Efficient perturbation analysis of elastic network models – Application to acetylcholinesterase of T. californica

Elastic network models in their different flavors have become useful models for the dynamics and functions of biomolecular systems such as proteins and their complexes. Perturbation to the interactions occur due to randomized and fixated changes (in molecular evolution) or designed modifications of the protein structures (in bioengineering). These perturbations are modifications in the topology and the strength of the interactions modeled by the elastic network models. We discuss how a naive approach to compute properties for a large number of perturbed structures and interactions by repeated diagonalization can be replaced with an identity found in linear algebra. We argue about the computational complexity and discuss the advantages of the protocol. We apply the proposed algorithm to the acetylcholinesterase, a well-known enzyme in neurobiology, and show how one can gain insight into the “breathing dynamics” of a structural funnel necessary for the function of the protein. The computational speed-up was a 60-fold increase in this example.