Achieving 80% ten-fold cross-validated accuracy for secondary structure prediction by large-scale training.
An integrated system of neural networks, called SPINE, is established and optimized for predicting structural properties of proteins. SPINE is applied to three-state secondary-structure and residue-solvent-accessibility (RSA) prediction in this paper. The integrated neural networks are carefully trained with a large dataset of 2640 chains, sequence profiles generated from multiple sequence alignment, representative amino acid properties, a slow learning rate, overfitting protection, and an optimized sliding-widow size. More than 200,000 weights in SPINE are optimized by maximizing the accuracy measured by Q(3) (the percentage of correctly classified residues). SPINE yields a 10-fold cross-validated accuracy of 79.5% (80.0% for chains of length between 50 and 300) in secondary-structure prediction after one-month (CPU time) training on 22 processors. An accuracy of 87.5% is achieved for exposed residues (RSA >95%). The latter approaches the theoretical upper limit of 88-90% accuracy in assigning secondary structures. An accuracy of 73% for three-state solvent-accessibility prediction (25%/75% cutoff) and 79.3% for two-state prediction (25% cutoff) is also obtained.
Dor
Ofer
O
Department of Physiology and Biophysics, Center for Single Molecule Biophysics, Howard Hughes Medical Institute, State University of New York at Buffalo, Buffalo, New York 14214, USA.
Zhou
Yaoqi
Y
D000465:Algorithms; D019295:Computational Biology / Q000379:methods* / Q000592:standards*; D018360:Crystallography, X-Ray; D030562:Databases, Protein; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012997:Solvents
D011506:Proteins; D012997:Solvents