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NOLB: Nonlinear Rigid Block Normal Mode Analysis Method

Alexandre Hoffmann 1 Sergei Grudinin 1
1 NANO-D - Algorithms for Modeling and Simulation of Nanosystems
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann
Abstract : We present a new conceptually simple and computationally efficient method for nonlinear normal mode analysis called NOLB. It relies on the rotations-translations of blocks (RTB) theoretical basis developed by Y.-H. Sanejouand and colleagues. We demonstrate how to physically interpret the eigenvalues computed in the RTB basis in terms of angular and linear velocities applied to the rigid blocks and how to construct a nonlinear extrapolation of motion out of these velocities. The key observation of our method is that the angular velocity of a rigid block can be interpreted as the result of an implicit force, such that the motion of the rigid block can be considered as a pure rotation about a certain center. We demonstrate the motions produced with the NOLB method on three different molecular systems and show that some of the lowest frequency normal modes correspond to the biologically relevant motions. For example, NOLB detects the spiral sliding motion of the TALE protein, which is capable of rapid diffusion along its target DNA. Overall, our method produces better structures compared to the standard approach, especially at large deformation amplitudes, as we demonstrate by visual inspection, energy and topology analyses, and also by the MolProbity service validation. Finally, our method is scalable and can be applied to very large molecular systems, such as ribosomes. Standalone executables of the NOLB normal mode analysis method are available at A graphical user interface created for the SAMSON software platform will be made available at https: //
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Submitted on : Friday, June 2, 2017 - 10:24:13 AM
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Alexandre Hoffmann, Sergei Grudinin. NOLB: Nonlinear Rigid Block Normal Mode Analysis Method. Journal of Chemical Theory and Computation, American Chemical Society, 2017, 13 (5), pp.2123-2134. ⟨10.1021/acs.jctc.7b00197⟩. ⟨hal-01505843v2⟩



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