Ajitha M, Sundar K, Arul Mugilan S, Arumugam S
Kalasalingam University, Krishnankoil, Tamil Nadu, India.
Raja Doraisingam Government Arts College, Sivaganga, Tamil Nadu, India.
Proteins. 2018 Mar;86(3):322-331. doi: 10.1002/prot.25441. Epub 2018 Jan 22.
The advent of whole genome sequencing leads to increasing number of proteins with known amino acid sequences. Despite many efforts, the number of proteins with resolved three dimensional structures is still low. One of the challenging tasks the structural biologists face is the prediction of the interaction of metal ion with any protein for which the structure is unknown. Based on the information available in Protein Data Bank, a site (METALACTIVE INTERACTION) has been generated which displays information for significant high preferential and low-preferential combination of endogenous ligands for 49 metal ions. User can also gain information about the residues present in the first and second coordination sphere as it plays a major role in maintaining the structure and function of metalloproteins in biological system. In this paper, a novel computational tool (ZINCCLUSTER) is developed, which can predict the zinc metal binding sites of proteins even if only the primary sequence is known. The purpose of this tool is to predict the active site cluster of an uncharacterized protein based on its primary sequence or a 3D structure. The tool can predict amino acids interacting with a metal or vice versa. This tool is based on the occurrence of significant triplets and it is tested to have higher prediction accuracy when compared to that of other available techniques.
全基因组测序的出现使得已知氨基酸序列的蛋白质数量不断增加。尽管付出了诸多努力，但已解析三维结构的蛋白质数量仍然较少。结构生物学家面临的一项具有挑战性的任务是预测金属离子与任何结构未知的蛋白质之间的相互作用。基于蛋白质数据库中可用的信息，创建了一个位点（金属活性相互作用位点），该位点展示了49种金属离子的内源性配体的显著高偏好和低偏好组合的信息。用户还可以获取有关第一和第二配位球中存在的残基的信息，因为这些残基在维持生物系统中金属蛋白的结构和功能方面起着主要作用。在本文中，开发了一种新型计算工具（锌簇），即使仅知道蛋白质的一级序列，它也能预测蛋白质的锌金属结合位点。该工具的目的是根据未表征蛋白质的一级序列或三维结构预测其活性位点簇。该工具可以预测与金属相互作用的氨基酸，反之亦然。该工具基于显著三联体的出现情况，并且经测试与其他现有技术相比具有更高的预测准确性。
