Assessment of Disordered Linker Predictions in the CAID2 Experiment

Author:

Wang Kui1,Hu Gang1,Wu Zhonghua2ORCID,Uversky Vladimir N.3ORCID,Kurgan Lukasz4ORCID

Affiliation:

1. School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China

2. School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China

3. Department of Molecular Medicine, USF Health Byrd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33613, USA

4. Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA

Abstract

Disordered linkers (DLs) are intrinsically disordered regions that facilitate movement between adjacent functional regions/domains, contributing to many key cellular functions. The recently completed second Critical Assessments of protein Intrinsic Disorder prediction (CAID2) experiment evaluated DL predictions by considering a rather narrow scenario when predicting 40 proteins that are already known to have DLs. We expand this evaluation by using a much larger set of nearly 350 test proteins from CAID2 and by investigating three distinct scenarios: (1) prediction residues in DLs vs. in non-DL regions (typical use of DL predictors); (2) prediction of residues in DLs vs. other disordered residues (to evaluate whether predictors can differentiate residues in DLs from other types of intrinsically disordered residues); and (3) prediction of proteins harboring DLs. We find that several methods provide relatively accurate predictions of DLs in the first scenario. However, only one method, APOD, accurately identifies DLs among other types of disordered residues (scenario 2) and predicts proteins harboring DLs (scenario 3). We also find that APOD’s predictive performance is modest, motivating further research into the development of new and more accurate DL predictors. We note that these efforts will benefit from a growing amount of training data and the availability of sophisticated deep network models and emphasize that future methods should provide accurate results across the three scenarios.

Funder

National Science Foundation

National Natural Science Foundation of China

Robert J. Mattauch Endowment funds

Publisher

MDPI AG

Reference74 articles.

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4. DisProt in 2024: Improving function annotation of intrinsically disordered proteins;Aspromonte;Nucleic Acids Res.,2023

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