Common functionally important motions of the nucleotide‐binding domain of Hsp70

Abstract

The 70 kDa Heat Shock Proteins (Hsp70) are a family of molecular chaperones involved in protein folding, aggregate prevention, and protein disaggregation. They consist of the substrate binding domain (SBD) that binds client substrates, and the nucleotide-binding domain (NBD), whose cycles of nucleotide hydrolysis and exchange underpin the activity of the chaperone. To characterize the structure-function relationships that link the binding state of the NBD to its conformational behavior, we analyzed the dynamics of the NBD of the Hsp70 chaperone from Bos taurus (pdb 3C7N:B) by all-atom canonical molecular dynamics simulations. It was found that essential motions within the NBD fall into three major classes: the mutual class, reflecting tendencies common to all binding states, and the ADP- and ATP-unique classes, which reflect conformational trends that are unique to either the ADP- or ATP-bound states, respectively. ‘Mutual’ class motions generally describe ‘in-plane’ and/or ‘out-of-plane’ (‘scissor-like’) rotation of the subdomains within the NBD. This result is consistent with experimental nuclear magnetic resonance data on the NBD. The ‘Unique’ class motions target specific regions on the NBD, usually surface loops or sites involved in nucleotide-binding and are, therefore, expected to be involved in allostery and signal transmission. For all classes, and especially for those of the ‘Unique’ type, regions of enhanced mobility can be identified; these are termed ‘hot-spots,’ and their locations generally parallel those found by NMR spectroscopy. The presence of magnesium and potassium cations in the nucleotide-binding pocket was also found to influence the dynamics of the NBD significantly.

Building Graphs To Describe Dynamics, Kinetics, and Energetics in the d-ALa:d-Lac Ligase VanA

“…Several approaches have been proposed in the literature to describe the conformational space of proteins as graph of local minima. The analysis performed in ref ( 22 ) is based on Principal Component Analysis (PCA) of protein motion. However, the PCA-based analysis detects only linear correlation, whereas SOM can capture nonlinear correlations.…”

“…Graphs are traditionally used for modeling biological datasets, as for the analysis of protein–protein and molecular interaction networks, 2 − 9 for description of drug function, 10 − 16 for the description of interactions within a protein, 17 − 19 for the description of the hierarchy of local minima in the conformational space. 20 − 22 In the description of protein conformational space, the determination of such a graph is hampered by the need to (i) simplify the protein local geometry without loss of information and (ii) find a generic approach for graph determination, while preserving the specificity of each protein. In contrast, the description of protein structure and dynamics through graphs would allow one to (i) relate structure description, conformational variability, and protein function; (ii) unify the structural and dynamical representations; and (iii) obtain, for a given protein, a model that could be interfaced with the graphs described at the cellular level, as the interactome network.…”

Dynamical Structures of Hsp70 and Hsp70-Hsp40 Complexes

“…Building upon this work, recent all-atom MD simulations of apo, ADP-, and ATP-bound Hsc70 NBD revealed nucleotide-mediated modulation of NBD dynamics at specific localized sites coinciding with those identified by solution-state NMR spectroscopy. (Gołaś et al, 2015). By utilizing a graph theory-based algorithm that models protein structures as networks and their residues as nodes, Liu et al identified “central residues,” or residues with high probabilities of comprising the shortest possible allosteric pathway, in the NBD of open (NEF-bound and apo) and closed (ATP-bound) Hsc70 (Liu et al, 2010).…”

“…Molecular simulations of FL Hsp70s (human and E. coli ) played an important role in modeling the allosteric network involved in propagating nucleotide-induced structural transitions (Chiappori et al, 2012; General et al, 2014; Gołaś et al, 2012; KA, 2010; Nicolaï et al, 2010, 2013). Here, we discuss only recent simulations of the bacterial chaperone, as previous studies have been summarized (Gołaś et al, 2015; Gołaś et al, 2012)…”