title: Prediction of protein crystallization using collocation of amino acid pairs.
abstract: While above 80% of protein structures in PDB were determined using X-ray crystallography, in some cases only 42% of soluble purified proteins yield crystals. Since experimental verification of protein's ability to crystallize is relatively expensive and time-consuming, we propose a new in silico prediction system, called CRYSTALP, which is based on the protein's sequence. CRYSTALP uses a novel feature-based sequence representation and applies a Naïve Bayes classifier. It was compared with recent, competing in silico method, SECRET [P. Smialowski, T. Schmidt, J. Cox, A. Kirschner, D. Frishman, Will my protein crystallize? A sequence-based predictor, Proteins 62 (2) (2006) 343-355], and other state-of-the-art classifiers. Based on experimental tests, CRYSTALP is shown to predict crystallization with 77.5% accuracy, which is better by over 10% than the SECRET's accuracy, and better than accuracy of the other considered classifiers. CRYSTALP uses different and over 50% less features to represent sequences than SECRET. Additionally, features used by CRYSTALP may help to discover intra-molecular markers that influence protein crystallization.
authors.affiliation: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alta., Canada.
authors.forename: Ke
authors.initials: K
authors.lastname: Chen
authors.forename: Lukasz
authors.initials: L
authors.lastname: Kurgan
authors.forename: Mandana
authors.initials: M
authors.lastname: Rahbari
mesh_terms: D000596:Amino Acids / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D003460:Crystallization; D030562:Databases, Protein; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D012984:Software
chemical_list: D000596:Amino Acids; D011506:Proteins