Abstract
Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule, thus serving as a first step towards the characterization of transition trajectories connecting these states.
Highlights
Global optimization is an objective of many disciplines, both in academic and industrial settings [1, 2]
We show that the ability of basin hopping (BH) to provide a map of the energy surface in terms of minima is useful when the goal is to locate the global minimum, and when the objective is to characterize proteins with more than one functionally relevant state
We have shown that BH is a general, versatile framework that allows structural characterization of important biological macromolecules, such as proteins
Summary
Global optimization is an objective of many disciplines, both in academic and industrial settings [1, 2]. Characterization of complex systems often poses very hard global optimization problems with many variables [3, 4]. Algorithms that target such problems largely build on or combine four main approaches: deterministic, stochastic, heuristic, and smoothing [3, 5,6,7]. All these algorithms are challenged by systems where the variable space contains multiple distinct minima. Adaptations that build on deterministic and stochastic numerical procedures, such as molecular dynamics (MD) and MC, are abundant in computational biology for the structural characterization of biological macromolecules (cf. [11, 12])
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