TY  - JOUR
AU  - Flöge, Klemens
AU  - Udayakumar, Srisruthi
AU  - Sommer, Johanna
AU  - Piraud, Marie
AU  - Kesselheim, Stefan
AU  - Fortuin, Vincent
AU  - Günnemann, Stephan
AU  - van der Weg, Karel J.
AU  - Gohlke, Holger
AU  - Merdivan, Erinc
AU  - Bazarova, Alina
TI  - OneProt: Towards multi-modal protein foundation models via latent space alignment of sequence, structure, binding sites and text encoders
JO  - PLoS Computational Biology
VL  - 21
IS  - 11
SN  - 1553-734X
CY  - San Francisco, Calif.
PB  - Public Library of Science
M1  - FZJ-2025-04662
SP  - e1013679
PY  - 2025
AB  - Recent advances in Artificial Intelligence have enabled multi-modal systems to model and translate diverse information spaces. Extending beyond text and vision, we introduce OneProt, a multi-modal Deep Learning model for proteins that integrates structural, sequence, text, and binding site data. Using the ImageBind framework, OneProt aligns the latent spaces of protein modality encoders in a lightweight fine-tuning scheme that focuses on pairwise alignment with sequence data, rather than requiring full matches. This novel approach comprises a mix of Graph Neural Networks and transformer architectures. It demonstrates good performance in retrieval tasks and showcases the efficacy of multi-modal systems in Protein Machine Learning through a broad spectrum of downstream baselines, including enzyme function prediction and binding site analysis. Furthermore, OneProt enables the transfer of representational information from specialized encoders to the sequence encoder, enhancing capabilities for distinguishing evolutionarily related and unrelated sequences and exhibiting representational properties where evolutionarily related proteins align in similar directions within the latent space. In addition, we extensively investigate modality ablations to identify the encoders that contribute the most to predictive performance, highlighting the significance of the binding site encoder, which has not been used in similar models previously. This work expands the horizons of multi-modal protein models, paving the way for transformative applications in drug discovery, biocatalytic reaction planning, and protein engineering.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1371/journal.pcbi.1013679
UR  - https://juser.fz-juelich.de/record/1048464
ER  -