  Hydrophobic patches on protein surfaces play important functional roles in
protein-protein and protein-ligand interactions. Large hydrophobic surfaces are
also involved in the progression of aggregation diseases. Predicting exposed
hydrophobic patches from a protein sequence has been shown to be a difficult
task. Fine-tuning foundation models allows for adapting a model to the specific
nuances of a new task using a much smaller dataset. Additionally, multi-task
deep learning offers a promising solution for addressing data gaps,
simultaneously outperforming single-task methods. In this study, we harnessed a
recently released leading large language model ESM-2. Efficient fine-tuning of
ESM-2 was achieved by leveraging a recently developed parameter-efficient
fine-tuning method. This approach enabled comprehensive training of model
layers without excessive parameters and without the need to include a
computationally expensive multiple sequence analysis. We explored several
related tasks, at local (residue) and global (protein) levels, to improve the
representation of the model. As a result, our fine-tuned ESM-2 model,
PatchProt, cannot only predict hydrophobic patch areas but also outperforms
existing methods at predicting primary tasks, including secondary structure and
surface accessibility predictions. Importantly, our analysis shows that
including related local tasks can improve predictions on more difficult global
tasks. This research sets a new standard for sequence-based protein property
prediction and highlights the remarkable potential of fine-tuning foundation
models enriching the model representation by training over related tasks.
