Structural relationships among proteins are important in the study of their evolution as well as in drug design and development. The protein 3D structure has been shown to be effective with respect to classifying proteins. Prior work has shown that the Double Centroid Reduced Representation (DCRR) model is a useful geometric representation for protein structure with respect to visual models, reducing the quantity of modeled information for each amino acid, yet retaining the most important geometrical and chemical features of each: the centroids of the backbone and of the side-chain. DCRR has not yet been applied in the calculation of geometric structural similarity. Meanwhile, multi-dimensional indexing (MDI) of protein structure combines protein structural analysis with distance metrics to facilitate structural similarity queries and is also used for clustering protein structures into related groups. In this respect, the combination of geometric models with MDI has been shown to be effective. Prior work, notably Distance and Density based Protein Indexing (DDPIn), applies MDI to protein models based on the geometry of the CA backbone. DDPIn distance metrics are based on radial and density functions that incorporate spherical-based metrics, and the indices are built from metric tree (M-tree) structures. This work combines DCRR with DDPIn for the development of new DCRR centroid-based metrics: spherical binning distance and inter-centroid spherical distance. The use of DCRR models will provide additional significant structural information via the inclusion of side-chain centroids. Additionally, the newly developed distance metric functions combined with DCRR and M-tree indexing attempt to improve upon the performance of prior work (DDPIn), given the same data set, with respect to both individual k-nearest neighbor (kNN) search queries as well as clustering all proteins in the index.
Graph neural networks and attention-based CNN-LSTM for protein classification
College of Artificial Intelligence, Nankai University（南开大学人工智能学院）；Shenzhen Research Institute, Nankai University（南开大学深圳研究院）；Ji Hua Laboratory（季华实验室）；State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Nankai University（南开大学药学院药物化学生物学国家重点实验室）
The eBDIMS path-sampling server: generation, classification and interactive visualization of protein ensembles and transition pathways in 2D-motion space
Weighted graphlets and deep neural networks for protein structure classification
University of Notre Dame（圣母大学）；Eck Institute for Global Health, University of Notre Dame（圣母大学埃克全球健康研究所）；Interdisciplinary Center for Network Science & Applications, University of Notre Dame（圣母大学网络科学与应用跨学科中心）；University of Tennessee at Knoxville（田纳西大学诺克斯维尔分校）
Comprehensive classification of the plant non-specific lipid transfer protein superfamily towards its Sequence -Structure -Function analysis
背景非特异性脂质转移蛋白（Non-specific Lipid Transfer Proteins，nsLTP）广泛分布于植物界，是一个相关蛋白超家族。到目前为止，已经鉴定了800多个不同的序列，但它们的生物学功能仍不清楚。多年来，很明显，它们对农学和营养问题产生了一定的兴趣。破译它们的功能意味着收集和分析从基因序列到蛋白质结构，从细胞定位到生理作用的各种数据。随着大量新的蛋白质序列的出现，从序列-结构-功能关系中提取有意义的知识需要开发新的工具和方法。由于nsLTP显示出高度的进化分歧，但保守的共同右手超螺旋结构折叠，并且由于它们参与了植物发育和防御中的大量关键作用，因此它们是验证这种方法的一个令人兴奋的案例研究。
Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification
The School of Computing and Informatic, University of Louisiana at Lafayette（计算与信息学院，路易斯安那州立大学拉法叶分校）；Department of Chemistry, University of Louisiana at Lafayette（化学系，路易斯安那州立大学拉法叶分校）
A Vectorization Method Induced By Maximal Margin Classification For Persistent Diagrams
College of Mathematics；Computer Science Zhejiang A \& F University Hangzhou, China；School of Mathematics；Statistics Beijing Institute of Technology Beijing, China；School of Electronics；Information Hangzhou Dianzi University Hangzhou, China；Institute of Automation Chinese Academy of Sciences Beijing, China；School of Computer Science \& Technology Beijing Institute of Technology Beijing, China
