Key result
Elastic networks of motor proteins exhibit robust, low-dimensional nonlinear relaxation dynamics that can be replicated in artificial networks using evolutionary optimization methods.
This computational study demonstrates that the robust conformational motions of motor proteins can be reproduced in artificial elastic networks designed via evolutionary optimization.
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May advance bio-inspired cardiac models; leaves open whether artificial networks translate to therapies.
Analyzing nonlinear conformational relaxation dynamics in elastic networks corresponding to two classical motor proteins, we find that they respond by well defined internal mechanical motions to various initial deformations and that these motions are robust against external perturbations. We show that this behavior is not characteristic for random elastic networks. However, special network architectures with such properties can be designed by evolutionary optimization methods. Using them, an example of an artificial elastic network, operating as a cyclic machine powered by ligand binding, is constructed.
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Togashi et al. (2007) studied this question. Elastic networks of motor proteins exhibit robust, low-dimensional nonlinear relaxation dynamics that can be replicated in artificial networks using evolutionary optimization methods.
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