Contact-based ligand-clustering approach for the identification of active compounds in virtual screening

Molecular Dynamics for the Optimal Design of Functionalized Nanodevices to Target Folate Receptors on Tumor Cells

Atomistic details on the mechanism of targeting activity by biomedical nanodevices of specific receptors are still scarce in the literature, where mostly ligand/receptor pairs are modeled. Here, we use atomistic molecular dynamics (MD) simulations, free energy calculations, and machine learning approaches on the case study of spherical TiO2 nanoparticles (NPs) functionalized with folic acid (FA) as the targeting ligand of the folate receptor (FR). We consider different FA densities on the surface and different anchoring approaches, i.e., direct covalent bonding of FA γ-carboxylate or through polyethylene glycol spacers. By molecular docking, we first identify the lowest energy conformation of one FA inside the FR binding pocket from the X-ray crystal structure, which becomes the starting point of classical MD simulations in a realistic physiological environment. We estimate the binding free energy to be compared with the existing experimental data. Then, we increase complexity and go from the isolated FA to a nanosystem decorated with several FAs. Within the simulation time framework, we confirm the stability of the ligand-receptor interaction, even in the presence of the NP (with or without a spacer), and no significant modification of the protein secondary structure is observed. Our study highlights the crucial role played by the spacer, FA protonation state, and density, which are parameters that can be controlled during the nanodevice preparation step.

MasitinibL shows promise as a drug-like analog of masitinib that elicits comparable SARS-Cov-2 3CLpro inhibition with low kinase preference

SARS-CoV-2 infection has led to several million deaths worldwide and ravaged the economies of many countries. Hence, developing therapeutics against SARS-CoV-2 remains a core priority in the fight against COVID-19. Most of the drugs that have received emergency use authorization for treating SARS-CoV-2 infection exhibit a number of limitations, including side effects and questionable efficacy. This challenge is further compounded by reinfection after vaccination and the high likelihood of mutations, as well as the emergence of viral escape mutants that render SARS-CoV-2 spike glycoprotein-targeting vaccines ineffective. Employing de novo drug synthesis or repurposing to discover broad-spectrum antivirals that target highly conserved pathways within the viral machinery is a focus of current research. In a recent drug repurposing study, masitinib, a clinically safe drug against the human coronavirus OC43 (HCoV-OC43), was identified as an antiviral agent with effective inhibitory activity against the SARS-CoV-2 3CLpro. Masitinib is currently under clinical trial in combination with isoquercetin in hospitalized patients (NCT04622865). Nevertheless, masitinib has kinase-related side effects; hence, the development of masitinib analogs with lower anti-tyrosine kinase activity becomes necessary. In this study, in an attempt to address this limitation, we executed a comprehensive virtual workflow in silico to discover drug-like compounds matching selected pharmacophore features in the SARS-CoV-2 3CLpro-bound state of masitinib. We identified a novel lead compound, "masitinibL", a drug-like analog of masitinib that demonstrated strong inhibitory properties against the SARS-CoV-2 3CLpro. In addition, masitinibL further displayed low selectivity for tyrosine kinases, which strongly suggests that masitinibL is a highly promising therapeutic that is preferable to masitinib.

PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results

Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file formats. PacDOCK allows a complete analysis of molecular docking results through root mean square deviation (RMSD) calculation, molecular visualization, and cluster analysis of docked poses. The RMSD calculation compares docked structures with a reference structure, also when atoms are randomly labelled, and their conformational and positional differences can be visualised. In addition, it is possible to visualise a ligand into the target binding pocket and investigate the key receptor–ligand interactions. Moreover, PacDOCK enables the clustering of docking results by identifying a restrained number of clusters from many docked poses. We believe that PacDOCK will contribute to facilitating the analysis of docking results to improve the efficiency of computer-aided drug design.

