In silico prediction of deleterious single nucleotide polymorphism in S100A4 metastatic gene: potential early diagnostic marker
S100A4 protein overexpression has been reported in different types of cancer and plays a key role by interacting with the tumor suppressor protein Tp53. Single nucleotide polymorphisms (SNP) in S100A4 could directly influence the biomolecular interaction with the tumor suppressor protein Tp53 due to...
| Main Authors: | , , , , , |
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| Format: | Article |
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Hindawi
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/101935/ |
| _version_ | 1848863671280730112 |
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| author | Aisha Farhana, . Kothandan, Sangeetha Alsrhani, Abdullah Mok, Pooi Ling Subbiah, Suresh Kumar Yusuf Saleem Khan |
| author_facet | Aisha Farhana, . Kothandan, Sangeetha Alsrhani, Abdullah Mok, Pooi Ling Subbiah, Suresh Kumar Yusuf Saleem Khan |
| author_sort | Aisha Farhana, . |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | S100A4 protein overexpression has been reported in different types of cancer and plays a key role by interacting with the tumor suppressor protein Tp53. Single nucleotide polymorphisms (SNP) in S100A4 could directly influence the biomolecular interaction with the tumor suppressor protein Tp53 due to their aberrant conformations. Hence, the study was designed to predict the deleterious SNP and its effect on the S100A4 protein structure and function. Twenty-one SNP data sets were screened for nonsynonymous mutations and subsequently subjected to deleterious mutation prediction using different computational tools. The screened deleterious mutations were analyzed for their changes in functionality and their interaction with the tumor suppressor protein Tp53 by protein-protein docking analysis. The structural effects were studied using the 3DMissense mutation tool to estimate the solvation energy and torsion angle of the screened mutations on the predicted structures. In our study, 21 deleterious nonsynonymous mutations were screened, including F72V, E74G, L5P, D25E, N65S, A28V, A8D, S20L, L58P, and K26N were found to be remarkably conserved by exhibiting the interaction either with the EF-hand 1 or EF-hand 2 domain. The solvation and torsion values significantly deviated for the mutant-type structures with S20L, N65S, and F72L mutations and showed a marked reduction in their binding affinity with the Tp53 protein. Hence, these deleterious mutations might serve as prospective targets for diagnosing and developing personalized treatments for cancer and other related diseases. |
| first_indexed | 2025-11-15T13:36:37Z |
| format | Article |
| id | upm-101935 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:36:37Z |
| publishDate | 2022 |
| publisher | Hindawi |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1019352023-10-04T07:14:19Z http://psasir.upm.edu.my/id/eprint/101935/ In silico prediction of deleterious single nucleotide polymorphism in S100A4 metastatic gene: potential early diagnostic marker Aisha Farhana, . Kothandan, Sangeetha Alsrhani, Abdullah Mok, Pooi Ling Subbiah, Suresh Kumar Yusuf Saleem Khan S100A4 protein overexpression has been reported in different types of cancer and plays a key role by interacting with the tumor suppressor protein Tp53. Single nucleotide polymorphisms (SNP) in S100A4 could directly influence the biomolecular interaction with the tumor suppressor protein Tp53 due to their aberrant conformations. Hence, the study was designed to predict the deleterious SNP and its effect on the S100A4 protein structure and function. Twenty-one SNP data sets were screened for nonsynonymous mutations and subsequently subjected to deleterious mutation prediction using different computational tools. The screened deleterious mutations were analyzed for their changes in functionality and their interaction with the tumor suppressor protein Tp53 by protein-protein docking analysis. The structural effects were studied using the 3DMissense mutation tool to estimate the solvation energy and torsion angle of the screened mutations on the predicted structures. In our study, 21 deleterious nonsynonymous mutations were screened, including F72V, E74G, L5P, D25E, N65S, A28V, A8D, S20L, L58P, and K26N were found to be remarkably conserved by exhibiting the interaction either with the EF-hand 1 or EF-hand 2 domain. The solvation and torsion values significantly deviated for the mutant-type structures with S20L, N65S, and F72L mutations and showed a marked reduction in their binding affinity with the Tp53 protein. Hence, these deleterious mutations might serve as prospective targets for diagnosing and developing personalized treatments for cancer and other related diseases. Hindawi 2022-07-31 Article PeerReviewed Aisha Farhana, . and Kothandan, Sangeetha and Alsrhani, Abdullah and Mok, Pooi Ling and Subbiah, Suresh Kumar and Yusuf Saleem Khan (2022) In silico prediction of deleterious single nucleotide polymorphism in S100A4 metastatic gene: potential early diagnostic marker. Contrast Media and Molecular Imaging, 2022 (spec.). art. no. 4202623. 01-12. ISSN 1555-4309; ESSN: 1555-4317 https://www.hindawi.com/journals/cmmi/2022/4202623/ 10.1155/2022/4202623 |
| spellingShingle | Aisha Farhana, . Kothandan, Sangeetha Alsrhani, Abdullah Mok, Pooi Ling Subbiah, Suresh Kumar Yusuf Saleem Khan In silico prediction of deleterious single nucleotide polymorphism in S100A4 metastatic gene: potential early diagnostic marker |
| title | In silico prediction of deleterious single nucleotide polymorphism in S100A4 metastatic gene: potential early diagnostic marker |
| title_full | In silico prediction of deleterious single nucleotide polymorphism in S100A4 metastatic gene: potential early diagnostic marker |
| title_fullStr | In silico prediction of deleterious single nucleotide polymorphism in S100A4 metastatic gene: potential early diagnostic marker |
| title_full_unstemmed | In silico prediction of deleterious single nucleotide polymorphism in S100A4 metastatic gene: potential early diagnostic marker |
| title_short | In silico prediction of deleterious single nucleotide polymorphism in S100A4 metastatic gene: potential early diagnostic marker |
| title_sort | in silico prediction of deleterious single nucleotide polymorphism in s100a4 metastatic gene: potential early diagnostic marker |
| url | http://psasir.upm.edu.my/id/eprint/101935/ http://psasir.upm.edu.my/id/eprint/101935/ http://psasir.upm.edu.my/id/eprint/101935/ |