Dataset Information

Projection of gene-protein networks to the functional space of the proteome and its application to analysis of organism complexity.

ABSTRACT:

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We consider the problem of biological complexity via a projection of protein-coding genes of complex organisms onto the functional space of the proteome. The latter can be defined as a set of all functions committed by proteins of an organism. Alternative splicing (AS) allows an organism to generate diverse mature RNA transcripts from a single mRNA strand and thus it could be one of the key mechanisms of increasing of functional complexity of the organism's proteome and a driving force of biological evolution. Thus, the projection of transcription units (TU) and alternative splice-variant (SV) forms onto proteome functional space could generate new types of relational networks (e.g. SV-protein function networks, SFN) and lead to discoveries of novel evolutionarily conservative functional modules. Such types of networks might provide new reliable characteristics of organism complexity and a better understanding of the evolutionary integration and plasticity of interconnection of genome-transcriptome-proteome functions.

Results

We use the InterPro and UniProt databases to attribute descriptive features (keywords) to protein sequences. UniProt database includes a controlled and curated vocabulary of specific descriptors or keywords. The keywords have been assigned to a protein sequence via conserved domains or via similarity with annotated sequences. Then we consider the unique combinations of keywords as the protein functional labels (FL), which characterize the biological functions of the given protein and construct the contingency tables and graphs providing the projections of transcription units (TU) and alternative splice-variants (SV) onto all FL of the proteome of a given organism. We constructed SFNs for organisms with different evolutionary history and levels of complexity, and performed detailed statistical parameterization of the networks.

Conclusions

The application of the algorithm to organisms with different evolutionary history and level of biological complexity (nematode, fruit fly, vertebrata) reveals that the parameters describing SFN correlate with the complexity of a given organism. Using statistical analysis of the links of the functional networks, we propose new features of evolution of protein function acquisition. We reveal a group of genes and corresponding functions, which could be attributed to an early conservative part of the cellular machinery essential for cell viability and survival. We identify and provide characteristics of functional switches in the polyform group of TUs in different organisms. Based on comparison of mouse and human SFNs, a role of alternative splicing as a necessary source of evolution towards more complex organisms is demonstrated. The entire set of FL across many organisms could be used as a draft of the catalogue of the functional space of the proteome world.

SUBMITTER: Kanapin AA

PROVIDER: S-EPMC2822532 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Publications

Projection of gene-protein networks to the functional space of the proteome and its application to analysis of organism complexity.

Kanapin Alexander A AA Mulder Nicola N Kuznetsov Vladimir A VA

BMC genomics 20100210

<h4>Unlabelled</h4>We consider the problem of biological complexity via a projection of protein-coding genes of complex organisms onto the functional space of the proteome. The latter can be defined as a set of all functions committed by proteins of an organism. Alternative splicing (AS) allows an organism to generate diverse mature RNA transcripts from a single mRNA strand and thus it could be one of the key mechanisms of increasing of functional complexity of the organism's proteome and a driv ...[more]

PMID: 20158875

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Project description:A substantial proportion of protein interactions relies on small domains binding to short peptides in the partner proteins. Many of these interactions are relatively low affinity and transient, and they impact on signal transduction. However, neither the number of potential interactions mediated by each domain nor the degree of promiscuity at a whole proteome level has been investigated. We have used a combination of phage display and SPOT synthesis to discover all the peptides in the yeast proteome that have the potential to bind to eight SH3 domains. We first identified the peptides that match a relaxed consensus, as deduced from peptides selected by phage display experiments. Next, we synthesized all the matching peptides at high density on a cellulose membrane, and we probed them directly with the SH3 domains. The domains that we have studied were grouped by this approach into five classes with partially overlapping specificity. Within the classes, however, the domains display a high promiscuity and bind to a large number of common targets with comparable affinity. We estimate that the yeast proteome contains as few as six peptides that bind to the Abp1 SH3 domain with a dissociation constant lower than 100 microM, while it contains as many as 50-80 peptides with corresponding affinity for the SH3 domain of Yfr024c. All the targets of the Abp1 SH3 domain, identified by this approach, bind to the native protein in vivo, as shown by coimmunoprecipitation experiments. Finally, we demonstrate that this strategy can be extended to the analysis of the entire human proteome. We have developed an approach, named WISE (whole interactome scanning experiment), that permits rapid and reliable identification of the partners of any peptide recognition module by peptide scanning of a proteome. Since the SPOT synthesis approach is semiquantitative and provides an approximation of the dissociation constants of the several thousands of interactions that are simultaneously analyzed in an array format, the likelihood of each interaction occurring in any given physiological settings can be evaluated. WISE can be easily extended to a variety of protein interaction domains, including those binding to modified peptides, thereby offering a powerful proteomic tool to help completing a full description of the cell interactome.

Dataset Information

Projection of gene-protein networks to the functional space of the proteome and its application to analysis of organism complexity.

Unlabelled

Results

Conclusions

Publications

Projection of gene-protein networks to the functional space of the proteome and its application to analysis of organism complexity.

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