DeepSol: a deep learning framework for sequence-based protein solubility prediction
DeepSol: a deep learning framework for sequence-based protein solubility prediction
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DOI：
10.1093/bioinformatics/bty166
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发表时间：
2018-08-01
期刊：
影响因子：
5.8
通讯作者：
Mall, Raghvendra
中科院分区：
文献类型：
--
作者：
Khurana, Sameer;Rawi, Reda;Mall, Raghvendra
动机：蛋白质溶解度在药物研究和生产产量中起着至关重要的作用。对于给定的蛋白质，其溶解度的程度可以代表其功能的质量，并最终由其序列定义。因此，必须开发新的、高度准确的基于计算机序列的蛋白质溶解度预测器。在这项工作中，我们提出了DeepSol，一种新的基于深度学习的蛋白质溶解度预测器。我们的框架的主干是一个卷积神经网络，利用k-mer结构和从蛋白质序列中提取的额外序列和结构特征。结果：DeepSol优于所有已知的基于序列的最先进的溶解度预测方法，准确度达到0.77，Matthew相关系数为0.55。DeepSol的上级预测精度允许筛选具有增强生产能力的序列，并且可以更可靠地预测新蛋白质的溶解度。可用性和实施：DeepSol的最佳性能模型和结果公开存放在https：//doi。org/10.5281/zenodo.1162886（Khurana和Mall，2018）。联系方式：skhurana@mit.eduorrmall@hbku.edu. qa补充信息：补充数据可在Bioinformatics在线获得。
Motivation: Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence-based protein solubility predictors. In this work we propose, DeepSol, a novel Deep Learning-based protein solubility predictor. The backbone of our framework is a convolutional neural network that exploits k-mer structure and additional sequence and structural features extracted from the protein sequence.Results: DeepSol outperformed all known sequence-based state-of-the-art solubility prediction methods and attained an accuracy of 0.77 and Matthew's correlation coefficient of 0.55. The superior prediction accuracy of DeepSol allows to screen for sequences with enhanced production capacity and can more reliably predict solubility of novel proteins.Availability and implementation: DeepSol's best performing models and results are publicly deposited at https://doi. org/10.5281/zenodo.1162886 (Khurana and Mall, 2018).Contact: skhurana@mit.eduorrmall@hbku.edu.qaSupplementary information: Supplementary data are available at Bioinformatics online.