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Protein structure prediction remains a central challenge in computational structural biology. Even at the coarse-grained level of detail, the protein conformational space is vast, and available energy functions contain many false local minima. In order to effectively characterize this space, a conformational search must sample a geometrically-diverse set of low-energy conformations. Our recently published...
Protein Modeling conceptualizes the protein energy landscape as a funnel with the native structure at the low-energy minimum. Current protein structure prediction algorithms seek the global minimum by searching for low-energy conformations in the hope that some of these reside in local minima near the native structure. The search techniques employed, however, fail to explicitly model these local minima...
The most successful ab-initio protein structure prediction methods employ fragment-based assembly. This technique is usually implemented in the context of a Metropolis Monte Carlo template. Fragment length is an important consideration, as it controls the complexity of the discretization of the search space and the associated energy surface. This work measures the impact of employing multiple, heterogenous...
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