Charting a Path to Success in Virtual Screening

Development of human lactate dehydrogenase a inhibitors: high-throughput screening, molecular dynamics simulation and enzyme activity assay

Lactate dehydrogenase A (LDHA) is highly expressed in many tumor cells and promotes the conversion of pyruvate to lactic acid in the glucose pathway, providing energy and synthetic precursors for rapid proliferation of tumor cells. Therefore, inhibition of LDHA has become a widely concerned tumor treatment strategy. However, the research and development of highly efficient and low toxic LDHA small molecule inhibitors still faces challenges. To discover potential inhibitors against LDHA, virtual screening based on molecular docking techniques was performed from Specs database of more than 260,000 compounds and Chemdiv-smart database of more than 1,000 compounds. Through molecular dynamics (MD) simulation studies, we identified 12 potential LDHA inhibitors, all of which can stably bind to human LDHA protein and form multiple interactions with its active central residues. In order to verify the inhibitory activities of these compounds, we established an enzyme activity assay system and measured their inhibitory effects on recombinant human LDHA. The results showed that Compound 6 could inhibit the catalytic effect of LDHA on pyruvate in a dose-dependent manner with an EC50 value of 14.54 ± 0.83 µM. Further in vitro experiments showed that Compound 6 could significantly inhibit the proliferation of various tumor cell lines such as pancreatic cancer cells and lung cancer cells, reduce intracellular lactic acid content and increase intracellular reactive oxygen species (ROS) level. In summary, through virtual screening and in vitro validation, we found that Compound 6 is a small molecule inhibitor for LDHA, providing a good lead compound for the research and development of LDHA related targeted anti-tumor drugs.

Predicting binding events in very flexible, allosteric, multi-domain proteins

Knowledge of the structures formed by proteins and small ligands is of fundamental importance for understanding molecular principles of chemotherapy and for designing new and more effective drugs. Due to the still high costs and to the several limitations of experimental techniques, it is most often desirable to predict these ligand-protein complexes in silico, particularly when screening for new putative drugs from databases of millions of compounds. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology aimed at generating bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites. Validation was performed on the enzyme adenylate kinase (ADK), a paradigmatic example of proteins that undergo very large conformational changes upon ligand binding. By only exploiting the unbound structure and the putative binding sites of the protein, we generated a significant fraction of bound-like structures, which employed in ensemble-docking calculations allowed to find native-like poses of substrates, inhibitors, and catalytically incompetent binders. Our protocol provides a general framework for the generation of bound-like conformations of flexible proteins that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening for difficult targets, prediction of the impact of amino acid mutations on structure and dynamics, and protein engineering.

The application of WaterMap-guided structure-based virtual screening in novel drug discovery

INTRODUCTION

Nowadays, it is widely accepted that water molecules play a key role in binding a ligand to a molecular target. Neglecting water molecules in the process of molecular recognition was the result of several failures of the structure-based drug discovery campaigns. The application of WaterMap, in particular WaterMap-guided molecular docking, enables the reasonably accurate and quick description of the location and energetics of water molecules at the ligand-protein interface.

AREAS COVERED

In this review, the authors shortly discuss the importance of water in drug design and discovery and provide a brief overview of the computational approaches used to predict the solvent-related effects for the purposes of presenting WaterMap in the context of other available techniques and tools. A concise description of WaterMap concept is followed by the presentation of WaterMap-assisted virtual screening literature published between 2013 and 2023.

EXPERT OPINION

In recent years, WaterMap software has been extensively used to support structure-based drug design, in particular structure-based virtual screening. Indeed, it is a useful tool to rescore docking results considering water molecules in the binding pocket. Although WaterMap allows for the consideration of the dynamic behavior of water molecules in the binding site, for best accuracy, its application in conjunction with other techniques such as molecular mechanics-generalized Born surface area of FEP (Free Energy Perturbation) is recommended.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Discovery of Mcl-1 inhibitors through virtual screening, molecular dynamics simulations and in vitro experiments

