RDbC2: an improved method to identify the residue-residue pairing in β strands.

Background: Despite the great advance of protein structure prediction, accurate prediction of the structures of mainly β proteins is still highly challenging, but could be assisted by the knowledge of residue-residue pairing in β strands. Previously, we proposed a ridge-detection-based algorithm RDbC that adopted a multi-stage random forest framework to predict the β-β pairing given the amino acid sequence of a protein.

Results: In this work, we developed a second version of this algorithm, RDbC2, by employing the residual neural network to further enhance the prediction accuracy. In the benchmark test, this new algorithm improves the F1-score by > 10 percentage points, reaching impressively high values of ~ 72% and ~ 73% in the BetaSheet916 and BetaSheet1452 sets, respectively.

Conclusion: Our new method promotes the prediction accuracy of β-β pairing to a new level and the prediction results could better assist the structure modeling of mainly β proteins. We prepared an online server of RDbC2 at http://structpred.life.tsinghua.edu.cn/rdb2c2.html.