PROSES: A Web Server for Sequence-Based Protein Encoding

Area of Science:

• Bioinformatics
• Computational Biology
• Machine Learning in Proteomics

Background:

• The exponential growth of protein sequence data in online databases necessitates efficient analytical tools.
• Machine learning methods, utilizing sequence-derived features, have shown success in various protein-related predictions.
• Existing tools may require programming expertise, limiting accessibility for some researchers.

Purpose of the Study:

• To introduce the Protein Sequence Encoding System (PROSES), a user-friendly web server.
• To provide researchers with a no-code solution for encoding protein sequences for computational analysis.
• To facilitate the application of machine learning algorithms to large-scale protein sequence data.

Main Methods:

• Development of a web server accessible to all researchers.
• Implementation of methods for encoding protein sequences based on their amino acid composition and other features.
• Integration of user-friendly interface for easy sequence input and processing.

Main Results:

• PROSES offers a freely accessible platform for protein sequence encoding.
• The system eliminates the need for users to write programming code for sequence data preparation.
• Enables straightforward application of sequence-derived features in machine learning models.

Conclusions:

• PROSES democratizes the use of computational methods in protein sequence analysis.
• The web server supports diverse research areas including functional classification and interaction prediction.
• Facilitates broader adoption of machine learning in proteomics research.