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In this paper, a multiscale virtual particle based elastic network model(MVP-ENM) is proposed for biomolecular normal mode analysis. The multiscalevirtual particle model is proposed for the discretization of biomoleculardensity data in different scales. Essentially, the model works as thecoarse-graining of the biomolecular structure, so that a delicate balancebetween biomolecular geometric representation and computational cost can beachieved. To form "connections" between these multiscale virtual particles, anew harmonic potential function, which considers the influence from both massdistributions and distance relations, is adopted between any two virtualparticles. Unlike the previous ENMs that use a constant spring constant, aparticle-dependent spring parameter is used in MVP-ENM. Two independent models,i.e., multiscale virtual particle based Gaussian network model (MVP-GNM) andmultiscale virtual particle based anisotropic network model (MVP-ANM), areproposed. Even with a rather coarse grid and a low resolution, the MVP-GNM isable to predict the Debye-Waller factors (B-factors) with considerable goodaccuracy. Similar properties have also been observed in MVP-ANM. Moreimportantly, in B-factor predictions, the mismatch between the predictedresults and experimental ones is predominantly from higher fluctuation regions.Further, it is found that MVP-ANM can deliver a very consistent low-frequencyeigenmodes in various scales. This demonstrates the great potential of MVP-ANMin the deformation analysis of low resolution data. With the multiscalerigidity function, the MVP-ENM can be applied to biomolecular data representedin density distribution and atomic coordinates. Further, the great advantage ofmy MVP-ENM model in computational cost has been demonstrated by using twopoliovirus virus structures. Finally, the paper ends with a conclusion.

Multiscale virtual particle based elastic network model (MVP-ENM) for biomolecular normal mode analysis