Computational resources and chemoinformatics for translational health research.

The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.

Computational exploration of microsomal cytochrome P450 3A1 enzyme modulation by phytochemicals of Cichorium intybus L.: Insights into drug metabolism.

Drug metabolism plays a crucial role in drug fate, including therapeutic inactivation or activation, as well as the formation of toxic compounds. This underscores the importance of understanding drug metabolism in drug discovery and development. Considering the substantial costs associated with traditional drug development methods, computational approaches have emerged as valuable tools for predicting the metabolic fate of drug candidates. With this in mind, the present study aimed to investigate the potential mechanisms underlying the modulation of microsomal cytochrome P450 3A1 (CYP3A1) enzyme activity by various phytochemicals found in Cichorium intybus L., commonly known as chicory. To achieve this goal, several in silico methods, including molecular docking and molecular dynamics (MD) simulation, were employed to explore computationally the microsomal CYP3A1 enzyme. Schrodinger software was utilized for the molecular docking study, which involved the interaction analysis between CYP3A1 and 28 phytoconstituents of Cichorium intybus. Virtual screening of 28 compounds from chicory led to the identification of the top five ranked compounds. These compounds were evaluated for drug-likeness properties, pharmacokinetic profiles, and predicted binding affinities to CYP3A1. Caffeoylshikimic acid and cichoric acid emerged as promising candidates due to their favorable characteristics, including good oral bioavailability and high binding affinities to CYP3A1. Molecular dynamics simulations were conducted to assess the stability of caffeoylshikimic acid within the CYP3A1 binding pocket. The results demonstrated that caffeoylshikimic acid maintained stable interactions with the enzyme throughout the simulation, suggesting its potential as an effective modulator of CYP3A1 activity. The findings of this study have the potential to provide valuable insights into the complex molecular mechanisms by which Cichorium intybus L. acts on hepatocytes and modulates CYP3A1 enzyme expression or activity. By elucidating the impact of these phytochemicals on drug metabolism, this research contributes to our understanding of how chicory may interact with drugs and influence their efficacy and safety profiles.

Splice-site identification for exon prediction using bidirectional LSTM-RNN approach.

Machine learning methods played a major role in improving the accuracy of predictions and classification of DNA (Deoxyribonucleic Acid) and protein sequences. In eukaryotes, Splice-site identification and prediction is though not a straightforward job because of numerous false positives. To solve this problem, here, in this paper, we represent a bidirectional Long Short Term Memory (LSTM) Recurrent Neural Network (RNN) based deep learning model that has been developed to identify and predict the splice-sites for the prediction of exons from eukaryotic DNA sequences. During the splicing mechanism of the primary mRNA transcript, the introns, the non-coding region of the gene are spliced out and the exons, the coding region of the gene are joined. This bidirectional LSTM-RNN model uses the intron features that start with splice site donor (GT) and end with splice site acceptor (AG) in order of its length constraints. The model has been improved by increasing the number of epochs while training. This designed model achieved a maximum accuracy of 95.5%. This model is compatible with huge sequential data such as the complete genome.

Machine learning approaches and their applications in drug discovery and design.

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non-linear datasets, as well as big data of increasing depth and complexity. Various machine learning-based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand-based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.

Simulation studies, 3D QSAR and molecular docking on a point mutation of protein kinase B with flavonoids targeting ovarian Cancer.

