Using inferred residue contacts to distinguish between correct and incorrect protein models.
BACKGROUND
The de novo prediction of 3D protein structure is enjoying a period of dramatic improvements. Often, a remaining difficulty is to select the model closest to the true structure from a group of low-energy candidates. To what extent can inter-residue contact predictions from multiple sequence alignments, information which is orthogonal to that used in most structure prediction algorithms, be used to identify those models most similar to the native protein structure?


RESULTS
We present a Bayesian inference procedure to identify residue pairs that are spatially proximal in a protein structure. The method takes as input a multiple sequence alignment, and outputs an accurate posterior probability of proximity for each residue pair. We exploit a recent metagenomic sequencing project to create large, diverse and informative multiple sequence alignments for a test set of 1656 known protein structures. The method infers spatially proximal residue pairs in this test set with good accuracy: top-ranked predictions achieve an average accuracy of 38% (for an average 21-fold improvement over random predictions) in cross-validation tests. Notably, the accuracy of predicted 3D models generated by a range of structure prediction algorithms strongly correlates with how well the models satisfy probable residue contacts inferred via our method. This correlation allows for confident rejection of incorrect structural models.


BACKGROUND
An implementation of the method is freely available at http://www.doe-mbi.ucla.edu/services.
UCLA-DOE Institute for Genomics & Proteomics, Molecular Biology Institute, Box 951570, UCLA, Los Angeles, CA 90095, USA.
Christopher S
CS
Miller
David
D
Eisenberg
D000465:Algorithms; D000595:Amino Acid Sequence; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D016678:Genome; D023281:Genomics; D015233:Models, Statistical; D008969:Molecular Sequence Data; D011336:Probability; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D017386:Sequence Homology, Amino Acid; D012984:Software
D011506:Proteins