ProtIR: Iterative Refinement between Retrievers and Predictors for
  Protein Function Annotation
Zuobai Zhang Jiarui Lu Vijil Chenthamarakshan Aurélie Lozano Payel Das Jian Tang
Protein function annotation is an important yet challenging task in biology.
Recent deep learning advancements show significant potential for accurate
function prediction by learning from protein sequences and structures.
Nevertheless, these predictor-based methods often overlook the modeling of
protein similarity, an idea commonly employed in traditional approaches using
sequence or structure retrieval tools. To fill this gap, we first study the
effect of inter-protein similarity modeling by benchmarking retriever-based
methods against predictors on protein function annotation tasks. Our results
show that retrievers can match or outperform predictors without large-scale
pre-training. Building on these insights, we introduce a novel variational
pseudo-likelihood framework, ProtIR, designed to improve function predictors by
incorporating inter-protein similarity modeling. This framework iteratively
refines knowledge between a function predictor and retriever, thereby combining
the strengths of both predictors and retrievers. ProtIR showcases around 10%
improvement over vanilla predictor-based methods. Besides, it achieves
performance on par with protein language model-based methods, yet without the
need for massive pre-training, highlighting the efficacy of our framework. Code
will be released upon acceptance.