Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application

Antitumoral activity of different Amaryllidaceae alkaloids: In vitro and in silico assays

ETHNOPHARMACOLOGY RELEVANCE

The plants of Amaryllidaceae family, such as Amaryllis belladonna L., have been used as herbal remedies for thousands of years to address various disorders, including diseases that might today be identified as cancer.

AIM OF THE STUDY

The objective of this work was to evaluate the potential of three Amaryllidaceae alkaloids against four cancer cell lines.

MATERIAL AND METHODS

The alkaloids lycorine, 1-O-acetylcaranine, and montanine were evaluated in vitro against colon adenocarcinoma cell line (HCT-116) and breast carcinoma cell lines (MCF-7, MDAMB231, and Hs578T). Computational experiments (target prediction and molecular docking) were conducted to gain a deeper comprehension of possible interactions between these alkaloids and potential targets associated with these tumor cells.

RESULTS

Montanine presented the best results against HCT-116, MDAMB231, and Hs578T cell lines, while lycorine was the most active against MCF-7. In alignment with the target prediction outcomes and existing literature, four potential targets were chosen for the molecular docking analysis: CDK8, EGFR, ER-alpha, and dCK. The docking scores revealed two potential targets for the alkaloids with scores similar to co-crystallized inhibitors and substrates: CDK8 and dCK. A visual analysis of the optimal docked configurations indicates that the alkaloids may interact with some key residues in contrast to the other docked compounds. This observation implies their potential to bind effectively to both targets.

CONCLUSIONS

In vitro and in silico results corroborate with data literature suggesting the Amaryllidaceae alkaloids as interesting molecules with antitumoral properties, especially montanine, which showed the best in vitro results against colorectal and breast carcinoma. More studies are necessary to confirm the targets and pharmaceutical potential of montanine against these cancer cell lines.

Kawsar SM. Marine-Derived Compounds as Potential Inhibitors of Hsp90 for Anticancer and Antimicrobial Drug Development: A Comprehensive In Silico Study

Marine compounds constitute a diverse and invaluable resource for the discovery of bioactive substances with promising applications in the pharmaceutical development of anti-inflammatory and antibacterial agents. In this study, a comprehensive methodology was employed, encompassing pharmacophore modeling, virtual screening, in silico ADMET assessment (encompassing aspects of absorption, distribution, metabolism, excretion, and toxicity), and molecular dynamics simulations. These methods were applied to identify new inhibitors targeting the Hsp90 protein (heat shock protein 90), commencing with a diverse assembly of compounds sourced from marine origins. During the virtual screening phase, an extensive exploration was conducted on a dataset comprising 31,488 compounds sourced from the CMNPD database, characterized by a wide array of molecular structures. The principal objective was the development of structure-based pharmacophore models, a valuable approach when the pool of known ligands is limited. The pharmacophore model DDRRR was successfully constructed within the active sites of the Hsp90 crystal structure. Subsequent docking studies led to the identification of six compounds (CMNPD 22591, 9335, 10015, 360799, 15115, and 20988) demonstrating substantial binding affinities, each with values below -8.3 kcal/mol. In the realm of in silico ADMET predictions, five of these compounds exhibited favorable pharmacokinetic properties. Furthermore, molecular dynamics simulations and total binding energy calculations using MM-PBSA indicated that these marine-derived compounds formed exceptionally stable complexes with the Hsp90 receptor over a 100-nanosecond simulation period. These findings underscore the considerable potential of these novel marine compounds as promising candidates for anticancer and antimicrobial drug development.

Fighting Antimicrobial Resistance: Insights on How the Staphylococcus aureus NorA Efflux Pump Recognizes 2-Phenylquinoline Inhibitors by Supervised Molecular Dynamics (SuMD) and Molecular Docking Simulations

The superbug Staphylococcus aureus (S. aureus) exhibits several resistance mechanisms, including efflux pumps, that strongly contribute to antimicrobial resistance. In particular, the NorA efflux pump activity is associated with S. aureus resistance to fluoroquinolone antibiotics (e.g., ciprofloxacin) by promoting their active extrusion from cells. Thus, since efflux pump inhibitors (EPIs) are able to increase antibiotic concentrations in bacteria as well as restore their susceptibility to these agents, they represent a promising strategy to counteract bacterial resistance. Additionally, the very recent release of two NorA efflux pump cryo-electron microscopy (cryo-EM) structures in complex with synthetic antigen-binding fragments (Fabs) represents a real breakthrough in the study of S. aureus antibiotic resistance. In this scenario, supervised molecular dynamics (SuMD) and molecular docking experiments were combined to investigate for the first time the molecular mechanisms driving the interaction between NorA and efflux pump inhibitors (EPIs), with the ultimate goal of elucidating how the NorA efflux pump recognizes its inhibitors. The findings provide insights into the dynamic NorA-EPI intermolecular interactions and lay the groundwork for future drug discovery efforts aimed at the identification of novel molecules to fight antimicrobial resistance.

