Single-nucleotide polymorphisms(SNPs)are recognized as one kind of major genetic variants in population scale.However,polymorphisms at the proteome level in population scale remain elusive.In the present study,we nam...Single-nucleotide polymorphisms(SNPs)are recognized as one kind of major genetic variants in population scale.However,polymorphisms at the proteome level in population scale remain elusive.In the present study,we named amino acid variances derived from SNPs within coding regions as single amino acid polymorphisms(SAPs)at the proteome level,and developed a pipeline of nontargeted and targeted proteomics to identify and quantify SAP peptides in human plasma.The absolute concentrations of three selected SAP-peptide pairs among 290 Asian individuals were measured by selected reaction monitoring(SRM)approach,and their associations with both obesity and diabetes were further analyzed.This work revealed that heterozygotes and homozygotes with various SAPs in a population could have different associations with particular traits.In addition,the SRM approach allows us for the first time to separately measure the absolute concentration of each SAP peptide in the heterozygotes,which also shows different associations with particular traits.展开更多
Single amino acid polymorphisms(SAPs),also known as non-synonymous single nucleotide polymorphisms(nsSNPs),are responsible for most of human genetic diseases.Discriminate the deleterious SAPs from neutral ones can hel...Single amino acid polymorphisms(SAPs),also known as non-synonymous single nucleotide polymorphisms(nsSNPs),are responsible for most of human genetic diseases.Discriminate the deleterious SAPs from neutral ones can help identify the disease genes and understand the mechanism of diseases.In this work,a method of deleterious SAP prediction at system level was established.Unlike most existing methods,our method not only considers the sequence and structure information,but also the network information.The integration of network information can improve the performance of deleterious SAP prediction.To make our method available to the public,we developed SySAP(a System-level predictor of deleterious Single Amino acid Polymorphisms),an easy-to-use and high accurate web server.SySAP is freely available at http://www.biosino.org/SySAP/and http://lifecenter.sgst.cn/SySAP/.展开更多
基金supported by the grants from the Ministry of Science and Technology(2011CB910200,2011CB910601)a grant from the National Natural Science Foundation of China(30821065)the grants from the Knowledge Innovation Program of the Chinese Academy of Sciences(KSCX1-YW-02,KJCX2-YW-M15).
文摘Single-nucleotide polymorphisms(SNPs)are recognized as one kind of major genetic variants in population scale.However,polymorphisms at the proteome level in population scale remain elusive.In the present study,we named amino acid variances derived from SNPs within coding regions as single amino acid polymorphisms(SAPs)at the proteome level,and developed a pipeline of nontargeted and targeted proteomics to identify and quantify SAP peptides in human plasma.The absolute concentrations of three selected SAP-peptide pairs among 290 Asian individuals were measured by selected reaction monitoring(SRM)approach,and their associations with both obesity and diabetes were further analyzed.This work revealed that heterozygotes and homozygotes with various SAPs in a population could have different associations with particular traits.In addition,the SRM approach allows us for the first time to separately measure the absolute concentration of each SAP peptide in the heterozygotes,which also shows different associations with particular traits.
基金by the National Basic Research Program of China(973 Program)(Grant Nos.2011CB910204,2010CB529206,and 2010CB912702)Research Program of the Chinese Academy of Sciences(KSCX2-EW-R-04,KSCX2-YW-R-190,2011KIP204)+3 种基金National Natural Science Foundation of China(Grant Nos.30900272 and 31070752)National Key Technology R&D Program in the 11th Five Year Plan of China(No.2008BAI64B01)National High-Tech R&D Program of China(863 Program)(Grant No.2009AA02Z304)National Scientific-Basic Special Fund(No.2009FY120100).
文摘Single amino acid polymorphisms(SAPs),also known as non-synonymous single nucleotide polymorphisms(nsSNPs),are responsible for most of human genetic diseases.Discriminate the deleterious SAPs from neutral ones can help identify the disease genes and understand the mechanism of diseases.In this work,a method of deleterious SAP prediction at system level was established.Unlike most existing methods,our method not only considers the sequence and structure information,but also the network information.The integration of network information can improve the performance of deleterious SAP prediction.To make our method available to the public,we developed SySAP(a System-level predictor of deleterious Single Amino acid Polymorphisms),an easy-to-use and high accurate web server.SySAP is freely available at http://www.biosino.org/SySAP/and http://lifecenter.sgst.cn/SySAP/.
