XANNpred: Neural nets that predict the propensity of a protein to yield diffraction-quality crystals: XANNpred is a pair of Artificial Neural Networks (XANNpred-PDB, XANNpred-SG). Proteins with XANNpred-PDB or XANNpred-SG scores respectively above 0.517 or 0.418 are predicted to be ”likely to produce diffraction-quality crystals by current structural biology techniques”.We suggest that the XANNpred-SG algorithm may be most applicable to ”high-throughput” efforts (e.g. structural genomics consortia), while the XANNpred-PDB algorithm may be more relevant to the structural biology community as a whole. This is because XANNpred-PDB predictions are based on PDB data, while XANNpred-SG predictions are based on structural genomics data. XANNpred utilizes 428 features, including 20 amino acid and 400 dipeptide frequencies, sequence length, predicted secondary structure, transmembrane regions, protein disorder, isoelectric point, hydrophobicity and molecular weight. On the data examined, XANNpred-PDB and XANNpred-SG each outperform the other publicly available algorithms (XtalPred, PXS, ParCrys and OB-Score). XANNpred results include sliding window plots to show the XANNpred score against the centre position of a 61-residue sliding window over the input sequence. We suggest that these sliding window plots may be helpful for construct design. If you use XANNpred, please cite: Overton, I.M., van Niekerk, C.A.J., and Barton, G.J. (2011), XANNpred: Neural nets that predict the propensity of a protein to yield diffraction-quality crystals
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- Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)