Title: Evolution-Aware Positive-Unlabeled Learning for Protein Design
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Positive-Unlabeled Learning, Protein Design, One-class classification

Abstract: We consider prediction of protein function, focusing on protein functionalities that enhance survival for one or more organisms. Sequencing these organisms provides plentiful positive training examples. In contrast, synthesizing and characterizing a protein with a mutation unseen in nature requires time-consuming wet lab experiments, making negative training examples scarce. Thus, datasets are often imbalanced, hindering classifier accuracy outside the training data.  Positive-unlabeled (PU) learning attempts to address this issue by considering unlabeled protein sequences to be part of the data and modeling them as positive with a probability called the class prior. This class prior is often constant. Our insight is that an understanding of evolution suggests a novel sequence-dependent class prior when learning from sequencing data. We propose Evo-PU, a PU learning framework that integrates our novel class prior to create a likelihood for training classifiers. We evaluate Evo-PU on multiple real-world tasks: identifying immune-evasive viral epitopes, predicting human receptor binding peptides and classifying peptide with membrane fusion capability. Using influenza genomic surveillance data and held-out laboratory assays of mutants unseen in nature, Evo-PU outperforms state-of-the-art PU learning, one-class classification, and evolutionary conservation-based methods, demonstrating the benefit of combining evolutionary modeling with data-driven learning for protein design.