A bioinformatics pipeline to search functional motifs within whole-proteome data: a case study of poxviruses

Abstract

Proteins harbor domains or short linear motifs, which facilitate their functions and interactions. Finding functional motifs in protein sequences could predict the putative cellular roles or characteristics of hypothetical proteins. In this study, we present Shetti-Motif, which is an interactive tool to (i) map UniProt and PROSITE flat files, (ii) search for multiple pre-defined consensus patterns or experimentally validated functional motifs in large datasets protein sequences (proteome-wide), (iii) search for motifs containing repeated residues (low-complexity regions, e.g., Leu-, SR-, PEST-rich motifs, etc.). As proof of principle, using this comparative proteomics pipeline, eleven proteomes encoded by member of Poxviridae family were searched against about 100 experimentally validated functional motifs. The closely related viruses and viruses infect the same host cells (e.g. vaccinia and variola viruses) show similar motif-containing proteins profile. The motifs encoded by these viruses are correlated, which explains why poxviruses are able to interact with wide range of host cells. In conclusion, this in silico analysis is useful to establish a dataset(s) or potential proteins for further investigation or compare between species.

Keywords: Comparative genomics; Functional genomics; Low-complexity regions (LCRs); Protein annotation; Protein domain; Protein function.

Fig. 1

Screenshot of Shetti-Motif main window (a), and flowchart of features and method used in this study (b)

Fig. 2

Poxviruses encode divergent number of motifs; the motif-containing proteins (McPs) profile of closely related viruses are correlated. The number of motif-containing proteins (i.e., protein containing at least one instance of the query motif) were counted and normalized (percentage) to total number of proteins encoded by a virus, Table 1, S3. a Box and whisker plot shows 1st, 2nd, 3rd quantiles (Q1, Q2 and Q3 respectively) of numbers of McPs, whiskers at 1.5 IQR (interquartile range) (Q3 + 1.5 IQR); b the average and maximum numbers of McPs, the error bars are based on standard deviation values; c Spearman correlation coefficient values and scatterplots (using STASTICA Data Miner; StatSoft, USA) of the number of McPs encoded by each virus. VacV: Vaccinia virus WR, VarV: Variola virus, MPxV: Monkeypox virus, YMTV: Yaba monkey tumor virus, FPxV: Fowlpox virus, CPxV: Canarypox virus, Orf V: Orf virus, CPxV: Cowpox virus, CmPxV: Camelpox virus M-96, MV: Myxoma virus, and NileCV: Nile crocodilepox virus