DEPICTER: Intrinsic Disorder and Disorder Function Prediction Server

Area of Science:

• Computational Biology
• Bioinformatics
• Structural Biology

Background:

• Millions of proteins lack functional annotation.
• Predicting intrinsic disorder and function is crucial for protein annotation.
• Existing disorder prediction tools are fragmented and difficult to access.

Purpose of the Study:

• To provide a centralized server for accessing multiple disorder prediction tools.
• To automate the prediction of protein intrinsic disorder and its functions.
• To offer consensus predictors for disorder and disordered protein binding.

Main Methods:

• Developed the DEPICTER (DisorderEd PredictIon CenTER) server.
• Integrated 10 popular disorder and disorder function prediction methods.
• Implemented server-side prediction automation, result visualization, and standardized output.
• Included two consensus predictors for disorder and disordered protein binding.

Main Results:

• DEPICTER provides unified access to diverse prediction tools.
• Automated predictions cover protein/nucleic acid binding, linkers, and moonlighting regions.
• Consensus predictors enhance disorder and binding site predictions.
• Empirical tests show DEPICTER predictions outperform sequence alignment on independent datasets.

Conclusions:

• DEPICTER simplifies and enhances the prediction of protein intrinsic disorder and function.
• The server offers a valuable resource for annotating uncharacterized proteins.
• Centralized access and automated predictions improve research efficiency in bioinformatics.