Mapping allostery through the covariance analysis of NMR chemical shifts

Abstract

Allostery is a fundamental mechanism of regulation in biology. The residues at the end points of long-range allosteric perturbations are commonly identified by the comparative analyses of structures and dynamics in apo and effector-bound states. However, the networks of interactions mediating the propagation of allosteric signals between the end points often remain elusive. Here we show that the covariance analysis of NMR chemical shift changes caused by a set of covalently modified analogs of the allosteric effector (i.e., agonists and antagonists) reveals extended networks of coupled residues. Unexpectedly, such networks reach not only sites subject to effector-dependent structural variations, but also regions that are controlled by dynamically driven allostery. In these regions the allosteric signal is propagated mainly by dynamic rather than structural modulations, which result in subtle but highly correlated chemical shift variations. The proposed chemical shift covariance analysis (CHESCA) identifies interresidue correlations based on the combination of agglomerative clustering (AC) and singular value decomposition (SVD). AC results in dendrograms that define functional clusters of coupled residues, while SVD generates score plots that provide a residue-specific dissection of the contributions to binding and allostery. The CHESCA approach was validated by applying it to the cAMP-binding domain of the exchange protein directly activated by cAMP (EPAC) and the CHESCA results are in full agreement with independent mutational data on EPAC activation. Overall, CHESCA is a generally applicable method that utilizes a selected chemical library of effector analogs to quantitatively decode the binding and allosteric information content embedded in chemical shift changes. L ong-range allosteric perturbations are propagated not only by structural changes but also by effector-dependent modulations in dynamics (1-23). The end points of these long-range allosteric signal propagations are effectively characterized by the comparative analysis of the structural and dynamic profiles of apo and effector-bound states (2, 7). However, what remains experimentally challenging is often defining the networks of residues that mediate the cross-talk between distal sites. Such clusters of coupled residues are particularly elusive in allosteric processes with a significant dynamically driven component (11-17), as in this case the allosteric signal propagation relies on subtle, but critical, conformational and side-chain packing rearrangements that often fall below the resolution of common X-ray or NMR structure determination methods (2, 7, 24).Here we introduce a general experimental method to map allosteric networks based on the covariance analysis of NMR chemical shifts. The chemical shift covariance analysis (CHESCA) is based on two simple but general notions. The first assumption is that the subtle but functionally relevant structural changes that underlie the allosteric modulations of dynamics are effectively probed by accurately ...

Signaling through dynamic linkers as revealed by PKA

“…2F), we anticipate that the allosteric networks controlled by cAMP reach sites in the linker region as well. To test this prediction, we implemented for R A the chemical shift covariance analysis (CHESCA), which has been shown to be effective in mapping effector-dependent networks in other allosteric systems based on a small library of agonist and antagonists (39). This library includes cAMP, the reverse-agonist Rp-cAMPS, the partial agonist 2′-OMe-cAMP, and the agonist Sp-cAMPS (27,40).…”

Tracking Equilibrium and Nonequilibrium Shifts in Data with TREND

“…No scaling has been used for MRI movies. Column centering and scaling are not necessary in our experience, but are available in trendmaingui and trendmain as they are sometimes used for PCA (22) and ICA (11). The descriptions of the data scaling and centering methods (21) are listed in Table S2.…”

Cracking the allosteric code of NMR chemical shifts

“…Hence, there is considerable interest in elucidating allosteric mechanisms, which typically involve the modulation of functional conformational equilibria by allosteric effectors. One of the most effective means to monitor allosteric transitions is through NMR chemical shift changes resulting from a library of perturbations designed to interrogate a given allosteric system (6,7). The perturbation library may include analogs of allosteric ligands and/or mutations designed to modulate allosteric conformational equilibria (7).…”

“…One of the most effective means to monitor allosteric transitions is through NMR chemical shift changes resulting from a library of perturbations designed to interrogate a given allosteric system (6,7). The perturbation library may include analogs of allosteric ligands and/or mutations designed to modulate allosteric conformational equilibria (7). Through statistical comparative analyses of the NMR chemical shift variations elicited by the selected perturbations it is possible to identify chemical shift patterns that serve as distinctive signatures for specific allosteric mechanisms (3, 7).…”

“…For example, amide 15 N and 1 H chemical shifts are hypersensitive to conformational variations, which poses a major challenge for their computational prediction and for their use in chemical shift-based structure determination but makes them an ideal atomic-resolution probe to sense the effect of allosteric perturbations through comparative NMR analyses (3, 6). The latter include not only conventional apo vs. holo or WT vs. mutant binary chemical shift comparisons, but they extend also to ternary or multiperturbation vectorial, correlative, and statistical analyses (6)(7)(8)(9)(10)(11)(12)(13).…”