The function of protein, RNA, and DNA is modulated by fast, dynamic exchangesbetween three-dimensional conformations. Conformational sampling ofbiomolecules with exact and nullspace inverse kinematics, using rotatable bondsas revolute joints and non-covalent interactions as holonomic constraints, canaccurately characterize these native ensembles. However, sampling biomoleculesremains challenging owing to their ultra-high dimensional configuration spaces,and the requirement to avoid (self-) collisions, which results in lowacceptance rates. Here, we present two novel mechanisms to overcome these limitations. First,we introduced temporary constraints between near-colliding links. The resultingconstraint varieties instantaneously redirect the search for collision-freeconformations, and couple motions between distant parts of the linkage. Second,we adapted a randomized Poisson-disk motion planner, which prevents localoversampling and widens the search, to ultra-high dimensions. We evaluated ouralgorithm on several model systems. Our contributions apply to generalhigh-dimensional motion planning problems in static and dynamic environmentswith obstacles.
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