutic alternatives including new molecules in the current treatments of infectious diseases. Methionine aminopeptidase (MetAP) is a promising target for developing new antibiotics because it is essential for bacterial survival.

OBJECTIVE

To screen for potential MetAP inhibitors by in silico virtual screening of the ZINC database and evaluate the best potential lead molecules by in vitro studies.

METHODS

We have considered 200,000 compounds from the ZINC database for virtual screening with FlexX software to identify potential inhibitors against bacterial MetAP. Nine chemical compounds of the top hits predicted were purchased and evaluated in vitro. The antimicrobial activity of each inhibitor of MetAP was tested by the disc-diffusion assay against one Gram-positive (Staphylococcus aureus) and two Gram-negative (Escherichia coli & Pseudomonas aeruginosa) bacteria. Among the studied compounds, compounds ZINC04785369 and ZINC03307916 showed promising antibacterial activity. To further characterize their efficacy, the minimum inhibitory concentration was determined for each compound by the microdilution method which showed significant results.

RESULTS

These results suggest compounds ZINC04785369 and ZINC03307916 as promising molecules for developing MetAP inhibitors.

CONCLUSION

Furthermore, they could therefore serve as lead molecules for further chemical modifications to obtain clinically useful antibacterial agents.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

Integrated support vector machine and pharmacophore based virtual screening driven identification of thiophene carboxamide scaffold containing compound as potential PARP1 inhibitor

Poly (ADP-ribose) polymerase-1 (PARP1) inhibition strategy for cancer treatment is gaining advantage particularly in patients having a mutation in BRCA1/BRCA2 gene. To date, four drugs have obtained FDA approval and some inhibitors are in clinical trials. To identify more potent PARP1 inhibitors extensive research is going on to enrich the library of PARP1 inhibitors with compounds belonging to different classes. We employed an integrated virtual screening approach to identify potential PARP1 inhibitors. The sequential support vector machine (SVM) and pharmacophore model based virtual screening was carried out on the Maybridge library. The obtained hits were docked in the binding site of the PARP1 catalytic domain and nine drug-like compounds showing good ADME properties and form critical molecular interactions with the binding site residues were considered for the in vitro PARP1 inhibition assay. MD simulations were performed to decipher the stability of the PARP1-ligand complexes. Hydrogen bond interactions were also probed for their stability during MD simulations. We have identified three compounds (BTB02767, GK01172, and KM09200) showing 50% inhibition of PARP1 enzyme activity at 25 μM. BTB02767 and KM09200 have phthalazinone scaffold, while GK01172 bears a thiophene carboxamide scaffold, which could be a new chemotype of PARP1 inhibitors. In conclusion, GK01172 may serve as an important compound for further development of PARP1 inhibitors containing thiophene carboxamide scaffold.Communicated by Ramaswamy H. Sarma.

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Molecular docking-based virtual drug screening revealing an oxofluorenyl benzamide and a bromonaphthalene sulfonamido hydroxybenzoic acid as HDAC6 inhibitors with cytotoxicity against leukemia cells