As a member of the B-cell lymphoma 2 (Bcl-2) protein family, the myeloid leukemia cell differentiation protein (Mcl-1) can inhibit apoptosis and plays an active role in the process of tumor escape from apoptosis. Therefore, inhibition of Mcl-1 protein can effectively promote the apoptosis of tumor cells and may also reduce tumor cell resistance to drugs targeting other anti-apoptotic proteins. This research is dedicated to the development of Mcl-1 inhibitors, aiming to provide more references for lead compounds with different scaffolds for the development of targeted anticancer drugs. We obtained a series of small molecules with a common core skeleton through molecular docking from Specs database and searched the core structure in ZINC database for more similar small molecules. Collecting these small molecules for preliminary experimental screening, we found a batch of active compounds, and selected two small molecules with the strongest inhibitory activity on B16F10 cells: compound 7 and compound 1. Their IC₅₀s are 7.86 ± 1.25 and 24.72 ± 1.94 μM, respectively. These two compounds were also put into cell scratch test for B16F10 cells and cell viability assay of other cell lines. Furthermore, through molecular dynamics (MD) simulation analysis, we found that compound 7 formed strong binding with the key P2, P3 pocket and ARG 263 of Mcl-1. Finally, ADME results showed that compound 7 performs well in terms of drug similarity. In conclusion, this study provides hits with co-scaffolds that may aid in the design of effective clinical drugs targeting Mcl-1 and the future drug development.

Identification of novel inhibitors of S-adenosyl-L-homocysteine hydrolase via structure-based virtual screening and molecular dynamics simulations

S-adenosyl-L-homocysteine hydrolase (SAHase) is an important regulator in the methylation reactions in many organisms and thus is crucial for numerous cellular functions. In recent years, SAHase has become one of the popular targets for drug design, and SAHase inhibitors have exhibited potent antiviral activity. In this study, we established the complex-based pharmacophore models based on the known crystal complex of SAHase (PDB ID: 1A7A) to screen the drug-likeness compounds of ChEMBL database. Then, three molecular docking programs were used to validate the reliability of compounds, involving Libdock, CDOCKER, and AutoDock Vina programs. The four promising hit compounds (CHEMBL420751, CHEMBL346387, CHEMBL1569958, and CHEMBL4206648) were performed molecular dynamics simulations and MM-PBSA calculations to evaluate their stability and binding-free energy in the binding site of SAHase. After screening and analyzing, the hit compounds CHEMBL420751 and CHEMBL346387 were suggested to further research to obtain novel potential SAHase inhibitors. A series of computer-aided drug design methods, including pharmacophore, molecular docking, molecular dynamics simulation and MM-PBSA calculations, were employed in this study to identity novel inhibitors of S-adenosyl-L-homocysteine hydrolase (SAHase). Some compounds from virtual screening could form various interactions with key residues of SAHase. Among them, compounds CHEMBL346387 and CHEMBL420751 exhibited potent binding affinity from molecular docking and MM-PBSA, and maintained good stability at the binding site during molecular dynamics simulations as well. All these results indicated that the selected compounds might have the potential to be novel SAHase inhibitors.

Identification of antidiabetic components in Uncariae Rammulus Cum Uncis based on phytochemical isolation and spectrum-effect relationship analysis

OBJECTIVES

Uncariae Rammulus Cum Uncis (URCU) is a commonly used herbal medicine to treat diabetes. This work is aimed to discover and identify the antidiabetic components from URCU extract.

METHODS

Column chromatography and recrystallisation were used to separate individual compounds from URCU extract, and the obtained individual compounds were used for determination of α-glucosidase inhibitory activity. Molecular docking was applied to predict the molecular interactions. High-performance liquid chromatography (HPLC) was used for fingerprint analysis of 12 batches of URCU. HPLC fingerprints were assessed by the similarity analysis (SA) and hierarchical clustering analysis (HCA). The spectrum-effect relationship analysis of URCU was assessed by orthogonal partial least squares (OPLS) and bivariate correlation analysis (BCA).

RESULTS

A total of 10 potential bioactive compounds were isolated and six of them showed potent α-glucosidase inhibitory activity (IC₅₀  = 4.21-166.10 μM). The molecular docking results revealed that the binding energy was consistent with the results of α-glucosidase inhibition activity analysis (-8.55 to -4.84 kcal/mol). The ethanol extracts of the 12 batches of URCU showed inhibitory effect on α-glucosidase in a dose-dependent manner, and the IC₅₀ values ranged from 0.94 μg/mL to 12.57 μg/mL. The spectrum-effect relationship analysis results indicated that 13 peaks might be potential antidiabetic compounds in URCU, including 18 (hyperoside) and 19 (rutin).

