The invention belongs to the crossing field of marine
biotechnology and
bioinformatics, and particularly relates to a method for high-
throughput identification of natural functional oligopeptides. At present, bottlenecks still exist in development and application of various functional oligopeptides such as penetrating peptides and antibacterial peptides in non-model species, and the high adaptability of the existing functional oligopeptides in complex and diverse non-model organisms is limited mainly due to differences (
amino acid preference and
receptor specificity) among species. Therefore, the invention provides a method for high-
throughput identification of functional oligopeptides from natural sequences of organisms by means of
machine learning. According to the method, based on a biological natural
protein sequence, an
oligopeptide sequence set meeting a preset length condition is generated through a sliding window strategy
system, a deep
machine learning model is utilized to perform high-
throughput functional prediction on the
oligopeptide sequence, and on the basis of performing'interruption-prediction-re-comparison 'on existing functional oligopeptides, the functional
oligopeptide sequence set meeting the preset length condition is obtained. The comparison threshold value for accurately identifying the functional oligopeptide can be determined, and the natural functional oligopeptide existing in the
protein sequence can be effectively identified based on the threshold value. The functional oligopeptide obtained by the method has strict screening of physicochemical properties of a
machine learning model and high-adaptability biological characteristics formed in species evolution. According to the method, the direct application capability of the functional oligopeptide in non-model species is remarkably improved, and an accurate and high-throughput technical means is provided for efficient development and application of the functional oligopeptide in non-model organisms.