Computational Approaches for Identification of Potential Plant Bioactives as Novel G6PD Inhibitors Using Advanced Tools and Databases.

In glucose metabolism, the pentose phosphate pathway (PPP) is the major metabolic pathway that plays a crucial role in cancer growth and metastasis. Although it has been pointed out that blockade of the PPP is a promising approach against cancer, in the clinical setting, effective anti-PPP agents are still not available. Dysfunction of the G6PD enzyme in this pathway leads to cancer development as this enzyme possesses oncogenic activity. In the present study, an attempt was made to identify bioactive compounds that can be developed as potential G6PD inhibitors. In the present study, 11 natural compounds and a controlled drug were taken. The physicochemical and toxicity properties of the compounds were determined via ADMET and ProTox-II analysis. In the present study, the findings of docking studies revealed that staurosporine was the most effective compound with the highest binding energy of -9.2 kcal/mol when docked against G6PD. Homology modeling revealed that 97.56% of the residues were occupied in the Ramachandran-favored region. The modeled protein gave a quality Z-score of -10.13 by ProSA tool. iMODS server provided significant insights into the mobility, stability and flexibility of the G6PD protein that described the collective functional protein motion. In the present study, the physical and functional interactions between proteins were determined by STRING. CASTp server determined the topological and geometric properties of the G6PD protein. The findings of the present study revealed that staurosporine could be developed as a potential G6PD inhibitor; however, further in vivo and in vitro studies are needed for further validation of these results.

Bioinformatics evaluation of anticancer properties of GP63 protein-derived peptides on MMP2 protein of melanoma cancer.

Background: GP63, also known as Leishmanolysin, is a multifunctional virulence factor abundant on the surface of Leishmania spp. small peptides with anticancer capabilities that are selective and toxic to cancer cells are known as anticancer peptides. We aimed to demonstrate the activity of GP63 and its anticancer properties on melanoma using a range of in silico tools and screening methods to identify predicted and designed anticancer peptides. Methods: Various in silico modeling methodologies are used to establish the three-dimensional (3D) structure of GP63. Refinement and re-evaluation of the modeled structures and the built models' quality evaluated using the different docking used to find the interacting amino acids between MMP2 and GP63 and its anticancer peptides. AntiCP2.0 is used for screening anticancer peptides. 2D interaction plots of protein-ligand complexes evaluated by Protein-Ligand Interaction Profiler server. It is for the first time that used anticancer peptides of GP63 and the predicted and designed peptides. Results: We used 3 peptides of GP63 based on the AntiCP 2.0 server with scores of 0.63, 0.53, and 0.49, and common peptides of GP63/MMP2 (continues peptide: mean the completely selected peptide after docking with non-anticancer effect, predicted with 0.58 score and designed peptides with 0.47 and 0.45 scores by AntiCP 2.0 server). Conclusions: The antileishmanial and anticancer peptide research topics exemplify the multidisciplinary nature of peptide research. The advancement of therapeutics targeting cancer and/or Leishmania requires an interconnected research strategy shown in this work.

Screening and identification of potential inhibitor for visceral leishmaniasis (VL) through computational analysis.

AIM: The aim of this investigation is to detect potential inhibitor for visceral leishmaniasis through computational analysis. BACKGROUND: Leishmaniasis is categorized as a vector born pathogenic infection prevalent in tropical, subtropical, and in Mediterranean zones spread by intra-macrophage protozoa. The clinical syndrome of leishmaniasis is divided into the following type's namely cutaneous leishmaniasis, mucocutaneous leishmaniasis, visceral leishmaniasis, and dermal leishmaniasis. Trypanothione synthetase is a key enzyme involving in glutathione biosynthesis as well as hydrolysis. Trypanothione is one of the promising drug targets for parasites. Parasites are inimitable with concern to their dependence on trypanothione to regulate intracellular thiol-redox balance in fighting against oxidative stress and biochemical anxiety. However, trypanothione synthetase was presumed as the target therapeutic alternate in VL therapy. OBJECTIVE: The important objective of this current investigation is to identify or analyze the potential inhibitor for V. leishmaniasis through computational approaches which include virtual screening, molecular docking, ADME prediction, and molecular dynamic simulation. METHODS: An investigation was performed to develop a 3D protein structure, using computational screening among associated similar structured proteins from popular compound database banks such as Specs, Maybridge, and Enamine, to detect novel staging with a series of validation for emerging innovative drugs molecules. Modeled protein ligand complex was further analyzed to know the binding ability of the complex. Molecular dynamics were performed to ascertain its stability at 50 ns. RESULTS: Trypanothione synthetase overall ability in the outcome of series of analysis. Among three database compounds screened, the compound from the Specs database exhibited the better protein-ligand docking scores and fulfilled the drug-like properties through ADMET analysis, and the docked complexes had better stability throughout the simulation. Besides, the other two database leads fulfilled the pharmacological properties, and the complexes were stable in the simulation. CONCLUSION: By analyzing the various compounds from different databases, we concluded that the Specs database compound exhibits potential activity against the target protein and is considered a promising inhibitor for trypanothione synthetase.

Computational screening of phytochemicals from three medicinal plants as inhibitors of transmembrane protease serine 2 implicated in SARS-CoV-2 infection.

