Reconstructing and mining protein energy landscape to understand disease

Many pathogenic mutations percolate to protein dysfunction by altering dynamics. Reconstructing protein energy landscapes promises to relate dynamics to function but is generally infeasible due to the disparate spatio-temporal scales involved. Recent algorithmic innovation allows reconstructing energy landscapes of medium-size proteins in the presence of sufficient prior wet-laboratory structure data. The ability to do so on healthy and pathogenic variants of a protein is renewing the need for landscape analysis and comparison. Here we describe a novel landscape analysis method that detects altered landscape features in response to mutations and allows formulating hypotheses on the impact of mutations on (dys)function. This work opens up interesting avenues into automated analysis and summarization of landscapes.

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