The first decade of genome sequencing saw a surge in the characterisation of proteins with unknown functionality. Even still, more than 20% of proteins in well-studied model animals have yet to be identified, making the discovery of their active site one of biology's greatest difficulties. Herein, we apply a transformer architecture to a language representation of bio-catalyzed chemical reactions to learn the signal at the base of the substrate-active site atomic interactions. The language representation comprises a reaction simplified molecular-input line-entry system (SMILES) for substrate and products, complemented with amino acid (AA) sequence information for the enzyme. Defining a custom tokenizer and a score based on attention values, we show we can capture the substrate-active site interaction signal and use it to detect the location of the active site in unknown protein sequences, hence elucidating complex 3D interactions solely relying on 1D representations. We consider a Transfomer-based model, BERT, trained with different losses and analyse the performance in comparison with a statistical baseline and methods based on sequence alignments. Our approach exhibits remarkable results and is able to recover, with no supervision, 31.51% of the active site when considering co-crystallized substrate-enzyme structures as a ground truth, largely outperforming sequence alignment-based approaches. Our findings are further corroborated by docking simulations on the 3D structure of few enzymes. This work confirms the unprecedented impact of natural language processing and more specifically of transformer architectures on domain-specific languages, paving the way to effective solutions for protein functional characterisation and bio-catalysis engineering. The implementation is publicly available on GitHub: https://github.com/rxn4chemistry/rxnaamapper.

Overlap score and false positive rates for the active site prediction using PLIP (DOIs: 10.1093/nar/gkab294, 10.1093/nar/gkv315) as a ground-truth for the four methods considered: a random model, Pfam (http://pfam.xfam.org/) alignment-based model, a pretrained BERT model and RXNAAMapper.

Loïc Kwate Dassi

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