Dataset Information

A comprehensive analysis of Trypanosoma brucei mitochondrial proteome.

ABSTRACT: The composition of the large, single, mitochondrion (mt) of Trypanosoma brucei was characterized by MS (2-D LC-MS/MS and gel-LC-MS/MS) analyses. A total of 2897 proteins representing a substantial proportion of procyclic form cellular proteome were identified, which confirmed the validity of the vast majority of gene predictions. The data also showed that the genes annotated as hypothetical (species specific) were overpredicted and that virtually all genes annotated as hypothetical, unlikely are not expressed. By comparing the MS data with genome sequence, 40 genes were identified that were not previously predicted. The data are placed in a publicly available web-based database (www.TrypsProteome.org). The total mitochondrial proteome is estimated at 1008 proteins, with 401, 196, and 283 assigned to the mt with high, moderate, and lower confidence, respectively. The remaining mitochondrial proteins were estimated by statistical methods although individual assignments could not be made. The identified proteins have predicted roles in macromolecular, metabolic, energy generating, and transport processes providing a comprehensive profile of the protein content and function of the T. brucei mt.

SUBMITTER: Panigrahi AK

PROVIDER: S-EPMC2869593 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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A comprehensive analysis of Trypanosoma brucei mitochondrial proteome.

Panigrahi Aswini K AK Ogata Yuko Y Zíková Alena A Anupama Atashi A Dalley Rachel A RA Acestor Nathalie N Myler Peter J PJ Stuart Kenneth D KD

Proteomics 20090101 2

The composition of the large, single, mitochondrion (mt) of Trypanosoma brucei was characterized by MS (2-D LC-MS/MS and gel-LC-MS/MS) analyses. A total of 2897 proteins representing a substantial proportion of procyclic form cellular proteome were identified, which confirmed the validity of the vast majority of gene predictions. The data also showed that the genes annotated as hypothetical (species specific) were overpredicted and that virtually all genes annotated as hypothetical, unlikely are ...[more]

PMID: 19105172

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Project description:BackgroundTrypanosoma brucei is the causative agent for trypanosomiasis in humans and livestock, which presents a growing challenge due to drug resistance. While identifying novel drug targets is vital, the process is delayed due to a lack of functional information on many of the pathogen's proteins. Accordingly, this paper presents a computational framework for prioritizing drug targets within the editosome, a vital molecular machinery responsible for mitochondrial RNA processing in T. brucei. Importantly, this framework may eliminate the need for prior gene or protein characterization, potentially accelerating drug discovery efforts.ResultsBy integrating protein-protein interaction (PPI) network analysis, PPI structural modeling, and residue interaction network (RIN) analysis, we quantitatively ranked and identified top hub editosome proteins, their key interaction interfaces, and hotspot residues. Our findings were cross-validated and further prioritized by incorporating them into gene set analysis and differential expression analysis of existing quantitative proteomics data across various life stages of T. brucei. In doing so, we highlighted PPIs such as KREL2-KREPA1, RESC2-RESC1, RESC12A-RESC13, and RESC10-RESC6 as top candidates for further investigation. This includes examining their interfaces and hotspot residues, which could guide drug candidate selection and functional studies.ConclusionRNA editing offers promise for target-based drug discovery, particularly with proteins and interfaces that play central roles in the pathogen's life cycle. This study introduces an integrative drug target identification workflow combining information from the PPI network, PPI 3D structure, and reside-level information of their interface which can be applicable to diverse pathogens. In the case of T. brucei, via this pipeline, the present study suggested potential drug targets with residue-resolution from RNA editing machinery. However, experimental validation is needed to fully realize its potential in advancing urgently needed antiparasitic drug development.

Dataset Information

A comprehensive analysis of Trypanosoma brucei mitochondrial proteome.

Publications

A comprehensive analysis of Trypanosoma brucei mitochondrial proteome.

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