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Reassessing domain architecture evolution of metazoan proteins: major impact of errors caused by confusing paralogs and epaktologs.

Nagy A, Bányai L, Patthy L - Genes (Basel) (2011)

Bottom Line: Based on these findings, we suggest that earlier genome-scale studies based on comparison of predicted (frequently mispredicted) protein sequences may have led to some erroneous conclusions about the evolution of novel domain architectures of multidomain proteins.In this manuscript we examine the impact of confusing paralogous and epaktologous multidomain proteins (i.e., those that are related only through the independent acquisition of the same domain types) on conclusions drawn about DA evolution of multidomain proteins in Metazoa.Our findings caution that earlier studies based on analysis of datasets of protein families that were contaminated with epaktologs may have led to some erroneous conclusions about the evolution of novel domain architectures of multidomain proteins.

View Article: PubMed Central - PubMed

Affiliation: Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, Budapest H-1113, Hungary. nagya@enzim.hu.

ABSTRACT
In the accompanying paper (Nagy, Szláma, Szarka, Trexler, Bányai, Patthy, Reassessing Domain Architecture Evolution of Metazoan Proteins: Major Impact of Gene Prediction Errors) we showed that in the case of UniProtKB/TrEMBL, RefSeq, EnsEMBL and NCBI's GNOMON predicted protein sequences of Metazoan species the contribution of erroneous (incomplete, abnormal, mispredicted) sequences to domain architecture (DA) differences of orthologous proteins might be greater than those of true gene rearrangements. Based on these findings, we suggest that earlier genome-scale studies based on comparison of predicted (frequently mispredicted) protein sequences may have led to some erroneous conclusions about the evolution of novel domain architectures of multidomain proteins. In this manuscript we examine the impact of confusing paralogous and epaktologous multidomain proteins (i.e., those that are related only through the independent acquisition of the same domain types) on conclusions drawn about DA evolution of multidomain proteins in Metazoa. To estimate the contribution of this type of error we have used as reference UniProtKB/Swiss-Prot sequences from protein families with well-characterized evolutionary histories. We have used two types of paralogy-group construction procedures and monitored the impact of various parameters on the separation of true paralogs from epaktologs on correctly annotated Swiss-Prot entries of multidomain proteins. Our studies have shown that, although public protein family databases are contaminated with epaktologs, analysis of the structure of sequence similarity networks of multidomain proteins provides an efficient means for the separation of epaktologs and paralogs. We have also demonstrated that contamination of protein families with epaktologs increases the apparent rate of DA change and introduces a bias in DA differences in as much as it increases the proportion of terminal over internal DA differences.We have shown that confusing paralogous and epaktologous multidomain proteins significantly increases the apparent rate of DA change in Metazoa and introduces a positional bias in favor of terminal over internal DA changes. Our findings caution that earlier studies based on analysis of datasets of protein families that were contaminated with epaktologs may have led to some erroneous conclusions about the evolution of novel domain architectures of multidomain proteins. A reassessment of the DA evolution of multidomain proteins is presented in an accompanying paper [1].

No MeSH data available.


Related in: MedlinePlus

Analysis of sequence similarity networks of paralogous human proteins defined through all-against-all sequence comparison of human Swiss-Prot entries. The matches were ranked in the order of decreasing sequence similarity scores, including in this list only the top-scoring 1, 2, 3, …. 20 matches with e-values of <10−5, excluding self-matches. Datasets containing the top-scoring one, two… twenty sequences were created (TSS = 1, TSS = 2, …. TSS = 20 datasets) and the component structure of sequence similarity networks defined for these datasets was analyzed as described in the text. The numbers on the abscissa indicate the number of top-scoring matches included in the analyses (TSS = 1, …. TSS = 20). Black diamonds represent the number of human Swiss-Prot entries with at least one significant human Swiss-Prot homolog. Blue triangles represent the number of weak components, red triangles represent the number of strong components. Blue rectangles represent the number of sequences in the Largest Connected Component of weak component analysis, red rectangles represent the number of sequences in the Largest Connected Component of strong component analysis.
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f5-genes-02-00516: Analysis of sequence similarity networks of paralogous human proteins defined through all-against-all sequence comparison of human Swiss-Prot entries. The matches were ranked in the order of decreasing sequence similarity scores, including in this list only the top-scoring 1, 2, 3, …. 20 matches with e-values of <10−5, excluding self-matches. Datasets containing the top-scoring one, two… twenty sequences were created (TSS = 1, TSS = 2, …. TSS = 20 datasets) and the component structure of sequence similarity networks defined for these datasets was analyzed as described in the text. The numbers on the abscissa indicate the number of top-scoring matches included in the analyses (TSS = 1, …. TSS = 20). Black diamonds represent the number of human Swiss-Prot entries with at least one significant human Swiss-Prot homolog. Blue triangles represent the number of weak components, red triangles represent the number of strong components. Blue rectangles represent the number of sequences in the Largest Connected Component of weak component analysis, red rectangles represent the number of sequences in the Largest Connected Component of strong component analysis.

Mentions: As shown in Figure 5, the total number of human Swiss-Prot sequences that are homologous (paralogous or epaktologous) with at least one human Swiss-Prot sequence (vertices in the network) is ∼15.000. Since the total number of human Swiss-Prot entries analyzed is 20.311, this indicates that ∼5000 human sequences are ‘solitary’, i.e., they have no human homolog that give a significant similarity score at the cut-off value used (e-value < 10−5).


