Deep residual learning has shown great success in protein contact prediction. In this study, a new deep residual learning-based protein contact prediction model was developed. Comparing with previous models, a new type of residual block hybridizing 1D and 2D convolutions was designed to increase the effective receptive field of the residual network, and a new loss function emphasizing the easily misclassified residue pairs was proposed to enhance the model training. The developed protein contact prediction model referred to as DRN-1D2D was first evaluated on 105 CASP11 targets, 76 CAMEO hard targets and 398 membrane proteins together with two in house-developed reference models based on either the standard 2D residual block or the traditional BCE loss function, from which we confirmed that both the dimensional hybrid residual block and the singularity enhanced loss function can be employed to improve the model performance for protein contact prediction. DRN-1D2D was further evaluated on 39 CASP13 and CASP14 free modeling targets together with the two reference models and six state-of-the-art protein contact prediction models including DeepCov, DeepCon, DeepConPred2, SPOT-Contact, RaptorX-Contact and TripleRes. The result shows that DRN-1D2D consistently achieved the best performance among all these models.

中文翻译：

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使用维度混合残差网络和奇异性增强损失函数改进蛋白质接触预测

深度残差学习在蛋白质接触预测方面取得了巨大成功。在这项研究中，开发了一种新的基于深度残差学习的蛋白质接触预测模型。与以前的模型相比，设计了一种新型的混合一维和二维卷积的残差块，以增加残差网络的有效感受野，并提出了一种强调容易误分类的残差对的新损失函数来增强模型训练。开发的称为 DRN-1D2D 的蛋白质接触预测模型首先在 105 个 CASP11 目标、76 个 CAMEO 硬目标和 398 个膜蛋白以及基于标准 2D 残差块或传统 BCE 损失的两个内部开发的参考模型上进行了评估功能，从中我们证实，维度混合残差块和奇异性增强损失函数都可以用来提高蛋白质接触预测的模型性能。DRN-1D2D 在 39 个无 CASP13 和 CASP14 的建模目标以及两个参考模型和六个最先进的蛋白质接触预测模型（包括 DeepCov、DeepCon、DeepConPred2、SPOT-Contact、RaptorX-Contact 和 TripleRes）上进行了进一步评估。结果表明，DRN-1D2D 在所有这些模型中始终取得最佳性能。DeepCon、DeepConPred2、SPOT-Contact、RaptorX-Contact 和 TripleRes。结果表明，DRN-1D2D 在所有这些模型中始终取得最佳性能。DeepCon、DeepConPred2、SPOT-Contact、RaptorX-Contact 和 TripleRes。结果表明，DRN-1D2D 在所有这些模型中始终取得最佳性能。