FAPM: Functional annotation of proteins using multi-modal models beyond structural modeling

Assigning accurate property labels to proteins, like functional terms and catalytic activity, is challenging, especially for proteins without homologs and “tail labels” with few known examples. Unlike previous methods that mainly focused on protein sequence features, we use a pretrained large natural language model to understand the semantic meaning of protein labels. Specifically, we introduce FAPM, a contrastive multi-modal model that links natural language with protein sequence language. This model combines a pretrained protein sequence model with a pretrained large language model to generate labels, such as Gene Ontology (GO) functional terms and catalytic activity predictions, in natural language. Our results show that FAPM excels in understanding protein properties, outperforming models based solely on protein sequences or structures. It achieves state-of-the-art performance on public benchmarks and in-house experimentally annotated phage proteins, which often have few known homol..., , , # FAPM: Functional annotation of proteins using multi-modal models beyond structural modeling

[https://doi.org/10.5061/dryad.m905qfv9p](https://doi.org/10.5061/dryad.m905qfv9p)

The online demo is at: [https://huggingface.co/spaces/wenkai/FAPM_demo](https://huggingface.co/spaces/wenkai/FAPM_demo)

### Description of the data and file structure

The dataset includes:

1. The information of GO (Gene Ontology). This is a system to describe the functions of proteins. 

-The basic version of the GO (file name: go1.4-basic.obo). Source: [https://geneontology.org/docs/download-ontology/](https://geneontology.org/docs/download-ontology/)

-The mapping between GO numbers and GO descriptions (file name: go_descriptions1.4.txt)

-GO terms (file names: bp_terms.pkl; mf_terms.pkl; cc_terms.pkl)
2. Manually annotated data derived from Uniprot database. These datasets are used to finetune the model.

-File names:

train_exp_prompt_bp.csv; train_exp_prompt_mf.csv; train_exp_prompt_cc.cs...