AlphaFunctor: Bridging The Gap Between Protein Function Annotation and Property Prediction

Abstract

The fundamental relationship among protein sequence, structure, function, and physicochemical properties is a central principle in biology. While in principle protein function and properties should be able to be derived directly from protein sequence, in practice protein function and property prediction methods have been designed around specific datasets and specific property or function subsets, leading to an enormous gap between function annotation and property prediction. To address these challenges, we introduce AlphaFunctor, a category theory based foundation model-like platform to bridge the gap between protein function annotation and property prediction. Based on the hypothesis that protein function and properties can be directly derived from protein sequence, AlphaFunctor predicts protein functions as represented by Gene Ontology terms directly from sequence. Using these function predictions, AlphaFunctor further maps protein functions using topological spectral theory, path-complex neural networks, and protein domain analysis onto downstream property prediction. AlphaFunctor is (pre)trained in nearly 0.6 million protein function data points to deliver the state-of-the-art protein function annotation on three benchmark datasets. Without task-specific network redesign, AlphaFunctor maps qualitative protein function annotation to various qualitative and quantitative protein property predictions, outperforming other dataset-specific and task-specific competing predictors.