CONCLUSION

A comprehensive connection between URCU chemical components and α-glucosidase inhibitory activity was established for the first time by using a spectrum-effect relationship model, which might be applicable to the quality control of URCU.

Immunotherapeutic Value of MAP1LC3C and Its Candidate FDA-Approved Drugs Identified by Pan-Cancer Analysis, Virtual Screening and Sensitivity Analysis

Background: The autophagy pathway within the tumour microenvironment can be regulated to inhibit or promote tumour development. In the fight against tumour growth, immunotherapy induces an anti-tumour immune response, whereas autophagy modulates this immune response. A key protein in the autophagy pathway, microtubule-associated protein 1 light chain 3 (MAP1LC3), has recently become a hotspot for tumour research. As a relatively novel member, the function of MAP1LC3C in tumours still need to be investigated. Therefore, the goal of this study was to look into the possible link between MAP1LC3C and immunotherapy for 33 kinds of human malignancies by using pan-cancer analysis. Methods: High-throughput sequencing data from The Cancer Genome Atlas, Genotype-Tissue Expression Project and Cancer Cell Line Encyclopedia databases, combined with clinical data, were used to analyze the expression of MAP1LC3C in 33 types of cancer, as well as patient prognosis and neoplasm staging. Activity scores were calculated using ssGSEA to assess the MAP1LC3C activity in pan-cancer. Associations between MAP1LC3C and the tumour microenvironment, including immune cell infiltration and immunomodulators, were analyzed. Moreover, tumour tissue ImmuneScores and StromalScores were analyzed using the ESTIMATE algorithm. Additionally, associations between MAP1LC3C and tumour mutational burden/microsatellite instability, were investigated. Finally, based on the expression and structure of MAP1LC3C, the United States Food and Drug Administration (FDA)-approved drugs, were screened by virtual screening, molecular docking and NCI-60 drug sensitivity analysis. Results: Our study found that MAP1LC3C was differentially expressed in tumour and normal tissues in 23 of 33 human cancer types, among which MAP1LC3C had prognostic effects in 12 cancer types, and MAP1LC3C expression was significantly correlated with tumour stage in four cancer types. In addition, MAP1LC3C activity in 14 cancer types was consistent with changes in transcription levels. Moreover, MAP1LC3C strongly correlated with immune infiltration, immune modulators and immune markers. Finally, a number of FDA-approved drugs were identified via virtual screening and drug sensitivity analysis. Conclusion: Our study investigated the prognostic and immunotherapeutic value of MAP1LC3C in 33 types of cancer, and several FDA-approved drugs were identified to be highly related to MAP1LC3C and can be potential cancer therapeutic candidates.

Targeting Epigenetic Regulators Using Machine Learning: Potential Sirtuin 2 Inhibitors

Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the [Formula: see text][Formula: see text]kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.

Fine tuning for success in structure-based virtual screening

Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.

RealVS: Toward Enhancing the Precision of Top Hits in Ligand-Based Virtual Screening of Drug Leads from Large Compound Databases

Accurate modeling of compound bioactivities is essential for the virtual screening of drug leads. In real-world scenarios, pharmacists tend to choose from the top-k hit compounds ranked by predicted bioactivities from a large database with interest to continue wet experiments for drug discovery. Significant improvement of the precision of the top hits in ligand-based virtual screening of drug leads is more valuable than conventional schemes for accurately predicting the bioactivities of all compounds from a large database. Here, we proposed a new method, RealVS, to significantly improve the top hits' precision and learn interpretable key substructures associated with compound bioactivities. The features of RealVS involve the following points. (1) Abundant transferable information from the source domain was introduced for alleviating the insufficiency of inactive ligands associated with drug targets. (2) The adversarial domain alignment was adopted to fit the distribution of generated features of compounds from the training data set and that from the screening database for greater model generalization ability. (3) A novel objective function was proposed to simultaneously optimize the classification loss, regression loss, and adversarial loss, where most inactive ligands tend to be screened out before activity regression prediction. (4) Graph attention networks were adopted for learning key substructures associated with ligand bioactivities for better model interpretability. The results on a large number of benchmark data sets show that our method has significantly improved the precision of top hits under various k values in ligand-based virtual screening of drug leads from large compound databases, which is of great value in real-world scenarios. The web server of RealVS is freely available at noveldelta.com/RealVS for academic purposes, where virtual screening of hits from large compound databases is accessible.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

