Into the Unknown: From Structure to Disorder in Protein Function Prediction

TL;DR

This paper reviews recent computational advances in predicting functions of intrinsically disordered regions in proteins, emphasizing new methods, challenges, and a community framework for better annotation.

Contribution

It introduces emerging computational techniques and proposes a community-driven framework to improve functional predictions of IDRs.

Findings

Identification of conserved features and motifs in IDRs linked to function

Highlighting challenges in IDR function annotation

Proposing a collaborative framework for interpretability

Abstract

Intrinsically disordered regions (IDRs) account for one-third of the human proteome and play essential biological roles. However, predicting the functions of IDRs remains a major challenge due to their lack of stable structures, rapid sequence evolution, and context-dependent behavior. Many predictors of protein function neglect or underperform on IDRs. Recent advances in computational biology and machine learning, including protein language models, alignment-free approaches, and IDR-specific methods, have revealed conserved bulk features and local motifs within IDRs that are linked to function. This review highlights emerging computational methods that map the sequence-function relationship in IDRs, outlines critical challenges in IDR function annotation, and proposes a community-driven framework to accelerate interpretable functional predictions for IDRs.