The Proteome Folding Project: proteome-scale prediction of structure and function

Area of Science:

• Proteomics
• Structural Biology
• Bioinformatics

Background:

• Incomplete proteome annotation hinders biological research, especially high-throughput experiments.
• Accurate protein structure and function data are crucial for understanding cellular processes.

Purpose of the Study:

• To develop a computational pipeline for comprehensive proteome annotation.
• To improve the prediction of protein structure and function using integrated methods.

Main Methods:

• Developed a proteome annotation pipeline integrating sequence comparison, fold recognition, and de novo structure prediction.
• Utilized grid computing (World Community Grid) for large-scale de novo structure predictions across 94 genomes.
• Predicted protein domain boundaries and 3D structures for domains in diverse species.

Main Results:

• Generated new confident fold annotations for 9% of previously unannotated domains.
• Successfully predicted protein structures and functions for domains across 94 genomes, including human and model organisms.
• Demonstrated the utility of predicted structures in conjunction with Gene Ontology for predicting specific molecular functions.

Conclusions:

• The developed pipeline significantly enhances proteome annotation completeness.
• Predicted protein structures are valuable for inferring molecular functions and interpreting experimental data.
• This approach advances our understanding of proteomes across a wide range of species.