Statistical Analysis of Computed Energy Landscapes to Understand Dysfunction in Pathogenic Protein Variants
Pages 679 - 684
Abstract
The energy landscape underscores the inherent nature of proteins as dynamic systems interconverting between structures with varying energies. The protein energy landscape contains much of the information needed to characterize protein equilibrium dynamics and relate it to function. It is now possible to reconstruct energy landscapes of medium-size proteins with sufficient prior structure data. These developments turn the focus to tools for analysis and comparison of energy landscapes as a means of formulating hypotheses on the impact of sequence mutations on (dys)function via altered landscape features. We present such a method here and provide a detailed evaluation of its capabilities on an enzyme central to human biology. The work presented here opens up an interesting avenue into automated analysis and summarization of landscapes that yields itself to machine learning approaches at the energy landscape level.
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Index Terms
- Statistical Analysis of Computed Energy Landscapes to Understand Dysfunction in Pathogenic Protein Variants
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Published In
August 2017
800 pages
ISBN:9781450347228
DOI:10.1145/3107411
- General Chairs:
- Nurit Haspel,
- Lenore J. Cowen,
- Program Chairs:
- Amarda Shehu,
- Tamer Kahveci,
- Giuseppe Pozzi
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Published: 20 August 2017
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