PDF(Pubmed)
Abstract：
Plant pathogens cause billions of dollars of crop loss every year and are a major threat to global food security. Identifying and characterizing pathogens effectors is crucial towards their improved control. Because of their poor sequence conservation, effector identification is challenging, and current methods generate too many candidates without indication for prioritizing experimental studies. In most phyla, effectors contain specific sequence motifs which influence their localization and targets in the plant. Therefore, there is an urgent need to develop bioinformatics tools tailored for pathogen effectors. To circumvent these limitations, we have developed MOnSTER a specific tool that identifies clusters of motifs of protein sequences (CLUMPs). MOnSTER can be fed with motifs identified by de novo tools or from databases such as Pfam and InterProScan. The advantage of MOnSTER is the reduction of motif redundancy by clustering them and associating a score. This score encompasses the physicochemical properties of AAs and the motif occurrences. We built up our method to identify discriminant CLUMPs in oomycetes effectors. Consequently, we applied MOnSTER on plant parasitic nematodes and identified six CLUMPs in about 60% of the known nematode candidate parasitism proteins. Furthermore, we found co-occurrences of CLUMPs with protein domains important for invasion and pathogenicity. The potentiality of this tool goes beyond the effector characterization and can be used to easily cluster motifs and calculate the CLUMP-score on any set of protein sequences.
摘要：
植物病原体每年造成数十亿美元的作物损失，是全球粮食安全的主要威胁。鉴定和表征病原体效应物对于改善其控制至关重要。由于它们的序列保守性差，效应器识别具有挑战性，目前的方法产生了太多的候选人，没有指示优先的实验研究。在大多数门，效应子包含特定的序列基序，这些基序会影响它们在植物中的定位和靶标。因此,迫切需要开发针对病原体效应物的生物信息学工具。为了规避这些限制，我们已经开发了MONSTER一种特定的工具，可以识别蛋白质序列（CLUMPs）的基序簇。MONSTER可以提供由从头工具或从Pfam和InterProScan等数据库识别的主题。MOnSTER的优点是通过对它们进行聚类并关联分数来减少基序冗余。该分数包括AA的物理化学性质和基序出现。我们建立了我们的方法来识别卵菌效应物中的判别式CLUMPs。因此,我们将MOnSTER应用于植物寄生线虫，并在约60％的已知线虫候选寄生蛋白中鉴定了6个CLUMPs。此外，我们发现CLUMPs与对侵袭和致病性重要的蛋白结构域同时出现.该工具的潜力超出了效应子表征，可用于轻松地对基序进行聚类并计算任何一组蛋白质序列的CLUMP得分。
