title: Improving the chances of successful protein structure determination with a random forest classifier.
abstract: Obtaining diffraction quality crystals remains one of the major bottlenecks in structural biology. The ability to predict the chances of crystallization from the amino-acid sequence of the protein can, at least partly, address this problem by allowing a crystallographer to select homologs that are more likely to succeed and/or to modify the sequence of the target to avoid features that are detrimental to successful crystallization. In 2007, the now widely used XtalPred algorithm [Slabinski et al. (2007), Protein Sci. 16, 2472-2482] was developed. XtalPred classifies proteins into five `crystallization classes' based on a simple statistical analysis of the physicochemical features of a protein. Here, towards the same goal, advanced machine-learning methods are applied and, in addition, the predictive potential of additional protein features such as predicted surface ruggedness, hydrophobicity, side-chain entropy of surface residues and amino-acid composition of the predicted protein surface are tested. The new XtalPred-RF (random forest) achieves significant improvement of the prediction of crystallization success over the original XtalPred. To illustrate this, XtalPred-RF was tested by revisiting target selection from 271 Pfam families targeted by the Joint Center for Structural Genomics (JCSG) in PSI-2, and it was estimated that the number of targets entered into the protein-production and crystallization pipeline could have been reduced by 30% without lowering the number of families for which the first structures were solved. The prediction improvement depends on the subset of targets used as a testing set and reaches 100% (i.e. twofold) for the top class of predicted targets.
authors.affiliation: Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92307, USA.
authors.forename: Samad
authors.initials: S
authors.lastname: Jahandideh
authors.affiliation: Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92307, USA.
authors.forename: Lukasz
authors.initials: L
authors.lastname: Jaroszewski
authors.affiliation: Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92307, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Godzik
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D001185:Artificial Intelligence; D003198:Computer Simulation; D003460:Crystallization; D018360:Crystallography, X-Ray; D057927:Hydrophobic and Hydrophilic Interactions; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D011897:Random Allocation; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D011506:Proteins
keywords: XtalPred; machine-learning methods; structural genomics; target selection