ProtReason: a reasoning-based framework for interpretable protein function prediction

Abstract

Understanding how protein sequence determines function remains a central challenge in computational biology. While some protein language models have advanced function prediction but most of them produce outputs without any justification or explainability. Protein function can be justified by connecting biological evidence to functional conclusions. We present ProtReason: A reasoning-augmented framework that generates interpretable protein function predictions with structured reasoning traces. In this study, a curated dataset of 87K proteins is constructed which is enriched with protein domain motifs, localization predictions and structural features transformed into reasoning traces linked to functional labels. ProtReason employs a two-stage architecture that first aligns protein sequence embeddings with textual representations and then generates structured outputs including reasoning traces, functional descriptions, and confidence scores. Compared to a sequence-tofunction baseline without reasoning, ProtReason achieves significantly improved BERT F1 scores, demonstrating the benefit of incorporating reasoning prior to function prediction. A systematic ablation study with 16 model variants shows the best design principles: a single unified reasoning path is better than a multi-step chain of reasoning and generating reasoning before function prediction yields superior performance. ProtReason performs competitively on standard benchmarks while providing biologically interpretable explanations with calibrated confidence estimates.