Costas Bouyioukos

Hadi Abdine, Michail Chatzianastasis, Costas Bouyioukos et al.

In recent years, significant progress has been made in the field of protein function prediction with the development of various machine-learning approaches. However, most existing methods formulate the task as a multi-classification problem, i.e. assigning predefined labels to proteins. In this work, we propose a novel approach, Prot2Text, which predicts a protein's function in a free text style, moving beyond the conventional binary or categorical classifications. By combining Graph Neural Networks(GNNs) and Large Language Models(LLMs), in an encoder-decoder framework, our model effectively integrates diverse data types including protein sequence, structure, and textual annotation and description. This multimodal approach allows for a holistic representation of proteins' functions, enabling the generation of detailed and accurate functional descriptions. To evaluate our model, we extracted a multimodal protein dataset from SwissProt, and demonstrate empirically the effectiveness of Prot2Text. These results highlight the transformative impact of multimodal models, specifically the fusion of GNNs and LLMs, empowering researchers with powerful tools for more accurate function prediction of existing as well as first-to-see proteins.

Xiao Fei, Sarah Almeida Carneiro, Yang Zhang et al.

Protein-protein interaction (PPI) modeling has been widely studied as a binary or multi-label classification task. While emerging multimodal large language models (LLMs) can now describe single proteins, they remain unable to generate free-form descriptions of interactions between protein pairs. Moving beyond controlled vocabulary annotations, we propose to model PPI using free-text description, enabling richer expressiveness, improved interpretability, and better integration with literature knowledge base. We present PPI2Text, a multimodal LLM for free-form PPI captioning from amino acid sequences, that encodes each protein using ESM3 encoder, constructs a pair map from the two representations to capture interactions across all residue pairs, and autoregressively generates descriptions using a Qwen3 language decoder. We further introduce PaCo-RoPE, a coordinate-aligned positional encoding that aligns each axis of the pair grid with the residue positions of the corresponding protein. In addition, we release PPI2Text-Dataset, a 351k-pair corpus of free-form PPI descriptions aggregated from ten curated biological databases and further synthesized with Gemini under evidence-tiered prompting. PPI2Text consistently outperforms strong baselines across multiple ablation settings and evaluation protocols. It not only achieves higher scores on linguistic metrics against synthesized references, but also excels on factuality metrics, where an LLM-based judge evaluates outputs against raw biological evidence.