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Bioinformatics-Aided Venomics.

Kaas Q, Craik DJ - Toxins (Basel) (2015)

Bottom Line: Bioinformatics is used to tackle specific challenges associated with the identification and annotations of toxins.Tracing the evolutionary relationships between toxins should consider specific mechanisms of rapid evolution as well as interactions between predatory animals and prey.Rapidly determining the activity of toxins is the main bottleneck in venomics discovery, but some recent bioinformatics and molecular modeling approaches give hope that accurate predictions of toxin specificity could be made in the near future.

View Article: PubMed Central - PubMed

Affiliation: Institute for Molecular Bioscience, the University of Queensland, Brisbane, QLD 4072, Australia. q.kaas@imb.uq.edu.au.

ABSTRACT
Venomics is a modern approach that combines transcriptomics and proteomics to explore the toxin content of venoms. This review will give an overview of computational approaches that have been created to classify and consolidate venomics data, as well as algorithms that have helped discovery and analysis of toxin nucleic acid and protein sequences, toxin three-dimensional structures and toxin functions. Bioinformatics is used to tackle specific challenges associated with the identification and annotations of toxins. Recognizing toxin transcript sequences among second generation sequencing data cannot rely only on basic sequence similarity because toxins are highly divergent. Mass spectrometry sequencing of mature toxins is challenging because toxins can display a large number of post-translational modifications. Identifying the mature toxin region in toxin precursor sequences requires the prediction of the cleavage sites of proprotein convertases, most of which are unknown or not well characterized. Tracing the evolutionary relationships between toxins should consider specific mechanisms of rapid evolution as well as interactions between predatory animals and prey. Rapidly determining the activity of toxins is the main bottleneck in venomics discovery, but some recent bioinformatics and molecular modeling approaches give hope that accurate predictions of toxin specificity could be made in the near future.

No MeSH data available.


Computational approaches in venomics. The different themes in this figure are discussed in the text. Generalist and specialized databases are a central source of information for both the discovery (top) and analysis (bottom) of venoms. Venomics computational tools combine data from proteomics and transcriptomics to discover the set of toxins in a venom gland. These tools use predictions of mature toxins from transcriptomes to support peptide sequencing, and sequencing by mass spectrometry uses transcript sequences as a database to rapidly identify peptides and their post-translational modifications. Bioinformatics tools use the standardized classification stored in databases to analyze transcripts, and phylogenetic analyses can be used to analyze toxin evolutionary relationships. Databases in turn record the newly identified toxin peptide and transcript sequences. These sequence data can be also used to refine toxin classification, for example when a new phylogenetic group of toxins is identified. The molecular target of toxins can be suggested by a phylogeny and an evolutionary analysis complemented by the prediction of toxin 3D structures. Molecular modeling of target/toxin complexes can be used to analyze in great depth structure-activity relationships of toxins. Finally, the affinity of these complexes can be predicted from the molecular models.
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toxins-07-02159-f001: Computational approaches in venomics. The different themes in this figure are discussed in the text. Generalist and specialized databases are a central source of information for both the discovery (top) and analysis (bottom) of venoms. Venomics computational tools combine data from proteomics and transcriptomics to discover the set of toxins in a venom gland. These tools use predictions of mature toxins from transcriptomes to support peptide sequencing, and sequencing by mass spectrometry uses transcript sequences as a database to rapidly identify peptides and their post-translational modifications. Bioinformatics tools use the standardized classification stored in databases to analyze transcripts, and phylogenetic analyses can be used to analyze toxin evolutionary relationships. Databases in turn record the newly identified toxin peptide and transcript sequences. These sequence data can be also used to refine toxin classification, for example when a new phylogenetic group of toxins is identified. The molecular target of toxins can be suggested by a phylogeny and an evolutionary analysis complemented by the prediction of toxin 3D structures. Molecular modeling of target/toxin complexes can be used to analyze in great depth structure-activity relationships of toxins. Finally, the affinity of these complexes can be predicted from the molecular models.

Mentions: Over the last few years, cutting-edge technologies are increasingly being employed in toxin research to unravel the pharmacological treasures hidden in animal venoms, dramatically changing the landscape of venom exploration [10]. Combined advances in proteomics and transcriptomics now give access to nearly complete toxin repertoires of a single venom [20], with this new approach termed “venomics” [2,10,21]. Venomics research is driven by techniques rather than by hypotheses [10], with old fractionation methods now replaced with the simultaneous discovery of massive amounts of toxin nucleic and protein sequences. This large amount of data cannot be efficiently comprehended without the use of computational and statistical methods [22]. This review will first focus on computational approaches that have been developed in recent years to help in the discovery and analysis of venom proteins and peptides. The main themes discussed are illustrated in Figure 1. Several specialized databases provide access to information on toxins, which are used by bioinformatics tools to analyze “omics” data and to orient new venomics research. The increased pace at which toxin sequences are being discovered is unfortunately not matched by the speed at which their functions are assayed because the functional characterization of a single toxin requires significant experimental efforts. Thus, we also describe some of the computational approaches that have been developed to suggest and study toxin functions.


