Rossana Zaru - Predicting protein metal binding sites with AI and ML in UniProt
Channel: Biocuration Conference 2023
Date: 2023-06-21

*Session:* April 25 - Ai And Text Mining 2 - 05

*Abstract:* Metal binding is essential for many protein functions. Metals can stabilise protein structure, be part of enzyme catalytic sites or regulate protein function in response to extra- or intracellular signals. Mutations affecting metal-binding residues often result in disease, highlighting the importance of identifying the amino acids involved in metal coordination in order to understand disease etiology and to design therapy drugs. The UniProt Knowledgebase (UniProtKB) collects and centralises functional information on proteins across a wide range of species. For each protein, we provide extensive annotation of sequence features. For example, for metal-binding proteins, UniProtKB specifies the specific amino acid residues that participate in metal binding sites and which metal is bound. Currently, around 16% of reviewed/Swiss-Prot proteins have annotations of metal binding site residues, which are identified from the literature or known structures from PDB. However, only 3% of unreviewed/TrEMBL entries have annotated metal binding sites, which are created by a variety of automated annotation methods. The difference in coverage between the reviewed/Swiss-Prot and unreviewed/TrEMBL entries suggests that there are many millions of missing metal binding site annotation predictions. To increase the coverage of unreviewed/TrEMBL entries, we decided to take the opportunity offered by the huge advances made by artificial intelligence and machine learning (AI/ML) in addressing protein biology such as the prediction of 3D structures by AlphaFold or the prediction of names for uncharacterised proteins by Google’s ProtNLM. We set a challenge for the AI/ML community to generate models to predict metal binding sites with the aim of identifying one or more software tools that are both accurate and scalable and that we can apply within the UniProt production environment. Here, we will provide an overview on how metal binding sites are identified and annotated in UniProtKB, discuss the challenges in annotating and predicting them and how we will evaluate the proposed AI/ML models.