Núñez-Vivanco Gabriel, Valdés-Jiménez Alejandro, Besoaín Felipe, Reyes-Parada Miguel
Escuela de Ingeniería Civil en Bioinformática, Universidad de Talca, Avenida Lircay s/n, Talca, Chile ; Centro de Bioinformática y Simulación Molecular, Universidad de Talca, 2 Norte 685, Talca, Chile.
Escuela de Ingeniería Civil en Bioinformática, Universidad de Talca, Avenida Lircay s/n, Talca, Chile.
J Cheminform. 2016 Apr 18;8:19. doi: 10.1186/s13321-016-0131-9. eCollection 2016.
Since the structure of proteins is more conserved than the sequence, the identification of conserved three-dimensional (3D) patterns among a set of proteins, can be important for protein function prediction, protein clustering, drug discovery and the establishment of evolutionary relationships. Thus, several computational applications to identify, describe and compare 3D patterns (or motifs) have been developed. Often, these tools consider a 3D pattern as that described by the residues surrounding co-crystallized/docked ligands available from X-ray crystal structures or homology models. Nevertheless, many of the protein structures stored in public databases do not provide information about the location and characteristics of ligand binding sites and/or other important 3D patterns such as allosteric sites, enzyme-cofactor interaction motifs, etc. This makes necessary the development of new ligand-independent methods to search and compare 3D patterns in all available protein structures.
Here we introduce Geomfinder, an intuitive, flexible, alignment-free and ligand-independent web server for detailed estimation of similarities between all pairs of 3D patterns detected in any two given protein structures. We used around 1100 protein structures to form pairs of proteins which were assessed with Geomfinder. In these analyses each protein was considered in only one pair (e.g. in a subset of 100 different proteins, 50 pairs of proteins can be defined). Thus: (a) Geomfinder detected identical pairs of 3D patterns in a series of monoamine oxidase-B structures, which corresponded to the effectively similar ligand binding sites at these proteins; (b) we identified structural similarities among pairs of protein structures which are targets of compounds such as acarbose, benzamidine, adenosine triphosphate and pyridoxal phosphate; these similar 3D patterns are not detected using sequence-based methods; (c) the detailed evaluation of three specific cases showed the versatility of Geomfinder, which was able to discriminate between similar and different 3D patterns related to binding sites of common substrates in a range of diverse proteins.
Geomfinder allows detecting similar 3D patterns between any two pair of protein structures, regardless of the divergency among their amino acids sequences. Although the software is not intended for simultaneous multiple comparisons in a large number of proteins, it can be particularly useful in cases such as the structure-based design of multitarget drugs, where a detailed analysis of 3D patterns similarities between a few selected protein targets is essential.
由于蛋白质的结构比序列更保守，因此在一组蛋白质中识别保守的三维（3D）模式对于蛋白质功能预测、蛋白质聚类、药物发现以及进化关系的建立可能很重要。因此，已经开发了几种用于识别、描述和比较3D模式（或基序）的计算应用程序。通常，这些工具将3D模式视为由X射线晶体结构或同源模型中可获得的共结晶/对接配体周围的残基所描述的模式。然而，公共数据库中存储的许多蛋白质结构并未提供有关配体结合位点和/或其他重要3D模式（如变构位点、酶辅因子相互作用基序等）的位置和特征的信息。这使得有必要开发新的不依赖配体的方法来搜索和比较所有可用蛋白质结构中的3D模式。
在这里，我们介绍了Geomfinder，这是一个直观、灵活、无需比对且不依赖配体的网络服务器，用于详细估计在任何两个给定蛋白质结构中检测到的所有3D模式对之间的相似性。我们使用了大约1100个蛋白质结构来形成蛋白质对，并使用Geomfinder对其进行评估。在这些分析中，每个蛋白质仅在一对中被考虑（例如，在100个不同蛋白质的子集中，可以定义50对蛋白质）。因此：（a）Geomfinder在一系列单胺氧化酶-B结构中检测到相同的3D模式对，这与这些蛋白质中实际相似的配体结合位点相对应；（b）我们在作为阿卡波糖、苯甲脒、三磷酸腺苷和磷酸吡哆醛等化合物靶点的蛋白质结构对之间鉴定出结构相似性；这些相似的3D模式无法使用基于序列的方法检测到；（c）对三个具体案例的详细评估显示了Geomfinder的多功能性，它能够区分一系列不同蛋白质中与常见底物结合位点相关的相似和不同的3D模式。
Geomfinder允许检测任意两个蛋白质结构对之间相似的3D模式，无论它们氨基酸序列之间的差异如何。虽然该软件并非用于对大量蛋白质进行同时的多重比较，但在多靶点药物的基于结构的设计等情况下可能特别有用，在这些情况下，对少数选定蛋白质靶点之间的3D模式相似性进行详细分析至关重要。