HDAC6 is a crucial epigenetic modifier that plays a vital role in tumor progression and carcinogenesis due to its multiple biological functions. It is a unique member of class-II HDAC enzymes. It possesses two catalytic domains, which function independently of the overall enzyme activity. Up to date, there are only a few selective HDAC6 inhibitors with anti-cancer activity. In this study, 175,204 ligands obtained from the ZINC15 and OTAVAchemical databases were used for virtual drug screening against HDAC6. Molecular docking studies were performed for 100 selected compounds. Furthermore, the top 10 compounds obtained from docking were tested for their efficacy to inhibit the function of HDAC6. Five compounds (N-(9-oxo-9H-fluoren-3-yl)benzamide, 2-hydroxy-5-[(5-oxo-6-phenyl-4,5-dihydro-1,2,4-triazin-3-yl)amino]benzoic acid, 5-(4-bromonaphthalene-1-sulfonamido)-2-hydroxybenzoic acid, 2-(naphthalen-2-yl)-N-(1H-1,2,3,4-tetrazol-5-yl)cyclopropane-1-carboxamide, and 4-oxa-5,6 diazapentacyclo[10.7.1.0³,⁷.0⁸,²⁰.0¹⁴,¹⁹]icosa-1,3(7),5,8(20),9,11,14,16,18-nonaen-13-one) inhibited enzymatic activity by more than 50 % compared to DMSO as the control. Two candidates, (N-(9-oxo-9H-fluoren-3-yl)benzamide and 5-(4-bromonaphthalene-1-sulfonamido)-2-hydroxybenzoic acid), were identified with considerable cytotoxicity towards drug-sensitive CCRF-CEM and multidrug-resistant CEM/ADR5000 leukemia cells. Microscale thermophoresis revealed the binding of N-(9-oxo-9H-fluoren-3-yl)benzamide and 5-(4-bromonaphthalene-1-sulfonamido)-2-hydroxybenzoic acid to purified HDAC6 protein. Both compounds induced apoptosis in a dose-dependent manner as analyzed by flow cytometry. In conclusion, we demonstrate for the first time that these two compounds bind to HDAC6, inhibit its function, and exert cytotoxic activity by apoptosis induction.

The ChemicalToolbox: reproducible, user-friendly cheminformatics analysis on the Galaxy platform

Here, we introduce the ChemicalToolbox, a publicly available web server for performing cheminformatics analysis. The ChemicalToolbox provides an intuitive, graphical interface for common tools for downloading, filtering, visualizing and simulating small molecules and proteins. The ChemicalToolbox is based on Galaxy, an open-source web-based platform which enables accessible and reproducible data analysis. There is already an active Galaxy cheminformatics community using and developing tools. Based on their work, we provide four example workflows which illustrate the capabilities of the ChemicalToolbox, covering assembly of a compound library, hole filling, protein-ligand docking, and construction of a quantitative structure-activity relationship (QSAR) model. These workflows may be modified and combined flexibly, together with the many other tools available, to fit the needs of a particular project. The ChemicalToolbox is hosted on the European Galaxy server and may be accessed via https://cheminformatics.usegalaxy.eu.

How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs

The early and late development of new anticancer drugs, small molecules or peptides can be slowed down by some issues such as poor selectivity for the target or poor ADME properties. Computer-aided drug design (CADD) and target drug delivery (TDD) techniques, although apparently far from each other, are two research fields that can give a significant contribution to overcome these problems. Their combination may provide mechanistic understanding resulting in a synergy that makes possible the rational design of novel anticancer based therapies. Herein, we aim to discuss selected applications, some also from our research experience, in the fields of anticancer small organic drugs and peptides.

Virtual Screening of Novel Hybrid Non-Steroidal Anti-Inflammatory Drugs (NSAIDs): Exploring Multiple Targeted Cancer Therapy by an In Silico Approach

Background:

Dual-targeting/Multi-targeting of oncoproteins by a single drug molecule represents an efficient, logical and alternative approach to drug combinations. In silico methods are useful tool for the search and design of selective multi-target agents.

Objective:

The objective of the present study was to design new hybrid compounds by linking the main structural unit of the NSAIDs with the benzothiazole and thiadiazole ring and to discover new hybrid NSAIDs as multi targeted anticancer agents through in silico approach.

Method:

Structure-based virtual screening was performed by applying ADMET filtration and Glide docking using Virtual screening Workflow. The docking studies were performed on three different types of receptors TNF-α, COX-II and protein kinase. Bioactivity prediction of screened compounds were done using Molinspiration online software tool.

Results:

Out of 54 designed compounds eighteen were screened on the basis of binding affinity on various receptors and ADMET filtration. Bioactivity prediction reveals that screened compounds may act through kinase inhibition or enzyme inhibition. Compounds 2sa, 5sa, 6sa and 7sa shows higher binding affinity with all three receptors.

Conclusion:

The study concluded that compound 2sa, 5sa, 6sa, and 7sa could be further explored for multiple targeted cancer therapy.

