{"title":"通过硅学筛选、分子动力学模拟和结合自由能确定可破坏 p53 稳定性并减少与 DNA 结合的单点突变","authors":"Shahidul M. Islam, Md. Mehedi Hasan, Jahidul Alam, Anonya Dey, Dylan Molineaux","doi":"10.1002/prot.26747","DOIUrl":null,"url":null,"abstract":"Considering p53's pivotal role as a tumor suppressor protein, proactive identification and characterization of potentially harmful p53 mutations are crucial before they appear in the population. To address this, four computational prediction tools—SIFT, Polyphen‐2, PhD‐SNP, and MutPred2—utilizing sequence‐based and machine‐learning algorithms, were employed to identify potentially deleterious p53 nsSNPs (nonsynonymous single nucleotide polymorphisms) that may impact p53 structure, dynamics, and binding with DNA. These computational methods identified three variants, namely, C141Y, C238S, and L265P, as detrimental to p53 stability. Furthermore, molecular dynamics (MD) simulations revealed that all three variants exhibited heightened structural flexibility compared to the native protein, especially the C141Y and L265P mutations. Consequently, due to the altered structure of mutant p53, the DNA‐binding affinity of all three variants decreased by approximately 1.8 to 9.7 times compared to wild‐type p53 binding with DNA (14 μM). Notably, the L265P mutation exhibited an approximately ten‐fold greater reduction in binding affinity. Consequently, the presence of the L265P mutation in p53 could pose a substantial risk to humans. Given that p53 regulates abnormal tumor growth, this research carries significant implications for surveillance efforts and the development of anticancer therapies.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":"33 1","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"In Silico Screening, Molecular Dynamics Simulation and Binding Free Energy Identify Single‐Point Mutations That Destabilize p53 and Reduce Binding to DNA\",\"authors\":\"Shahidul M. Islam, Md. Mehedi Hasan, Jahidul Alam, Anonya Dey, Dylan Molineaux\",\"doi\":\"10.1002/prot.26747\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Considering p53's pivotal role as a tumor suppressor protein, proactive identification and characterization of potentially harmful p53 mutations are crucial before they appear in the population. To address this, four computational prediction tools—SIFT, Polyphen‐2, PhD‐SNP, and MutPred2—utilizing sequence‐based and machine‐learning algorithms, were employed to identify potentially deleterious p53 nsSNPs (nonsynonymous single nucleotide polymorphisms) that may impact p53 structure, dynamics, and binding with DNA. These computational methods identified three variants, namely, C141Y, C238S, and L265P, as detrimental to p53 stability. Furthermore, molecular dynamics (MD) simulations revealed that all three variants exhibited heightened structural flexibility compared to the native protein, especially the C141Y and L265P mutations. Consequently, due to the altered structure of mutant p53, the DNA‐binding affinity of all three variants decreased by approximately 1.8 to 9.7 times compared to wild‐type p53 binding with DNA (14 μM). Notably, the L265P mutation exhibited an approximately ten‐fold greater reduction in binding affinity. Consequently, the presence of the L265P mutation in p53 could pose a substantial risk to humans. Given that p53 regulates abnormal tumor growth, this research carries significant implications for surveillance efforts and the development of anticancer therapies.\",\"PeriodicalId\":56271,\"journal\":{\"name\":\"Proteins-Structure Function and Bioinformatics\",\"volume\":\"33 1\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proteins-Structure Function and Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1002/prot.26747\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proteins-Structure Function and Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/prot.26747","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
In Silico Screening, Molecular Dynamics Simulation and Binding Free Energy Identify Single‐Point Mutations That Destabilize p53 and Reduce Binding to DNA
Considering p53's pivotal role as a tumor suppressor protein, proactive identification and characterization of potentially harmful p53 mutations are crucial before they appear in the population. To address this, four computational prediction tools—SIFT, Polyphen‐2, PhD‐SNP, and MutPred2—utilizing sequence‐based and machine‐learning algorithms, were employed to identify potentially deleterious p53 nsSNPs (nonsynonymous single nucleotide polymorphisms) that may impact p53 structure, dynamics, and binding with DNA. These computational methods identified three variants, namely, C141Y, C238S, and L265P, as detrimental to p53 stability. Furthermore, molecular dynamics (MD) simulations revealed that all three variants exhibited heightened structural flexibility compared to the native protein, especially the C141Y and L265P mutations. Consequently, due to the altered structure of mutant p53, the DNA‐binding affinity of all three variants decreased by approximately 1.8 to 9.7 times compared to wild‐type p53 binding with DNA (14 μM). Notably, the L265P mutation exhibited an approximately ten‐fold greater reduction in binding affinity. Consequently, the presence of the L265P mutation in p53 could pose a substantial risk to humans. Given that p53 regulates abnormal tumor growth, this research carries significant implications for surveillance efforts and the development of anticancer therapies.
期刊介绍:
PROTEINS : Structure, Function, and Bioinformatics publishes original reports of significant experimental and analytic research in all areas of protein research: structure, function, computation, genetics, and design. The journal encourages reports that present new experimental or computational approaches for interpreting and understanding data from biophysical chemistry, structural studies of proteins and macromolecular assemblies, alterations of protein structure and function engineered through techniques of molecular biology and genetics, functional analyses under physiologic conditions, as well as the interactions of proteins with receptors, nucleic acids, or other specific ligands or substrates. Research in protein and peptide biochemistry directed toward synthesizing or characterizing molecules that simulate aspects of the activity of proteins, or that act as inhibitors of protein function, is also within the scope of PROTEINS. In addition to full-length reports, short communications (usually not more than 4 printed pages) and prediction reports are welcome. Reviews are typically by invitation; authors are encouraged to submit proposed topics for consideration.