Abstract

BackgroundThe massive accumulation of protein sequences arising from the rapid development of high-throughput sequencing, coupled with automatic annotation, results in high levels of incorrect annotations. In this study, we describe an approach to decrease annotation errors of protein families characterized by low overall sequence similarity. The GDSL lipolytic family comprises proteins with multifunctional properties and high potential for pharmaceutical and industrial applications. The number of proteins assigned to this family has increased rapidly over the last few years. In particular, the natural abundance of GDSL enzymes reported recently in plants indicates that they could be a good source of novel GDSL enzymes. We noticed that a significant proportion of annotated sequences lack specific GDSL motif(s) or catalytic residue(s). Here, we applied motif-based sequence analyses to identify enzymes possessing conserved GDSL motifs in selected proteomes across the plant kingdom.ResultsMotif-based HMM scanning (Viterbi decoding-VD and posterior decoding-PD) and the here described PD/VD protocol were successfully applied on 12 selected plant proteomes to identify sequences with GDSL motifs. A significant number of identified GDSL sequences were novel. Moreover, our scanning approach successfully detected protein sequences lacking at least one of the essential motifs (171/820) annotated by Pfam profile search (PfamA) as GDSL. Based on these analyses we provide a curated list of GDSL enzymes from the selected plants. CLANS clustering and phylogenetic analysis helped us to gain a better insight into the evolutionary relationship of all identified GDSL sequences. Three novel GDSL subfamilies as well as unreported variations in GDSL motifs were discovered in this study. In addition, analyses of selected proteomes showed a remarkable expansion of GDSL enzymes in the lycophyte, Selaginella moellendorffii. Finally, we provide a general motif-HMM scanner which is easily accessible through the graphical user interface (http://compbio.math.hr/).ConclusionsOur results show that scanning with a carefully parameterized motif-HMM is an effective approach for annotation of protein families with low sequence similarity and conserved motifs. The results of this study expand current knowledge and provide new insights into the evolution of the large GDSL-lipase family in land plants.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0919-7) contains supplementary material, which is available to authorized users.

Highlights

  • The massive accumulation of protein sequences arising from the rapid development of high-throughput sequencing, coupled with automatic annotation, results in high levels of incorrect annotations

  • We aimed to scan for the presence of GDSL motifs in proteomes selected across the plant kingdom

  • While analyzing previously reported protein sequences annotated as GDSL enzymes we noticed that numerous sequences were lacking specific block(s) and/or catalytic residues known to be essential for GDSL enzyme activities

Read more

Summary

Introduction

The massive accumulation of protein sequences arising from the rapid development of high-throughput sequencing, coupled with automatic annotation, results in high levels of incorrect annotations. The search for novel enzymes with beneficial functions has great potential in the GDSL lipolytic family These enzymes have five consensus sequence Blocks (I-V), among which Blocks I, III and V show higher conservation. Searching for new GDSL enzymes across the plant kingdom is of general interest Since these enzymes exhibit low overall sequence similarity [5], motif scanning [17] is an appropriate method for in silico annotation of GDSL proteins. The standard decoding algorithm within the HMM framework is Viterbi decoding (VD) which finds the most probable path through the model (i.e. HMM) assuming that the given HMM has generated the analyzed sequence Another possible approach within the HMM framework is posterior decoding (PD) which maximizes the posterior probability of assigning an HMM state to the given residue, over all possible states. In such cases PD might be preferable to Viterbi decoding since in posterior decoding all paths that contribute to a given assignment are taken into account [21,22,23]

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call