BACKGROUND: Ovarian cancer is the world's dreaded disease and its prevalence is expanding globally. The study of integrated molecular networks is crucial for the basic mechanism of cancer cells and their progression. During the present investigation, we have examined different flavonoids that target protein kinases B (AKT1) protein which exerts their anticancer efficiency intriguing the role in cross-talk cell signalling, by metabolic processes through in-silico approaches. METHOD: Molecular dynamics simulation (MDS) was performed to analyze and evaluate the stability of the complexes under physiological conditions and the results were congruent with molecular docking. This investigation revealed the effect of a point mutation (W80R), considered based on their frequency of occurrence, with AKT1 protein. RESULTS: The ligand with high docking scores and favourable behaviour on dynamic simulations are proposed as potential W80R inhibitors. A virtual screening analysis was performed with 12,000 flavonoids satisfying Lipinski's rule of five according to which drug-likeness is predicted based on its pharmacological and biological properties to be active and taken orally. The pharmacokinetic ADME (adsorption, digestion, metabolism, and excretion) studies featured drug-likeness. Subsequently, a statistically significant 3D-QSAR model of high correlation coefficient (R2) with 0.822 and cross-validation coefficient (Q2) with 0.6132 at 4 component PLS (partial least square) were used to verify the accuracy of the models. Taxifolin holds good interactions with the binding domain of W80R, highest Glide score of - 9.63 kcal/mol with OH of GLU234 and H bond ASP274 and LEU156 amino acid residues and one pi-cation interaction and one hydrophobic bond with LYS276. CONCLUSION: Natural compounds have always been a richest source of active compounds with a wide variety of structures, therefore, these compounds showed a special inspiration for medical chemists. The present study has aimed molecular docking and molecular dynamics simulation studies on taxifolin targeting W80R mutant protein of protein kinase B/serine- threonine kinase/AKT1 (EC:2.7.11.1) protein of ovarian cancer for designing therapeutic intervention. The expected result supported the molecular cause in a mutant form which resulted in a gain of ovarian cancer. Here we discussed validations computationally and yet experimental evaluation or in vivo studies are endorsed for further study. Several of these compounds should become the next marvels for early detection of ovarian cancer.

Chemo-informatics guided study of natural inhibitors targeting rho GTPase: a lead for treatment of glaucoma.

Glaucoma, the most perilous disease leading to blindness is a result of optical neuropathy. Accumulation of aqueous humor in the posterior chamber due to a large difference in the rate of formation and its drainage in the anterior chamber causes an increase in intraocular pressure (IOP) leading to damage of nerve cells. A literature survey has revealed that inhibition of the Rho guanosine triphosphatases (rho GTPase) pathway by specific inhibitors leads to the relaxation of contractile cells involved in the aqueous outflow pathway. Relaxation of the strained contractile cells results in increased outflow thereby releasing IOP. In the present study molecular docking has been used to screen twenty seven bioactive (17 natural compounds and 10 conventional drugs) compounds that may play a significant role in relaxing contractile cells by inhibiting rho-GTPase protein. Docking results showed that among all-natural bioactive compounds Cyanidin and Delphinidine have a good binding affinity (- 8.4 kcal/mol) than the top screened conventional drug molecule Mitomycin, (- 6.3 kcal/mol) when docked with rho-GTPase protein. Cyanidin and Delphinidin belong to anthocyanidin, a glycoside form of anthocyanins from Vaccinium myrtillus L. and Punica granatum. The resembling potential of Cyanidin and Delphinidin concerning the drug Mitomycin was confirmed through simulation analysis. Molecular dynamics study (MDS) for 100 ns, showed that the rho GTPase-Delphinidine complex structure was energetically more stable than rho GTPase-Cyaniding complex in comparison to rho GTPase-Mitomycin complex. The comparative study of both the selected hits (Cyanidin and Delphinidin) was assessed by RMSD, RMSF, Rg, SASA, H-bond, PCA MM/PBSA analysis. The analysis revealed that Delphinidine is more potent to inhibit the rho GTPase as compare to Cyaniding and available conventional drugs in terms of stability and binding free energy. Based on the results, these molecules have good pharmacokinetic and pharmacodynamics properties and will prove to be a promising lead compound as a future drug for Glaucoma.

Comparative structural analysis of bifunctional methylenetetrahydrofolate dehydrogenase/methenyltetrahydrofolate cyclohydrolase from Bordetella pertussis and Bordetella parapertussis: a drug target against pertussis.