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies.

An in silico Approach for Integrating Phenotypic and Target-based Approaches in Drug Discovery

Phenotypic and target-based approaches are useful methods in drug discovery. The phenotypic approach is an experimental approach for evaluating the phenotypic response. The target-based approach is a rational approach for screening drug candidates targeting a biomolecule that causes diseases. These approaches are widely used for drug discovery. However, two serious problems of target deconvolution and polypharmacology are encountered in these conventional experimental approaches. To overcome these two problems, we developed a new in silico method using a probabilistic framework. This method integrates both the phenotypic and target-based approaches to estimate a relevant network from compound to phenotype. Our method can computationally execute target deconvolution considering polypharmacology and can provide keys for understanding the pathway and mechanism from compound to phenotype, thereby promoting drug discovery.

Investigation of Crystal Structures in Structure-Based Virtual Screening for Protein Kinase Inhibitors

Protein kinases are important drug targets in several therapeutic areas ,and structure-based virtual screening (SBVS) is an important strategy in discovering lead compounds for kinase targets. However, there are multiple crystal structures available for each target, and determining which one is the most favorable is a key step in molecular docking for SBVS due to the ligand induce-fit effect. This work aimed to find the most desirable crystal structures for molecular docking by a comprehensive analysis of the protein kinase database which covers 190 different kinases from all eight main kinase families. Through an integrated self-docking and cross-docking evaluation, 86 targets were eventually evaluated on a total of 2608 crystal structures. Results showed that molecular docking has great capability in reproducing conformation of crystallized ligands and for each target, the most favorable crystal structure was selected, and the AGC family outperformed the other family targets based on RMSD comparison. In addition, RMSD values, GlideScore, and corresponding bioactivity data were compared and demonstrated certain relationships. This work provides great convenience for researchers to directly select the optimal crystal structure in SBVS-based kinase drug design and further validates the effectiveness of molecular docking in drug discovery.

Identification of Potential Inhibitors of Cathepsin-B using Shape & Pharmacophore-based Virtual Screening, Molecular Docking and Explicit Water Thermodynamics

Lysosome has been long understood as a vital digestive organelle. Increasing reports indicate that the lysosome also plays a crucial role in the pathogenesis of a variety of neurodegenerative diseases, including Huntington's disease, Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. Abnormal protein degradation and deposition stimulated by lysosomal dysfunction may cause age-related neurodegeneration. Enormous efforts have been devoted to the development of effective therapeutics against Alzheimer's disease, the most debilitating neurodegenerative disease. Endopeptidase activity of the Cathepsin-B is associated with the pathological processes. Work presented here focuses on identification of new inhibitors against Cathepsin-B protein using diverse computational approaches together. The inhibitors identified were further tested for in-vitro activity using enzyme based assay method. The identified inhibitors provided interesting understanding on how the water thermodynamic properties along with hydrophobic, steric, electronic, and structural requirements contribute to cathepsin-B inhibitory activity. These water thermodynamic studies, may further be used in computer aided drug discovery pipeline to design and predict more potent derivatives of various scaffolds as cathepsin-B inhibitors.

The novel small molecular BH3 mimetics SM3 and its regulation of cell apoptosis and autophagy

Bcl-2 family proteins play an important role in regulation of the cell survival and death. The inhibition of the anti-apoptotic proteins of Bcl-2 family leads to the apoptosis of cancer. BH3 mimetics have been developed targeting anti-apoptotic proteins of Bcl-2 family as small molecular drugs. It has been proved that BH3 mimetics has effect on apoptosis and proliferation in leukemia and some of them has been used in phase one or two clinical trials. Besides, with the development of the research on autophagic cell death, the antagonism and the synergism of autophagy and apoptosis is significant in cell death. As a hub of these two pathways of cell death, Bcl-2 protein is a potential target in basic research and clinical applications. In our studies, we found 32 potential BH3 mimetics compounds from 140,000 small molecular compounds via pharmacophore-based virtual screening. Furthermore, we demonstrated SM3, one of the 32 potential BH3 mimetics, induced autophagy and apoptosis simultaneously in dose-time dependence in A549 cell. SM3 induced apoptosis by intrinsic apoptosis pathway and induced autophagy by weakening the interaction between Beclin-1 and Bcl-2 complex. We wish to provide evidences and clues for the structural optimizing and further study of new compounds in the future.

