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Understanding the sequence requirements of protein families: insights from the BioVis 2013 contests.

Ray WC, Rumpf RW, Sullivan B, Callahan N, Magliery T, Machiraju R, Wong B, Krzywinski M, Bartlett CW - BMC Proc (2014)

Bottom Line: The data was grounded in actual experimental results in triosephosphate isomerase(TIM) enzymes.Judges found valuable and novel contributions in each of the entries, including interesting ways to hierarchicalize the protein into domains of informational interaction, tools for simultaneously understanding both sequential and spatial order, and approaches for conveying some types of inter-residue dependencies.In this manuscript we document the problem presented to the contestants, summarize the biological contributions of their entries, and suggest opportunities that this work has highlighted for even more improved tools in the future.

View Article: PubMed Central - HTML - PubMed

Affiliation: Nationwide Children's Hospital, 575 Children's Crossroad, 43215, Columbus, OH, USA ; The Ohio State University, 100 W. 18th Ave, 43210, Columbus, OH, USA ; Contest Chairs.

ABSTRACT

Introduction: In 2011, the BioVis symposium of the IEEE VisWeek conferences inaugurated a new variety of data analysis contest. Aimed at fostering collaborations between computational scientists and biologists, the BioVis contest provided real data from biological domains with emerging visualization needs, in the hope that novel approaches would result in powerful new tools for the community. In 2011 and 2012 the theme of these contests was expression Quantitative Trait Locus analysis, within and across tissues respectively. In 2013 the topic was updated to protein sequence and mutation visualization.

Methods: The contest was framed in the context of a real protein with numerous mutations that had lost function, and the question posed "what minimal set of changes would you propose to rescue function, or how could you support a biologist attempting to answer that question?". The data was grounded in actual experimental results in triosephosphate isomerase(TIM) enzymes. Seven teams composed of 36 individuals submitted entries with proposed solutions and approaches to the challenge. Their contributions ranged from careful analysis of the visualization and analytical requirements for the problem through integration of existing tools for analyzing the context and consequences of protein mutations, to completely new tools addressing the problem.

Results: Judges found valuable and novel contributions in each of the entries, including interesting ways to hierarchicalize the protein into domains of informational interaction, tools for simultaneously understanding both sequential and spatial order, and approaches for conveying some types of inter-residue dependencies. In this manuscript we document the problem presented to the contestants, summarize the biological contributions of their entries, and suggest opportunities that this work has highlighted for even more improved tools in the future.

No MeSH data available.


Knisley's graph-theoretic approach. Knisley's approach portrays the protein data as a hierarchy of network-connected graphs, from the atomic level, up through coordinated subsets of amino acids and groupings of these such as secondary or tertiary structures and domains.
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Figure 11: Knisley's graph-theoretic approach. Knisley's approach portrays the protein data as a hierarchy of network-connected graphs, from the atomic level, up through coordinated subsets of amino acids and groupings of these such as secondary or tertiary structures and domains.

Mentions: Knisley and Knisley[23] presented the most theoretical approach to the problem, developing a multi-layer nested graph-theoretic model. Their approach casts the protein into a hierarchical graph structure with an overall protein view (domains as vertices), a domain view (residues as vertices), and an amino acid view (atoms as vertices), each with edges indicating physical or proximity-based interaction inferences between the features, as shown in Figure 11.


Understanding the sequence requirements of protein families: insights from the BioVis 2013 contests.

Ray WC, Rumpf RW, Sullivan B, Callahan N, Magliery T, Machiraju R, Wong B, Krzywinski M, Bartlett CW - BMC Proc (2014)

Knisley's graph-theoretic approach. Knisley's approach portrays the protein data as a hierarchy of network-connected graphs, from the atomic level, up through coordinated subsets of amino acids and groupings of these such as secondary or tertiary structures and domains.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4155613&req=5

Figure 11: Knisley's graph-theoretic approach. Knisley's approach portrays the protein data as a hierarchy of network-connected graphs, from the atomic level, up through coordinated subsets of amino acids and groupings of these such as secondary or tertiary structures and domains.
Mentions: Knisley and Knisley[23] presented the most theoretical approach to the problem, developing a multi-layer nested graph-theoretic model. Their approach casts the protein into a hierarchical graph structure with an overall protein view (domains as vertices), a domain view (residues as vertices), and an amino acid view (atoms as vertices), each with edges indicating physical or proximity-based interaction inferences between the features, as shown in Figure 11.

Bottom Line: The data was grounded in actual experimental results in triosephosphate isomerase(TIM) enzymes.Judges found valuable and novel contributions in each of the entries, including interesting ways to hierarchicalize the protein into domains of informational interaction, tools for simultaneously understanding both sequential and spatial order, and approaches for conveying some types of inter-residue dependencies.In this manuscript we document the problem presented to the contestants, summarize the biological contributions of their entries, and suggest opportunities that this work has highlighted for even more improved tools in the future.

View Article: PubMed Central - HTML - PubMed

Affiliation: Nationwide Children's Hospital, 575 Children's Crossroad, 43215, Columbus, OH, USA ; The Ohio State University, 100 W. 18th Ave, 43210, Columbus, OH, USA ; Contest Chairs.

ABSTRACT

Introduction: In 2011, the BioVis symposium of the IEEE VisWeek conferences inaugurated a new variety of data analysis contest. Aimed at fostering collaborations between computational scientists and biologists, the BioVis contest provided real data from biological domains with emerging visualization needs, in the hope that novel approaches would result in powerful new tools for the community. In 2011 and 2012 the theme of these contests was expression Quantitative Trait Locus analysis, within and across tissues respectively. In 2013 the topic was updated to protein sequence and mutation visualization.

Methods: The contest was framed in the context of a real protein with numerous mutations that had lost function, and the question posed "what minimal set of changes would you propose to rescue function, or how could you support a biologist attempting to answer that question?". The data was grounded in actual experimental results in triosephosphate isomerase(TIM) enzymes. Seven teams composed of 36 individuals submitted entries with proposed solutions and approaches to the challenge. Their contributions ranged from careful analysis of the visualization and analytical requirements for the problem through integration of existing tools for analyzing the context and consequences of protein mutations, to completely new tools addressing the problem.

Results: Judges found valuable and novel contributions in each of the entries, including interesting ways to hierarchicalize the protein into domains of informational interaction, tools for simultaneously understanding both sequential and spatial order, and approaches for conveying some types of inter-residue dependencies. In this manuscript we document the problem presented to the contestants, summarize the biological contributions of their entries, and suggest opportunities that this work has highlighted for even more improved tools in the future.

No MeSH data available.