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.
Peng Cheng Laboratory, 2 Xingke 1st Street, Nanshan District, Shenzhen 518055, China.
Brief Bioinform. 2024 May 23;25(4). doi: 10.1093/bib/bbae348.
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.
功能肽在各种生物过程中发挥着关键作用，并在药物发现和生物技术等许多领域具有重要的应用潜力。准确预测肽的功能对于理解其多样化的作用和设计基于肽的治疗方法至关重要。在这里，我们提出了 CELA-MFP，这是一种深度学习框架，它将特征对比增强和标签适应纳入其中，用于预测多功能治疗性肽。CELA-MFP 利用蛋白质语言模型（pLM）从肽序列中提取特征，然后将其输入到 Transformer 解码器中进行功能预测，有效地对不同功能之间的相关性进行建模。为了增强每个肽序列的表示，在训练过程中使用了对比学习。实验结果表明，CELA-MFP 在两个广泛使用的数据集 MFBP 和 MFTP 上的大多数评估指标上均优于最先进的方法。通过可视化 pLM 和 Transformer 解码器中的注意力模式，证明了 CELA-MFP 的可解释性。最后，建立了一个用户友好的在线服务器，用于预测多功能肽，作为所提出的 CELA-MFP 的实现，可以在 http://dreamai.cmii.online/CELA-MFP 上免费访问。
