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An effective approach for annotation of protein families with low sequence similarity and conserved motifs: identifying GDSL hydrolases across the plant kingdom.

ABSTRACT: 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. 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.Motif-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/ ).Our 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.

SUBMITTER: Vujaklija I

PROVIDER: S-EPMC4757993 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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An effective approach for annotation of protein families with low sequence similarity and conserved motifs: identifying GDSL hydrolases across the plant kingdom.

Vujaklija Ivan I Bielen Ana A Paradžik Tina T Biđin Siniša S Goldstein Pavle P Vujaklija Dušica D

BMC bioinformatics 20160218

<h4>Background</h4>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. 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. Th ...[more]

PMID: 26892257

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Project description:BackgroundThe Plasmodium genus of malaria parasites encodes several families of antigen-encoding genes. These genes tend to be hyper-variable, highly recombinogenic and variantly expressed. The best-characterized family is the var genes, exclusively found in the Laveranian subgenus of malaria parasites infecting humans and great apes. Var genes encode major virulence factors involved in immune evasion and the maintenance of chronic infections. In the human parasite P. falciparum, var gene recombination and diversification appear to be promoted by G-quadruplex (G4) DNA motifs, which are strongly associated with var genes in P. falciparum. Here, we investigated how this association might have evolved across Plasmodium species - both Laverania and also more distantly related species which lack vars but encode other, more ancient variant gene families.ResultsThe association between var genes and G4-forming motifs was conserved across Laverania, spanning ~ 1 million years of evolutionary time, with suggestive evidence for evolution of the association occurring within this subgenus. In rodent malaria species, G4-forming motifs were somewhat associated with pir genes, but this was not conserved in the Laverania, nor did we find a strong association of these motifs with any gene family in a second outgroup of avian malaria parasites. Secondly, we compared two different G4 prediction algorithms in their performance on extremely A/T-rich Plasmodium genomes, and also compared these predictions with experimental data from G4-seq, a DNA sequencing method for identifying G4-forming motifs. We found a surprising lack of concordance between the two algorithms and also between the algorithms and G4-seq data.ConclusionsG4-forming motifs are uniquely strongly associated with Plasmodium var genes, suggesting a particular role for G4s in recombination and diversification of these genes. Secondly, in the A/T-rich genomes of Plasmodium species, the choice of prediction algorithm may be particularly influential when studying G4s in these important protozoan pathogens.

Project description:BackgroundBiological knowledge, and therefore Gene Ontology annotation sets, for human genes is incomplete. Recent studies have reported that biases in available GO annotations result in biased estimates of functional similarities of genes, but it is still unclear what the effect of incompleteness itself may be, even in the absence of bias. Pairwise gene similarities are used in a number of contexts, including gene "functional similarity" clustering and the related problem of functional ontology structure inference, but it is not known how different similarity measures or clustering methods perform on this task, and how the clusters are affected by annotation completeness.ResultsWe developed representations of both "complete" and "incomplete" GO annotation datasets based on experimentally-supported annotations from the GO database-specifically designed to model the incompleteness of human gene annotations-and computed semantic similarities for each set using a variety of different published measures. We then assessed the clusters derived from these measures using two different clustering methods: hierarchical clustering, and the CliXO algorithm. We find the CliXO algorithm, combined with appropriate measures, performs better than hierarchical clustering in reconstructing GO both when the data are complete, and incomplete. Some measures, particularly those that create a pairwise gene similarity by averaging over all pairwise annotation similarities, had consistently poor performance, and a few measures, such as Lin best-matched average and Relevance maximum, were generally among the best performers for a broad range in annotation completeness and types of GO classes. Finally, we show that for semantic similarity-based clustering, the multicellular organism process branch of the GO biological process ontology is more challenging to represent than the cellular process branch.ConclusionsWe assessed the effects of annotation completeness on the distribution of pairwise gene semantic similarity scores, and subsequent effects on the clusters derived from these scores. Our results suggest combinations of semantic similarity measures, gene-level scoring methods and clustering method that perform best for functional gene clustering using annotation sets of varying completeness. Overall, our results underscore the importance of increasing the completeness of GO annotations to for supporting computational analyses of gene function.

Dataset Information

An effective approach for annotation of protein families with low sequence similarity and conserved motifs: identifying GDSL hydrolases across the plant kingdom.

Publications

An effective approach for annotation of protein families with low sequence similarity and conserved motifs: identifying GDSL hydrolases across the plant kingdom.

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