Identification of Diseases caused by non-Synonymous Single Nucleotide Polymorphism using Machine Learning Algorithms DOI Open Access
Muhammad Junaid Anjum, Fatima Tariq,

Khadeeja Anjum

et al.

VFAST Transactions on Software Engineering, Journal Year: 2024, Volume and Issue: 12(4), P. 312 - 325

Published: Dec. 31, 2024

The production of vaccines for diseases depends entirely on its analysis. However, to test every disease extensively is costly as it would involve the investigation known gene related a disease. This issue further elevated when different variations are considered. As such use computational methods considered tackle this issue. research makes machine learning algorithms in identification and prediction Single Nucleotide Polymorphism. presents that Gradient Boosting algorithm performs better comparison other genic variation predictions with an accuracy 70%.

Language: Английский

Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study DOI Creative Commons
Md. Mostafa Kamal,

Kazi Fahmida Haque Shantanu,

Shamiha Tabassum Teeya

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0316465 - e0316465

Published: Jan. 24, 2025

The cytotoxic T-lymphocyte antigen-4 (CTLA4) is essential in controlling T cell activity within the immune system. Thus, uncovering molecular dynamics of single nucleotide polymorphisms (SNPs) CTLA4 gene critical. We identified non-synonymous SNPs (nsSNPs), examined their impact on protein stability, and sequences associated with them human gene. There were 3134 (rsIDs) our study. Out these, 186 missense variants (5.93%), 1491 intron (47.57%), 91 synonymous (2.90%), while remaining unspecified. utilized SIFT, PolyPhen-2, PROVEAN, SNAP for identifying deleterious nsSNPs, SNPs&GO, PhD SNP, PANTHER verifying risk nsSNPs Following SIFT analysis, six as reporting second third probably damaging one benign, respectively. From upstream rs138279736, rs201778935, rs369567630, rs376038796 found to be deleterious, damaging, disease associated. ConSurf predicted conservation scores four Project Hope suggested that all mutations could disrupt interactions. Furthermore, mCSM DynaMut2 analyses indicated a decrease ΔΔG stability mutants. GeneMANIA STRING networks highlighted correlations CD86 CD80 genes. Finally, MD simulation revealed consistent fluctuation RMSD RMSF, consequently Rg, hydrogen bonds, PCA mutant proteins compared wild-type, which might alter functional structural protein. current comprehensive study shows how various can modify characteristics protein, potentially influencing pathogenesis diseases humans. Further, experimental studies are needed analyze effect these susceptibility pathological phenotype populations.

Language: Английский

Citations

1
In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene DOI Creative Commons
Md. Mostafa Kamal, Md. Sohel Mia, Md. Omar Faruque

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 21, 2024

Single nucleotide polymorphisms (SNPs) are one of the most common determinants and potential biomarkers human disease pathogenesis. SNPs could alter amino acid residues, leading to loss structural functional integrity encoded protein. In humans, members minichromosome maintenance (MCM) family play a vital role in cell proliferation have significant impact on tumorigenesis. Among MCM members, molecular mechanism how missense complex component 6 (MCM6) contribute DNA replication tumor pathogenesis is underexplored needs be elucidated. Hence, series sequence structure-based computational tools were utilized determine mutations affect corresponding MCM6 From dbSNP database, among 15,009 gene, 642 (4.28%), 291 synonymous (1.94%), 12,500 intron (83.28%) observed. Out SNPs, 33 found deleterious during SIFT analysis. these, 11 (I123S, R207C, R222C, L449F, V456M, D463G, H556Y, R602H, R633W, R658C, P815T) as deleterious, probably damaging, affective disease-associated. Then, I123S, R658C highly harmful. Six R633W) had destabilize protein predicted by DynaMut2. Interestingly, five high-risk distributed two domains (PF00493 PF14551). During dynamics simulations analysis, consistent fluctuation RMSD RMSF values, high Rg hydrogen bonds mutant proteins compared wild-type revealed that these might structure stability results from analyses guide exploration which gene properties protein, identification ways minimize harmful effects humans.