Sensitivity of Binding Free Energy Calculations to Initial Protein Crystal Structure

Binding free energy calculations using alchemical free energy (AFE) methods are widely considered to be the most rigorous tool in the computational drug discovery arsenal. Despite this, the calculations suffer from accuracy, precision, and reproducibility issues. In this publication, we perform a high-throughput study of more than a thousand AFE calculations, utilizing over 220 μs of total sampling time, on three different protein systems to investigate the impact of the initial crystal structure on the resulting binding free energy values. We also consider the influence of equilibration time and discover that the initial crystal structure can have a significant effect on free energy values obtained at short timescales that can manifest itself as a free energy difference of more than 1 kcal/mol. At longer timescales, these differences are largely overtaken by important rare events, such as torsional ligand motions, typically resulting in a much higher uncertainty in the obtained values. This work emphasizes the importance of rare event sampling and long-timescale dynamics in free energy calculations even for routinely performed alchemical perturbations. We conclude that an optimal protocol should not only concentrate computational resources on achieving convergence in the alchemical coupling parameter (λ) space but also on longer simulations and multiple repeats.

Identification and characterisation of putative drug binding sites in human ATP-binding cassette B5 (ABCB5) transporter

The human ATP-binding cassette B5 (ABCB5) transporter, a member of the ABC transporter superfamily, is linked to chemoresistance in tumour cells by drug effluxion. However, little is known about its structure and drug-binding sites. In this study, we generated an atomistic model of the full-length human ABCB5 transporter with the highest quality using the X-ray crystal structure of mouse ABCB1 (Pgp1), a close homologue of ABCB5 and a well-studied member of the ABC family. Molecular dynamics simulations were used to validate the atomistic model of ABCB5 and characterise its structural properties in model cell membranes. Molecular docking simulations of known ABCB5 substrates such as taxanes, anthracyclines, camptothecin and etoposide were then used to identify at least three putative binding sites for chemotherapeutic drugs transported by ABCB5. The location of these three binding sites is predicted to overlap with the corresponding binding sites in Pgp1. These findings will serve as the basis for future in vitro studies to validate the nature of the identified substrate-binding sites in the full-length ABCB5 transporter.

Lapatinib, Nilotinib and Lomitapide Inhibit Haemozoin Formation in Malaria Parasites

With the continued loss of antimalarials to resistance, drug repositioning may have a role in maximising efficiency and accelerating the discovery of new antimalarial drugs. Bayesian statistics was previously used as a tool to virtually screen USFDA approved drugs for predicted β-haematin (synthetic haemozoin) inhibition and in vitro antimalarial activity. Here, we report the experimental evaluation of nine of the highest ranked drugs, confirming the accuracy of the model by showing an overall 93% hit rate. Lapatinib, nilotinib, and lomitapide showed the best activity for inhibition of β-haematin formation and parasite growth and were found to inhibit haemozoin formation in the parasite, providing mechanistic insights into their mode of antimalarial action. We then screened the USFDA approved drugs for binding to the β-haematin crystal, applying a docking method in order to evaluate its performance. The docking method correctly identified imatinib, lapatinib, nilotinib, and lomitapide. Experimental evaluation of 22 of the highest ranked purchasable drugs showed a 24% hit rate. Lapatinib and nilotinib were chosen as templates for shape and electrostatic similarity screening for lead hopping using the in-stock ChemDiv compound catalogue. The actives were novel structures worthy of future investigation. This study presents a comparison of different in silico methods to identify new haemozoin-inhibiting chemotherapeutic alternatives for malaria that proved to be useful in different ways when taking into consideration their strengths and limitations.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Virtual screening for potential inhibitors of β(1,3)-D-glucan synthase as drug candidates against fungal cell wall

Background

To enhance the outcome in patients with invasive candidiasis, initiation of an efficient antifungal treatment in a suitable dosage is necessary. Echinocandins (e.g. caspofungin) inhibit the enzyme β(1,3)-D-glucan synthase of the fungal cell wall. Compared to azoles and other antifungal agents, echinocandins have lower adverse effects and toxicity in humans. Echinocandins are available in injectable (intravenous) form.

