Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden.
Bioinformatics. 2019 Aug 1;35(15):2677-2679. doi: 10.1093/bioinformatics/bty1036.
Residue contact prediction was revolutionized recently by the introduction of direct coupling analysis (DCA). Further improvements, in particular for small families, have been obtained by the combination of DCA and deep learning methods. However, existing deep learning contact prediction methods often rely on a number of external programs and are therefore computationally expensive.
Here, we introduce a novel contact predictor, PconsC4, which performs on par with state of the art methods. PconsC4 is heavily optimized, does not use any external programs and therefore is significantly faster and easier to use than other methods.
PconsC4 is freely available under the GPL license from https://github.com/ElofssonLab/PconsC4. Installation is easy using the pip command and works on any system with Python 3.5 or later and a GCC compiler. It does not require a GPU nor special hardware.
Supplementary data are available at Bioinformatics online.
最近,直接耦合分析 (DCA) 的引入彻底改变了残基接触预测。通过将 DCA 与深度学习方法相结合,进一步提高了(特别是对于小家族的)预测精度。然而,现有的深度学习接触预测方法通常依赖于许多外部程序,因此计算成本很高。
在这里,我们引入了一种新的接触预测器 PconsC4,它与最先进的方法表现相当。PconsC4 经过了大量优化,不使用任何外部程序,因此比其他方法快得多,使用也更容易。
PconsC4 可在 GPL 许可证下从 https://github.com/ElofssonLab/PconsC4 免费获得。使用 pip 命令进行安装非常简单,适用于任何具有 Python 3.5 或更高版本和 GCC 编译器的系统。它不需要 GPU 也不需要特殊硬件。
补充数据可在 Bioinformatics 在线获得。