TopSuite Web Server: A Meta-Suite for Deep-Learning-Based Protein Structure and Quality Prediction.

Area of Science:

• Computational biology
• Structural biology
• Bioinformatics

Background:

• Proteins perform essential life functions, requiring knowledge of their 3D structures.
• Experimental methods for protein structure determination are costly, time-consuming, and not universally applicable.
• Computational protein structure prediction offers a valuable alternative using existing structural data.

Purpose of the Study:

• To introduce TopSuite, a comprehensive web server for protein structure prediction and quality assessment.
• To provide a user-friendly platform for researchers needing accurate protein structural models.
• To enhance the efficiency and reliability of computational protein structure analysis.

Main Methods:

• TopModel employs a top-down consensus approach for template selection in structure prediction.
• TopScore utilizes deep neural networks for global and residue-wise protein model quality estimation.
• The TopSuite server integrates prediction and assessment for a streamlined workflow.

Main Results:

• TopScore accurately estimates the quality of predicted protein models.
• TopModel effectively predicts protein structures using template-based methods.
• The integrated TopSuite server provides reliable protein structure predictions and quality assessments.

Conclusions:

• TopSuite offers a powerful and accessible tool for computational protein structure prediction and quality assessment.
• The server leverages advanced deep learning techniques for improved accuracy.
• TopSuite is freely available, facilitating broader research in structural biology.