XANNpred: neural nets that predict the propensity of a protein to yield diffraction-quality crystals.

ABSTRACT: 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 o

SUBMITTER: Overton IM 

PROVIDER: S-EPMC3084997 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature