RBO Aleph: leveraging novel information sources for protein structure prediction

Area of Science:

• Computational Biology
• Structural Biology
• Bioinformatics

Background:

• Protein structure prediction is crucial for understanding protein function.
• Accurate prediction remains challenging, especially for proteins lacking detectable templates.
• Existing methods often struggle with difficult protein targets.

Purpose of the Study:

• Introduce RBO Aleph, a novel web server for protein structure prediction.
• Address limitations in predicting structures for difficult protein targets.
• Provide a versatile tool for both template-based and ab initio modeling.

Main Methods:

• Utilizes combined evolutionary and physicochemical information for contact prediction.
• Employs predicted contact information in conformational space search for ab initio modeling.
• Integrates automatic switching between template-based and ab initio methods based on template availability.

Main Results:

• RBO Aleph demonstrated leading performance in ab initio and contact prediction at CASP11 (2014).
• The server effectively models difficult protein targets where templates are not found.
• Offers integrated tools for visualization of predicted models, contacts, and error estimation.

Conclusions:

• RBO Aleph provides a robust and user-friendly platform for diverse protein structure prediction needs.
• The server's approach enhances accuracy, particularly for challenging targets.
• Facilitates protein structure analysis for both expert and non-expert users.