Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with\r\nmultiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of\r\nmolecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a\r\ncoarse-grained representation of the protein energy surface in terms of local minima. In this paper, we show that the BH framework\r\nis general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion\r\ndue to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations\r\nof the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein\r\nstructure prediction and rigid protein-protein docking.We also show that BH can map intermediate minima related with motions\r\nconnecting diverse stable functionally relevant states in a protein molecule, thus serving as a first step towards the characterization\r\nof transition trajectories connecting these states.
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