Language: Английский

Citations

4
Prediction and assessment of deleterious and disease causing nonsynonymous single nucleotide polymorphisms (nsSNPs) in human FOXP4 gene: An in-silico study DOI Creative Commons
Md. Mostafa Kamal, Shamiha Tabassum Teeya, Md. Mahfuzur Rahman

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(12), P. e32791 - e32791

Published: June 1, 2024

In humans, FOXP gene family is involved in embryonic development and cancer progression. The FOXP4 (Forkhead box protein P4) belongs to this family. plays a crucial role oncogenesis. Single nucleotide polymorphisms are biological markers common determinants of human diseases. Mutations can largely affect the function corresponding protein. Therefore, molecular mechanism nsSNPs needs be elucidated. Initially, SNPs were extracted from dbSNP database total 23124 was found, where 555 nonsynonymous, 20525 intronic, 1114 noncoding transcript, 334 synonymous obtained rest unspecified. Then, series bioinformatics tools (SIFT, PolyPhen2, SNAP2, PhD SNP, PANTHER, I-Mutant2.0, MUpro, GOR IV, ConSurf, NetSurfP 2.0, HOPE, DynaMut2, GeneMANIA, STRING Schrodinger) used explore effect on structural stability. First, analyzed using SIFT, which 57 found as deleterious. Following, SNP PANTHER analyses, 10 (rs372762294, rs141899153, rs142575732, rs376938850, rs367607523, rs112517943, rs140387832, rs373949416, rs373949416 rs376160648) observed deleterious, damaging diseases associated. Following that, I-Mutant2.0 MUpro servers, 7 most unstable. IV predicted that these seven structure by altering contents alpha helixes, extended strands, random coils. 5 showed decrease ΔΔG value compared with wild-type responsible for destabilizing GeneMANIA network revealed interaction other genes. Finally, dynamics simulation analysis consistent fluctuation RMSD RMSF values, Rg hydrogen bonds mutant proteins WT, might alter functional stability As result, aforementioned integrated comprehensive bioinformatic analyses provide insight into how various change properties protein, potentially proceeding pathophysiology

Language: Английский

Citations

4
In Silico Identification and Functional Impact of Deleterious Nonsynonymous Single‐Nucleotide Polymorphisms (nsSNPs) in Type 2 Diabetes–Associated Genes in South Asian Populations DOI Creative Commons
Md. Hafizur Rahman, Md. Numan Islam,

Md. Golam Rabby

et al.

Genetics Research, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

This study explores the impact of nonsynonymous single‐nucleotide polymorphisms (nsSNPs) on type 2 diabetes (T2D). The nsSNPs are genetic variations that alter amino acids within proteins, affecting protein structure and function. investigated seven candidate genes associated with T2D pathogenesis from genome‐wide association studies (GWASs) catalog datasets. Subsequently, six mutation‐prediction tools were employed to identify most harmful these genes. Further analysis involved evaluating evolutionary conservation using ConSurf server assessing stability I‐Mutant MUpro. Functional structural effects predicted MutPred2, Project HOPE, FoldAmyloid tools. We obtained 42 deleterious identified Among these, 38 located in highly conserved residues a conservative score 7–9. Furthermore, 20 found decrease stability, 18 them classified as pathogenic mutations. These mutations can either reduce or increase size charge hydrophobic characteristics affected proteins. In addition, eight mutants four amyloidogenic regions, suggesting potential link aggregation. findings provide valuable insights into physicochemical properties changes nsSNPs. concludes distinctive significant suggest for future research. Understanding variants through large‐scale may pave way developing therapeutic interventions targeting variations, ultimately improving our understanding treatment.

Language: Английский

Citations

0
Identification of Diseases caused by non-Synonymous Single Nucleotide Polymorphism using Machine Learning Algorithms DOI Open Access
Muhammad Junaid Anjum, Fatima Tariq,

Khadeeja Anjum

et al.

VFAST Transactions on Software Engineering, Journal Year: 2024, Volume and Issue: 12(4), P. 312 - 325

Published: Dec. 31, 2024

The production of vaccines for diseases depends entirely on its analysis. However, to test every disease extensively is costly as it would involve the investigation known gene related a disease. This issue further elevated when different variations are considered. As such use computational methods considered tackle this issue. research makes machine learning algorithms in identification and prediction Single Nucleotide Polymorphism. presents that Gradient Boosting algorithm performs better comparison other genic variation predictions with an accuracy 70%.

Language: Английский

Citations

0