Insight into the Initial Stages of the Folding Process in Onconase Revealed by UNRES

The unfolded state of proteins presents many challenges to elucidate the structural basis for biological function. This state is characterized by a large degree of structural heterogeneity which makes it difficult to generate structural models. However, recent experiments into the initial folding events of the 104-residue ribonuclease homologue onconase (ONC) were able to identify the regions in the protein that participate in the initial folding of this protein. Therefore, to gain additional structural insight into the unfolded state of proteins, this study utilized molecular dynamics simulations using the UNited-RESidue (UNRES) force field to evaluate whether there is a good agreement between the experimentally determined initial structures and the structures identified by computer simulations along a folding pathway. Indeed, these UNRES simulations accurately identified the two regions experimentally observed to form the initial native structure along the folding pathway of ONC. In addition, these regions are determined to be chain folding initiation sites (CFIS) according to methods developed previously. Subsequent self-organization maps (SOM) analysis has revealed key structural states involved in these early folding events.

Synthesis of Four Steroidal Carbamates with Antitumor Activity against Mouse Colon Carcinoma CT26WT Cells: In Vitro and In Silico Evidence

Colorectal cancer (CRC) is one of the most lethal cancers worldwide. If detected on time, surgery can expand life expectations of patients up to five more years. However, if metastasis has grown deliberately, the use of chemotherapy can play a crucial role in CRC control. Moreover, the lack of selectivity of current anticancer drugs, plus mutations that occur in cancerous cells, demands the development of new chemotherapeutic agents. Several steroids have shown their potentiality as anticancer agents, while some other compounds, such as Taxol and its derivatives bearing a carbamate functionality, have reached the market. In this article, the synthesis, characterization, and antiproliferative activity of four steroidal carbamates on mouse colon carcinoma CT26WT cells are described. Carbamate synthesis occurred via direct reaction between diosgenin, its B-ring modified derivative, and testosterone with phenyl isocyanate under a Brønsted acid catalysis. All obtained compounds were characterized by ¹H and ¹³C Nuclear Magnetic Resonance (NMR), High Resolution Mass Spectroscopy (HRMS); their melting points are also reported. Results obtained from antiproliferative activity assays indicated that carbamates compounds have inhibitory effects on the growth of this colon cancer cell line. A molecular docking study carried out on Human Prostaglandin E Receptor (EP4) showed a high affinity between carbamates and protein, thus providing a valuable theoretical explanation of the in vitro results.

PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations

Understanding the process of ligand–protein recognition is important to unveil biological mechanisms and to guide drug discovery and design. Enhanced-sampling molecular dynamics is now routinely used to simulate the ligand binding process, resulting in the need for suitable tools for the analysis of large data sets of binding events. Here, we designed, implemented, and tested PathDetect-SOM, a tool based on self-organizing maps to build concise visual models of the ligand binding pathways sampled along single simulations or replicas. The tool performs a geometric clustering of the trajectories and traces the pathways over an easily interpretable 2D map and, using an approximate transition matrix, it can build a graph model of concurrent pathways. The tool was tested on three study cases representing different types of problems and simulation techniques. A clear reconstruction of the sampled pathways was derived in all cases, and useful information on the energetic features of the processes was recovered. The tool is available at https://github.com/MottaStefano/PathDetect-SOM.

An Expanded Survey of the Moth PBP/GOBP Clade in Bombyx mori: New Insight into Expression and Functional Roles

We studied the expression profile and ontogeny (from the egg stage through the larval stages and pupal stages, to the elderly adult age) of four OBPs from the silkworm moth Bombyx mori. We first showed that male responsiveness to female sex pheromone in the silkworm moth B. mori does not depend on age variation; whereas the expression of BmorPBP1, BmorPBP2, BmorGOBP1, and BmorGOBP2 varies with age. The expression profile analysis revealed that the studied OBPs are expressed in non-olfactory tissues at different developmental stages. In addition, we tested the effect of insecticide exposure on the expression of the four OBPs studied. Exposure to a toxic macrolide insecticide endectocide molecule (abamectin) led to the modulated expression of all four genes in different tissues. The higher expression of OBPs was detected in metabolic tissues, such as the thorax, gut, and fat body. All these data strongly suggest some alternative functions for these proteins other than olfaction. Finally, we carried out ligand docking studies and reported that PBP1 and GOBP2 have the capacity of binding vitamin K1 and multiple different vitamins.

