There is increasing interest in the use of nature-based approaches for mitigation of storm surges along coasts, deltas, and estuaries. However, very few studies have quantified the effectiveness of storm surge height reduction by a real-existing, estuarine-scale, nature-based, and engineered flood defense system, under specific storm surge conditions. Here, we present data and modelling results from a specific storm surge in the Scheldt estuary (Belgium), where a hybrid flood defense system is implemented, consisting of flood control areas, of which some are restored into tidal marsh ecosystems, by use of culvert constructions that allow daily reduced tidal in- and outflow. We present a hindcast simulation of the storm surge of 6 December 2013, using a TELEMAC-3D model of the Scheldt estuary, and model scenarios showing that the hybrid flood defense system resulted in a storm surge height reduction of up to half a meter in the estuary. An important aspect of the work was the implementation of model formulations for calculating flow through culverts of restored marshes. The latter was validated comparing simulated and measured discharges through a physical scale model of a culvert, and through a real-scale culvert of an existing restored marsh during the storm surge.

Proteins often undergo slow structural rearrangements that involve several angstroms and surpass the nanosecond timescale. These spatiotemporal scales challenge physics-based simulations and open the way to sample-based models of structural dynamics. This article improves an understanding of current capabilities and limitations of sample-based models of dynamics. Borrowing from widely used concepts in evolutionary computation, this article introduces two conflicting aspects of sampling capability and quantifies them via statistical (and graphical) analysis tools. This allows not only conducting a principled comparison of different sample-based algorithms but also understanding which algorithmic ingredients to use as knobs via which to control sampling and, in turn, the accuracy and detail of modeled structural rearrangements. We demonstrate the latter by proposing two powerful variants of a recently published sample-based algorithm. We believe that this work will advance the adoption of sample-based models as reliable tools for modeling slow protein structural rearrangements.

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BackgroundThe protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein’s energy landscape holds the key to characterizing a protein’s equilibrium conformational dynamics and its relationship to function. Many pathogenic mutations in protein sequences alter the equilibrium dynamics that regulates molecular interactions and thus protein function. In principle, reconstructing energy landscapes of a protein’s healthy and diseased variants is a central step to understanding how mutations impact dynamics, biological mechanisms, and function.ResultsRecent computational advances are yielding detailed, sample-based representations of protein energy landscapes. In this paper, we propose and describe two novel methods that leverage computed, sample-based representations of landscapes to reconstruct them and extract from them informative local structures that reveal the underlying organization of an energy landscape. Such structures constitute landscape features that, as we demonstrate here, can be utilized to detect alterations of landscapes upon mutation.ConclusionsThe proposed methods detect altered protein energy landscape features in response to sequence mutations. By doing so, the methods allow formulating hypotheses on the impact of mutations on specific biological activities of a protein. This work demonstrates that the availability of energy landscapes of healthy and diseased variants of a protein opens up new avenues to harness the quantitative information embedded in landscapes to summarize mechanisms via which mutations alter protein dynamics to percolate to dysfunction.