Prediction of Intrinsic Disorder Functions with DEPICTER2

Area of Science:

• Bioinformatics
• Computational Biology
• Structural Biology

Background:

• Intrinsically disordered proteins (IDPs) play crucial roles in cellular regulation and signaling.
• Predicting disordered regions and their binding functions is essential for understanding protein mechanisms.
• Existing tools often lack comprehensive coverage or user-friendly interfaces for diverse disorder predictions.

Purpose of the Study:

• To introduce DEPICTER2, a novel web server for predicting intrinsic disorder and disorder functions.
• To provide a unified platform integrating multiple state-of-the-art prediction methods.
• To facilitate the analysis of disordered regions and their interactions with various biomolecules.

Main Methods:

• Integration of six advanced sequence-based prediction algorithms: ANCHOR2, DFLpred, DisoLipPred, DisoRDPbind, flDPnn, and MoRF_{CHiBi_Light}.
• Development of a server-side, fully automated prediction pipeline.
• Implementation of batch prediction capabilities for multiple protein sequences.
• User-friendly web interface for method selection and result interpretation.

Main Results:

• DEPICTER2 successfully predicts disordered linkers and regions involved in binding proteins, peptides, DNA, RNA, and lipids.
• The server allows flexible combinations of the six integrated methods for tailored predictions.
• Automated, server-side processing eliminates the need for local software installation.
• A case study demonstrates effective interpretation of predicted intrinsic disorder functions.

Conclusions:

• DEPICTER2 offers a valuable, accessible resource for researchers studying protein intrinsic disorder and function.
• The integrated approach provides comprehensive insights into the diverse roles of disordered regions.
• The web server simplifies and enhances the process of predicting and analyzing protein disorder.