Mechanistic insights on nsSNPs on binding site of renin and cytochrome P450 proteins: A computational perceptual study for pharmacogenomics evaluation

HTNpedia: A Knowledge Base for Hypertension Research

BACKGROUND

Hypertension is notably a serious public health concern due to its high prevalence and strong association with cardiovascular disease and renal failure. It is reported to be the fourth leading disease that leads to death worldwide.

OBJECTIVES

Currently, there is no active operational knowledge base or database for hypertension or cardiovascular illness.

METHODS

The primary data source was retrieved from the research outputs obtained from our laboratory team working on hypertension research. We have presented a preliminary dataset and external links to the public repository for detailed analysis to readers.

RESULTS

As a result, HTNpedia was created to provide information regarding hypertension-related proteins and genes.

CONCLUSION

The complete webpage is accessible via www.mkarthikeyan.bioinfoau.org/HTNpedia.

Computational study of the potential impact of AURKC missense SNPs on AURKC-INCENP interaction and their correlation to macrozoospermia

Aurora Kinase C (AURKC) is considered an important element in Chromosome Passenger Complex (CPC), its interaction with Inner Centromere Protein (INCENP) plays a critical role in the establishment and the recruitment of a stable CPC during spermatogenesis. Genetic variations of AURKC gene are susceptible to impact AURKC-INCENP interaction, which may affect CPC stability and predispose male subjects to macrozoospermia. In this study, we systematically applied computational approaches using different bioinformatic tools to predict the effect of missense SNPs reported on AURKC gene, we selected the deleterious ones and we introduced their corresponding amino acid substitutions on AURKC protein structure. Then we did a protein-protein docking between AURKC variants and INCENP followed by a structural assessment of each resulting complex using PRODIGY server, Yassara view, Ligplot + and we choose the complexes of the most impactful variants for molecular dynamics (MD) simulation study. Seventeen missense SNPs of AURKC were identified as deleterious between all reported ones. All of them were located on relatively conserved positions on AURKC protein according to Consurf server. Only the four missense SNPs; E91K, D166V, D221Y and G235V were ranked as the most impactful ones and were chosen for MD simulation. D221Y and G235V were responsible for the most remarkable changes on AURKC-INCENP structural stability, therefore, they were selected as the most deleterious ones. Experimental studies are recommended to test the actual effect of these two variants and their actual impact on the morphology of sperm cells.Communicated by Ramaswamy H. Sarma.

In silico analysis of missense SNPs in TNFR1a and their possible therapeutic or pathogenic role in immune diseases

Tumor necrosis factor alpha (TNFa) is an inflammatory cytokine that is involved in the pathogenesis of various inflammatory disorders including rheumatoid arthritis. TNF-alpha receptor I (TNFR1a) is one of the receptors TNFa binds with for its activation. Any variation in this receptor might affect the role of TNFa in successive events. Amino acid residue substitutions might happen in TNFR1a through non-synonymous single nucleotide polymorphisms (nsSNPs) which may alter the functioning of TNFa, hence, identifying any such substitutions is of paramount significance. In this study, six nsSNPs at five different evolutionary conserved regions are predicted to be detrimental to the structure and/or function of TNFR1a by using numerous computational tools. Their 3D models are also proposed in this study. Besides, they were found to reduce the stability and affect the molecular mechanisms of this protein. Two contrasting possibilities might happen because of these substitutions. One, they might reduce the production of TNFa which is overexpressed in inflammatory diseases, hence can play therapeutic role in such diseases. Second, they might possibly hinder the apoptosis to occur which can effectuate the uncontrolled division of cells, hence can be pathogenic in diseases like cancer. Further investigations on these nsSNPs using animal models and at cellular level will open doors to understand the underlying mechanisms behind various diseases.

Theoretical investigation on known renin inhibitors and generation of ligand-based pharmacophore models for hypertension treatment

