PepAnno: A structure-aware deep learning framework for bioactive peptide prediction, structural visualization, and physicochemical profiling
Fig 5
Backend workflow of the PepAnno platform.
The process involves: (1) User data input (peptide sequences and parameters) followed by preprocessing. (2) Calculation of various peptide physicochemical features using toolkits. (3) Tertiary structure prediction of peptides. (4) Input of processed data into functional prediction model. (5) Final output of three main data files: comprehensive feature data, structural information, and integrated prediction results for all functions.