Methods

In this study, to identify the novel oral drug-like compounds that affect the fungal cell wall, downloaded oral drug-like compounds from the ZINC database were processed with a virtual screening procedure. The docking free energies were calculated and compared with the known inhibitor caspofungin. Four molecules were selected as the most potent ligands and subjected to hydrogen bonds analysis.

Results

Considering the hydrogen bond analysis, two compounds (ZINC71336662 and ZINC40910772) were predicted to better interact with the active site of β(1,3)-D-glucan synthase compared with caspofungin.

Conclusion

The introduced compound in this study may be valuable to analyze experimentally as a novel oral drug candidate targeting fungal cell walls.

Virtual screening as a tool to discover new β-haematin inhibitors with activity against malaria parasites

Malaria remains a major public health problem. With the loss of antimalarials to resistance, the malaria burden will likely continue for decades. New antimalarial scaffolds are crucial to avoid cross-resistance. Here, we present the first structure based virtual screening using the β-haematin crystal as a target for new inhibitor scaffolds by applying a docking method. The ZINC15 database was searched for compounds with high binding affinity with the surface of the β-haematin crystal using the PyRx Virtual Screening Tool. Top-ranked compounds predicted to interact with β-haematin were submitted to a second screen applying in silico toxicity and drug-likeness predictions using Osiris DataWarrior. Fifteen compounds were purchased for experimental testing. An NP-40 mediated β-haematin inhibition assay and parasite growth inhibition activity assay were performed. The benzoxazole moiety was found to be a promising scaffold for further development, showing intraparasitic haemozoin inhibition using a cellular haem fractionation assay causing a decrease in haemozoin in a dose dependent manner with a corresponding increase in exchangeable haem. A β-haematin inhibition hit rate of 73% was found, a large enrichment over random screening, demonstrating that virtual screening can be a useful and cost-effective approach in the search for new haemozoin inhibiting antimalarials.

Molecular docking of novel 5-O-benzoylpinostrobin derivatives as wild type and L858R/T790M/V948R mutant EGFR inhibitor

Background Previous studies have shown that 5-O-benzoylpinostrobin derivatives is a potential anti-breast cancer, with the highest potential being the HER2 inhibitors, is a protein's member of the epidermal growth factor receptor (EGFR) family. Overexpression of EGFR itself is known to be one of the causes of other cancer, including non-small cell lung cancer (NSCLC). Thus, it is possible that 5-O-benzoylpinostrobin derivatives can also inhibit the overexpression of EGFR in NSCLC. In the case of NSCLC, mutations of EGFR are often found in several amino acids, such as L858R, T790M, and V948R. This study aimed to determine the potential of 5-O-benzoylpinostrobin derivatives as an inhibitor of wild type and L858R/T790M/V948R-mutant EGFR. Methods Docking was performed using AutoDock Vina 1.1.2 on both wild type and L858R/T790M/V948R-mutant EGFR. Parameters observed, consisted of free energy of binding (ΔG) and amino acid interactions of each ligand. Results Docking results showed that all 5-O-benzoylpinostrobin derivatives showed a lower ΔG for both wild type and L858R/T790M/V948R-mutant EGFR, with the lowest ΔG shown by 4-methyl-5-O-benzoylpinostrobin and 4-trifluoromethyl-5-O-benzoylpinostrobin. Both the ligands have the similarity of interacting amino acids compared to reference ligands between 76.47 and 88.24%. Specifically, the ΔG of all test ligands was lower in mutant EGFR than in the wild type, which indicates the potential of the ligand as EGFR inhibitors where a mutation to EGFR occurs. Conclusions These results confirm that 5-O-benzoylpinostrobin derivatives have the potential to inhibit EGFR in both wild type and L858R/T790M/V948R-mutant.