Modulation of Pseudomonas aeruginosa quorum sensing by ajoene through direct competition with small RNAs for binding at the proximal site of Hfq - a structure-based perspective

Bacteria can communicate to each other via quorum sensing, a cell density-dependent gene regulation system that stimulates the expression of virulence factors in the neighboring cells. Although the interaction of the natural product ajoene with the Hfq protein has been associated with the disruption of the quorum sensing system in Pseudomonas aeruginosa, there is no information concerning the corresponding ligand-target interaction process. Herein we observed a strong correlation (p < 0.00001) between the estimated affinities for the binding of 23 ajoene analogues at the proximal site of the Hfq protein of P. aeruginosa and their corresponding IC₅₀ values, which reflect the reduction in the transcription of a virulence factor after quorum sensing inhibition. In this concern, our analyses reinforces previous propositions suggesting that ajoene could target the Hfq protein and affects its interaction with RNAs. Based on docking simulations, we tried to elucidate the binding mode of ajoene into the proximal Hfq site and the also to established the minimum set of groups that would be necessary for a good interaction at this site, which includes a single hydrogen bond acceptor feature surrounded by groups that interact via π-sulfur (i.e., disulfide sulfurs) and/or π-alkyl/π-π stacking interactions (e.g., vinyl or small aryl/heteroaryl/heterocyclic groups). Because of the widespread role of Hfq as a matchmaker between messenger and small regulatory RNAs in Gram-negatives, we believe the discussion here provided for P. aeruginosa could be extrapolated for Gram-negatives in general, while the interaction of ajoene over the Hfq protein of Gram-positives would still remain more controversial.

Bovine Serum Amine Oxidase and Polyamine Analogues: Chemical Synthesis and Biological Evaluation Integrated with Molecular Docking and 3-D QSAR Studies

Natural polyamines (PAs) are key players in cellular homeostasis by regulating cell growth and proliferation. Several observations highlight that PAs are also implicated in pathways regulating cell death. Indeed, the PA accumulation cytotoxic effect, maximized with the use of bovine serum amine oxidase (BSAO) enzyme, represents a valuable strategy against tumor progression. In the present study, along with the design, synthesis, and biological evaluation of a series of new spermine (Spm) analogues (1-23), a mixed structure-based (SB) and ligand-based (LB) protocol was applied. Binding modes of BSAO-PA modeled complexes led to clarify electrostatic and steric features likely affecting the BSAO-PA biochemical kinetics. LB and SB three-dimensional quantitative structure-activity relationship (Py-CoMFA and Py-ComBinE) models were developed by means of the 3d-qsar.com portal, and their analysis represents a strong basis for future design and synthesis of PA BSAO substrates for potential application in oxidative stress-induced chemotherapy.

Anticancer Potential of Hydroxychavicol Derived from Piper betle L: An in Silico and Cytotoxicity Study

Piper betle L. is a popular medicinal plant in Asia, and extracts of the plant leaf are used for several therapeutics. It is known for its rich source of phenolic compounds, including hydroxychavicol. Hydroxychavicol is an allylbenzene that has gained much attention due to its anticancer properties. The current study quantified and purified hydroxychavicol from P. betle L. and predicted its anticancer competence through in silico and cytotoxicity studies. Leaf samples of 22 P. betle L. accessions from different locations of Tamil Nadu, India, were analyzed using reverse phase-high performance liquid chromatography for quantification of hydroxychavicol. The highest quantity of hydroxychavicol was obtained from the accession BV22 (89.2%). Chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis of hydroxychavicol using SwissADME satisfied the physicochemical property guidelines of Lipinski's Rule of Five, ensuring its drug-likeness behavior. Molecular docking studies confirmed the interaction of hydroxychavicol with all 16 tested cancer targets. In Vitro MTT assay of hydroxychavicol in bone cancer cell lines (MG63) also demonstrated the anticancer competency, indicating the requirement to formulate the molecule as a drug in treating various types of cancers.

