Dynamical network of residue–residue contacts reveals coupled allosteric effects in recognition, catalysis, and mutation

Abstract

Detailed understanding of how conformational dynamics orchestrates function in allosteric regulation of recognition and catalysis remains ambiguous. Here, we simulate CypA using multiple-microsecond-long atomistic molecular dynamics in explicit solvent and carry out NMR experiments. We analyze a large amount of time-dependent multidimensional data with a coarse-grained approach and map key dynamical features within individual macrostates by defining dynamics in terms of residue-residue contacts. The effects of substrate binding are observed to be largely sensed at a location over 15 Å from the active site, implying its importance in allostery. Using NMR experiments, we confirm that a dynamic cluster of residues in this distal region is directly coupled to the active site. Furthermore, the dynamical network of interresidue contacts is found to be coupled and temporally dispersed, ranging over 4 to 5 orders of magnitude. Finally, using network centrality measures we demonstrate the changes in the communication network, connectivity, and influence of CypA residues upon substrate binding, mutation, and during catalysis. We identify key residues that potentially act as a bottleneck in the communication flow through the distinct regions in CypA and, therefore, as targets for future mutational studies. Mapping these dynamical features and the coupling of dynamics to function has crucial ramifications in understanding allosteric regulation in enzymes and proteins, in general.allostery | enzyme dynamics | residue-residue contacts | Cyclophilin A | molecular dynamics

Structural and biochemical insights of CypA and AIF interaction

“…On the contrary, the region H126-K131 is not included in those proposed by the 3D model. Furthermore, for these residues, included in the loop α2-β7, NMR-CSP studies indicate that they are weakly perturbed in presence of AIF(370-394), suggesting that they could be not directly involved in the interaction but could instead undergo a conformational rearrangement following the interaction with AIF(370-394), as recently suggested by a computational and NMR study 21 .

Figure 4MALDI-TOF spectra of tryptic ( A ,  B ) and chymotryptic ( C ,  D ) CypA peptides protected from proteolysis following hydrolyses performed in the absence (upper panel) and in the presence (lower panel) of AIF(370-394) peptide. Representative CypA control peptides for which no relative intensity changes were detected are reported in ( E) (sequence region 50-55) and ( F) (sequence region 32-37).

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Networks of Dynamic Allostery Regulate Enzyme Function

“…As a comparison, Val20, which is representative of the majority of distal residues unaffected by changes in active-site dynamics, exhibits no increase in R ex in response to either substrate addition or active-site mutation. Our identification of Val29 here as a hotspot has recently prompted us to examine this residue by standard MD simulations and functional studies, which revealed that the conformational distributions within the active site are altered upon introducing a single mutation to Val29 (V29L) with a slower rate of catalytic turnover monitored experimentally (Doshi et al, 2016). However, the underlying communication network mediating this allostery remained unclear.…”

“…By two-sided t test, *p < 0.05 compared with CypA WT , #p < 0.05 compared with CypA V29L (CypA V6TV29L ), or CypA V6T (CypA V6TV29T ). k iso values for CypA WT and CypA V29L have been published previously (Doshi et al, 2016; Holliday et al, 2015b). See Figure S3 for the dynamic impacts of CypA V6TV29L .…”

“…WT, wild-type. See Figure S3 and Table S1 for equivalent binding and catalytic data with the hemagglutinin peptide. a Errors in K D-app are in fits to a single experiment. b Errors in k iso are SDs of two or more independent experiments. c Published previously by Doshi et al (2016) and Holliday et al (2015b). …”

Structure-Based Analysis of Single Nucleotide Variants in the Renin-Angiotensinogen Complex

“…A cut-off distance of 6.7 angstroms [27] was used to define any residue-residue contact (contact = 1, no contact = 0) around designated SNV positions. A total of 10,001 frames (in PDB format) were traversed to generate edge lists that were used by an in-house R script – using the igraph library [28] – to produce a weighted adjacency matrix, which was converted to frequencies by dividing by the number of frames, similar to the approach used by Doshi et al [29]. Edge weights were displayed as log2 re-scaled values, while the contact frequencies were used as edge labels.…”