Molecular docking-based computational platform for high-throughput virtual screening

Flavonostilbenes natural hybrids from Rhamnoneuron balansae as potential antitumors targeting ALDH1A1: molecular docking, ADMET, MM-GBSA calculations and molecular dynamics studies

Several studies have linked Cancer stem cells (CSCs) to cancer resistance development to chemotherapy and radiotherapy. ALDH1A1 is a key enzyme that regulates the gene expression of CSCs and creates an immunosuppressive tumor microenvironment. It was reported that quercetin and resveratrol were among the inhibitors of ALDH1A1. In early 2022, it was reported that new 11 flavonostilbenes (rhamnoneuronal D-N) were isolated from Rhamnoneuron balansae as potential antiaging natural products. Rhamnoneuronal H (5) could be envisioned as a natural hybrid of quercetin and resveratrol. It was therefore hypothesized that 5 and its analogous isolates rhamnoneuronal D-G (1-4) and rhamnoneuronal I-N (6-11) would have potential ALDH1A1 inhibitory activity. To this end, all isolates were subjected to molecular docking, MM-GBSA, ADMET, and molecular dynamics simulations studies to assess their potential as new leads for cancer treatment targeting ALDH1A1. In silico findings revealed that natural hybrid 5 has a similar binding affinity, judged by MM-GBSA, to the ALDH1A1 active site when compared to the co-crystalized ligand (-64.71 kcal/mole and -64.12 kcal/mole, respectively). Despite having lesser affinity than that of the co-crystalized ligand, the rest of the flavonostilbenes, except 2-4, displayed better binding affinities (-37.55 kcal/mole to -58.6 kcal/mole) in comparison to either resveratrol (-34.44 kcal/mole) or quercetin (-36.48 kcal/mole). Molecular dynamic simulations showed that the natural hybrids 1, 5-11 are of satisfactory stability up to 100 ns. ADMET outcomes indicate that these hybrids displayed acceptable properties and hence could represent an ideal starting point for the development of potent ALDH1A1 inhibitors for cancer treatment.Communicated by Ramaswamy H. Sarma.

Screening Techniques for Drug Discovery in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive and irreversible impairment of memory and other cognitive functions of the aging brain. Pathways such as amyloid beta neurotoxicity, tau pathogenesis and neuroinflammatory have been used to understand AD, despite not knowing the definite molecular mechanism which causes this progressive disease. This review attempts to summarize the small molecules that target these pathways using various techniques involving high-throughput screening, molecular modeling, custom bioassays, and spectroscopic detection tools. Novel and evolving screening methods developed to advance drug discovery initiatives in AD research are also highlighted.

DockOpt: A Tool for Automatic Optimization of Docking Models

Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

Targeting Cathepsin L in Cancer Management: Leveraging Machine Learning, Structure-Based Virtual Screening, and Molecular Dynamics Studies

Cathepsin L (CTSL) expression is dysregulated in a variety of cancers. Extensive empirical evidence indicates their direct participation in cancer growth, angiogenic processes, metastatic dissemination, and the development of treatment resistance. Currently, no natural CTSL inhibitors are approved for clinical use. Consequently, the development of novel CTSL inhibition strategies is an urgent necessity. In this study, a combined machine learning (ML) and structure-based virtual screening strategy was employed to identify potential natural CTSL inhibitors. The random forest ML model was trained on IC50 values. The accuracy of the trained model was over 90%. Furthermore, we used this ML model to screen the Biopurify and Targetmol natural compound libraries, yielding 149 hits with prediction scores >0.6. These hits were subsequently selected for virtual screening using a structure-based approach, yielding 13 hits with higher binding affinity compared to the positive control (AZ12878478). Two of these hits, ZINC4097985 and ZINC4098355, have been shown to strongly bind CTSL proteins. In addition to drug-like properties, both compounds demonstrated high affinity, ligand efficiency, and specificity for the CTSL binding pocket. Furthermore, in molecular dynamics simulations spanning 200 ns, these compounds formed stable protein-ligand complexes. ZINC4097985 and ZINC4098355 can be considered promising candidates for CTSL inhibition after experimental validation, with the potential to provide therapeutic benefits in cancer management.

Identification of potential death-associated protein kinase-1 (DAPK1) inhibitors by an integrated ligand-based and structure-based computational drug design approach

Death-associated protein kinase 1 (DAPK1) is a calcium/calmodulin (Ca2+/CaM)-dependent serine/threonine kinase that is abundantly expressed in the memory- and cognition-related brain areas. DAPK1 is associated with several pathological hallmarks of Alzheimer's disease (AD); it is an attractive target for designing a novel DAPK1 inhibitor as an effective therapeutic treatment for AD. In the present study, we have used an integrated ligand-based and structure-based drug design method to identify DAPK1 inhibitors. The pharmacophoric features of compound 38 G (PDB ID 4TXC) were mapped, and the models were evaluated using enrichment factor (EF) and goodness of hit (GH) score. The selected models were used to screen Zinc 15 compounds library. The identified hits were passed through drug-likeliness and PAINS filtering. The docking study was performed in three steps to yield molecules with good binding energy and ligand-target interactions. Finally, three hits were obtained, that is, ZINC000020648330, ZINC000006755051 and ZINC000020650468, which were subjected to rigorous molecular dynamics simulation. All three hits exhibited optimal stability under simulated conditions and low predicted toxicity.Communicated by Ramaswamy H. Sarma.

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Identification of new inhibitors of NS5 from dengue virus using saturation transfer difference (STD-NMR) and molecular docking studies

The rapid spread of dengue virus has now emerged as a major health problem worldwide, particularly in tropical and sub-tropical regions. Nearly half of the human population is at risk of getting infection. Among the proteomes of dengue virus, nonstructural protein NS5 is conserved across the genus Flavivirus. NS5 comprises methyltransferase enzyme (MTase) domain, which helps in viral RNA capping, and RNA-dependent RNA polymerase (RdRp) domain, which is important for the virus replication. Negative modulation of NS5 decreases its activity and associated functions. Despite recent advances, there is still an immense need for effective approaches toward drug discovery against dengue virus. Drug repurposing is an approach to identify the new therapeutic indications of already approved drugs, for the treatment of both common and rare diseases, and can potentially lower the cost, and time required for drug discovery and development. In this study, we evaluated 75 compounds (grouped into 15 mixtures), including 13 natural compounds and 62 drugs, by using biophysical methods, for their ability to interact with NS5 protein, which were further validated by molecular docking and simulation studies. Our current study led to the identification of 12 ligands, including both 9 US-FDA approved drugs and 3 natural products that need to be further studied as potential antiviral agents against dengue virus.

Acute Toxicity and Anti-Inflammatory Activity of Trattinnickia rhoifolia Willd (Sucuruba) Using the Zebrafish Model

The species Trattinnickia rhoifolia Willd, (T. rhoifolia), which belongs to the Burseraceae family, is widely used in ethnopharmacological cultural practices by traditional Amazonian people for anti-inflammatory purposes, sometimes as their only therapeutic resource. Although it is used in teas, infusions, macerations and in food, the species is still unexplored in regard to its pharmacophoric potential and chemical profile. Therefore, the aim of this study was to conduct a phytochemical characterization of the hydroethanolic extract of T. rhoifolia leaves (HELTr) and to evaluate the acute toxicity and anti-inflammatory activity of this species using zebrafish (Danio rerio). The extract was analyzed by gas chromatography−mass spectrometry (GC-MS). The evaluation of the acute toxicity of the HELTr in adult zebrafish was determined using the limit test (2000 mg/kg), with behavioral and histopathological evaluations, in addition to the analysis of the anti-inflammatory potential of HELTr in carrageenan-induced abdominal edema, followed by the use of the computational method of molecular docking. The phytochemical profile of the species is chemically diverse, suggesting the presence of the fatty acids, ester, alcohol and benzoic acid classes, including propanoic acid, ethyl ester and hexadecanoic acid. In the studies of zebrafish performed according to the index of histopathological changes (IHC), the HELTr did not demonstrate toxicity in the behavioral and histopathological assessments, since the vital organs remained unchanged. Carrageenan-induced abdominal edema was significantly reduced at all HELTr doses (100, 200 and 500 mg/kg) in relation to the negative control, dimethyl sulfoxide (DMSO), while the 200 mg/kg dose showed significant anti-inflammatory activity in relation to the positive control (indomethacin). With these activities being confirmed by molecular docking studies, they showed a good profile for the inhibition of the enzyme Cyclooxygenase-2 (COX-2), as the interactions established at the sites of the receptors used in the docking study were similar to the controls (RCX, IMN and CEL). Therefore, the HELTr has an acceptable degree of safety for acute toxicity, defined in the analysis of behavioral changes, mortality and histopathology, with a significant anti-inflammatory action in zebrafish at all doses, which demonstrates the high pharmacophoric potential of the species. These results may direct future applications and drug development but still require further elucidation.

In-silico investigation of a novel inhibitors against the antibiotic-resistant Neisseria gonorrhoeae bacteria

Antibiotics are drugs that are used to treat or prevent bacterial infections. They work by either killing or stopping bacteria from spreading. Nevertheless, it appeared in the last decade, Antibiotic-resistant bacteria are bacteria resistant to antibiotics and cannot be controlled or killed by them. In the presence of an antibiotic, they can live and even reproduce. The Neisseria gonorrhoeae bacteria is appearing to be a multidrug-resistant pathogen. Many factors contribute to antibiotic resistance, including unfettered access to antimicrobials, incorrect drug selection, misuse, and low-quality antibiotics. Here, we investigated in-silico docking screening and analysis for ten natural marine fungus extracted compounds. The resulted data were examined for the best binding affinity, toxicity, and chemical interactions. The most superior compound was elipyrone A with six hydrogen bonds, −8.5 of binding affinity, and preferable results in the SWISS-ADME examination. It is well known that “Declining corporate investment and a lack of innovation in the development of new antibiotics are weakening efforts to battle drug-resistant illnesses,” according to the World Health Organization (WHO). So, we extended our effort to predict a new natural compound to overcome the resistance of this bacteria.

Combining virtual screening and in vitro evaluation for the discovery of potential CYP11B2 inhibitors

Aim: To search for highly bioactive hits for CYP11B2 inhibitors by virtual screening and in vitro evaluation. Materials & methods: Virtual screening of potential CYP11B2 inhibitors was performed by molecular docking and molecular dynamics simulation. Compound activity was determined by in vitro evaluation using MTT and ELISA assays. Results & conclusion: Based on the results of molecular docking and molecular dynamics simulation, nine lead hits were selected for in vitro biochemical testing. All hits in in vitro experiments had lower inhibitory effects on cell proliferation and certain inhibitory effects on aldosterone secretion. These hits may be excellent candidates for CYP11B2 inhibitors.