Protein structure classification (PSC) uses supervised learning to predict a protein's CATH/SCOP(e) class from the protein's sequence or 3D structural feature(s). We already modeled 3D structures as (static) protein structure networks (PSNs), demonstrating the competitiveness of PSN-based features to sequence or direct (i.e. non-network) 3D structural features in the PSC task. More recently, we demonstrated the power of features extracted from dynamic PSNs over features extracted from static PSNs (and thus by transitivity over sequence and direct 3D structural features) in the same task. That dynamic PSN approach used traditional machine learning (ML), combining manual (pre-engineered) features with an off-the-shelf classifier. Here, we evaluate whether automatic deep learning (DL) from the dynamic PSNs yields improvements. Our evaluation on 72 datasets spanning ~44,000 CATH- or SCOPe-labeled dynamic PSNs reveals that in terms of PSC accuracy, traditional ML and DL are (close to) tied for a large majority of the datasets, while DL is on average 10+ times slower. We are the first to evaluate traditional ML vs. DL in the dynamic PSN-based PSC task.

翻译：蛋白质结构分类（PSC）利用监督学习，根据蛋白质的序列或三维结构特征预测其CATH/SCOP(e)类别。我们先前已将三维结构建模为（静态）蛋白质结构网络（PSN），并证明基于PSN的特征在PSC任务中与序列或直接（即非网络）三维结构特征相比具有竞争力。最近，我们证明了在同一任务中，从动态PSN中提取的特征优于从静态PSN（从而通过传递性也优于序列和直接三维结构特征）中提取的特征。该动态PSN方法采用传统机器学习（ML），将手动（预设计）特征与现成分类器相结合。本研究评估了从动态PSN中自动进行深度学习（DL）能否带来改进。我们对涵盖约44,000个CATH或SCOPe标记动态PSN的72个数据集进行的评估表明，就PSC准确率而言，传统ML与DL在绝大多数数据集上（接近）持平，而DL平均慢10倍以上。我们是首个在基于动态PSN的PSC任务中评估传统ML与DL的研究。