In Silico Study to Identify New Antituberculosis Molecules from Natural Sources by Hierarchical Virtual Screening and Molecular Dynamics Simulations

Tuberculosis (TB) is an infection caused by Mycobacterium tuberculosis, responsible for 1.5 million documented deaths in 2016. The increase in reported cases of M. tuberculosis resistance to the main drugs show the need for the development of new and efficient drugs for better TB control. Based on these facts, this work aimed to use combined in silico techniques for the discovery of potential inhibitors to β-ketoacyl-ACP synthase (MtKasA). Initially compounds from natural sources present in the ZINC database were selected, then filters were sequentially applied by virtual screening, initially with pharmacophoric modeling, and later the selected compounds (based on QFIT scores) were submitted to the DOCK 6.5 program. After recategorization of the variables (QFIT score and GRID score), compounds ZINC35465970 and ZINC31170017 were selected. These compounds showed great hydrophobic contributions and for each established system 100 ns of molecular dynamics simulations were performed and the binding free energy was calculated. ZINC35465970 demonstrated a greater capacity for the KasA enzyme inhibition, with a ΔG_bind = -30.90 kcal/mol and ZINC31170017 presented a ΔG_bind = -27.49 kcal/mol. These data can be used in other studies that aim at the inhibition of the same biological targets through drugs with a dual action.

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.

Virtual screening-driven repositioning of etoposide as CD44 antagonist in breast cancer cells

CD44 is a receptor for hyaluronan (HA) that promotes epithelial-to-mesenchymal transition (EMT), induces cancer stem cell (CSC) expansion, and favors metastasis. Thus, CD44 is a target for the development of antineoplastic agents. In order to repurpose drugs as CD44 antagonists, we performed consensus-docking studies using the HA-binding domain of CD44 and 11,421 molecules. Drugs that performed best in docking were examined in molecular dynamics simulations, identifying etoposide as a potential CD44 antagonist. Ligand competition and cell adhesion assays in MDA-MB-231 cells demonstrated that etoposide decreased cell binding to HA as effectively as a blocking antibody. Etoposide-treated MDA-MB-231 cells developed an epithelial morphology; increased their expression of E-cadherin; and reduced their levels of EMT-associated genes and cell migration. By gene expression analysis, etoposide reverted an EMT signature similarly to CD44 knockdown, whereas other topoisomerase II (TOP2) inhibitors did not. Moreover, etoposide decreased the proportion of CD44⁺/CD24⁻ cells, lowered chemoresistance, and blocked mammosphere formation. Our data indicate that etoposide blocks CD44 activation, impairing key cellular functions that drive malignancy, thus rendering it a candidate for further translational studies and a potential lead compound in the development of new CD44 antagonists.

Identification of Phytochemicals Targeting c-Met Kinase Domain using Consensus Docking and Molecular Dynamics Simulation Studies

c-Met receptor tyrosine kinase is a proto-oncogene whose aberrant activation is attributed to a lower rate of survival in most cancers. Natural product-derived inhibitors known as “fourth generation inhibitors” constitute more than 60% of anticancer drugs. Furthermore, consensus docking approach has recently been introduced to augment docking accuracy and reduce false positives during a virtual screening. In order to obtain novel small-molecule Met inhibitors, consensus docking approach was performed using Autodock Vina and Autodock 4.2 to virtual screen Naturally Occurring Plant-based Anti-cancer Compound–Activity–Target database against active and inactive conformation of c-Met kinase domain structure. Two hit molecules that were in line with drug-likeness criteria, desired docking score, and binding pose were subjected to molecular dynamics simulations to elucidate intermolecular contacts in protein–ligand complexes. Analysis of molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area studies showed that ZINC08234189 is a plausible inhibitor for the active state of c-Met, whereas ZINC03871891 may be more effective toward active c-Met kinase domain compared to the inactive form due to higher binding energy. Our analysis showed that both the hit molecules formed hydrogen bonds with key residues of the hinge region (P1158, M1160) in the active form, which is a hallmark of kinase domain inhibitors. Considering the pivotal role of HGF/c-Met signaling in carcinogenesis, our results propose ZINC08234189 and ZINC03871891 as the therapeutic options to surmount Met-dependent cancers.