Bordetella pertussis and Bordetella parapertussis are Gram-negative, aerobic, and pathogenic bacteria and cause pertussis disease (whooping cough) in humans. Genomic island analysis indicated the presence of an important protein bifunctional methylenetetrahydrofolate dehydrogenase/methenyltetrahydrofolate cyclohydrolase (BMD/MC) in both B. pertussis and B. parapertussis. BMD/MC is associated with carbon fixation, folate pathway, and microbial metabolism in a diverse environment. Sequence comparison analysis indicates two amino acid variations between BMD/MC of B. pertussis and B. parapertussis and this difference reflects a good extent of variation in their 3D model. After refinement, BMD/MC model assessment result shows that 96.77% residue of B. pertussis and 97.49% residues of B. pertussis belong to the favored region of the Ramachandran plot, indicating a good quality model. During structural alignment, chain A of BMD/MC for B. pertussis and B. parapertussis shows the RMSD of 0.058 angstroms between 281 pruned atom pairs. Cavity analysis predicts a single cavity with an area (362.723 Å2) and volume (216.631 Å3) in the BMD/MC of B. pertussis, whereas the area and volume of cavity in B. parapertussis is 479.689 Å2 and 350.982 Å3 respectively. Several residues in the predicted cavity of both organisms are common with a good extent of variation in their area and volume. The average value of RMSD, RMSF, the radius of gyration, and principal component analysis (eigenvectors) for the BMD/MC model (B. parapertussis) was found smaller as compared to B. pertussis, which indicates that the B. parapertussis model is comparatively better than B. pertussis. MDS analysis suggests that both modeled structures are stable, good quality, and a compact model with a small degree of motions.

The Impact of Pharmacogenomics in Personalized Medicine.

Recent advances in Pharmacogenomics have made it possible to understand the reasons behind the different response of a drug. Discovery of genetic variants and its association with the varying response of drug provide the basis for recommending a drug and its dose to an individual patient. Genetic makeup-based prescription, design, and implementation of therapy not only improve the outcome of treatments but also reduce the risk of toxicity and other adverse effects. A better understanding of individual variations and their effect on drug response, metabolism excretion, and toxicity will replace the trial-and-error approach of treatment. Evidence of the clinical utility of pharmacogenetics testing is only available for a few medications, and FDA labels only require pharmacogenetics testing for a small number of drugs. Although there is a great promise, there are not many examples where Pharmacogenomics impacts clinical utility. Some genetic variants related to different diseases have been reported, and many have not been studied yet. The information related to the outcome of treatment with a particular drug and a genetic variant can be used to release a warning/label for the use of that drug. There are many limitations in the way of implementing the goal of personalized medicine. Future advances in the field of genomics, diagnosis approaches, data analysis, clinical decision-making, and sustainable business model for personalization of therapy can speed up the individualization of therapy based on genetic makeup.

Identification of potential inhibitors of Fasciola gigantica thioredoxin1: computational screening, molecular dynamics simulation, and binding free energy studies.

Fasciola gigantica is the causative organism of fascioliasis and is responsible for major economic losses in livestock production globally. F. gigantica thioredoxin1 (FgTrx1) is an important redox-active enzyme involved in maintaining the redox homeostasis in the cell. To identify a potential anti-fasciolid compound, we conducted a structure-based virtual screening of natural compounds from the ZINC database (n = 1,67,740) against the FgTrx1 structure. The ligands were docked against FgTrx1 and 309 ligands were found to have better docking score. These compounds were evaluated for Lipinski and ADMET prediction, and 30 compounds were found to fit well for re-docking studies. After refinement by molecular docking and drug-likeness analysis, three potential inhibitors (ZINC15970091, ZINC9312362, and ZINC9312661) were identified. These three ligands were further subjected to molecular dynamics simulation (MDS) to compare the dynamics and stability of the protein structure after binding of the ligands. The binding free energy analyses were calculated to determine the intermolecular interactions. The results suggested that the two compounds had a binding free energy of -82.237, and -109.52 kJ.mol-1 for compounds with IDs ZINC9312362 and ZINC9312661, respectively. These predicted compounds displayed considerable pharmacological and structural properties to be drug candidates. We concluded that these two compounds could be potential drug candidates to fight against F. gigantica parasites.

Structural insight into binding mode of inhibitor with SAHH of Plasmodium and human: interaction of curcumin with anti-malarial drug targets.