Heat Shock Protein 90 (HSP90) Inhibitors as Anticancer Medicines: A Review on the Computer-Aided Drug Discovery Approaches over the Past Five Years

Cancer is a disease caused by the uncontrolled, abnormal growth of cells in different anatomic sites. In 2018, it was predicted that the worldwide cancer burden would rise to 18.1 million new cases and 9.6 million deaths. Anticancer compounds, often known as chemotherapeutic medicines, have gained much interest in recent cancer research. These medicines work through various biological processes in targeting cells at various stages of the cell's life cycle. One of the most significant roadblocks to developing anticancer drugs is that traditional chemotherapy affects normal cells and cancer cells, resulting in substantial side effects. Recently, advancements in new drug development methodologies and the prediction of the targeted interatomic and intermolecular ligand interaction sites have been beneficial. This has prompted further research into developing and discovering novel chemical species as preferred therapeutic compounds against specific cancer types. Identifying new drug molecules with high selectivity and specificity for cancer is a prerequisite in the treatment and management of the disease. The overexpression of HSP90 occurs in patients with cancer, and the HSP90 triggers unstable harmful kinase functions, which enhance carcinogenesis. Therefore, the development of potent HSP90 inhibitors with high selectivity and specificity becomes very imperative. The activities of HSP90 as chaperones and cochaperones are complex due to the conformational dynamism, and this could be one of the reasons why no HSP90 drugs have made it beyond the clinical trials. Nevertheless, HSP90 modulations appear to be preferred due to the competitive inhibition of the targeted N-terminal adenosine triphosphate pocket. This study, therefore, presents an overview of the various computational models implored in the development of HSP90 inhibitors as anticancer medicines. We hereby suggest an extensive investigation of advanced computational modelling of the three different domains of HSP90 for potent, effective inhibitor design with minimal off-target effects.

Human Estrogen Receptor Alpha Antagonists, Part 3: 3-D Pharmacophore and 3-D QSAR Guided Brefeldin A Hit-to-Lead Optimization toward New Breast Cancer Suppressants

The estrogen receptor α (ERα) is an important biological target mediating 17β-estradiol driven breast cancer (BC) development. Aiming to develop innovative drugs against BC, either wild-type or mutated ligand-ERα complexes were used as source data to build structure-based 3-D pharmacophore and 3-D QSAR models, afterward used as tools for the virtual screening of National Cancer Institute datasets and hit-to-lead optimization. The procedure identified Brefeldin A (BFA) as hit, then structurally optimized toward twelve new derivatives whose anticancer activity was confirmed both in vitro and in vivo. Compounds as SERMs showed picomolar to low nanomolar potencies against ERα and were then investigated as antiproliferative agents against BC cell lines, as stimulators of p53 expression, as well as BC cell cycle arrest agents. Most active leads were finally profiled upon administration to female Wistar rats with pre-induced BC, after which 3DPQ-12, 3DPQ-3, 3DPQ-9, 3DPQ-4, 3DPQ-2, and 3DPQ-1 represent potential candidates for BC therapy.

Synthesis and Biological Evaluation of Tetrahydropyrimidine and Dihydropyridine Derivatives Against Leishmania Major

PURPOSE

A number of tetrahydropyrimidines and their bioisosteric dihydropyridines bearing chloro substituent at various positions of phenyl ring in C4 of main scaffolds were designed, synthesized and evaluated for antileishmanial activity.

METHODS

The antileishmanial activity of the synthesized compounds was evaluated against promastigote and amastigote forms. Moreover, molecular docking studies of the compounds in pteridine reductase 1 (PTR1) pocket were carried out to describe the results of biological experiments.

RESULTS

The compounds exhibited moderate to good antileishmanial activity against promastigote and amastigote forms. Among the screened compounds, 1d and 2c were found as the most potent compounds against promastigote form with EC₅₀ values of 15.5 and 10.5 µM, respectively. Compounds 2a and 2c were the most potent compounds against amastigote form with EC₅₀ values of 5.4 and 2.2 µM, respectively.

