Protein embeddings and deep learning predict binding residues for various ligand classes

Abstract

One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we proposed bindEmbed21, a method predicting whether a protein residue binds to metal ions, nucleic acids, or small molecules. The Artificial Intelligence (AI)-based method exclusively uses embeddings from the Transformer-based protein Language Model ProtT5 as input. Using only single sequences without creating multiple sequence alignments (MSAs), bindEmbed21DL outperformed existing MSA-based methods. Combination with homology-based inference increased performance to F1=29±6%, F1=24±7%, and F1=41±% for metal ions, nucleic acids, and small molecules, respectively; it reached F1=45±2% when merging all three ligand classes into one. Focusing on very reliably predicted residues could complement experimental evidence: the 25% most strongly predicted binding residues, at least 73% were correctly predicted even when counting missing annotations as incorrect. The new method bindEmbed21 is fast, simple, and broadly applicable - neither using structure nor MSAs. Thereby, it found binding residues in over 42% of all human proteins not otherwise implied in binding.