ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction...

Area of Science:

• Proteomics and Bioinformatics
• Enzymology
• Computational Biology

Background:

• Proteases are crucial for cellular functions, and understanding their substrate specificity is key.
• Developing accurate sequence-based predictors for protease substrate cleavage sites is essential but challenging due to data growth.
• Existing protease-specific predictors struggle to keep pace with the increasing number of known protease types and substrate data.

Purpose of the Study:

• To develop a versatile bioinformatics platform, ProsperousPlus, for users to efficiently build custom predictors of protease substrate cleavage sites.
• To empower researchers, regardless of their bioinformatics expertise, to leverage accumulating substrate cleavage data for predictive modeling.
• To provide a computational solution for the growing need for protease-specific cleavage site prediction.

Main Methods:

• Conceptualization, development, and testing of the ProsperousPlus bioinformatics platform.
• Implementation of features for users to train, assess, and deploy predictive models for user-selected substrate types.
• Benchmarking the platform's predictive performance against existing state-of-the-art approaches using test datasets.

Main Results:

• ProsperousPlus was successfully developed and released as a webserver and stand-alone software package.
• The platform enables users with limited programming or bioinformatics background to build accurate substrate cleavage site predictors.
• Benchmarking demonstrated that ProsperousPlus predictors outperform current state-of-the-art methods on average.

Conclusions:

• ProsperousPlus offers a powerful and accessible solution for predicting protease substrate cleavage sites.
• The platform facilitates the utilization of vast and growing substrate cleavage data for biological research.
• ProsperousPlus enhances the understanding of protease functions and substrate specificity through efficient predictive modeling.