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Keyword: sequence conservation ×Clear all

3 claims from 1 paper

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

   Wang, Sun, Li, Zhang and Xu · PLoS Computational Biology · 2017

   The paper presents an ultra-deep neural network that predicts protein contact maps from evolutionary coupling and sequence conservation, and reports better contact prediction and contact-assisted folding than existing methods.

   Unchecked3 claims
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   1. UncheckedOn three test sets of 579 proteins, the authors' method predicted long-range residue contacts more accurately than CCMpred and MetaPSICOV.“Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV…”
   2. UncheckedFolding guided by the authors' predicted contacts gave correct folds (TMscore>0.6) for 203 test proteins, versus 79 with MetaPSICOV and 62 with CCMpred.“Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively.”
   3. UncheckedThe authors report that their contact-prediction method works very well on membrane proteins even though no membrane proteins were used in training.“One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction.”