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Discrimination and quantification of homologous keratins from goat and sheep with dual protease digestion and PRM assays.
Miao, Chen; Yang, Yunfei; Li, Shanshan; Guo, Yufeng; Shui, Wenqing; Cao, Qichen.
Afiliação
  • Miao C; Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Yang Y; Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
  • Li S; iHuman Institute, Shanghai Tech University, Shanghai 201210, China.
  • Guo Y; Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
  • Shui W; iHuman Institute, Shanghai Tech University, Shanghai 201210, China; School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China. Electronic address: shuiwq@shanghaitech.edu.cn.
  • Cao Q; Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China. Electronic address: cao_qc@tib.cas.cn.
J Proteomics ; 186: 38-46, 2018 08 30.
Article em En | MEDLINE | ID: mdl-30030164
ABSTRACT
Mass spectrometry (MS) technology has a special advantage in species determination for protein-rich samples which requires identification of species-specific peptides. However, for species discrimination of highly homologous proteins, it remains challenging to select the species unique peptides with routine proteomics approaches. In this work, we chose keratins and keratin-associated proteins (KAPs) present in cashmere fibers from goat and wool fibers from sheep as targets, to develop a dual-protease digestion workflow based on in-silico and experimental analysis. Combined usage of Glu-C and trypsin proteases showed the best digestion performance for MS identification of keratins and KAPs from different species. The parallel reaction monitoring (PRM) technique was implemented to validate and quantify the selected species discriminable peptides. The fiber composition of both blended animal hair fibers and industrial textile fabrics were successfully determined with the PRM assay. Furthermore, we identified over 360 peptides from the cashmere fiber beyond the current Uniprot goat proteome database. We expect our new workflow would improve the identification and quantification of keratin and KAPs, and provide inspiration for distinguishing other highly homologous proteins. We also anticipate the set of species-specific peptides from keratin or KAPs validated in this work would benefit the quality assessment for industrial fiber materials and textile products.

SIGNIFICANCE:

Discriminating species from highly homologous proteins is challenging for MS-based shotgun proteomics. The large percentage of overlapped protein sequence hinders the identification of the species unique peptides. In this work, we aimed to discriminate sample species between goat and sheep from keratins and keratin-associated proteins (KAPs). A dedicated workflow was developed to boost the exposure and quantification of species discriminable peptides. The dual-proteases digestion approach was optimized based on amino acid sequence analysis and protein in-silico digestion analysis. The PRM assays were established to validate and quantify the selected species unique peptides. Additionally, we have identified about 360 novel candidate peptides complementary to the current goat protein sequence database. We expect our workflow would improve the species discrimination for highly homologous proteins and benefit the proteomics study of keratin and KAPs in the human proteome.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Homologia de Sequência de Aminoácidos / Proteólise / Queratinas Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Homologia de Sequência de Aminoácidos / Proteólise / Queratinas Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2018 Tipo de documento: Article