title: Structure prediction of partial-length protein sequences.
abstract: Protein structure information is essential to understand protein function. Computational methods to accurately predict protein structure from the sequence have primarily been evaluated on protein sequences representing full-length native proteins. Here, we demonstrate that top-performing structure prediction methods can accurately predict the partial structures of proteins encoded by sequences that contain approximately 50% or more of the full-length protein sequence. We hypothesize that structure prediction may be useful for predicting functions of proteins whose corresponding genes are mapped expressed sequence tags (ESTs) that encode partial-length amino acid sequences. Additionally, we identify a confidence score representing the quality of a predicted structure as a useful means of predicting the likelihood that an arbitrary polypeptide sequence represents a portion of a foldable protein sequence ("foldability"). This work has ramifications for the prediction of protein structure with limited or noisy sequence information, as well as genome annotation.
authors.affiliation: Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195-2350, USA. alaurenz@uw.edu
authors.forename: Adrian
authors.initials: A
authors.lastname: Laurenzi
authors.forename: Ling-Hong
authors.initials: LH
authors.lastname: Hung
authors.forename: Ram
authors.initials: R
authors.lastname: Samudrala
mesh_terms: D030562:Databases, Protein; D020224:Expressed Sequence Tags; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012984:Software
chemical_list: D011506:Proteins