Large-scale determination of previously unsolved protein structures using evolutionary information.

Area of Science:

• Structural Biology
• Computational Biology
• Bioinformatics

Background:

• Accurate protein structure prediction is crucial for understanding biological function.
• Predicting structures for proteins lacking sequence similarity to known structures is a significant challenge.
• Co-evolutionary information offers insights into protein residue interactions.

Purpose of the Study:

• To improve de novo protein structure prediction accuracy for proteins without known structural homologs.
• To generate novel 3D structure models for large prokaryotic protein families lacking experimental structures.
• To provide structural insights for a vast number of uncharacterized proteins.

Main Methods:

• Incorporation of residue-residue co-evolution information into the Rosetta structure prediction program.
• De novo blind structure prediction for two proteins in large families during the CASP11 competition.
• Application of the developed method to generate structure models for 58 prokaryotic protein families.

Main Results:

• Achieved unprecedented accuracy in de novo blind structure predictions for selected proteins.
• Generated 3D structure models for 58 large prokaryotic protein families, previously lacking structural data.
• Provided structural models for over 400,000 proteins, enabling functional and mechanistic hypothesis generation.

Conclusions:

• Co-evolutionary information significantly enhances the accuracy of de novo protein structure prediction.
• The developed method provides valuable structural models for a large number of uncharacterized proteins.
• These publicly accessible models will accelerate research in structural biology and functional genomics.