In Search of a Dynamical Vocabulary: A Pipeline to Construct a Basis of Shared Traits in Large-Scale Motions of Proteins

Abstract

The paradigmatic sequence–structure–dynamics–function relation in proteins is currently well established in the scientific community; in particular, a large effort has been made to probe the first connection, indeed providing convincing evidence of its strength and rationalizing it in a quantitative and general framework. In contrast, however, the role of dynamics as a link between structure and function has eluded a similarly clear-cut verification and description. In this work, we propose a pipeline aimed at building a basis for the quantitative characterization of the large-scale dynamics of a set of proteins, starting from the sole knowledge of their native structures. The method hinges on a dynamics-based clusterization, which allows a straightforward comparison with structural and functional protein classifications. The resulting basis set, obtained through the application to a group of related proteins, is shown to reproduce the salient large-scale dynamical features of the dataset. Most interestingly, the basis set is shown to encode the fluctuation patterns of homologous proteins not belonging to the initial dataset, thus highlighting the general applicability of the pipeline used to build it.

Information-theoretical measures identify accurate low-resolution representations of protein configurational space

“…To this end, a dataset of 107 proteins, including many of the known folds and structure classes, was constructed and clustered based on their dynamics. 37 For each protein, the first 10 normal modes of fluctuation were analysed using an elastic network model, 38 and superimposed by means of the ALADYN 39 protocol, which performs a hybrid structural/dynamical alignment. The similarity between the essential spaces spanned by the first 10 normal modes was quantified by means of the root mean square inner product 40 (RMSIP).…”

Special Issue on “Computational Approaches for Protein Dynamics and Function”

“…ENMs were also used by Tarenzi et al [3] to decipher structure-dynamics-function relationships. ENM-NMA was applied to a dataset of 116 different proteases, and proteins were clustered together based on their "dynamic distance" in the space of normal modes.…”