Ten quick tips for sequence-based prediction of protein properties using machine learning
Fig 4
Possible model inputs and outputs.
A machine learning architecture may take protein sequence data in different ways: residue-level features, windows or fragments of adjacent residues in the sequence, or a whole protein sequence. Some models may also include global features at the protein level, for example, protein length, amino acid composition, or average hydrophobicity. The output of the model can also vary, including residue-level predictions, region/fragment classification (e.g., secondary structure elements), or protein-level labels (e.g., transmembrane or not). Created with Biorender.com.