Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties

TL;DR

Multi-Peptide integrates transformer language models and graph neural networks with multimodal contrastive learning to accurately predict peptide properties, demonstrating state-of-the-art performance on key biological datasets.

Contribution

It introduces a novel multimodal framework combining PeptideBERT, GNNs, and CLIP for enhanced peptide property prediction.

Findings

Achieved 86.185% accuracy in hemolysis prediction.

Demonstrated robustness across multiple peptide datasets.

Showcased the effectiveness of multimodal learning in bioinformatics.

Abstract

Peptides are essential in biological processes and therapeutics. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with Graph Neural Networks (GNNs) to predict peptide properties. We combine PeptideBERT, a transformer model tailored for peptide property prediction, with a GNN encoder to capture both sequence-based and structural features. By employing Contrastive Language-Image Pre-training (CLIP), Multi-Peptide aligns embeddings from both modalities into a shared latent space, thereby enhancing the model's predictive accuracy. Evaluations on hemolysis and nonfouling datasets demonstrate Multi-Peptide's robustness, achieving state-of-the-art 86.185% accuracy in hemolysis prediction. This study highlights the potential of multimodal learning in bioinformatics, paving the way for accurate and reliable predictions in peptide-based research and applications.