Prot2Text-V2: Protein Function Prediction with Multimodal Contrastive Alignment
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918169758662656 |
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| author | Fei, Xiao Chatzianastasis, Michail Carneiro, Sarah Almeida Abdine, Hadi Petalidis, Lawrence P. Vazirgiannis, Michalis |
| author_facet | Fei, Xiao Chatzianastasis, Michail Carneiro, Sarah Almeida Abdine, Hadi Petalidis, Lawrence P. Vazirgiannis, Michalis |
| contents | Predicting protein function from sequence is a central challenge in computational biology. While existing methods rely heavily on structured ontologies or similarity-based techniques, they often lack the flexibility to express structure-free functional descriptions and novel biological functions. In this work, we introduce Prot2Text-V2, a novel multimodal sequence-to-text model that generates free-form natural language descriptions of protein function directly from amino acid sequences. Our method combines a protein language model as a sequence encoder (ESM-3B) and a decoder-only language model (LLaMA-3.1-8B-Instruct) through a lightweight nonlinear modality projector. A key innovation is our Hybrid Sequence-level Contrastive Alignment Learning (H-SCALE), which improves cross-modal learning by matching mean- and std-pooled protein embeddings with text representations via contrastive loss. After the alignment phase, we apply instruction-based fine-tuning using LoRA on the decoder to teach the model how to generate accurate protein function descriptions conditioned on the protein sequence. We train Prot2Text-V2 on about 250K curated entries from SwissProt and evaluate it under low-homology conditions, where test sequences have low similarity with training samples. Prot2Text-V2 consistently outperforms traditional and LLM-based baselines across various metrics. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_11194 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prot2Text-V2: Protein Function Prediction with Multimodal Contrastive Alignment Fei, Xiao Chatzianastasis, Michail Carneiro, Sarah Almeida Abdine, Hadi Petalidis, Lawrence P. Vazirgiannis, Michalis Computational Engineering, Finance, and Science Predicting protein function from sequence is a central challenge in computational biology. While existing methods rely heavily on structured ontologies or similarity-based techniques, they often lack the flexibility to express structure-free functional descriptions and novel biological functions. In this work, we introduce Prot2Text-V2, a novel multimodal sequence-to-text model that generates free-form natural language descriptions of protein function directly from amino acid sequences. Our method combines a protein language model as a sequence encoder (ESM-3B) and a decoder-only language model (LLaMA-3.1-8B-Instruct) through a lightweight nonlinear modality projector. A key innovation is our Hybrid Sequence-level Contrastive Alignment Learning (H-SCALE), which improves cross-modal learning by matching mean- and std-pooled protein embeddings with text representations via contrastive loss. After the alignment phase, we apply instruction-based fine-tuning using LoRA on the decoder to teach the model how to generate accurate protein function descriptions conditioned on the protein sequence. We train Prot2Text-V2 on about 250K curated entries from SwissProt and evaluate it under low-homology conditions, where test sequences have low similarity with training samples. Prot2Text-V2 consistently outperforms traditional and LLM-based baselines across various metrics. |
| title | Prot2Text-V2: Protein Function Prediction with Multimodal Contrastive Alignment |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2505.11194 |