Identification and characterization of specific motifs in effector proteins of plant parasites using MOnSTER.

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 in protein sequences predicted from genomes is challenging and current methods generate too many candidates without indication for prioritizing further experimental studies. In most phyla, effectors contain specific sequence motifs which influence their localization and targets in the plant. Although bacterial, fungal and oomycetes effectors have been studied extensively and conserved characteristic motifs have been identified, research on plant-parasitic nematode effectors (PPN) identified some enriched degenerate motifs in only one species so far. The different lifestyles of PPNs might reflect effectors with different functions according to the nematode's specific needs, thus presenting a high variety of characteristic motifs. To circumvent these limitations, we have developed MOnSTER a novel tool that identifies clusters of motifs of protein sequences (CLUMP) and associates a score to each CLUMP. This score encompasses the physicochemical properties of AAs and the motif occurrences. We built up our method to identify discriminant CLUMPs in effector proteins of plant-pathogenic oomycetes. We showed the reliability of MOnSTER by identifying five CLUMPs that correspond to the known motifs: RxLR, -dEER and LxLFLAK-HVLVxxP. Consequently, we applied MOnSTER on PPN effector proteins and identified peculiar motifs in their sequences. We identified six CLUMPs in about 60% of the known nematode effectors. Furthermore, we found that specific co-occurrences of at least two CLUMPs are present in PPN effector sequences bearing protein domains important for invasion and pathogenicity. The potentiality of this tool goes behind the effector proteins and can be used to easily cluster motifs and calculate the CLUMPs score on any set of protein sequences. ### Competing Interest Statement The authors have declared no competing interest.