XANNpred: neural nets that predict the propensity of a protein to yield diffraction-quality crystals.
Production of diffracting crystals is a critical step in determining the three-dimensional structure of a protein by X-ray crystallography. Computational techniques to rank proteins by their propensity to yield diffraction-quality crystals can improve efficiency in obtaining structural data by guiding both protein selection and construct design. XANNpred comprises a pair of artificial neural networks that each predict the propensity of a selected protein sequence to produce diffraction-quality crystals by current structural biology techniques. Blind tests show XANNpred has accuracy and Matthews correlation values ranging from 75% to 81% and 0.50 to 0.63 respectively; values of area under the receiver operator characteristic (ROC) curve range from 0.81 to 0.88. On blind test data XANNpred outperforms the other available algorithms XtalPred, PXS, OB-Score, and ParCrys. XANNpred also guides construct design by presenting graphs of predicted propensity for diffraction-quality crystals against residue sequence position. The XANNpred-SG algorithm is likely to be most useful to target selection in structural genomics consortia, while the XANNpred-PDB algorithm is more suited to the general structural biology community. XANNpred predictions that include sliding window graphs are freely available from http://www.compbio.dundee.ac.uk/xannpred
School of Life Sciences Research, College of Life Sciences, University of Dundee, Dundee, UK.
Ian M
IM
Overton
C A Johannes
CA
van Niekerk
Geoffrey J
GJ
Barton
D019295:Computational Biology / Q000379:methods*; D003460:Crystallization / Q000379:methods*; D030562:Databases, Protein; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry*; D012372:ROC Curve; D012984:Software
D011506:Proteins
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