Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors

Identification of potential inhibitors of thymidylate synthase (TS) (PDB ID: 6QXH) and nuclear factor kappa-B (NF–κB) (PDB ID: 1A3Q) from Capsicum annuum (bell pepper) towards the development of new therapeutic drugs against colorectal cancer (CRC)

Colorectal cancer is the third most deadly cancer globally. Drug resistance and attendant side effects make the available standard anti-colorectal cancer drugs against target receptors inefficient. Phytochemicals from medicinal plants are safer, cheaper, effective, and heal diseases from the cellular level. This study is aimed at identifying potential inhibitors of thymidylate synthase (TS) and nuclear factor kappa-B (NF–κB) target receptors from Capsicum annuum towards the development of new therapeutic drugs against colorectal cancer via in silico approach. One hundred and fifty (150) ligands previously reported from Capsicum annuum were downloaded from the PubChem database and were subjected to chemo-informatics analyses such as ADMET, drug-likeness, oral bioavailability, bioactivity, and PASS prediction to ascertain their therapeutic and safety profile before docking. The ligands that passed the analyses were docked against TS and NF–κB in duplicate using a creditable docking tool (PyRx). Raltitrexed and emetine were used as the standard drug inhibitors for TS and NF–κB, respectively. The results obtained from this study showed that feruloyl-beta-D-glucose (8.45 kcal/mol), 5-O-caffeoylquinic acid (−8.40 kcal/mol), 5-O-caffeoylquinic acid methyl ester (−7.89 kcal/mol), feruloyl hexoside (−7.40 kcal/mol), O-glucopyranoside (−7.55 kcal/mol), and quercetin (−7.00 kcal/mol) shared the same binding pocket with TS while feruloyl-beta-D-glucose (−7.00 kcal/mol), chlorogenic acid (−6.90 kcal/mol), 5-O-caffeoylquinic acid (−6.90 kcal/mol) and feruloyl hexoside (−6.50 kcal/mol) shared the same pocket with NF–κB. These compounds were selected as best hits due to their excellent inhibitory efficiency and chemoinformatic profiles. Thus, the compounds may function as prospective lead compounds for developing a new anti-colorectal cancer drug.

Towards Effective Consensus Scoring in Structure-Based Virtual Screening

Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein-ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository ( http://dude.docking.org/ ) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand-protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning.

Target-based drug discovery, ADMET profiling and bioactivity studies of antibiotics as potential inhibitors of SARS-CoV-2 main protease (Mpro)

A recent outbreak of a new strain of Coronavirus (SARS-CoV-2) has become a global health burden, which has resulted in deaths. No proven drug has been found to effectively cure this fast-spreading infection, hence the need to explore old drugs with the known profile in tackling this pandemic. A computer-aided drug design approach involving virtual screening was used to obtain the binding scores and inhibiting efficiencies of previously known antibiotics against SARS-CoV-2 main protease (Mpro). The drug-likeness analysis of the repurposed drugs were done using the Molinspiration chemoinformatics tool, while the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis was carried out using ADMET SAR-2 webserver. Other analyses performed include bioactivities of the repurposed drug as a probable anti-SARS-CoV-2 agent and oral bioavailability analyses among others. The results were compared with those of drugs currently involved in clinical trials in the ongoing pandemic. Although antibiotics have been speculated to be of no use in the treatment of viral infections, literature has emerged lately to reveal the antiviral potential and immune-boosting ability of antibiotics. This study identified Tarivid and Ciprofloxacin with binding affinities of - 8.3 kcal/mol and - 8.1 kcal/mol, respectively as significant inhibitors of SARS-CoV-2 (Mpro) with better pharmacokinetics, drug-likeness and oral bioavailability, bioactivity properties, ADMET properties and inhibitory strength compared to Remdesivir (- 7.6 kcal/mol) and Azithromycin (- 6.3 kcal/mol). These observations will provide insight for further research (clinical trial) in the cure and management of COVID-19.

Molecular Modeling of Histamine Receptors-Recent Advances in Drug Discovery

The recent developments of fast reliable docking, virtual screening and other algorithms gave rise to discovery of many novel ligands of histamine receptors that could be used for treatment of allergic inflammatory disorders, central nervous system pathologies, pain, cancer and obesity. Furthermore, the pharmacological profiles of ligands clearly indicate that these receptors may be considered as targets not only for selective but also for multi-target drugs that could be used for treatment of complex disorders such as Alzheimer's disease. Therefore, analysis of protein-ligand recognition in the binding site of histamine receptors and also other molecular targets has become a valuable tool in drug design toolkit. This review covers the period 2014-2020 in the field of theoretical investigations of histamine receptors mostly based on molecular modeling as well as the experimental characterization of novel ligands of these receptors.