Reassessing domain architecture evolution of metazoan proteins: major impact of errors caused by confusing paralogs and epaktologs.

Nagy A, Bányai L, Patthy L - Genes (Basel) (2011)

Analysis of sequence similarity networks of paralogous human proteins defined through all-against-all sequence comparison of human Swiss-Prot entries. The matches were ranked in the order of decreasing sequence similarity scores, including in this list only the top-scoring 1, 2, 3, …. 20 matches with e-values of <10−5, excluding self-matches. Datasets containing the top-scoring one, two… twenty sequences were created (TSS = 1, TSS = 2, …. TSS = 20 datasets) and the component structure of sequence similarity networks defined for these datasets was analyzed as described in the text. The numbers on the abscissa indicate the number of top-scoring matches included in the analyses (TSS = 1, …. TSS = 20). Black diamonds represent the number of human Swiss-Prot entries with at least one significant human Swiss-Prot homolog. Blue triangles represent the number of weak components, red triangles represent the number of strong components. Blue rectangles represent the number of sequences in the Largest Connected Component of weak component analysis, red rectangles represent the number of sequences in the Largest Connected Component of strong component analysis.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3927612&req=5

f5-genes-02-00516: Analysis of sequence similarity networks of paralogous human proteins defined through all-against-all sequence comparison of human Swiss-Prot entries. The matches were ranked in the order of decreasing sequence similarity scores, including in this list only the top-scoring 1, 2, 3, …. 20 matches with e-values of <10−5, excluding self-matches. Datasets containing the top-scoring one, two… twenty sequences were created (TSS = 1, TSS = 2, …. TSS = 20 datasets) and the component structure of sequence similarity networks defined for these datasets was analyzed as described in the text. The numbers on the abscissa indicate the number of top-scoring matches included in the analyses (TSS = 1, …. TSS = 20). Black diamonds represent the number of human Swiss-Prot entries with at least one significant human Swiss-Prot homolog. Blue triangles represent the number of weak components, red triangles represent the number of strong components. Blue rectangles represent the number of sequences in the Largest Connected Component of weak component analysis, red rectangles represent the number of sequences in the Largest Connected Component of strong component analysis.
Mentions: As shown in Figure 5, the total number of human Swiss-Prot sequences that are homologous (paralogous or epaktologous) with at least one human Swiss-Prot sequence (vertices in the network) is ∼15.000. Since the total number of human Swiss-Prot entries analyzed is 20.311, this indicates that ∼5000 human sequences are ‘solitary’, i.e., they have no human homolog that give a significant similarity score at the cut-off value used (e-value < 10−5).

Bottom Line: Based on these findings, we suggest that earlier genome-scale studies based on comparison of predicted (frequently mispredicted) protein sequences may have led to some erroneous conclusions about the evolution of novel domain architectures of multidomain proteins.In this manuscript we examine the impact of confusing paralogous and epaktologous multidomain proteins (i.e., those that are related only through the independent acquisition of the same domain types) on conclusions drawn about DA evolution of multidomain proteins in Metazoa.Our findings caution that earlier studies based on analysis of datasets of protein families that were contaminated with epaktologs may have led to some erroneous conclusions about the evolution of novel domain architectures of multidomain proteins.

View Article: PubMed Central - PubMed

Affiliation: Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, Budapest H-1113, Hungary. nagya@enzim.hu.

ABSTRACT
In the accompanying paper (Nagy, Szláma, Szarka, Trexler, Bányai, Patthy, Reassessing Domain Architecture Evolution of Metazoan Proteins: Major Impact of Gene Prediction Errors) we showed that in the case of UniProtKB/TrEMBL, RefSeq, EnsEMBL and NCBI's GNOMON predicted protein sequences of Metazoan species the contribution of erroneous (incomplete, abnormal, mispredicted) sequences to domain architecture (DA) differences of orthologous proteins might be greater than those of true gene rearrangements. Based on these findings, we suggest that earlier genome-scale studies based on comparison of predicted (frequently mispredicted) protein sequences may have led to some erroneous conclusions about the evolution of novel domain architectures of multidomain proteins. In this manuscript we examine the impact of confusing paralogous and epaktologous multidomain proteins (i.e., those that are related only through the independent acquisition of the same domain types) on conclusions drawn about DA evolution of multidomain proteins in Metazoa. To estimate the contribution of this type of error we have used as reference UniProtKB/Swiss-Prot sequences from protein families with well-characterized evolutionary histories. We have used two types of paralogy-group construction procedures and monitored the impact of various parameters on the separation of true paralogs from epaktologs on correctly annotated Swiss-Prot entries of multidomain proteins. Our studies have shown that, although public protein family databases are contaminated with epaktologs, analysis of the structure of sequence similarity networks of multidomain proteins provides an efficient means for the separation of epaktologs and paralogs. We have also demonstrated that contamination of protein families with epaktologs increases the apparent rate of DA change and introduces a bias in DA differences in as much as it increases the proportion of terminal over internal DA differences.We have shown that confusing paralogous and epaktologous multidomain proteins significantly increases the apparent rate of DA change in Metazoa and introduces a positional bias in favor of terminal over internal DA changes. Our findings caution that earlier studies based on analysis of datasets of protein families that were contaminated with epaktologs may have led to some erroneous conclusions about the evolution of novel domain architectures of multidomain proteins. A reassessment of the DA evolution of multidomain proteins is presented in an accompanying paper [1].

No MeSH data available.


Related in: MedlinePlus