Shared protein complex subunits contribute to explaining disrupted co-occurrence.
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ABSTRACT:
SUBMITTER: Schneider A
PROVIDER: S-EPMC3715415 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Comparing and aligning protein sequences is an essential task in bioinformatics. More specifically, local alignment tools like BLAST are widely used for identifying conserved protein sub-sequences, which likely correspond to protein domains or functional motifs. However, to limit the number of false positives, these tools are used with stringent sequence-similarity thresholds and hence can miss several hits, especially for species that are phylogenetically distant from reference organisms. A solution to this problem is then to integrate additional contextual information to the procedure. Here, we propose to use domain co-occurrence to increase the sensitivity of pairwise sequence comparisons. Domain co-occurrence is a strong feature of proteins, since most protein domains tend to appear with a limited number of other domains on the same protein. We propose a method to take this information into account in a typical BLAST analysis and to construct new domain families on the basis of these results. We used Plasmodium falciparum as a case study to evaluate our method. The experimental findings showed an increase of 14% of the number of significant BLAST hits and an increase of 25% of the proteome area that can be covered with a domain. Our method identified 2240 new domains for which, in most cases, no model of the Pfam database could be linked. Moreover, our study of the quality of the new domains in terms of alignment and physicochemical properties show that they are close to that of standard Pfam domains. Source code of the proposed approach and supplementary data are available at: https://gite.lirmm.fr/menichelli/pairwise-comparison-with-cooccurrence.
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Project description:Multipass membrane proteins have a myriad of functions, including transduction of cell-cell signals, ion transport, and photoreception. Insertion of these proteins into the membrane depends on the endoplasmic reticulum (ER) membrane protein complex (EMC). Recently, birth defects have been observed in patients with variants in the gene encoding a member of this complex, EMC1. Patient phenotypes include congenital heart disease, craniofacial malformations, and neurodevelopmental disease. However, a molecular connection between EMC1 and these birth defects is lacking. Using Xenopus, we identified defects in neural crest cells (NCCs) upon emc1 depletion. We then used unbiased proteomics and discovered a critical role for emc1 in WNT signaling. Consistent with this, readouts of WNT signaling and Frizzled (Fzd) levels were reduced in emc1-depleted embryos, while NCC defects could be rescued with β-catenin. Interestingly, other transmembrane proteins were mislocalized upon emc1 depletion, providing insight into additional patient phenotypes. To translate our findings back to humans, we found that EMC1 was necessary for human NCC development in vitro. Finally, we tested patient variants in our Xenopus model and found the majority to be loss-of-function alleles. Our findings define molecular mechanisms whereby EMC1 dysfunction causes disease phenotypes through dysfunctional multipass membrane protein topogenesis.
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Project description:In most eukaryotes, telomere maintenance relies on telomeric repeat synthesis by a reverse transcriptase named telomerase. To synthesize telomeric repeats, the catalytic subunit telomerase reverse transcriptase (TERT) uses the RNA subunit (TER) as a template. In the ciliate Tetrahymena thermophila, the telomerase holoenzyme consists of TER, TERT, and eight additional proteins, including the telomeric repeat single-stranded DNA-binding protein Teb1 and its heterotrimer partners Teb2 and Teb3. Teb1 is paralogous to the large subunit of the general single-stranded DNA binding heterotrimer replication protein A (RPA). Little is known about the function of Teb2 and Teb3, which are structurally homologous to the RPA middle and small subunits, respectively. Here, epitope-tagging Teb2 and Teb3 expressed at their endogenous gene loci enabled affinity purifications that revealed that, unlike other Tetrahymena telomerase holoenzyme subunits, Teb2 and Teb3 are not telomerase-specific. Teb2 and Teb3 assembled into other heterotrimer complexes, which when recombinantly expressed had the general single-stranded DNA binding activity of RPA complexes, unlike the telomere-specific DNA binding of Teb1 or the TEB heterotrimer of Teb1, Teb2, and Teb3. TEB had no more DNA binding affinity than Teb1 alone. In contrast, heterotrimers reconstituted with Teb2 and Teb3 and two other Tetrahymena RPA large subunit paralogs had higher DNA binding affinity than their large subunit alone. Teb1 and TEB, but not RPA, increased telomerase processivity. We conclude that in the telomerase holoenzyme, instead of binding DNA, Teb2 and Teb3 are Teb1 assembly factors. These findings demonstrate that Tetrahymena telomerase holoenzyme and RPA complexes share subunits and that RPA subunits have distinct functions in different heterotrimer assemblies.
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Project description:Protein Domain Co-occurrence Network (DCN) is a biological network that has not been fully-studied. We analyzed the properties of the DCNs of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes. These DCNs have the hallmark features of scale-free networks. We investigated the possibility of using DCNs to predict protein and domain functions. Based on our experiment conducted on 66 randomly selected proteins, the best of top 3 predictions made by our DCN-based aggregated neighbor-counting method achieved a semantic similarity score of 0.81 to the actual Gene Ontology terms of the proteins. Moreover, the top 3 predictions using neighbor-counting, ?(2), and a SVM-based method achieved an accuracy of 66%, 59%, and 61%, respectively, when used to predict specific Gene Ontology terms of human target domains. These predictions on average had a semantic similarity score of 0.82, 0.80, and 0.79 to the actual Gene Ontology terms, respectively. We also used DCNs to predict whether a domain is an enzyme domain, and our SVM-based and neighbor-inference method correctly classified 79% and 77% of the target domains, respectively. When using DCNs to classify a target domain into one of the six enzyme classes, we found that, as long as there is one EC number available in the neighboring domains, our SVM-based and neighboring-counting method correctly classified 92.4% and 91.9% of the target domains, respectively. Furthermore, we benchmarked the performance of using DCNs to infer species phylogenies on six different combinations of 398 single-chromosome prokaryotic genomes. The phylogenetic tree of 54 prokaryotic taxa generated by our DCNs-alignment-based method achieved a 93.45% similarity score compared to the Bergey's taxonomy. In summary, our studies show that genome-wide DCNs contain rich information that can be effectively used to decipher protein function and reveal the evolutionary relationship among species.
