SELFprot: Effective and Efficient Multitask Finetuning Methods for Protein Parameter Prediction

Accurately predicting protein-ligand interactions and enzymatic kinetics remains a challenge for computational biology. Here, we present SELFprot, a suite of modular transformer-based machine learning architectures that leverage the ESM2-35M model architecture for protein sequence and small molecule embeddings to improve predictions of complex biochemical interactions. SELFprot employs multitask learning and parameter-efficient finetuning through low-rank adaptation, allowing for adaptive, data-driven model refinement. Furthermore, ensemble learning techniques are used to enhance the robustness and reduce the prediction variance. Evaluated on the BindingDB and CatPred-DB data sets, SELFprot achieves competitive performance with notable improvements in parameter-efficient prediction of k, K, K, K, IC, and EC values as well as the classification of functional site residues. With comparable accuracy to existing models and an order of magnitude fewer parameters, SELFprot demonstrates versatility and efficiency, making it a valuable tool for protein-ligand interaction studies in bioengineering.