DockNmine, a Web Portal to Assemble and Analyse Virtual and Experimental Interaction Data

Scientists have to perform multiple experiments producing qualitative and quantitative data to determine if a compound is able to bind to a given target. Due to the large diversity of the potential ligand chemical space, the possibility of experimentally exploring a lot of compounds on a target rapidly becomes out of reach. Scientists therefore need to use virtual screening methods to determine the putative binding mode of ligands on a protein and then post-process the raw docking experiments with a dedicated scoring function in relation with experimental data. Two of the major difficulties for comparing docking predictions with experiments mostly come from the lack of transferability of experimental data and the lack of standardisation in molecule names. Although large portals like PubChem or ChEMBL are available for general purpose, there is no service allowing a formal expert annotation of both experimental data and docking studies. To address these issues, researchers build their own collection of data in flat files, often in spreadsheets, with limited possibilities of extensive annotations or standardisation of ligand descriptions allowing cross-database retrieval. We have conceived the dockNmine platform to provide a service allowing an expert and authenticated annotation of ligands and targets. First, this portal allows a scientist to incorporate controlled information in the database using reference identifiers for the protein (Uniprot ID) and the ligand (SMILES description), the data and the publication associated to it. Second, it allows the incorporation of docking experiments using forms that automatically parse useful parameters and results. Last, the web interface provides a lot of pre-computed outputs to assess the degree of correlations between docking experiments and experimental data.

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

Docking calculations can accelerate drug discovery by predicting the bound poses of ligands for a targeted protein. However, it is not clear which docking methods work best. Furthermore, predicting poses requires steps outside the docking algorithm itself, such as preparation of the protein and ligand, and it is not known which components are most in need of improvement. The Continuous Evaluation of Ligand Protein Predictions (CELPP) is a blinded prediction challenge designed to address these issues. Participants create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. The results can be used to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

A primer on natural product-based virtual screening

Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.

A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data

Virtual screening has become a widely used technique for helping in drug discovery processes. The key to this success is its ability to aid in the identification of novel bioactive compounds by screening large molecular databases. Several web servers have emerged in the last few years supplying platforms to guide users in screening publicly accessible chemical databases in a reasonable time. In this review, we discuss a representative set of online virtual screening servers and their underlying similarity algorithms. Other related topics, such as molecular representation or freely accessible databases are also treated. The most relevant contributions to this review arise from critical discussions concerning the pros and cons of servers and algorithms, and the challenges that future works must solve in a virtual screening framework.

Protein structure and computational drug discovery

The first protein structures revealed a complex web of weak interactions stabilising the three-dimensional shape of the molecule. Small molecule ligands were then found to exploit these same weak binding events to modulate protein function or act as substrates in enzymatic reactions. As the understanding of ligand–protein binding grew, it became possible to firstly predict how and where a particular small molecule might interact with a protein, and then to identify putative ligands for a specific protein site. Computer-aided drug discovery, based on the structure of target proteins, is now a well-established technique that has produced several marketed drugs. We present here an overview of the various methodologies being used for structure-based computer-aided drug discovery and comment on possible future developments in the field.

Homology Modeling of 5-alpha-Reductase 2 Using Available Experimental Data

5-Alpha-reductase 2 is an interesting pharmaceutical target for the treatment of several diseases, including prostate cancer, benign prostatic hyperplasia, male pattern baldness, acne, and hirsutism. One of the main approaches in computer aided drug design is structure-based drug discovery. However, the experimental 3D structure of 5-alpha-reductase 2 is not available at present. Therefore, a homology modeling method and molecular dynamics simulation were used to develop a reliable model of 5-alpha-reductase 2 for inhibitor pose prediction and virtual screening. Despite the low sequence identity between the target and template sequences, a useful 3D model of 5-alpha-reductase 2 was generated by the inclusion of experimental data.

A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment

Ebola virus disease (EVD) is a deadly global public health threat, with no currently approved treatments. Traditional drug discovery and development is too expensive and inefficient to react quickly to the threat. We review published research studies that utilize computational approaches to find or develop drugs that target the Ebola virus and synthesize its results. A variety of hypothesized and/or novel treatments are reported to have potential anti-Ebola activity. Approaches that utilize multi-targeting/polypharmacology have the most promise in treating EVD.

