Laboratoire GBCM, EA 7528, Conservatoire National des Arts et Métiers, Hesam Université, Paris 75003, France.
Laboratoire XLIM, UMR CNRS 7252, Université de Limoges, Limoges 87000, France.
Bioinformatics. 2021 Dec 7;37(23):4375-4382. doi: 10.1093/bioinformatics/btab511.
The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes.
Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure-function paradigm toward a protein structure-surface(s)-function paradigm.
All data are available online at http://datasetmachat.drugdesign.fr.
Supplementary data are available at Bioinformatics online.
在分子水平上研究生物系统的结构可以深入了解它们的功能和动态。生物分子的形状和表面是分子识别事件的基础。对其几何形状进行特征描述可以更准确地预测它们的相互作用。在本工作中，我们评估了计算机视觉领域中参考形状检索方法在蛋白质形状上的性能。
形状检索方法在识别同源蛋白和跟踪大构象变化方面非常有效。这项工作说明了蛋白质表面形状作为蛋白质结构的一种更高层次的表示形式的重要性，它（i）抽象出了潜在的蛋白质序列、结构或折叠，（ii）允许使用形状检索方法来筛选大型蛋白质结构数据库，以识别表面同源物和可能的相互作用伙伴，（iii）将蛋白质结构-功能范式扩展到蛋白质结构-表面（s）-功能范式。
所有数据均可在 http://datasetmachat.drugdesign.fr 上获得。
补充数据可在 Bioinformatics 在线获得。
