Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases

Computational prediction of intrinsic disorder in protein sequences is an impactful and growing research area, recently infused with deep learning and protein language models, prompting the need to assess the impact of these advancements. We systematically surveyed 128 disorder predictors, many of which are accurate, and some that have been cited thousands of times. We demonstrated that recent methods utilizing protein language models outperform those that do not, particularly when combined with deep learning, yielding substantial gains in predictive quality. We place these observations within the context of other key factors, including runtime and coverage. We also identified and discussed resources that expedite and ease the collection of disorder predictions, including meta-web servers and large databases of pre-computed disorder predictions. Altogether, this work guides users in their pursuit of efficiently and conveniently obtaining accurate disorder predictions and offers practical insights for the developers of disorder predictors.