A Multimodal Protein Representation Framework for Quantifying Transferability Across Biochemical Downstream Tasks.

Abstract

Proteins are the building blocks of life, carrying out fundamental functions in biology. In computational biology, an effective protein representation facilitates many important biological quantifications. Most existing protein representation methods are derived from self-supervised language models designed for text analysis. Proteins, however, are more than linear sequences of amino acids. Here, a multimodal deep learning framework for incorporating ≈1 million protein sequence, structure, and functional annotation (MASSA) is proposed. A multitask learning process with five specific pretraining objectives is presented to extract a fine-grained protein-domain feature. Through pretraining, multimodal protein representation achieves state-of-the-art performance in specific downstream tasks such as protein properties (stability and fluorescence), protein-protein interactions (shs27k/shs148k/string/skempi), and protein-ligand interactions (kinase, DUD-E), while achieving competitive results in secondary structure and remote homology tasks. Moreover, a novel optimal-transport-based metric with rich geometry awareness is introduced to quantify the dynamic transferability from the pretrained representation to the related downstream tasks, which provides a panoramic view of the step-by-step learning process. The pairwise distances between these downstream tasks are also calculated, and a strong correlation between the inter-task feature space distributions and adaptability is observed.