CONCLUSION

According to structure-activity relationship (SAR) studies, the chloro substituent in different positions of phenyl ring at C4 of 1,2,3,4-tetrahydropyrimidine (THPM) and 1,4-dihydropyridine (DHP) rings and also the length of the chain belonging to the ester groups could be important for antileishmanial activity of these compounds. Most of these compounds exhibited low cytotoxicity against macrophages. Compounds 1 h, 2a, 2b and 2c revealed higher activity than glucantime while all compounds showed lower activity toward amphotericine B. Docking studies showed that the synthesized compounds were fit well in the PTR1 pocket. Compounds 1 h and 2c indicated the highest score docking among screened compounds in PTR1 enzyme.

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition-Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils

Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, the investigation of 61 assayed essential oils is reported focusing on their inhibition activity against Microsporum spp. including development of machine learning models with the aim of highlining the possible chemical components mainly related to the inhibitory potency. The application of machine learning and deep learning techniques for predictive and descriptive purposes have been applied successfully to many fields. Quantitative composition-activity relationships machine learning-based models were developed for the 61 essential oils tested as Microsporum spp. growth modulators. The models were built with in-house python scripts implementing data augmentation with the purpose of having a smoother flow between essential oils' chemical compositions and biological data. High statistical coefficient values (Accuracy, Matthews correlation coefficient and F1 score) were obtained and model inspection permitted to detect possible specific roles related to some components of essential oils' constituents. Robust machine learning models are far more useful tools to reveal data augmentation in comparison with raw data derived models. To the best of the authors knowledge this is the first report using data augmentation to highlight the role of complex mixture components, in particular a first application of these data will be for the development of ingredients in the dermo-cosmetic field investigating microbial species considering the urge for the use of natural preserving and acting antimicrobial agents.

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Background:

Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation.

Results:

Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile.

Conclusion:

In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

New heat shock protein (Hsp90) inhibitors, designed by pharmacophore modeling and virtual screening: synthesis, biological evaluation and molecular dynamics studies

Inhibition of heat shock protein 90 (Hsp90) is known to be a significantly effective strategy in cancer therapy. Here, pyrazolopyranopyrimidine derivatives were characterized as new Hsp90 inhibitors. The molecules' key structure (ZINC02819805) was determined by utilizing a pharmacophore model virtual screening workflow. Structural optimization was then carried out on compound ZINC02819805, pyrazolopyranopyrimidine derivatives were designed and six chosen derivatives were synthesized. The inhibition of Hsp90 ATPase activity of synthesized compounds revealed that para methylphenyl derivative of pyrazolopyranopyrimidine (HM3) was the most potent inhibitor (IC₅₀ = 5.5 µM). The anti-proliferative activity of this compound was evaluated against a panel of cell lines including MCF-7, HeLa and HUVEC (IC₅₀ = 1.28 µM, IC₅₀ = 1.74 µM and IC₅₀ = 61.48 µM respectively) by MTT method. The western blot analysis of treated MCF-7 cells with compound HM3 showed that the expression level of Hsp70 and Her2 proteins changed. The high level of Hsp70 expression and low level of Her2 expression suggest that compound HM3 exhibits inhibitory effect on Hsp90. Finally, the key interactions between HM3 and Hsp90 protein were studied by molecular dynamics simulation and showed that compound HM3 was stable in Hsp90 active cite during 200 ns simulation. AbbreviationsHsp90Heat shock protein 90MTT3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromideATPadenosine triphosphateMDmolecular dynamics simulationRMSDroot-mean-square deviationRMSFroot-mean-square fluctuationRggyration radiusm-SABNPsboehmite nanoparticles-supported sulfamic acidCommunicated by Ramaswamy H. Sarma.

An update on the discovery and development of selective heat shock protein inhibitors as anti-cancer therapy

INTRODUCTION

Over the years, not a single HSP inhibitor has progressed into the post-market phase of drug development despite the success recorded in various pre-clinical and clinical studies. The inability of existing drugs to specifically target oncogenic HSPs has majorly accounted for these setbacks. Recent combinatorial strategies that incorporated computer-aided drug design (CADD) techniques are geared towards the development of highly specific HSP inhibitors with increased activities and minimal toxicities. Areas covered: In this review, strategic therapeutic approaches that have recently aided the development of selective HSP inhibitors were highlighted. Also, the significant contributions of CADD techniques over the years were discussed in detail. This article further describes promising computational paradigms and their applications towards the discovery of highly specific inhibitors of oncogenic HSPs. Expert opinion: The recent shift towards highly selective and specific HSP inhibition has shown great promise as evidenced by the development of paralog/isoform-selective HSP drugs. It could be further augmented with computer-aided drug design strategies, which incorporate reliable methods that would greatly enhance the design and optimization of novel inhibitors with improved activities and minimal toxicities.