Role of Structural Bioinformatics in Drug Discovery by Computational SNP Analysis: Analyzing Variation at the Protein Level

With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps toward an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as genome-wide association studies and candidate gene association studies. However, these statistical methods do not provide an understanding of the functional effects of variation. Structure-based drug discovery and design is increasingly incorporating structural bioinformatics techniques to model and analyze protein targets, perform large scale virtual screening to identify hit to lead compounds, and simulate molecular interactions. These techniques are fast, cost-effective, and complement existing experimental techniques such as high throughput sequencing. In this paper, we discuss the contributions of structural bioinformatics to drug discovery, focusing particularly on the analysis of nonsynonymous single nucleotide polymorphisms. We conclude by suggesting a protocol for future analyses of the structural effects of nonsynonymous single nucleotide polymorphisms on proteins and protein complexes.

Small molecules as therapeutic agents for inborn errors of metabolism

Most inborn errors of metabolism (IEM) remain without effective treatment mainly due to the incapacity of conventional therapeutic approaches to target the neurological symptomatology and to ameliorate the multisystemic involvement frequently observed in these patients. However, in recent years, the therapeutic use of small molecules has emerged as a promising approach for treating this heterogeneous group of disorders. In this review, we focus on the use of therapeutically active small molecules to treat IEM, including readthrough agents, pharmacological chaperones, proteostasis regulators, substrate inhibitors, and autophagy inducers. The small molecules reviewed herein act at different cellular levels, and this knowledge provides new tools to set up innovative treatment approaches for particular IEM. We review the molecular mechanism underlying therapeutic properties of small molecules, methodologies used to screen for these compounds, and their applicability in preclinical and clinical practice.

Theoretical and experimental study of polycyclic aromatic compounds as β-tubulin inhibitors

In this work, through a docking analysis of compounds from the ZINC chemical library on human β-tubulin using high performance computer cluster, we report new polycyclic aromatic compounds that bind with high energy on the colchicine binding site of β-tubulin, suggesting three new key amino acids. However, molecular dynamic analysis showed low stability in the interaction between ligand and receptor. Results were confirmed experimentally in in vitro and in vivo models that suggest that molecular dynamics simulation is the best option to find new potential β-tubulin inhibitors. Graphical abstract Bennett's acceptance ratio (BAR) method.

Hybrid Receptor-Bound/MM-GBSA-Per-residue Energy-Based Pharmacophore Modelling: Enhanced Approach for Identification of Selective LTA4H Inhibitors as Potential Anti-inflammatory Drugs

Leukotriene A₄ hydrolase has been identified as an enzyme with dual anti- and pro-inflammatory role, thus, the conversion of leukotriene to leukotriene B₄ in the initiation stage of inflammation and the removal of the chemotactic Pro-Gly-Pro tripeptide. These findings make leukotriene A₄ hydrolase an attractive drug target: suggesting an innovative approach towards the identification and design of novel class of compounds that can selectively inhibit leukotriene B₄ synthesis while sparing the aminopeptidase activity. Previous inhibitors block the dual activity of the enzyme. Recently, a small lead molecule inhibitor denoted as ARM1 has been identified to block the hydrolase activity of leukotriene A₄ hydrolase whilst sparing the aminopeptidase activity. In this study, a hybrid receptor-bound/MM-GBSA-per-residue energy based pharmacophore modeling approach was implemented to identify potential selective hydrolase inhibitors of leukotriene A₄ hydrolase. In this approach, active site residues that favorably contributed to the binding of the bound conformation of ARM1 were derived from MD ensembles and MM/GBSA thermodynamic calculations. These residues were then mapped to key pharmacophore features of ARM1. The generated pharmacophore model was used to search the ZINC database for 3D structures that match the pharmacophore. Five new compounds have been identified and proposed as potential epoxide hydrolase selective inhibitors of leukotriene A₄ hydrolase. Molecular docking and MM/GBSA analyses revealed that, these top five lead-like compounds ZINC00142747, ZINC94260794, ZINC01382396, ZINC02508448, and ZINC53994447 showed better binding affinities to the hydrolase active site pocket compared to ARM1. Per-residue energy decomposition analysis revealed that amino acid residues Phe314, Tyr378, Pro382, Trp311, Val367, and Ala377 are key residues critical in the selective inhibition of these hits. Information highlighted in this study may guide the the design the next generation of novel and potent epoxide hydrolase selective inhibitors of leukotriene A₄ hydrolase.

