Computational analysis of non-synonymous single nucleotide polymorphism in the bovine PKLR geneComputational analysis of bovine PKLR gene

In Silico Studies of Phytoconstituents to Identify Potential Inhibitors for ERα Protein of Breast Cancer

BACKGROUND

It is noteworthy that a wide array of plants and nutraceuticals are effectively utilized in the treatment of various cancers, demonstrating potent effects on different cancer targets with fewer side effects. Notably, estrogen alpha has been identified as a crucial factor in breast cancer cell proliferation. Agents that can antagonize its action hold promise as potential drug leads for the treatment of breast cancer.

OBJECTIVE

This study aims to discover and identify the potential inhibitors against the most influential ERα receptor by the computational approach of 134 phytochemicals from 17 medicinal plants by using in silico docking studies.

METHODS

The molecular docking was performed by a genetic algorithm using the Auto Dock Vina program, and the validation of docking was also performed by using Molecular Dynamic (MD) simulation by the Desmond tool of Schrödinger molecular modeling. Drug-likeness properties and toxicity studies were conducted using SWISS PRO.

RESULTS

The top ten highest binding energy phytochemicals ginicidin (-10.8 kcal/mol), lemairone (-10.5 kcal/mol), ixoratannin (-10.0 kcal/mol), hydnocarpine (-9.8 kcal/mol), arabelline (-9.8 kcal/mol), acutilobine E (-9.8 kcal/mol), chaparinone (-8.9 kcal/mol), plumieride coumerate (-8.8 kcal/mol), acutilobine C (-8.7 kcal/mol), and mezerein (-8.7 kcal/mol) were taken for drug-likeness test and ADMET profile prediction with the help of web-based server SWISS ADME and protoxII. Docking's study dictated that ten phytochemical constituents showed greater binding interactions than standard tamoxifen (-6.6 kcal/mol) towards the target protein ERα. MSD study was achieved for the most active 4 phytoconstituents, and the stability of the ligand-protein complex was confirmed and showed that all the four compounds possess comparatively stable ligand-protein complexes with ERα target as compared to the tamoxifen-ERα complex.

CONCLUSION

Among the top ten phytochemicals, ginicidin (glycoside) formed a more stable complex and had greater binding affinity than standard tamoxifen with better safety profiles. Hence, this compound can be further studied for lead optimization and drug development for the treatment of breast cancer.

Comprehensive in silico analysis of prolactin receptor (PRLR) gene nonsynonymous single nucleotide polymorphisms (nsSNPs) reveals multifaceted impact on protein structure, function, and interactions

This study delves into the functional and structural implications of non-synonymous single nucleotide polymorphisms (nsSNPs) within the Prolactin Receptor (PRLR) gene. Thirteen deleterious nsSNPs were identified through bioinformatics tools, with SIFT predicting 168 out of 395 nsSNPs as detrimental, exhibiting tolerance index (TI) scores ranging from 0 to 0.05. Polyphen2 assigned likelihood scores >0.99 to all 13 nsSNPs, indicating high probability of harm, while Panther scores classified most nsSNPs as 'probably damaging', with specific mutations like W218R scoring 0.74, suggesting a higher impact. Stability analysis using DDG I-Mutant and DDG Mupro consistently predicted decreased stability for all mutations, with CUPSAT indicating mutations like V125G and W218R significantly decreasing stability. Structural analysis through DynaMut predicted destabilization for all mutations except L196I and L292H. MutPred2 highlighted structural alterations for all nsSNPs except L196I, L293V, R315W, and S353N. Domain analysis revealed key mutations within essential functional domains, with five nsSNPs located within Fibronectin type-III domains. Bayesian analysis through ConSurf identified 9 critical residues, with 11 nsSNPs exhibiting notably high conservation. STRING analysis unveiled a complex interaction network, indicating involvement in vital biological processes like lactation. Molecular dynamics (MD) simulations, spanning 100 nanoseconds, elucidated structural dynamics induced by detrimental missense SNPs. Post-translational modification (PTM) analysis identified specific mutations, such as R351, involved in methylation, while S353 was implicated in phosphorylation and glycosylation. These findings offer comprehensive insights into the molecular and phenotypic effects of deleterious nsSNPs in the PRLR gene, crucial for selective breeding.Communicated by Ramaswamy H. Sarma.

Unraveling the function and structure impact of deleterious missense SNPs in the human OX1R receptor by computational analysis

The orexin/hypocretin receptor type 1 (OX1R) plays a crucial role in regulating various physiological functions, especially feeding behavior, addiction, and reward. Genetic variations in the OX1R have been associated with several neurological disorders. In this study, we utilized a combination of sequence and structure-based computational tools to identify the most deleterious missense single nucleotide polymorphisms (SNPs) in the OX1R gene. Our findings revealed four highly conserved and structurally destabilizing missense SNPs, namely R144C, I148N, S172W, and A297D, located in the GTP-binding domain. Molecular dynamics simulations analysis demonstrated that all four most detrimental mutant proteins altered the overall structural flexibility and dynamics of OX1R protein, resulting in significant changes in the structural organization and motion of the protein. These findings provide valuable insights into the impact of missense SNPs on OX1R function loss and their potential contribution to the development of neurological disorders, thereby guiding future research in this field.