S-adenosyl-L-homocysteine hydrolase of Plasmodium falciparum (PfSAHH) is a potential drug target against malaria, and selective inhibition of PfSAHH is the excellent strategy to prevent the growth of parasite inside the host. Therefore, a comparative analysis of human S-adenosyl-L-homocysteine hydrolase (HsSAHH) and PfSAHH has been performed to explore the structural differences. Structural superimposition of PfSAHH and HsSAHH has generated the RMSD of 0.749 Å over 394 alpha carbon pairs. Residues of PfSAHH from position Tyr152 to Lys193 aligned with insertion/deletion region in HsSAHH, and these extra residues results in an extent of variation in cavity region of PfSAHH. Nicotinamide adenine dinucleotide (NAD) was observed to form hydrogen bonding with Thr201, Thr202, Thr203, Asn235, Val268, Glu287, Asn322, Ile343, Asn391, Lys473, and Tyr477 and also forms hydrophobic interactions with Val268, Ile288, and Thr320 of PfSAHH. In comparison to HsSAHH, Asn322, Lys473, and Tyr477 residues of PfSAHH are unique in interaction with NAD. 2-Fluoroaristeromycin and other analogues of aristeromycin have shown the good binding affinity for both enzymes. Structural differences between PfSAHH and HsSAHH might be employed to design the potential inhibitor of PfSAHH. To find the target enzyme responsible for an anti-malarial effect, molecular docking and interaction analysis of curcumin were performed with 34 drug targets of P. falciparum. Curcumin shows high affinity for binding with HGPRT of PfHGPRT, and an anti-malarial effect of curcumin might be due to binding with PfHGPRT.

An Approach for Identification of Novel Drug Targets in Streptococcus pyogenes SF370 Through Pathway Analysis.

Streptococcus pyogenes is one of the most important pathogens as it is involved in various infections affecting upper respiratory tract and skin. Due to the emergence of multidrug resistance and cross-resistance, S. Pyogenes is becoming more pathogenic and dangerous. In the present study, an in silico comparative analysis of total 65 metabolic pathways of the host (Homo sapiens) and the pathogen was performed. Initially, 486 paralogous enzymes were identified so that they can be removed from possible drug target list. The 105 enzymes of the biochemical pathways of S. pyogenes from the KEGG metabolic pathway database were compared with the proteins from the Homo sapiens by performing a BLASTP search against the non-redundant database restricted to the Homo sapiens subset. Out of these, 83 enzymes were identified as non-human homologous while 30 enzymes of inadequate amino acid length were removed for further processing. Essential enzymes were finally mined from remaining 53 enzymes. Finally, 28 essential enzymes were identified in S. pyogenes SF370 (serotype M1). In subcellular localization study, 18 enzymes were predicted with cytoplasmic localization and ten enzymes with the membrane localization. These ten enzymes with putative membrane localization should be of particular interest. Acyl-carrier-protein S-malonyltransferase, DNA polymerase III subunit beta and dihydropteroate synthase are novel drug targets and thus can be used to design potential inhibitors against S. pyogenes infection. 3D structure of dihydropteroate synthase was modeled and validated that can be used for virtual screening and interaction study of potential inhibitors with the target enzyme.

A Quantitative Measure of Conformational Changes in Apo, Holo and Ligand-Bound Forms of Enzymes.

Determination of the native geometry of the enzymes and ligand complexes is a key step in the process of structure-based drug designing. Enzymes and ligands show flexibility in structural behavior as they come in contact with each other. When ligand binds with active site of the enzyme, in the presence of cofactor some structural changes are expected to occur in the active site. Motivation behind this study is to determine the nature of conformational changes as well as regions where such changes are more pronounced. To measure the structural changes due to cofactor and ligand complex, enzyme in apo, holo and ligand-bound forms is selected. Enzyme data set was retrieved from protein data bank. Fifteen triplet groups were selected for the analysis of structural changes based on selection criteria. Structural features for selected enzymes were compared at the global as well as local region. Accessible surface area for the enzymes in entire triplet set was calculated, which describes the change in accessible surface area upon binding of cofactor and ligand with the enzyme. It was observed that some structural changes take place during binding of ligand in the presence of cofactor. This study will helps in understanding the level of flexibility in protein-ligand interaction for computer-aided drug designing.

A quantitative measure of conformational changes in Apo, holo and ligand bound form of enzymes.

