RDbC2：一种改进的β链残基-残基配对识别方法。
RDbC2: an improved method to identify the residue-residue pairing in β strands.
机构信息
MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, 100084, China.
出版信息
BMC Bioinformatics. 2020 Apr 3;21(1):133. doi: 10.1186/s12859-020-3476-z.
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.
背景
尽管蛋白质结构预测取得了巨大进展，但准确预测主要为β的蛋白质的结构仍然极具挑战性，但可以通过β链中残基-残基配对的知识来辅助。此前，我们提出了一种基于脊检测的算法 RDbC，该算法采用多阶段随机森林框架，根据蛋白质的氨基酸序列预测β-β配对。
结果
在这项工作中，我们通过使用残差神经网络开发了该算法的第二个版本 RDbC2，以进一步提高预测准确性。在基准测试中，该新算法将 F1 得分提高了>10 个百分点，在 BetaSheet916 和 BetaSheet1452 数据集上分别达到了令人印象深刻的约 72%和 73%的高值。
结论
我们的新方法将β-β 配对的预测准确性提升到了一个新的水平，预测结果可以更好地辅助主要为β的蛋白质的结构建模。我们在 http://structpred.life.tsinghua.edu.cn/rdb2c2.html 上准备了 RDbC2 的在线服务器。
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