Abstract: Therapeutic peptides are widely used in disease treatment due to their minimal toxicity,high absorption rate and high biological activity as an effective alternative to traditional antibiotic drugs in the field of biomedicine.While there has been limited consideration given to predicting multi-functions of therapeutic peptides in the perspective of deep learning until now.Therefore,a neural network prediction model with projected gradient descent(PGD),called PrMFTP-PGD,is proposed based on publicly available multi-functional therapeutic peptide(MFTP) datasets.The approach involves three steps.First,a multi-encoder is incorporated with a multi-head attention mechanism to extract the features of the input vectors and obtain a better representation capability.Then,a linear attention mechanism is introduced to further enhance the representation and extraction of features.Finally,adversarial training with PGD is used to mitigate the challenges posed by the inherent class imbalance problem in the MFTP datasets for the prediction task.The proposed method is compared with the existing methods,MPMAB,MLBP,PrMFTP and SP-RNN,on an independent test set.It demonstrates the biggest improvements across four key metrics-precision(2.55%),coverage(2.81%),accuracy(2.59%),and absolute correctness(2.39%),indicating that this method can enhance the model’s ability to capture sequence features,so as to better predict multifunctional therapeutic peptides.

Key words: Multi-functional therapeutic peptide(MFTP), Function prediction, Multi-label classification, Multi-coded neural networks, Deep learning