Background: SARS-CoV-2 infection or COVID-19 is a major global public health issue that requires urgent attention in terms of drug development. Transmembrane Protease Serine 2 (TMPRSS2) is a good drug target against SARS-CoV-2 because of the role it plays during the viral entry into the cell. Virtual screening of phytochemicals as potential inhibitors of TMPRSS2 can lead to the discovery of drug candidates for the treatment of COVID-19. Purpose: The study was designed to screen 132 phytochemicals from three medicinal plants traditionally used as antivirals; Zingiber officinalis Roscoe (Zingiberaceae), Artemisia annua L. (Asteraceae), and Moringa oleifera Lam. (Moringaceae), as potential inhibitors of TMPRSS2 for the purpose of finding therapeutic options to treat COVID-19. Methods: Homology model of TMPRSS2 was built using the ProMod3 3.1.1 program of the SWISS-MODEL. Binding affinities and interaction between compounds and TMPRSS2 model was examined using molecular docking and molecular dynamics simulation. The drug-likeness and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of potential inhibitors of TMPRSS2 were also assessed using admetSAR web tool. Results: Three compounds, namely, niazirin, quercetin, and moringyne from M. oleifera demonstrated better molecular interactions with binding affinities ranging from -7.1 to -8.0 kcal/mol compared to -7.0 kcal/mol obtained for camostat mesylate (a known TMPRSS2 inhibitor), which served as a control. All the three compounds exhibited good drug-like properties by not violating the Lipinski rule of 5. Niazirin and moringyne possessed good ADMET properties and were stable in their interactions with the TMPRSS2 based on the molecular dynamics simulation. However, the ADMET tool predicted the potential hepatotoxic and mutagenic effects of quercetin. Conclusion: This study demonstrated the potentials of niazirin, quercetin, and moringyne from M. oleifera, to inhibit the activities of human TMPRSS2, thus probably being good candidates for further development as new drugs for the treatment or management of COVID-19.

Titanium dioxide nanoparticle-protein interaction explained by docking approach.

Titanium dioxide has been proven for toxicity by in vitro and in vivo approaches, however, further studies are needed in nano-toxicological research using in silico analysis. In this study, Autodock 4.0.5 was used in an attempt to evaluate the interaction of titanium dioxide with proteins. Different cellular proteins were sorted to study the interaction, binding sites, and active sites as a pocket. These pockets have been determined using CastP - an online server. The analysis for the docked structures was performed with regard to the most efficient binding with amino acids. This study is the first of its kind to report on the in silico docking interaction of titanium dioxide nanoparticles without any surface modification. The higher negative binding energy shows strong binding of titanium dioxide with proteins. A strong interaction with different cellular proteins was observed, and more specifically, titanium dioxide nanoparticles showed frequent interaction with proline, lysine, as well as leusine.

Strand 6B deformation and residues exposure towards N-terminal end of helix B during proteinase inhibition by Serpins.

UNLABELLED: Serine Protease inhibitors (Serpins) like antithrombin, antitrypsin, neuroserpin, antichymotrypsin, protein C-inhibitor and plasminogen activator inhibitor is involved in important biological functions like blood coagulation, fibrinolysis, inflammation, cell migration and complement activation. Serpins native state is metastable, which undergoes transformation to a more stable state during the process of protease inhibition. Serpins are prone to conformation defects, however little is known about the factors and mechanisms which promote its conformational change and misfolding. Helix B region in serpins is with several point mutations which result in pathological conditions due to polymerization. Helix B analysis for residue burial and cavity was undertaken to understand its role in serpin structure function. A structural overlap and an accessible surface area analysis showed the deformation of strand 6B and exposure of helix B at N-terminal end in cleaved conformation but not in the native and latent conformation of various inhibitory serpins. A cleaved polymer like conformation of antitrypsin also showed deformation of s6B and helix B exposure. Cavity analysis showed that helix B residues were part of the largest cavity in most of the serpins in the native state which increase in size during the transformation to cleaved and latent states. These data for the first time show the importance of strand 6B deformation and exposure of helix B in smooth insertion of the reactive center loop during serpin inhibition and indicate that helix B exposure due to variants may increase its polymer propensity. ABBREVIATIONS: serpin -serine protease inhibitors RCL -reactive center loop ASA -accessible surface area.

Chapter 4. Predicting and characterizing protein functions through matching geometric and evolutionary patterns of binding surfaces.

Predicting protein functions from structures is an important and challenging task. Although proteins are often thought to be packed as tightly as solids, closer examination based on geometric computation reveals that they contain numerous voids and pockets. Most of them are of random nature, but some are binding sites providing surfaces to interact with other molecules. A promising approach for function prediction is to infer functions through discovery of similarity in local binding pockets, as proteins binding to similar substrates/ligands and carrying out similar functions have similar physical constraints for binding and reactions. In this chapter, we describe computational methods to distinguish those surface pockets that are likely to be involved in important biological functions, and methods to identify key residues in these pockets. We further describe how to predict protein functions at large scale from structures by detecting binding surfaces similar in residue make-ups, shape, and orientation. We also describe a Bayesian Monte Carlo method that can separate selection pressure due to biological function from pressure due to protein folding. We show how this method can be used to reconstruct the evolutionary history of binding surfaces for detecting similar binding surfaces. In addition, we briefly discuss how the negative image of a binding pocket can be casted, and how such information can be used to facilitate drug discovery.