CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction

Author:

Fang Yitian12ORCID,Luo Mingshuang2,Ren Zhixiang2,Wei Leyi34ORCID,Wei Dong-Qing12ORCID

Affiliation:

1. State Key Laboratory of Microbial Metabolism , Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China

2. Peng Cheng Laboratory , 2 Xingke 1st Street, Nanshan District, Shenzhen 518055 , China

3. Centre for Artificial Intelligence Driven Drug Discovery , Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao 999078, China

4. School of Informatics, Xiamen University , 422 Siming South Road, Xiamen 361005 , China

Abstract

Abstract Functional peptides play crucial roles in various biological processes and hold significant potential in many fields such as drug discovery and biotechnology. Accurately predicting the functions of peptides is essential for understanding their diverse effects and designing peptide-based therapeutics. Here, we propose CELA-MFP, a deep learning framework that incorporates feature Contrastive Enhancement and Label Adaptation for predicting Multi-Functional therapeutic Peptides. CELA-MFP utilizes a protein language model (pLM) to extract features from peptide sequences, which are then fed into a Transformer decoder for function prediction, effectively modeling correlations between different functions. To enhance the representation of each peptide sequence, contrastive learning is employed during training. Experimental results demonstrate that CELA-MFP outperforms state-of-the-art methods on most evaluation metrics for two widely used datasets, MFBP and MFTP. The interpretability of CELA-MFP is demonstrated by visualizing attention patterns in pLM and Transformer decoder. Finally, a user-friendly online server for predicting multi-functional peptides is established as the implementation of the proposed CELA-MFP and can be freely accessed at http://dreamai.cmii.online/CELA-MFP.

Funder

Intergovernmental International Scientific and Technological Innovation and Cooperation Program of The National Key R&D Program

National Natural Science Foundation of China

Joint Research Funds for Medical and Engineering and Scientific Research at Shanghai Jiao Tong University

Internal Research Grants of Macao Polytechnic University

Science and Technology Development Fund

Peng Cheng Laboratory and the Center for High-Performance Computing

Shanghai Jiao Tong University

Publisher

Oxford University Press (OUP)

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