Determination of the native geometry of the enzymes and ligand complexe is a key step in the process of structure based drug designing. Enzymes and ligands show flexibility in structural behavior as they come in contact with each other. When ligand binds with active site of the enzyme, in presence of cofactor some structural changes are expected to occur in the active site. Motivation behind this study is to determine the nature of conformational changes as well as regions where such changes are more pronounced. To measure the structural changes due to cofactor and ligand complex, enzyme in Apo, holo and ligand bound form is selected. Enzyme data set was retrieved from protein data bank (PDB). 15 triplet groups were selected for the analysis of structural changes based on selection criteria. Structural features for selected enzymes were compared at the global as well as local region. Accessible surface area for the enzymes in entire triplet set was calculated, which describes the change in accessible surface area upon binding of cofactor and ligand with the enzyme. It was observed that some structural changes take place during binding of ligand in presence of cofactor. This study will helps in understanding the level of flexibility in protein-ligand interaction for computer aided drug designing.

Functional classification and biochemical characterization of a novel rho class glutathione S-transferase in Synechocystis PCC 6803.

We report a novel class of glutathione S-transferase (GST) from the model cyanobacterium Synechocystis PCC 6803 (sll1545) which catalyzes the detoxification of the water pollutant dichloroacetate and also shows strong glutathione-dependent peroxidase activity representing the classical activities of zeta and theta/alpha class respectively. Interestingly, sll1545 has very low sequence and structural similarity with these classes. This is the first report of dichloroacetate degradation activity by any bacterial GST. Based on these results we classify sll1545 to a novel GST class, rho. The present data also indicate potential biotechnological and industrial applications of cyanobacterial GST in dichloroacetate-polluted areas.

A comprehensive metabolic modeling of thyroid pathway in relation to thyroid pathophysiology and therapeutics.

The thyroid pathway represents a complex interaction of different glands for thyroid hormone synthesis. Thyrotropin releasing hormone is synthesized in the hypothalamus and regulates thyrotropin stimulating hormone gene expression in the pituitary gland. In order to understand the complexity of the thyroid pathways, and using experimental data retrieved from the biomedical literature (e.g., NCBI, HuGE Navigator, Protein Data Bank, and KEGG), we constructed a metabolic map of the thyroid hormone pathway at a molecular level and analyzed it topologically. A total of five hub nodes were predicted in regards to the transcription thyroid receptor (TR), cAMP response element-binding protein (CREB), signal transducer and activator of transcription 3 (STAT 3), nuclear factor kappa-light-chain-enhancer of activated B cells (NFkB), and activator protein 1 (AP-1) as being potentially important in study of thyroid disorders and as novel putative therapeutic drug targets. Notably, the thyroid receptor is a highly connected hub node and currently used as a therapeutic target in hypothyroidism. Our analysis represents the first comprehensive description of the thyroid pathway, which pertains to understanding the function of the protein and gene interaction networks. The findings from this study are therefore informative for pathophysiology and rational therapeutics of thyroid disorders.

Docking and in silico ADMET studies of noraristeromycin, curcumin and its derivatives with Plasmodium falciparum SAH hydrolase: a molecular drug target against malaria.

The Plasmodium falciparum S-adenosyl-L-homocysteine hydrolase (pfSAHH) enzyme has been considered as a potential chemotherapeutic target against malaria due to the amino acid differences found on binding sites of pfSAHH related to human SAHH. It has been reported that noraristeromycin and some curcumin derivatives have potential binding with the largest cavity of pfSAHH, which is also related to the binding with Nicotinamide-Adenine-Dinucleotide (NAD) and Adenosine (ADN). Our present work focuses on docking and ADMET studies to select potential inhibitors of pfSAHH. The binding of the selected inhibitor of the PfSAHH active site was analyzed using Molegro Virtual Docker. In this study, curcumin and its derivatives have been found to have higher binding affinity with pfSAHH than noraristeromycin. Seven amino acid residues Leu53, His54, Thr56, Lys230, Gly397, His398 and Phe407 of pfSAHH involved in binding with curcumin, are the same as those for noraristeromycin, which reveals that curcumin and noraristeromycin bind in the same region of pfSAHH. Curcumin has shown a strong interaction with hydrophobic amino acid residues of pfSAHH. Molecular Docking and ADMET predictions suggest that curcumin can be a potent inhibitor of pfSAHH with ability to modulate the target in comparatively smaller dose. Therefore, curcumin is likely to become a good lead molecule for the development of effective drug against malaria.