title: The utility of artificially evolved sequences in protein threading and fold recognition.
abstract: Template-based protein structure prediction plays an important role in Functional Genomics by providing structural models of gene products, which can be utilized by structure-based approaches to function inference. From a systems level perspective, the high structural coverage of gene products in a given organism is critical. Despite continuous efforts towards the development of more sensitive threading approaches, confident structural models cannot be constructed for a considerable fraction of proteins due to difficulties in recognizing low-sequence identity templates with a similar fold to the target. Here we introduce a new modeling stratagem, which employs a library of synthetic sequences to improve template ranking in fold recognition by sequence profile-based methods. We developed a new method for the optimization of generic protein-like amino acid sequences to stabilize the respective structures using a combined empirical scoring function, which is compatible with these commonly used in protein threading and fold recognition. We show that the artificially evolved sequences, whose average sequence identity to the wild-type sequences is as low as 13.8%, have significant capabilities to recognize the correct structures. Importantly, the quality of the corresponding threading alignments is comparable to these constructed using conventional wild-type approaches (the average TM-score is 0.48 and 0.54, respectively). Fold recognition that uses data fusion to combine ranks calculated for both wild-type and synthetic template libraries systematically improves the detection of structural analogs. Depending on the threading algorithm used, it yields on average 4-16% higher recognition rates than using the wild-type template library alone. Synthetic sequences artificially evolved for the template structures provide an orthogonal source of signal that could be exploited to detect these templates unrecognized by standard modeling techniques. It opens up new directions in the development of more sensitive threading methods with the enhanced capabilities of targeting difficult, midnight zone templates.
authors.affiliation: Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA. michal@brylinski.org
authors.forename: Michal
authors.initials: M
authors.lastname: Brylinski
mesh_terms: D030562:Databases, Protein; D008958:Models, Molecular; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D016415:Sequence Alignment / Q000379:methods*
chemical_list: D011506:Proteins