COX Inhibition Profiles and Molecular Docking Studies of the Lignan Hinokinin and Some Synthetic Derivatives

Encouraged by the anti-inflammatory activity of hinokinin in vivo, which is also observed for the analogues dinitrohinokinin and diidrocubebin, herein we used in vitro and in silico methods to assess their selectivity profiles and predict their binding modes with Cyclooxygenases (COX-1 and 2). The in vitro assays demonstrated dinitrohinokinin is about 13 times more selective for COX-2 than for COX-1, a similar profile observed for the drugs celecoxib (selective index ≈9) and meloxicam (selective index ≈11). Predictions of the binding modes suggested dinitrohinokinin interacts with COX-2 very similarly to rofecoxib, exploring residues at the hydrophilic pocket of the enzyme that accessible to ligands only in this isoform. This lignan also interacts with COX-1 in a similar mode to meloxicam, blocking the access of the substrate to the catalytic cleft. Therefore, dinitrohinokinin is a promising lead for the design of selective COX-2 inhibitors.

Effect of N-1 arylation of monastrol on kinesin Eg5 inhibition in glioma cell lines

An original and focused library of two sets of dihydropyrimidin-2-thiones (DHPMs) substituted with N-1 aryl groups derived from monastrol was designed and synthesized in order to discover a more effective Eg5 ligand than the template. Based on molecular docking studies, four ligands were selected to perform pharmacological investigations against two glioma cell lines. The results led to the discovery of two original compounds, called 20h and 20e, with an anti-proliferative effects, achieving IC50 values of about half that of the IC50 of monastrol in both cell lines. As with monastrol, flow cytometry analyses showed that the 20e and 20h compounds induced cell cycle arrest in the G2/M phase, and immunocytochemistry essays revealed the formation of monopolar spindles due to Eg5 inhibition without any toxicity to Caenorhabditis elegans.

Disruptor of telomeric silencing 1-like (DOT1L): disclosing a new class of non-nucleoside inhibitors by means of ligand-based and structure-based approaches

Chemical inhibition of chromatin-mediated signaling involved proteins is an established strategy to drive expression networks and alter disease progression. Protein methyltransferases are among the most studied proteins in epigenetics and, in particular, disruptor of telomeric silencing 1-like (DOT1L) lysine methyltransferase plays a key role in MLL-rearranged acute leukemia Selective inhibition of DOT1L is an established attractive strategy to breakdown aberrant H3K79 methylation and thus overexpression of leukemia genes, and leukemogenesis. Although numerous DOT1L inhibitors have been several structural data published no pronounced computational efforts have been yet reported. In these studies a first tentative of multi-stage and LB/SB combined approach is reported in order to maximize the use of available data. Using co-crystallized ligand/DOT1L complexes, predictive 3-D QSAR and COMBINE models were built through a python implementation of previously reported methodologies. The models, validated by either modeled or experimental external test sets, proved to have good predictive abilities. The application of these models to an internal library led to the selection of two unreported compounds that were found able to inhibit DOT1L at micromolar level. To the best of our knowledge this is the first report of quantitative LB and SB DOT1L inhibitors models and their application to disclose new potential epigenetic modulators.

Identification of a novel putative inhibitor of the Plasmodium falciparum purine nucleoside phosphorylase: exploring the purine salvage pathway to design new antimalarial drugs

Malaria, a tropical parasitic disease caused by Plasmodium spp., continues to place a heavy social burden, with almost 200 million cases and more than 580,000 deaths per year. Plasmodium falciparum purine nucleoside phosphorylase (PfPNP) can be targeted for antimalarial drug design since its inhibition kills malaria parasites both in vitro and in vivo. Although the currently known inhibitors of PfPNP, immucillins, are orally available and of low toxicity to animals and humans, to the best of our knowledge, none of these compounds has entered clinical trials for the treatment of malaria. Using a pharmacophore-based virtual screening coupled to a consensual molecular docking approach, we identified 59 potential PfPNP inhibitors that are predicted to be orally absorbed in a Caco-2 cell model. Although most of these compounds are predicted to have high plasma protein binding levels, poor water solubility (except for compound 25) and CYP3A4 metabolic stability (except for 4, 7 and 8), four structures (4, 7, 8 and 25) remain as potential leads because of their plausible interaction with a specific hydrophobic pocket of PfPNP, which would confer them higher selectivity for PfPNP over human PNP. Additionally, both predicted Gibbs free energies for binding and molecular dynamics suggest that compound 4 may form a more stable complex with PfPNP than 5[Formula: see text]-methylthio-immucillin-H, a potent and selective inhibitor of PfPNP.

Vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors: development and validation of predictive 3-D QSAR models through extensive ligand- and structure-based approaches

Vascular endothelial growth factor receptor-2, (VEGFR-2), is a key element in angiogenesis, the process by which new blood vessels are formed, and is thus an important pharmaceutical target. Here, 3-D quantitative structure-activity relationship (3-D QSAR) were used to build a quantitative screening and pharmacophore model of the VEGFR-2 receptors for design of inhibitors with improved activities. Most of available experimental data information has been used as training set to derive optimized and fully cross-validated eight mono-probe and a multi-probe quantitative models. Notable is the use of 262 molecules, aligned following both structure-based and ligand-based protocols, as external test set confirming the 3-D QSAR models' predictive capability and their usefulness in design new VEGFR-2 inhibitors. From a survey on literature, this is the first generation of a wide-ranging computational medicinal chemistry application on VEGFR2 inhibitors.

Heat-shock protein 90 (Hsp90) as anticancer target for drug discovery: an ample computational perspective

There are over 100 different types of cancer, and each is classified based on the type of cell that is initially affected. If left untreated, cancer can result in serious health problems and eventually death. Recently, the paradigm of cancer chemotherapy has evolved to use a combination approach, which involves the use of multiple drugs each of which targets an individual protein. Inhibition of heat-shock protein 90 (Hsp90) is one of the novel key cancer targets. Because of its ability to target several signaling pathways, Hsp90 inhibition emerged as a useful strategy to treat a wide variety of cancers. Molecular modeling approaches and methodologies have become 'close counterparts' to experiments in drug design and discovery workflows. A wide range of molecular modeling approaches have been developed, each of which has different objectives and outcomes. In this review, we provide an up-to-date systematic overview on the different computational models implemented toward the design of Hsp90 inhibitors as anticancer agents. Although this is the main emphasis of this review, different topics such as background and current statistics of cancer, different anticancer targets including Hsp90, and the structure and function of Hsp90 from an experimental perspective, for example, X-ray and NMR, are also addressed in this report. To the best of our knowledge, this review is the first account, which comprehensively outlines various molecular modeling efforts directed toward identification of anticancer drugs targeting Hsp90. We believe that the information, methods, and perspectives highlighted in this report would assist researchers in the discovery of potential anticancer agents.

Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design

The docking methods used in structure-based virtual database screening offer the ability to quickly and cheaply estimate the affinity and binding mode of a ligand for the protein receptor of interest, such as a drug target. These methods can be used to enrich a database of compounds, so that more compounds that are subsequently experimentally tested are found to be pharmaceutically interesting. In addition, like all virtual screening methods used for drug design, structure-based virtual screening can focus on curated libraries of synthesizable compounds, helping to reduce the expense of subsequent experimental verification. In this review, we introduce the protein-ligand docking methods used for structure-based drug design and other biological applications. We discuss the fundamental challenges facing these methods and some of the current methodological topics of interest. We also discuss the main approaches for applying protein-ligand docking methods. We end with a discussion of the challenging aspects of evaluating or benchmarking the accuracy of docking methods for their improvement, and discuss future directions.

Hsp90 inhibitors, part 1: definition of 3-D QSAutogrid/R models as a tool for virtual screening

The multichaperone heat shock protein (Hsp) 90 complex mediates the maturation and stability of a variety of oncogenic signaling proteins. For this reason, Hsp90 has emerged as a promising target for anticancer drug development. Herein, we describe a complete computational procedure for building several 3-D QSAR models used as a ligand-based (LB) component of a comprehensive ligand-based (LB) and structure-based (SB) virtual screening (VS) protocol to identify novel molecular scaffolds of Hsp90 inhibitors. By the application of the 3-D QSAutogrid/R method, eight SB PLS 3-D QSAR models were generated, leading to a final multiprobe (MP) 3-D QSAR pharmacophoric model capable of recognizing the most significant chemical features for Hsp90 inhibition. Both the monoprobe and multiprobe models were optimized, cross-validated, and tested against an external test set. The obtained statistical results confirmed the models as robust and predictive to be used in a subsequent VS.