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Reassessing domain architecture evolution of metazoan proteins: major impact of gene prediction errors.

ABSTRACT: In view of the fact that appearance of novel protein domain architectures (DA) is closely associated with biological innovations, there is a growing interest in the genome-scale reconstruction of the evolutionary history of the domain architectures of multidomain proteins. In such analyses, however, it is usually ignored that a significant proportion of Metazoan sequences analyzed is mispredicted and that this may seriously affect the validity of the conclusions. To estimate the contribution of errors in gene prediction to differences in DA of predicted proteins, we have used the high quality manually curated UniProtKB/Swiss-Prot database as a reference. For genome-scale analysis of domain architectures of predicted proteins we focused on RefSeq, EnsEMBL and NCBI's GNOMON predicted sequences of Metazoan species with completely sequenced genomes. Comparison of the DA of UniProtKB/Swiss-Prot sequences of worm, fly, zebrafish, frog, chick, mouse, rat and orangutan with those of human Swiss-Prot entries have identified relatively few cases where orthologs had different DA, although the percentage with different DA increased with evolutionary distance. In contrast with this, comparison of the DA of human, orangutan, rat, mouse, chicken, frog, zebrafish, worm and fly RefSeq, EnsEMBL and NCBI's GNOMON predicted protein sequences with those of the corresponding/orthologous human Swiss-Prot entries identified a significantly higher proportion of domain architecture differences than in the case of the comparison of Swiss-Prot entries. Analysis of RefSeq, EnsEMBL and NCBI's GNOMON predicted protein sequences with DAs different from those of their Swiss-Prot orthologs confirmed that the higher rate of domain architecture differences is due to errors in gene prediction, the majority of which could be corrected with our FixPred protocol. We have also demonstrated that contamination of databases with incomplete, abnormal or mispredicted sequences introduces a bias in DA differences in as much as it increases the proportion of terminal over internal DA differences. Here we have shown that in the case of RefSeq, EnsEMBL and NCBI's GNOMON predicted protein sequences of Metazoan species, the contribution of gene prediction errors to domain architecture differences of orthologs is comparable to or greater than those due to true gene rearrangements. We have also demonstrated that domain architecture comparison may serve as a useful tool for the quality control of gene predictions and may thus guide the correction of sequence errors. Our findings caution 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. A reassessment of the DA evolution of orthologous and paralogous proteins is presented in an accompanying paper [1].

SUBMITTER: Nagy A

PROVIDER: S-EPMC3927609 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Reassessing domain architecture evolution of metazoan proteins: major impact of gene prediction errors.

Nagy Alinda A Szláma György G Szarka Eszter E Trexler Mária M Bányai László L Patthy László L

Genes 20110713 3

In view of the fact that appearance of novel protein domain architectures (DA) is closely associated with biological innovations, there is a growing interest in the genome-scale reconstruction of the evolutionary history of the domain architectures of multidomain proteins. In such analyses, however, it is usually ignored that a significant proportion of Metazoan sequences analyzed is mispredicted and that this may seriously affect the validity of the conclusions. To estimate the contribution of ...[more]

PMID: 24710207

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Project description: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].

Project description:MotivationSimulation is an essential technique for generating biomolecular data with a 'known' history for use in validating phylogenetic inference and other evolutionary methods. On longer time scales, simulation supports investigations of equilibrium behavior and provides a formal framework for testing competing evolutionary hypotheses. Twenty years of molecular evolution research have produced a rich repertoire of simulation methods. However, current models do not capture the stringent constraints acting on the domain insertions, duplications, and deletions by which multidomain architectures evolve. Although these processes have the potential to generate any combination of domains, only a tiny fraction of possible domain combinations are observed in nature. Modeling these stringent constraints on domain order and co-occurrence is a fundamental challenge in domain architecture simulation that does not arise with sequence and gene family simulation.ResultsHere, we introduce a stochastic model of domain architecture evolution to simulate evolutionary trajectories that reflect the constraints on domain order and co-occurrence observed in nature. This framework is implemented in a novel domain architecture simulator, DomArchov, using the Metropolis-Hastings algorithm with data-driven transition probabilities. The use of a data-driven event module enables quick and easy redeployment of the simulator for use in different taxonomic and protein function contexts. Using empirical evaluation with metazoan datasets, we demonstrate that domain architectures simulated by DomArchov recapitulate properties of genuine domain architectures that reflect the constraints on domain order and adjacency seen in nature. This work expands the realm of evolutionary processes that are amenable to simulation.Availability and implementationDomArchov is written in Python 3 and is available at http://www.cs.cmu.edu/~durand/DomArchov. The data underlying this article are available via the same link.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:ImportanceMajor depressive disorder (MDD) is associated with deficits in representing reward prediction errors (RPEs), which are the difference between experienced and predicted reward. Reward prediction errors underlie learning of values in reinforcement learning models, are represented by phasic dopamine release, and are known to affect momentary mood.ObjectiveTo combine functional neuroimaging, computational modeling, and smartphone-based large-scale data collection to test, in the absence of learning-related concerns, the hypothesis that depression attenuates the impact of RPEs.Design, setting, and participantsFunctional magnetic resonance imaging (fMRI) data were collected on 32 individuals with moderate MDD and 20 control participants who performed a probabilistic reward task. A risky decision task with repeated happiness ratings as a measure of momentary mood was also tested in the laboratory in 74 participants and with a smartphone-based platform in 1833 participants. The study was conducted from November 20, 2012, to February 17, 2015.Main outcomes and measuresBlood oxygen level-dependent activity was measured in ventral striatum, a dopamine target area known to represent RPEs. Momentary mood was measured during risky decision making.ResultsOf the 52 fMRI participants (mean [SD] age, 34.0 [9.1] years), 30 (58%) were women and 32 had MDD. Of the 74 participants in the laboratory risky decision task (mean age, 34.2 [10.3] years), 44 (59%) were women and 54 had MDD. Of the smartphone group, 543 (30%) had a depression history and 1290 (70%) had no depression history; 918 (50%) were women, and 593 (32%) were younger than 30 years. Contrary to previous results in reinforcement learning tasks, individuals with moderate depression showed intact RPE signals in ventral striatum (z = 3.16; P = .002) that did not differ significantly from controls (z = 0.91; P = .36). Symptom severity correlated with baseline mood parameters in laboratory (ρ = -0.54; P < 1 × 10-6) and smartphone (ρ = -0.30; P < 1 × 10-39) data. However, participants with depression showed an intact association between RPEs and happiness in a computational model of momentary mood dynamics (z = 4.55; P < .001) that was not attenuated compared with controls (z = -0.42; P = .67).Conclusions and relevanceThe neural and emotional impact of RPEs is intact in major depression. These results suggest that depression does not affect the expression of dopaminergic RPEs and that attenuated RPEs in previous reports may reflect downstream effects more closely related to aberrant behavior. The correlation between symptom severity and baseline mood parameters supports an association between depression and momentary mood fluctuations during cognitive tasks. These results demonstrate a potential for smartphones in large-scale computational phenotyping, which is a goal for computational psychiatry.

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Reassessing domain architecture evolution of metazoan proteins: major impact of gene prediction errors.

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Reassessing domain architecture evolution of metazoan proteins: major impact of gene prediction errors.

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