Bioinformatics-Aided Venomics.

Kaas Q, Craik DJ - Toxins (Basel) (2015)

Computational approaches in venomics. The different themes in this figure are discussed in the text. Generalist and specialized databases are a central source of information for both the discovery (top) and analysis (bottom) of venoms. Venomics computational tools combine data from proteomics and transcriptomics to discover the set of toxins in a venom gland. These tools use predictions of mature toxins from transcriptomes to support peptide sequencing, and sequencing by mass spectrometry uses transcript sequences as a database to rapidly identify peptides and their post-translational modifications. Bioinformatics tools use the standardized classification stored in databases to analyze transcripts, and phylogenetic analyses can be used to analyze toxin evolutionary relationships. Databases in turn record the newly identified toxin peptide and transcript sequences. These sequence data can be also used to refine toxin classification, for example when a new phylogenetic group of toxins is identified. The molecular target of toxins can be suggested by a phylogeny and an evolutionary analysis complemented by the prediction of toxin 3D structures. Molecular modeling of target/toxin complexes can be used to analyze in great depth structure-activity relationships of toxins. Finally, the affinity of these complexes can be predicted from the molecular models.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4488696&req=5

toxins-07-02159-f001: Computational approaches in venomics. The different themes in this figure are discussed in the text. Generalist and specialized databases are a central source of information for both the discovery (top) and analysis (bottom) of venoms. Venomics computational tools combine data from proteomics and transcriptomics to discover the set of toxins in a venom gland. These tools use predictions of mature toxins from transcriptomes to support peptide sequencing, and sequencing by mass spectrometry uses transcript sequences as a database to rapidly identify peptides and their post-translational modifications. Bioinformatics tools use the standardized classification stored in databases to analyze transcripts, and phylogenetic analyses can be used to analyze toxin evolutionary relationships. Databases in turn record the newly identified toxin peptide and transcript sequences. These sequence data can be also used to refine toxin classification, for example when a new phylogenetic group of toxins is identified. The molecular target of toxins can be suggested by a phylogeny and an evolutionary analysis complemented by the prediction of toxin 3D structures. Molecular modeling of target/toxin complexes can be used to analyze in great depth structure-activity relationships of toxins. Finally, the affinity of these complexes can be predicted from the molecular models.
Mentions: Over the last few years, cutting-edge technologies are increasingly being employed in toxin research to unravel the pharmacological treasures hidden in animal venoms, dramatically changing the landscape of venom exploration [10]. Combined advances in proteomics and transcriptomics now give access to nearly complete toxin repertoires of a single venom [20], with this new approach termed “venomics” [2,10,21]. Venomics research is driven by techniques rather than by hypotheses [10], with old fractionation methods now replaced with the simultaneous discovery of massive amounts of toxin nucleic and protein sequences. This large amount of data cannot be efficiently comprehended without the use of computational and statistical methods [22]. This review will first focus on computational approaches that have been developed in recent years to help in the discovery and analysis of venom proteins and peptides. The main themes discussed are illustrated in Figure 1. Several specialized databases provide access to information on toxins, which are used by bioinformatics tools to analyze “omics” data and to orient new venomics research. The increased pace at which toxin sequences are being discovered is unfortunately not matched by the speed at which their functions are assayed because the functional characterization of a single toxin requires significant experimental efforts. Thus, we also describe some of the computational approaches that have been developed to suggest and study toxin functions.

Bottom Line: Bioinformatics is used to tackle specific challenges associated with the identification and annotations of toxins.Tracing the evolutionary relationships between toxins should consider specific mechanisms of rapid evolution as well as interactions between predatory animals and prey.Rapidly determining the activity of toxins is the main bottleneck in venomics discovery, but some recent bioinformatics and molecular modeling approaches give hope that accurate predictions of toxin specificity could be made in the near future.

View Article: PubMed Central - PubMed

Affiliation: Institute for Molecular Bioscience, the University of Queensland, Brisbane, QLD 4072, Australia. q.kaas@imb.uq.edu.au.

ABSTRACT
Venomics is a modern approach that combines transcriptomics and proteomics to explore the toxin content of venoms. This review will give an overview of computational approaches that have been created to classify and consolidate venomics data, as well as algorithms that have helped discovery and analysis of toxin nucleic acid and protein sequences, toxin three-dimensional structures and toxin functions. Bioinformatics is used to tackle specific challenges associated with the identification and annotations of toxins. Recognizing toxin transcript sequences among second generation sequencing data cannot rely only on basic sequence similarity because toxins are highly divergent. Mass spectrometry sequencing of mature toxins is challenging because toxins can display a large number of post-translational modifications. Identifying the mature toxin region in toxin precursor sequences requires the prediction of the cleavage sites of proprotein convertases, most of which are unknown or not well characterized. Tracing the evolutionary relationships between toxins should consider specific mechanisms of rapid evolution as well as interactions between predatory animals and prey. Rapidly determining the activity of toxins is the main bottleneck in venomics discovery, but some recent bioinformatics and molecular modeling approaches give hope that accurate predictions of toxin specificity could be made in the near future.

No MeSH data available.