title: Complete fold annotation of the human proteome using a novel structural feature space.
abstract: Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.
authors.affiliation: Genomics and Computational Biology Program, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Sarah A
authors.initials: SA
authors.lastname: Middleton
authors.affiliation: Department of Computer Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Joseph
authors.initials: J
authors.lastname: Illuminati
authors.affiliation: Genomics and Computational Biology Program, University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Junhyong
authors.initials: J
authors.lastname: Kim
mesh_terms: D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D006801:Humans; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding; D020543:Proteome; D040901:Proteomics / Q000379:methods; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship
chemical_list: D020543:Proteome