Novel FXa Inhibitor Identification through Integration of Ligand- and Structure-Based Approaches

Factor Xa (FXa), a vitamin K-dependent serine protease plays a pivotal role in the coagulation cascade, one of the most interesting targets for the development of new anticoagulants. In the present work, we performed a virtual screening campaign based on ligand-based shape and electrostatic similarity search and protein-ligand docking to discover novel FXa-targeted scaffolds for further development of inhibitors. From an initial set of 260,000 compounds from the NCI Open database, 30 potential FXa inhibitors were identified and selected for in vitro biological evaluation. Compound 5 (NSC635393, 4-(3-methyl-4H-1,4-benzothiazin-2-yl)-2,4-dioxo-N-phenylbutanamide) displayed an IC₅₀ value of 2.02 nM against human FXa. The identified compound may serve as starting point for the development of novel FXa inhibitors.

Structure-Based Design of Scaffolds Targeting PDE10A by INPHARMA-NMR

Phosphodiesterases (PDE) hydrolyze both cyclic AMP and GMP (cAMP/cGMP) and are responsible for the regulation of their levels in a multitude of cellular functions. PDE10A is expressed in the brain and is a validated target for both schizophrenia and Huntington disease. Here, we address the identification of novel chemical scaffolds that may bind PDE10A via structure-based drug design. For this task, we use INPHARMA, an NMR-based method that measures protein-mediated interligand NOEs between pairs of weakly, competitively binding ligands. INPHARMA is applied to a combination of four chemically diverse PDE10A binding fragments, with the aim of merging their pharmacophoric features into a larger, tighter binding molecule. All four ligands bind the PDE10A cAMP binding domain with affinity in the micromolar range. The application of INPHARMA to identify the correct docking poses of these ligands is challenging due to the nature of the binding pocket and the high content of water-mediated intermolecular contacts. Nevertheless, ensemble docking in the presence of conserved water molecules generates docking poses that are in agreement with all sets of INPHARMA data. These poses are used to build a pharmacophore model with which we search the ZINC database.

Virtual Screening for Potential Inhibitors of CTX-M-15 Protein of Klebsiella pneumoniae

The Gram-negative bacterium Klebsiella pneumoniae, responsible for a wide variety of nosocomial infections in immuno-deficient patients, involves the respiratory, urinary and gastrointestinal tract infections and septicemia. Extended spectrum β-lactamases (ESBL) belong to β-lactamases capable of conferring antibiotic resistance in Gram-negative bacteria. CTX-M-15, a prevalent ESBL reported from Enterobacteriaceae including K. pneumoniae, was selected as a potent anti-bacterial target. To identify the novel drug-like compounds, structure-based screening procedure was employed against downloaded drug-like compounds from ZINC database. An acronym for "ZINC" is not commercial. The docking free energy values were investigated and compared to the known inhibitor Avibactam. Six best novel drug-like compounds were selected and their hydrogen bindings with the receptor were determined. Based on the binding efficiency mode, three among these six identified most potential inhibitors, ZINC21811621, ZINC93091917 and ZINC19488569, were predicted as potential competitive inhibitors against CTX-M-15 compared to Avibactam. These three inhibitors may provide a framework for the experimental studies to develop anti-Klebsiella novel drug candidates targeting CTX-M-15.

In silico and in vitro methods to identify ebola virus VP35-dsRNA inhibitors

Ebola virus continues to be problematic as sporadic outbreaks in Africa continue to arise, and as terrorist organizations have considered the virus for bioterrorism use. Several proteins within the virus have been targeted for antiviral chemotherapy, including VP35, a dsRNA binding protein that promotes viral replication, protects dsRNA from degradation, and prevents detection of the viral genome by immune complexes. To augment the scope of our antiviral research, we have now employed molecular modeling techniques to enrich the population of compounds for further testing in vitro. In the initial docking of a static VP35 structure with an 80,000 compound library, 40 compounds were selected, of which four compounds inhibited VP35 with IC₅₀ <200μM, with the best compounds having an IC₅₀ of 20μM. By superimposing 26 VP35 structures, we determined four aspartic acid residues were highly flexible and the docking was repeated under flexible parameters. Of 14 compounds chosen for testing, five compounds inhibited VP35 with IC₅₀ <200μM and one compound with an IC₅₀ of 4μM. These studies demonstrate the value of docking in silico for enriching compounds for testing in vitro, and specifically using multiple structures as a guide for detecting flexibility and provide a foundation for further development of small molecule inhibitors directed towards VP35.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.