A fully open-source framework for deep learning protein real-valued distances
A fully open-source framework for deep learning protein real-valued distances
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DOI：
10.1038/s41598-020-70181-0
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发表时间：
2020-08-07
影响因子：
4.6
通讯作者：
Adhikari, Badri
中科院分区：
文献类型：
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作者：
Adhikari, Badri
随着深度学习算法推动蛋白质结构预测的发展，在深度学习和蛋白质结构预测这条融合的高速公路上，还有很多需要研究的地方。最近的发现表明，残基间距离预测是预测准确模型的关键，它是众所周知的接触预测问题的更细粒度版本。然而，预测这些距离的深度学习方法仍处于发展的早期阶段。为了推进这些方法和开发其他新的方法，需要一个小的和有代表性的数据集，以便于更快的开发和测试。在这项工作中，我们引入了蛋白质距离网(PDNET)，这是一个由一个这样的代表性数据集以及用于训练和测试深度学习方法的脚本组成的框架。该框架还包括用于管理数据集和生成输入要素和距离图的所有脚本。深度学习模型也可以使用谷歌Colab等免费平台在网络浏览器中进行培训和测试。我们将讨论如何使用PDNET来预测联系人、距离间隔和实值距离。
As deep learning algorithms drive the progress in protein structure prediction, a lot remains to be studied at this merging superhighway of deep learning and protein structure prediction. Recent findings show that inter-residue distance prediction, a more granular version of the well-known contact prediction problem, is a key to predicting accurate models. However, deep learning methods that predict these distances are still in the early stages of their development. To advance these methods and develop other novel methods, a need exists for a small and representative dataset packaged for faster development and testing. In this work, we introduce protein distance net (PDNET), a framework that consists of one such representative dataset along with the scripts for training and testing deep learning methods. The framework also includes all the scripts that were used to curate the dataset, and generate the input features and distance maps. Deep learning models can also be trained and tested in a web browser using free platforms such as Google Colab. We discuss how PDNET can be used to predict contacts, distance intervals, and real-valued distances.