Analysis of deep learning methods for blind protein contact prediction in CASP12

Wang, S.; Sun, S.; Xu, J.

doi:10.1101/181586

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bioRxiv · 10.1101/181586

Analysis of deep learning methods for blind protein contact prediction in CASP12

Wang, S.·Sun, S.·Xu, J.

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Abstract

Here we present the results of protein contact prediction achieved in CASP12 by our RaptorX-Contact server, which is an early implementation of our deep learning method for contact prediction. On a set of 38 free-modeling target domains with a median family size of around 58 effective sequences, our server obtained an average top L/5 long- and medium-range contact accuracy of 47% and 44%, respectively (L=length). A more advanced implementation has an average accuracy of 59% and 57%, respectively. Our deep learning method formulates contact prediction as an image pixel-level labeling problem and simultaneously predicts all residue pairs of a protein using a combination of two deep residual neural networks, taking as input the residue conservation information, predicted secondary structure and solvent accessibility, contact potential, and co-evolution information. Our approach differs from existing methods mainly in (1) formulating contact prediction as a pixel-level image labeling problem instead of an image-level classification problem; (2) simultaneously predicting all contacts of an individual protein to make effective use of contact occurrence patterns; and (3) integrating both 1D and 2D deep convolutional neural networks to effectively learn complex sequence-structure relationship including high-order residue correlation. This paper discusses the RaptorX-Contact pipeline, both contact prediction and contact-based folding results, and finally the strength and weakness of our method.

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Wang, S., Sun, S., Xu, J.. 2017-08-28. Analysis of deep learning methods for blind protein contact prediction in CASP12. https://doi.org/10.1101/181586

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