Structure-Based Virtual Screening and In Vitro Evaluation of New Trypanosoma cruzi Cruzain Inhibitors

Chagas disease (CD), or American trypanosomiasis, causes more than 10,000 deaths per year in the Americas. Current medical therapy for CD has low efficacy in the chronic phase of the disease and serious adverse effects; therefore, it is necessary to search for new pharmacological treatments. In this work, the ZINC¹⁵ database was filtered using the N-acylhydrazone moiety and a subsequent structure-based virtual screening was performed using the cruzain enzyme of Trypanosoma cruzi to predict new potential cruzain inhibitors. After a rational selection process, four compounds, Z2 (ZINC9873043), Z3 (ZINC9870651), Z5 (ZINC9715287), and Z6 (ZINC9861447), were chosen to evaluate their in vitro trypanocidal activity and enzyme inhibition. Compound Z5 showed the best trypanocidal activity against epimatigote (IC₅₀ = 36.26 ± 9.9 μM) and trypomastigote (IC₅₀ = 166.21 ± 14.5 μM and 185.1 ± 8.5 μM on NINOA and INC-5 strains, respectively) forms of Trypanosoma cruzi. In addition, Z5 showed a better inhibitory effect on Trypanosoma cruzi proteases than S1 (STK552090, 8-chloro-N-(3-morpholinopropyl)-5H-pyrimido[5,4-b]-indol-4-amine), a known cruzain inhibitor. This study encourages the use of computational tools for the rational search for trypanocidal drugs.

Target Engagement and Binding Mode of an Antituberculosis Drug to Its Bacterial Target Deciphered in Whole Living Cells by NMR

Detailed information on hit-target interaction is very valuable for drug discovery efforts and indispensable for rational hit to lead optimization. We developed a new approach combining NMR in whole-cells in-cell NMR) and docking to characterize hit-target interaction at the atomic level. By using in-cell NMR, we validated target engagement of the antituberculosis imidazopyridine amide (IPA) series with the subunit b of the cytochrome bc₁:aa₃, the major respiratory terminal oxidase in mycobacteria. The most advanced IPA called Q203 is currently in clinical trial. Using its derivative IPA317, we identified the atoms of the drug interacting with the cytochrome b in whole cells. NMR data and the self-organizing map algorithm were used to cluster a large set of drug-target complex models. The selected ensemble revealed IPA317 in a transient cavity of the cytochrome b, interacting directly with the residue T313, which is the site of spontaneous mutation conferring resistance to the IPA series. Our approach constitutes a pipeline to obtain atomic information on hit-target interactions in the cellular context.

Virtual screening to identify Leishmania braziliensis N-myristoyltransferase inhibitors: pharmacophore models, docking, and molecular dynamics

Leishmaniasis is caused by several protozoa species belonging to genus Leishmania that are hosted by humans and other mammals. Millions of new cases are recorded every year and the drugs available on the market do not show satisfactory efficacy and safety. A hierarchical virtual screening approach based on the pharmacophore model, molecular docking, and molecular dynamics was conducted to identify possible Leishmania braziliensis N-misristoyltransferase (LbNMT) inhibitors. The adopted pharmacophore model had three main features: four hydrophobic centers, four hydrogen-bond acceptor atoms, and one positive nitrogen center. The molecules (n=15,000) were submitted to alignment with the pharmacophore model and only 27 molecules aligned to model. Six molecules were submitted to molecular docking, using receptor PDB ID 5A27. After docking, the ZINC35426134 was a top-ranked molecule (- 64.61 kcal/mol). The molecule ZINC35426134 shows hydrophobic interactions with Phe82, Tyr209, Val370, and Leu391 and hydrogen bonds with Asn159, Tyr318, and Val370. Molecular dynamics simulations were performed with the protein in its APO and HOLO forms for 37 ns in order to assess the stability of the protein-ligand complex. Results showed that the HOLO form was more stable than the APO one, and it suggests that the ZINC35426134 binding stabilizes the enzyme. Therefore, the selected molecule has the potential to meet the herein proposed target.