Discovery of new small molecules inhibiting 67 kDa laminin receptor interaction with laminin and cancer cell invasion

The 67 kDa laminin receptor (67LR) is a non-integrin receptor for laminin (LM) that derives from a 37 kDa precursor (37LRP). 67LR expression is increased in neoplastic cells and correlates with an enhanced invasive and metastatic potential. We used structure-based virtual screening (SB-VS) to search for 67LR inhibitory small molecules, by focusing on a 37LRP sequence, the peptide G, able to specifically bind LM. Forty-six compounds were identified and tested on HEK-293 cells transfected with 37LRP/67LR (LR-293 cells). One compound, NSC47924, selectively inhibited LR-293 cell adhesion to LM with IC50 and Ki values of 19.35 and 2.45 μmol/L. NSC47924 engaged residues W176 and L173 of peptide G, critical for specific LM binding. Indeed, NSC47924 inhibited in vitro binding of recombinant 37LRP to both LM and its YIGSR fragment. NSC47924 also impaired LR-293 cell migration to LM and cell invasion. A subsequent hierarchical similarity search with NSC47924 led to the identification of additional four compounds inhibiting LR-293 cell binding to LM: NSC47923, NSC48478, NSC48861, and NSC48869, with IC50 values of 1.99, 1.76, 3.4, and 4.0 μmol/L, respectively, and able to block in vitro cancer cell invasion. These compounds are promising scaffolds for future drug design and discovery efforts in cancer progression.

Drug Repositioning for Cancer Therapy Based on Large-Scale Drug-Induced Transcriptional Signatures

An in silico chemical genomics approach is developed to predict drug repositioning (DR) candidates for three types of cancer: glioblastoma, lung cancer, and breast cancer. It is based on a recent large-scale dataset of ~20,000 drug-induced expression profiles in multiple cancer cell lines, which provides i) a global impact of transcriptional perturbation of both known targets and unknown off-targets, and ii) rich information on drug's mode-of-action. First, the drug-induced expression profile is shown more effective than other information, such as the drug structure or known target, using multiple HTS datasets as unbiased benchmarks. Particularly, the utility of our method was robustly demonstrated in identifying novel DR candidates. Second, we predicted 14 high-scoring DR candidates solely based on expression signatures. Eight of the fourteen drugs showed significant anti-proliferative activity against glioblastoma; i.e., ivermectin, trifluridine, astemizole, amlodipine, maprotiline, apomorphine, mometasone, and nortriptyline. Our DR score strongly correlated with that of cell-based experimental results; the top seven DR candidates were positive, corresponding to an approximately 20-fold enrichment compared with conventional HTS. Despite diverse original indications and known targets, the perturbed pathways of active DR candidates show five distinct patterns that form tight clusters together with one or more known cancer drugs, suggesting common transcriptome-level mechanisms of anti-proliferative activity.

Fragment pharmacophore-based in silico screening: a powerful approach for efficient lead discovery

A fragmentation–recombination process coupled with fragment-based pharmacophore in silico screening: a tailor-made approach for the discovery of new cosmetics.