Abstract
Simple protein elastic networks which neglect amino-acid information often yield reasonable predictions of conformational dynamics and are broadly used. Recently, model variants which incorporate sequence-specific and distance-dependent interactions of residue pairs have been constructed and demonstrated to improve agreement with experimental data. We have applied the new variants in a systematic study of protein fluctuation properties and compared their predictions with those of conventional anisotropic network models. We find that the quality of predictions is frequently linked to poor estimations in highly flexible protein regions. An analysis of a large set of protein structures shows that fluctuations of very weakly connected network residues are intrinsically prone to be significantly overestimated by all models. This problem persists in the new models and is not resolved by taking into account sequence information. The effect becomes even enhanced in the model variant which takes into account very soft long-ranged residue interactions. Beyond these shortcomings, we find that model predictions are largely insensitive to the integration of chemical information, at least regarding the fluctuation properties of individual residues. One can furthermore conclude that the inherent drawbacks may present a serious hindrance when improvement of elastic network models are attempted.
Highlights
Proteins are involved in most cellular processes
The heterogeneity of sequence related stiffness constants in those models ranges from values between 0.226 for glycine-glycine pairs to 2.348 for isoleucine-valine pairs, which corresponds to a dispersion of one order of magnitude as compared to the homogeneous stiffness constants in anisotropic network models (ANMs)
While we originally attempted to better understand how sequence-specific elastic networks can improve model predictions, we found this drawback to be present in the traditional ANMs as well as in the considered proposed model variants with sequence specificity and distance dependence
Summary
Proteins are involved in most cellular processes. Their functions are often accompanied by conformational motions which can have timescales ranging from picoseconds (atomic group fluctuations), nanoseconds (collective movement of residue groups), to micro- and even milliseconds (relative domain motions). Slow dynamics cannot typically be followed in atomistic molecular dynamics (MD) simulations, despite the use of supercomputers. Coarse-grained models are often employed [1,2]. Elastic network models (ENMs) are simple and widely used [3,4,5,6].
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