In-Silico analysis of missense SNPs in Human HPPD gene associated with Tyrosinemia type III and Hawkinsinuria

Comprehensive characterization of pathogenic missense CTRP6 variants and their association with cancer

BACKGROUND

Previous genome-wide association studies have linked three missense single nucleotide polymorphisms (SNPs) in C1q/TNF-related protein 6 (CTRP6) to diseases such as type 1 diabetes and autoimmune diseases. However, the potential association of newly identified missense CTRP6 variants with diseases, especially cancer, remains unclear.

METHODS

We used several pathogenicity prediction algorithms to identify deleterious mutations within the highly conserved C1q domain of human CTRP6, following the retrieval of all SNPs from the Ensembl database. We systematically analyzed the effects of these mutations on the protein's stability, flexibility, structural conformation, compactness, stiffness, and overall functionality using various bioinformatics tools. Additionally, we investigated the association of these mutations with different cancer types using the cBioPortal and canSAR databases.

RESULTS

We identified 11 detrimental missense SNPs within the C1q domain, a region critical for this protein's functionality. Using various computational methods, we predicted the functional impact of these missense variants and assessed their effects on the stability and flexibility of the CTRP6 structure. Molecular dynamics simulations revealed significant structural differences between the native and mutated structures, including changes in structural conformation, compactness, solvent accessibility, and flexibility. Additionally, our study shows a strong association between two mutations, G181S and R247W, and certain types of cancer: colon adenocarcinoma and uterine corpus endometrial carcinoma, respectively. We also found that the mutational status of CTRP6 and other cancer-related genes, such as MAP2K3, p16, TP53, and JAK1, affected each other's expression, potentially contributing to cancer development.

CONCLUSIONS

Our screening and predictive analysis of pathogenic missense variants in CTRP6 advance the understanding of the functional implications of these mutations, potentially facilitating more focused and efficient research in the future.

In-silico screening and analysis of missense SNPs in human CYP3A4/5 affecting drug-enzyme interactions of FDA-approved COVID-19 antiviral drugs

Single nucleotide polymorphisms (SNPs) represent the prevailing form of genetic variations observed in the human population. Such variations could alter the encoded enzymes' activities. CYP3A4/5 enzymes are involved in metabolizing drugs, notably antivirals against SARS-CoV-2. In this work, we computationally investigated antiviral-enzyme interactions of CYP3A4/5 genetic variants. We also examined the deleterious impact of 751 missense single nucleotide polymorphisms (SNPs) within the CYP3A4/5 genes. An ensemble of bioinformatics tools, [SIFT, PolyPhen-2, cadd, revel, metaLr, mutation assessor, Panther, SNP&GO, PhD-SNP, SNAP, Meta-SNP, FATHMM, I-Mutant, MuPro, INPS, CONSURF, GPS 5.0, MusiteDeep and NetPhos], identified a total of 94 variants (47 SNPs in CYP3A4, 47 SNPs in CYP3A5) to potentially impact the structural integrity as well as the activity of the CYP3A4/5 enzymes. Molecular docking was done to recognize the structural stability and binding properties of the CYP3A4/5 protein isoforms with 3 FDA-approved antiviral drugs. Our findings indicated that the CYP3A4 gene variants; R418T, I335T and R130P and the CYP3A5 gene variants; I335T, L133P and R130Q are considered the most deleterious missense SNPs. These mutants potentially affect drug-enzyme binding and hence may alter therapeutic response. Cataloguing deleterious SNPs is essential for personalized gene-based pharmacotherapy.

Prediction of the most deleterious non-synonymous SNPs in the human IL1B gene: evidence from bioinformatics analyses

BACKGROUND

Polymorphisms in IL1B play a significant role in depression, multiple inflammatory-associated disorders, and susceptibility to infection. Functional non-synonymous SNPs (nsSNPs) result in changes in the encoded amino acids, potentially leading to structural and functional alterations in the mutant proteins. So far, most genetic studies have concentrated on SNPs located in the IL1B promoter region, without addressing nsSNPs and their association with multifactorial diseases. Therefore, this study aimed to explore the impact of deleterious nsSNPs retrieved from the dbSNP database on the structure and functions of the IL1B protein.

RESULTS

Six web servers (SIFT, PolyPhen-2, PROVEAN, SNPs&GO, PHD-SNP, PANTHER) were used to analyze the impact of 222 missense SNPs on the function and structure of IL1B protein. Five novel nsSNPs (E100K, T240I, S53Y, D128Y, and F228S) were found to be deleterious and had a mutational impact on the structure and function of the IL1B protein. The I-mutant v2.0 and MUPro servers predicted that these mutations decreased the stability of the IL1B protein. Additionally, these five mutations were found to be conserved, underscoring their significance in protein structure and function. Three of them (T240I, D128Y, and F228S) were predicted to be cancer-causing nsSNPs. To analyze the behavior of the mutant structures under physiological conditions, we conducted a 50 ns molecular dynamics simulation using the WebGro online tool. Our findings indicate that the mutant values differ from those of the IL1B wild type in terms of RMSD, RMSF, Rg, SASA, and the number of hydrogen bonds.