Multitarget Drug Design, Molecular Docking and PLIF Studies of Novel Tacrine-Coumarin Hybrids for the Treatment of Alzheimer's Disease

Alzheimer's disease (AD) as a complicated and progressive neurodegenerative disorder is the most common form of dementia and memory loss. On account of the multifactorial etiology of AD, the multi-target-directed ligand (MTDL) approach is a promising method in searching new drug candidates for this disease. Here, in this paper more than 500 tacrine-coumarin hybrids have been designed and drug-likeness, molecular docking and descriptor analysis of them were performed to find out a drug candidate with less toxicity and better binding affinity than tacrine. The docking analysis was carried out using human acetylcholineesterase (1ACJ), human butyrylcholineesterase (4BDS) and β-secretase (BACE1) (1W51) enzymes using AutoDock 4.2 and Vina. The promising results were obtained on the types of interactions. Based on docking on three targets and PLIF studies, the compounds that have better results were introduced as good candidates for synthesis. The validity of docking protocols was verified using a set of known active ligands and decoys on these targets.

In silico-based identification of human α-enolase inhibitors to block cancer cell growth metabolically

Unlimited growth of cancer cells requires an extensive nutrient supply. To meet this demand, cancer cells drastically upregulate glucose uptake and metabolism compared to normal cells. This difference has made the blocking of glycolysis a fascinating strategy to treat this malignant disease. α-enolase is not only one of the most upregulated glycolytic enzymes in cancer cells, but also associates with many cellular processes or conditions important to cancer cell survival, such as cell migration, invasion, and hypoxia. Targeting α-enolase could simultaneously disturb cancer cells in multiple ways and, therefore, is a good target for anticancer drug development. In the current study, more than 22 million chemical structures meeting the criteria of Lipinski’s rule of five from the ZINC database were docked to α-enolase by virtual screening. Twenty-four chemical structures with docking scores better than that of the enolase substrate, 2-phosphoglycerate, were further screened by the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties prediction. Four of them were classified as non-mutagenic, non-carcinogenic, and capable of oral administration where they showed steady interactions to α-enolase that were comparable, even superior, to the currently available inhibitors in molecular dynamics (MD) simulation. These compounds may be considered promising leads for further development of the α-enolase inhibitors and could help fight cancer metabolically.

A Comparative QSAR Analysis, Molecular Docking and PLIF Studies of Some N-arylphenyl-2, 2-Dichloroacetamide Analogues as Anticancer Agents

Dichloroacetate (DCA) is a simple and small anticancer drug that arouses the activity of the enzyme pyruvate dehydrogenase (PDH) through inhibition of the enzyme pyruvate dehydrogenase kinases (PDK1-4). DCA can selectively promote mitochondria-regulated apoptosis, depolarizing the hyperpolarized inner mitochondrial membrane potential to normal levels, inhibit tumor growth and reduce proliferation by shifting the glucose metabolism in cancer cells from anaerobic to aerobic glycolysis. In this study, a series of DCA analogues were applied to quantitative structure-activity relationship (QSAR) analysis. A collection of chemometrics methods such as multiple linear regression (MLR), factor analysis-based multiple linear regression (FA-MLR), principal component regression (PCR), and partial least squared combined with genetic algorithm for variable selection (GA-PLS) were applied to make relations between structural characteristics and cytotoxic activities of a variety of DCA analogues. The best multiple linear regression equation was obtained from genetic algorithms partial least squares, which predict 90% of variances. Based on the resulted model, an in silico-screening study was also conducted and new potent lead compounds based on new structural patterns were designed. Molecular docking as well as protein ligand interaction fingerprints (PLIF) studies of these compounds were also investigated and encouraging results were acquired. There was a good correlation between QSAR and docking results.