2019
DOI: 10.3390/ijms20071786
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Prediction of Bioactive Peptides from Chlorella sorokiniana Proteins Using Proteomic Techniques in Combination with Bioinformatics Analyses

Abstract: Chlorella is one of the most nutritionally important microalgae with high protein content and can be a good source of potential bioactive peptides. In the current study, isolated proteins from Chlorella sorokiniana were subjected to in silico analysis to predict potential peptides with biological activities. Molecular characteristics of proteins were analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and proteomics techniques. A total of eight proteins were identified by proteomic… Show more

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Cited by 61 publications
(53 citation statements)
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“…The amino acid composition and sequence of the protein greatly determine the presence of antioxidant peptides with required bioactivities (Tejano et al, 2019). The amino acid sequence of the protein can be obtained from databases, such as UniProtKB and NCBI, if the protein source is studied and reported that the hydrolysate from this protein exhibited particular bioactivity.…”
Section: Bioinformatic (In Silico) Approachmentioning
confidence: 99%
See 1 more Smart Citation
“…The amino acid composition and sequence of the protein greatly determine the presence of antioxidant peptides with required bioactivities (Tejano et al, 2019). The amino acid sequence of the protein can be obtained from databases, such as UniProtKB and NCBI, if the protein source is studied and reported that the hydrolysate from this protein exhibited particular bioactivity.…”
Section: Bioinformatic (In Silico) Approachmentioning
confidence: 99%
“…Conventional and bioinformatic approaches are widely employed to investigate in vitro bioactivity and physicochemical properties of antioxidant peptides possibly obtained from selected protein sources (Tejano et al, 2019;Yang et al, 2017). The peptides are inactive since they are fused in their parent proteins via peptide bonds.…”
Section: Introductionmentioning
confidence: 99%
“…For organisms like C. reinhardtii, Chlorella spp., P. tricornutum, and some blue-green algae (cyanobacteria), the genome-scale metabolic models are available. The core metabolic models and genome-scale system-level metabolic networks available for (Li et al, 2013(Li et al, , 2014bGao et al, 2014a;Sibi et al, 2014;Wu et al, 2015;Park and Choi, 2018;Vogler et al, 2018;Xing et al, 2018) Bathycoccus prasinos ( (May et al, 2009;Chen et al, 2010;Baba et al, 2011;Nguyen et al, 2011;Longworth et al, 2012;Mastrobuoni et al, 2012;Choi et al, 2013;Chaiboonchoe et al, 2014;Wase et al, 2014;Sithtisarn et al, 2017;Salguero et al, 2019) Chlorella (Lu et al, 2013;Ma et al, 2013;Rosenberg et al, 2014;Li et al, 2015a;Chen et al, 2017;Kumar et al, 2018;Tejano et al, 2019)…”
Section: Metabolomics and Metabolic Modelsmentioning
confidence: 99%
“…235,240 Once the sequences are obtained, bioinformatic tools can be used to predict peptides structure and estimate bioactive peptides. 254 More recently, an interactive and free web software platform, MixProTool, was developed, aiming to process multigroup proteomics data sets. This tool is compiled in R (www.r-project.…”
Section: Evaluating Proteomic Datamentioning
confidence: 99%