The renin enzyme is considered a promising target for hypertension and renal diseases. Over the last three decades, several experimental and theoretical studies have been engaged in the discovery of potent renin inhibitors. The identified inhibitors that undergo clinical trials are still failing to meet the criteria of potency and safety. To date, there is no specific FDA-approved drug for renin inhibition. Our theoretical opinion describes that the most potent compounds identified in experimental studies but lacking safety and overdose issues could be solved by finding similar molecules that are stable, very active, and have no side effects, which will kick start the drug discovery process. Here, we utilized the most potent direct renin inhibitors reported earlier, followed further by our theoretical study reported in 2019. Ligand-based virtual screening, density functional theory, and dynamic simulation studies were employed to explore the identified compounds and co-crystallized molecule in the protein structure. From the diverse databases, we have identified several identical molecules based on their structural features, such as functional groups like hydrophobic (H), aromatic rings (R), hydrogen bond acceptor (A), and donor (D). The HHHPR five-point pharmacophore feature was identified as a template pharmacophore to search the potential compounds from the Enamine and LifeChemical databases and have a good fitness score with known renin inhibitors. Furthermore, theoretical validation was done through several studies that confirmed the activity of the identified molecules. Overall, we propose that these compounds might break the failure in adverse events and improve the potency of hypertension treatment.Communicated by Ramaswamy H. Sarma.

Identification of the effects of pathogenic genetic variations of human CYP2C9 and CYP2D6: an in silico approach

Genetic variations in the human cytochrome P450 family 2 subfamily C member 9 (CYP2C9) and cytochrome P450 family 2 subfamily D member 6 (CYP2D6) genes may affect drug metabolism and lead to alterations in phenotypes. Genetic variations are associated with toxicity, adverse drug reactions, inefficient treatment. Various in silico tools were combined to investigate the deleterious effects of missense non-synonymous single nucleotide polymorphisms (nsSNPs) of the human CYP2C9 and CYP2D6. The structural and functional effects of the high-risk non-synonymous SNPs in the human CYP2C9 and CYP2D6 were predicted by numerous computational mutation analysis methods. Out of 24 pathogenic missense SNPs in the CYP2C9, 22 nsSNPs had a decreasing effect on protein stability and 13 SNPs were showed to be located at conserved positions. Out of 27 high-risk deleterious non-synonymous SNPs in the human CYP2D6, 21 SNPs decreased protein stability and 16 nsSNPs were predicted to be positioned at conserved regions. Our present study suggests that the identified functional SNPs may affect drug metabolism associated with CYP2C9 and CYP2D6 enzymes.

Identification of molecular signatures and molecular dynamics simulation of highly deleterious missense variants of key autophagy regulator beclin 1: a computational based approach

Beclin 1 is a key autophagy regulator that also plays significant roles in other intracellular processes such as vacuolar protein sorting. Beclin 1 protein functions as a scaffold in the formation of a multiprotein assemblage during autophagy. Beclin 1 is involved in various diseases such as cancers, neurodegenerative and autophagy-related disorders. In this study, we have used various in silico tools to scan beclin 1 at the molecular level to find its molecular signatures. We have predicted and analysed deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of beclin 1 causing alterations in its structure and also affecting its interactions with other proteins. In total, twelve coding region deleterious variants were predicted using sequence-based tools and nine were predicted using various structure-based tools. The molecular dynamics (MD) simulations revealed an altered stability of the native structure due to the introduction of mutations. Destabilization of beclin 1 ECD domain was observed due to nsSNPs W300R and E302K. Beclin 1 deleterious nsSNPs were predicted to show significant effects on beclin 1 interactions with ATG14L1, UVRAG and VPS34 proteins and were also predicted to alter the protein-protein interface of beclin 1 complexes. Additionally, beclin 1 was predicted to have thirty-one potential phosphorylation and three ubiquitination sites. In conclusion, the molecular details of beclin 1 could help in the better understanding of its functioning. The study of nsSNPs and their effect on beclin 1 and its interactions might aid in understanding the basis of anomalies caused due to beclin 1 dysfunction.Communicated by Ramaswamy H. Sarma.

Mechanistic insights into the role of vitamin D and computational identification of potential lead compounds for Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder that affects dopaminergic neurons in the midbrain. A recent study suggests that Orphan Nuclear Receptor 1 (NURR1) impairment may contribute to PD pathogenesis. Our study found three potent agonists for NURR1 protein based on structural and ligand-based screening methods. The pharmacophore is comprised of a hydrogen bond donor, a hydrophobic group, and two aromatic rings (DHRR). The Pharmacophore screening method screened 3142 compounds, of which 3 were screened using structure-based screening. An analysis of the molecules using Molecular Mechanics-Generalized Born Surface Area (binding free energy) revealed a range of -46.77 to -59.06 Kcal/mol. After that, chemical reactivity was investigated by density functional theory, and molecular dynamics simulation was performed (protein-ligand stability). Based on the computational studies, Lifechemical_16901310, Maybridge_2815310, and NPACT_392450 are promising agonists with respect to NURR1. To confirm the potency of the identified compounds, further validation and experiments must be conducted.

Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food–Drug Interactions (FDI)

Modifications of the activity of Cytochrome 450 (CYP) enzymes by compounds in food might impair medical treatments. These CYP-mediated food–drug interactions (FDI) play a major role in drug clearance in the intestine and liver. Inter-individual variation in both CYP expression and structure is an important determinant of FDI. Traditional targeted approaches have highlighted a limited number of dietary inhibitors and single-nucleotide variations (SNVs), each determining personal CYP activity and inhibition. These approaches are costly in time, money and labor. Here, we review computational tools and databases that are already available and are relevant to predicting CYP-mediated FDIs. Computer-aided approaches such as protein–ligand interaction modeling and the virtual screening of big data narrow down hundreds of thousands of items in databanks to a few putative targets, to which the research resources could be further directed. Structure-based methods are used to explore the structural nature of the interaction between compounds and CYP enzymes. However, while collections of chemical, biochemical and genetic data are available today and call for the implementation of big-data approaches, ligand-based machine-learning approaches for virtual screening are still scarcely used for FDI studies. This review of CYP-mediated FDIs promises to attract scientists and the general public.

Structural and Dynamic Investigation of non-synonymous variations in Renin-AGT complex revealed altered binding via hydrogen bonding network reprogramming to accelerate the hypertension pathway

Hypertension is one of the major issues worldwide and one of the main factors involved in heart and kidney failure. Angiotensinogen and renin are key components of the renin-angiotensin-aldosterone system (RAAS), which plays an indispensable role in hypertension. The aimed of this study to find out the non-synonymous mutations and structure-based mutation-function correlation in the Renin-AGT complex and reveal the most deleterious mutations to accelerated hypertension. In the current study, we employed computational modelling and molecular simulation approaches to demonstrate the impact of specific mutations in the REN-AGT interface in hypertension. Computational algorithms i.e. PhD-SNP, PolyPhen-1, MAPP, SIFT, SNAP, PredictSNP, PolyPhen-2, and PANTHER predicted 20 mutations as deleterious in AGT while only five mutations were conformed as deleterious in the Renin protein. Investigation of the bonding analysis revealed that two mutations S107L and V193F in Renin altered the hydrogen-bonding paradigm at the interface site. Furthermore, exploration of structural-dynamic behaviors demonstrated by that these mutations also increases the structural stability to regulate the expression of disease pathway. The flexibility index of each residues and structural compactness analysis further validated the findings by portraying the difference in the dynamic behavior in contrast to the wild type. Binding energy calculations based on molecular mechanics/generalized Born surface area (MM/GBSA) methods were used which further established the binding differences between the wild type, S107L, and V193F mutant variants. The total binding energy for wild type, S107L, and V193F were reported to be -27.79 kcal/mol, -47.72 kcal/mol, and -38.25 kcal/mol respectively. In conclusion, these two mutations increase the binding free energy alongside the docking score to enhance the binding between Renin and AGT to overexpress this pathway in a hypertension disease condition. Patients with these mutations may be screened for potential therapeutic intervention.

An in-silico analysis to identify structural, functional and regulatory role of SNPs in hMRE11

Meiotic recombination 11 (MRE11) is a component of the tri-molecular MRE11-RAD50-NBS1 (MRN) complex, which functions as an exonuclease and endonuclease which is involved in identifying, signalling, protecting and repairing double-strand breaks in DNA (DSBs). Ataxia-telangiectasia-like disorder (ATLD) 1 and Nijmegen breakage syndrome (NBS)-like disorder are MRE11 associated diseases. In the present study, we used an integrated computational approach to identify the most deleterious SNPs and their structural and functional impact on human MRE11. Five of the 68 observed non-synonymous SNP (nsSNPs; I162T, S273C, W210C, D311Y and R364L) should be worked on due to their strong possible pathogenicity and the risk of changing protein properties. All the nsSNPs were highly conserved and decrease the protein stability located in the MRE11 nuclease and MRE11 DNA binding presumed domain. R364L and I162T were predicted to be involved in post-translational modification (PTM) sites. Furthermore, we also analysed the regulatory effect of noncoding SNPs on MRE11 gene regulation in which 6 SNPs were found to affect gene regulation. All six noncoding SNPs predicted chromatin interactive site whereas only one SNP was noted its association with miRNA binding site which disrupts 5 miRNA conserved site. These findings help future studies to get more insights into the role of these variants in the alteration of the MRE11 function. Communicated by Ramaswamy H. Sarma.