Surveying over 100 predictors of intrinsic disorder in proteins

Introduction Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources. Areas covered We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends. Expert opinion We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.

Automated summary

The field of intrinsic disorder prediction has seen a consistent trend of improvement in predictive quality with the development of newer and more advanced predictors. The focus has shifted from machine learning methods to meta-predictors in the early 2010s, and most recently to deep learning. The use of deep learners is expected to continue in the foreseeable future due to their recent success. Additionally, there is a wide range of resources available to facilitate accurate disorder predictions for users, including web servers, standalone programs, and databases of pre-computed predictions. Addressing the shortage of accurate methods for predicting disordered binding regions is also highlighted as a need.