Systematic Evaluation of the CS-Rosetta De Novo Structure Prediction Method for Membrane Proteins

Abstract

In silico structure prediction of proteins on the basis of their sequence is a major challenge in computational structural biology, even though the ca. 100.000 entries stored in the Protein Data Bank now give a broad sampling of folds. Membrane proteins are particularly challenging cases due to their size and complexity, and high-resolution structural data is often lacking due to experimental challenges. We have investigated how the recently developed RASREC CS-Rosetta methodology benefits from integrating sparse NMR data for de novo structure prediction of membrane proteins. In particular, we tested the 4-TM disulfide binding protein B and sensory rhodopsin (full-length and 4-TM subdomain) for which both NMR and high-resolution X-ray crystal structures are available. We systematically investigated the effect of varying the type of data (chemical shifts, NOE distance restraints) and the amount of data (number of long-range NOEs) on the accuracy of the structure prediction. Our results show that RASREC CS-Rosetta can reliably predict membrane protein structures even with very sparse NMR data, and determine the minimal amount of NMR data needed.