CONCLUSIONS

This study provides valuable insights into nsSNPs located in the coding regions of IL1B, which lead to direct deleterious effects on the functional and structural aspects of the IL1B protein. Thus, these nsSNPs could be considered significant candidates in the pathogenesis of disorders caused by IL1B dysfunction, contributing to effective drug discovery and the development of precision medications. Thorough research and wet lab experiments are required to verify our findings. Moreover, bioinformatic tools were found valuable in the prediction of deleterious nsSNPs.

Identification of High-Risk Single Nucleotide Polymorphisms in the Human CYB5R3 Gene Responsible for Recessive Congenital Methemoglobinemia: A Computational Approach

Introduction

NADH-cytochrome b5 reductase deficiency due to pathogenic variants in the CYB5R3 gene causes recessive congenital methemoglobinemia (RCM) type I or type II. In type I, cyanosis from birth is the only major symptom, and the enzyme deficiency is restricted only to erythrocytes. Whereas in type II, cyanosis is associated with severe neurological manifestations, and the enzyme deficiency is generalized to all tissues.

Methods

In this study, several computational methods (SIFT, Polyphen-2, PROVEAN, Mutation Assessor, Panther, Phd-SNP, SNPs&GO, SNAP2, Align, GVGD, MutPred2, I-Mutant 2.0, MUpro, Duet, ConSurf and Netsurf-2.0 tools) were used to find the most deleterious nsSNPs in the CYB5R3 gene. Furthermore, structural analysis by Swiss-PDB viewer, protein-ligand docking using FTSite, and protein-protein interaction using STRING were carried out to evaluate the impact of these nsSNPs on the protein structure and function.

Results

Our in silico analysis suggested that out of 339 nsSNPs of the CYB5R3 gene, 17 (L47H, L47P, R61P, L73R G76D, G76C, P96H, G104C, S128P, G144D, P145S, L149P, Y151H, M177T, I178T, I216N, and G251V), are the most deleterious. Among them, two (P96H and S128P) were reported to be associated with the severe form RCM type II, six are related to RCM type I (G104C, G144D, P145S, L149P, M177T, and I178T), and the remaining nine high-risk nsSNPs have not yet been reported in RCM patients.

Discussion

This study highlighted the potential pathogenic nsSNPs of the CYB5R3 gene. To comprehend how these most harmful nsSNPs contribute to disease, it is crucial to experimentally validate their functional effects.

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.

Insights into 4-hydroxyphenylpyruvate dioxygenase-inhibitor interactions from comparative structural biology

4-Hydroxyphenylpyruvate dioxygenase (HPPD) plays a key role in tyrosine metabolism and has been identified as a promising target for herbicide and drug discovery. The structures of HPPD complexed with different types of inhibitors have been determined previously. We summarize the structures of HPPD complexed with structurally diverse molecules, including inhibitors, natural products, substrates, and catalytic intermediates; from these structures, the detailed inhibitory mechanisms of different inhibitors were analyzed and compared, and the key structural factors determining the slow-binding behavior of inhibitors were identified. Further, we propose four subpockets that accommodate different inhibitor substructures. We believe that these analyses will facilitate in-depth understanding of the enzymatic reaction mechanism and enable the design of new inhibitors with higher potency and selectivity.

Identification of deleterious nsSNPs in human HGF gene: in silico approach

HGF is a protein that binds to the hepatocyte growth factor receptor to regulate cell growth, cell motility and morphogenesis in different cells and tissues. Several bioinformatics tools and in silico methods were used to identify most deleterious nsSNPs that might change the structure and function of HGF protein. The in silico tools such as SIFT, SNP&GO and PolyPhen2 were used to distinguish deleterious nsSNPs from neutral ones. Protein stability is analysed by I-Mutant, MUpro and iStable. The functional and structural effects are predicted by other tools like MutPred2, Maestro, DUET etc. Analysis of structure was performed by HOPE and Mutation3D. SWISS-MODEL. server, was used for wild type and mutant proteins 3-D Modelling. Gene-gene and protein-protein interaction were predicted by GeneMANIA and STRING, respectively. The wildtype HGF protein and these three variants were independently docked with their close interactor protein MET by the use of ClusPro. Our study suggested that out of 392 missense nsSNPs of the HGF gene, five nsSNPs (D358G, G648R, I550N, N175S and R220Q), are the most deleterious in HGF gene. Gene-gene interactions showed relation of HGF with other genes depicting its importance in several pathways and co-expressions. The protein-protein interacting network is composed of 11 nodes. Analysis of protein stability by different tools indicated that the five nsSNPS decreased the stability of the protein. Anyway these nsSNPs need a confirmation analysis by experimental investigation and GWAS studiesCommunicated by Ramaswamy H. Sarma.