Enigmatic Histamine Receptor H4 for Potential Treatment of Multiple Inflammatory, Autoimmune, and Related Diseases

The histamine H4 receptor, belonging to the family of G-protein coupled receptors, is an increasingly attractive drug target. It plays an indispensable role in many cellular pathways, and numerous H4R ligands are being studied for the treatment of several inflammatory, allergic, and autoimmune disorders, including pulmonary fibrosis. Activation of H4R is involved in cytokine production and mediates mast cell activation and eosinophil chemotaxis. The importance of this receptor has also been shown in inflammatory models: peritonitis, respiratory tract inflammation, colitis, osteoarthritis, and rheumatoid arthritis. Recent studies suggest that H4R acts as a modulator in cancer, neuropathic pain, vestibular disorders, and type-2 diabetes, however, its role is still not fully understood.

3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery

eScience technologies are needed to process the information available in many heterogeneous types of protein-ligand interaction data and to capture these data into models that enable the design of efficacious and safe medicines. Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i) structure-based bioactivity data mapping, ii) structure-based identification of scaffold replacement strategies for ligand design, iii) ligand-based target prediction, iv) protein sequence-based binding site identification and ligand repurposing, and v) structure-based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well-established standards allows the re-use of these protocols and facilitates the design of customized computer-aided drug discovery workflows.

Consensus queries in ligand-based virtual screening experiments

Background

In ligand-based virtual screening experiments, a known active ligand is used in similarity searches to find putative active compounds for the same protein target. When there are several known active molecules, screening using all of them is more powerful than screening using a single ligand. A consensus query can be created by either screening serially with different ligands before merging the obtained similarity scores, or by combining the molecular descriptors (i.e. chemical fingerprints) of those ligands.

Results

We report on the discriminative power and speed of several consensus methods, on two datasets only made of experimentally verified molecules. The two datasets contain a total of 19 protein targets, 3776 known active and ~ 2 × 10⁶ inactive molecules. Three chemical fingerprints are investigated: MACCS 166 bits, ECFP4 2048 bits and an unfolded version of MOLPRINT2D. Four different consensus policies and five consensus sizes were benchmarked.

Conclusions

The best consensus method is to rank candidate molecules using the maximum score obtained by each candidate molecule versus all known actives. When the number of actives used is small, the same screening performance can be approached by a consensus fingerprint. However, if the computational exploration of the chemical space is limited by speed (i.e. throughput), a consensus fingerprint allows to outperform this consensus of scores.

Identification of potential inhibitors against the Zika virus using consensus scoring

The Zika virus (ZIKV) is a life threatening pathogen of zoonotic importance with prevalence in some parts of Africa and America. Unfortunately, there is yet to be a single approved vaccine or antiviral drug to treat the diseases and deformations being caused by the Zika virus infection. In this study, about 36 million compounds from MCULE database were virtually screened against a real matured ZIKV protein using a consensus scoring method to get improved hit rates. The consensus scoring method combined the result from the 25 top ranked molecules from both MCULE and Drug Score eXtended (DSX) docking programs which led to the selection of two hit compounds. The inhibition constant (Ki) values of 0.08 and 0.30μm were obtained for the two selected compounds MCULE-8830369631-0-1 and MCULE-9236850811-0-1 respectively, to remark them as hit compounds. The molecular interactions of the two selected hit compounds with the amino acids (ALA 48, ILE 49, ILE 468 and LEU 472) present in the ZIKV protein indicated that they both have similar binding modes. The result of the computationally predicted physicochemical properties including ADMET for the selected compounds showed their great potential in becoming lead compounds upon optimization and thus could be used in treating the Zika virus diseases.

Molecular interaction fingerprint approaches for GPCR drug discovery

Protein-ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein-ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein-ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.

Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models

Neuraminidase (NA) is a critical enzyme in the life cycle of influenza virus, which is known as a successful paradigm in the design of anti-influenza agents. However, to date there are no classification models for the virtual screening of NA inhibitors. In this work, we built support vector machine and Naïve Bayesian models of NA inhibitors and non-inhibitors, with different ratios of active-to-inactive compounds in the training set and different molecular descriptors. Four models with sensitivity or Matthews correlation coefficients greater than 0.9 were chosen to predict the NA inhibitory activities of 15,600 compounds in our in-house database. We combined the results of four optimal models and selected 60 representative compounds to assess their NA inhibitory profiles in vitro. Nine NA inhibitors were identified, five of which were oseltamivir derivatives with large C-5 substituents exhibiting potent inhibition against H1N1 NA with IC50 values in the range of 12.9-185.0 nM, and against H3N2 NA with IC50 values between 18.9 and 366.1 nM. The other four active compounds belonged to novel scaffolds, with IC50 values ranging 39.5-63.8 μM against H1N1 NA and 44.5-114.1 μM against H3N2 NA. This is the first time that classification models of NA inhibitors and non-inhibitors are built and their prediction results validated experimentally using in vitro assays.

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for “growing” the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.

Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H4 receptor

Structure-based virtual screening using H₁R- and β₂R-based histamine H₄R homology models identified 9 fragments with an affinity ranging from 0.14 to 6.3 μm for H₄R.