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Project description:The PDZ domains of the trimeric DegS protease bind unassembled outer-membrane proteins (OMPs) that accumulate in the Escherichia coli periplasm. This cooperative binding reaction triggers a proteolytic cascade that activates a transcriptional stress response. To dissect the mechanism of allosteric activation, we generated hybrid DegS trimers with different numbers of PDZ domains and/or protease-domain mutations. By studying the chemical reactivity and enzymatic properties of these hybrids, we show that all subunits experience a strongly coupled energetic landscape. For example, OMP peptide binding to a single PDZ domain stimulates active site chemical modification and proteolytic cleavage in the attached and neighboring protease domains. OMP peptide binding relieves inhibitory PDZ interactions, whereas the interfaces between protease domains in the trimeric DegS core mediate positively cooperative activation driven by both substrate binding and inhibition relief.
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Project description:The Esx and PE/PPE families of proteins are among the most immunodominant mycobacterial antigens and have thus been the focus of research to develop vaccines and immunological tests for diagnosis of bovine and human tuberculosis, mainly caused by Mycobacterium bovis and Mycobacterium tuberculosis, respectively. In non-tuberculous mycobacteria (NTM), multiple copies of genes encoding homologous proteins have mainly been identified in pathogenic Mycobacterium species phylogenically related to Mycobacterium tuberculosis and Mycobacterium bovis. Only ancestral copies of these genes have been identified in nonpathogenic NTM species like Mycobacterium smegmatis, Mycobacterium sp. KMS, Mycobacterium sp. MCS, and Mycobacterium sp. JLS. In this study we elucidated the genomes of four nonpathogenic NTM species, viz Mycobacterium komanii sp. nov., Mycobacterium malmesburii sp. nov., Mycobacterium nonchromogenicum, and Mycobacterium fortuitum ATCC 6841. These genomes were investigated for genes encoding for the Esx and PE/PPE (situated in the esx cluster) family of proteins as well as adjacent genes situated in the ESX-1 to ESX-5 regions. To identify proteins actually expressed, comparative proteomic analyses of purified protein derivatives from three of the NTM as well as Mycobacterium kansasii ATCC 12478 and the commercially available purified protein derivatives from Mycobacterium bovis and Mycobacterium avium was performed. The genomic analysis revealed the occurrence in each of the four NTM, orthologs of the genes encoding for the Esx family, the PE and PPE family proteins in M. bovis and M. tuberculosis. The identification of genes of the ESX-1, ESX-3, and ESX-4 region including esxA, esxB, ppe68, pe5, and pe35 adds to earlier reports of these genes in nonpathogenic NTM like M. smegmatis, Mycobacterium sp. JLS and Mycobacterium KMS. This report is also the first to identify esxN gene situated within the ESX-5 locus in M. nonchromogenicum. Our proteomics analysis identified a total of 609 proteins in the six PPDs and 22 of these were identified as shared between PPD of M.bovis and one or more of the NTM PPDs. Previously characterized M tuberculosis/M. bovis homologous immunogenic proteins detected in one or more of the nonpathogenic NTM in this study included CFP-10 (detected in M. malmesburii sp. nov. PPD), GroES (detected in all NTM PPDs but M. malmesburii sp. nov.), DnaK (detected in all NTM PPDs), and GroEL (detected in all NTM PPDs). This study confirms reports that the ESX-1, ESX-3, and ESX-4 regions are ancestral regions and thus found in the genomes of most mycobacteria. Identification of NTM homologs of immunogenic proteins warrants further investigation of their ability to cause cross-reactive immune responses with MTBC antigens.
| S-EPMC4894912 | biostudies-literature
Project description:BackgroundThe recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases. Then, a physician, when practicing on a patient, can suggest several diseases that are highly likely to co-occur with a primary disease according to the scores. In this study, we propose a method of implementing 'n-of-1 utility' (n potential diseases of one patient) to human disease network-the translational disease network.ResultsWe first construct a disease network by introducing the notion of walk in graph theory to protein-protein interaction network, and then provide a scoring algorithm quantifying the likelihoods of disease co-occurrence given a primary disease. Metabolic diseases, that are highly prevalent but have found only a few associations in previous studies, are chosen as entries of the network.ConclusionsThe proposed method substantially increased connectivity between metabolic diseases and provided scores of co-occurring diseases. The increase in connectivity turned the disease network info-richer. The result lifted the AUC of random guessing up to 0.72 and appeared to be concordant with the existing literatures on disease comorbidity.
| S-EPMC6854734 | biostudies-literature