Motivation Intrinsically disordered protein regions interact with proteins, nucleic acids and lipids. Regions that bind lipids are implicated in a wide spectrum of cellular functions and several human diseases. Motivated by the growing amount of experimental data for these interactions and lack of tools that can predict them from the protein sequence, we develop DisoLipPred, the first predictor of the disordered lipid-binding residues (DLBRs). Results DisoLipPred relies on a deep bidirectional recurrent network that implements three innovative features: transfer learning, bypass module that sidesteps predictions for putative structured residues, and expanded inputs that cover physiochemical properties associated with the protein–lipid interactions. Ablation analysis shows that these features drive predictive quality of DisoLipPred. Tests on an independent test dataset and the yeast proteome reveal that DisoLipPred generates accurate results and that none of the related existing tools can be used to indirectly identify DLBR. We also show that DisoLipPred’s predictions complement the results generated by predictors of the transmembrane regions. Altogether, we conclude that DisoLipPred provides high-quality predictions of DLBRs that complement the currently available methods. Availability and implementation DisoLipPred’s webserver is available at http://biomine.cs.vcu.edu/servers/DisoLipPred/. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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DisoLipPred：使用深度循环网络和迁移学习准确预测蛋白质序列中无序脂质结合残基

动机 本质上无序的蛋白质区域与蛋白质、核酸和脂质相互作用。结合脂质的区域与广泛的细胞功能和多种人类疾病有关。由于这些相互作用的实验数据越来越多，并且缺乏可以从蛋白质序列预测它们的工具，我们开发了 DisoLipPred，这是无序脂质结合残基 (DLBR) 的第一个预测因子。结果 DisoLipPred 依赖于实现三个创新功能的深度双向循环网络：迁移学习、绕过推定结构化残基预测的旁路模块，以及涵盖与蛋白质-脂质相互作用相关的物理化学特性的扩展输入。消融分析表明，这些特征推动了 DisoLipPred 的预测质量。对独立测试数据集和酵母蛋白质组的测试表明，DisoLipPred 生成准确的结果，并且没有任何相关的现有工具可用于间接识别 DLBR。我们还表明，DisoLipPred 的预测补充了跨膜区域预测因子生成的结果。总之，我们得出结论，DisoLipPred 提供了 DLBR 的高质量预测，补充了当前可用的方法。可用性和实施​​ DisoLipPred 的网络服务器可在 http://biomine.cs.vcu.edu/servers/DisoLipPred/ 获得。补充信息 补充数据可在 Bioinformatics 在线获取。我们还表明，DisoLipPred 的预测补充了跨膜区域预测因子生成的结果。总之，我们得出结论，DisoLipPred 提供了 DLBR 的高质量预测，补充了当前可用的方法。可用性和实施​​ DisoLipPred 的网络服务器可在 http://biomine.cs.vcu.edu/servers/DisoLipPred/ 获得。补充信息 补充数据可在 Bioinformatics 在线获取。我们还表明，DisoLipPred 的预测补充了跨膜区域预测因子生成的结果。总之，我们得出结论，DisoLipPred 提供了 DLBR 的高质量预测，补充了当前可用的方法。可用性和实施​​ DisoLipPred 的网络服务器可在 http://biomine.cs.vcu.edu/servers/DisoLipPred/ 获得。补充信息 补充数据可在 Bioinformatics 在线获取。