Identifying coupled clusters of allostery participants through chemical shift perturbations

Abstract

Allosteric couplings underlie many cellular signaling processes and provide an exciting avenue for development of new diagnostics and therapeutics. A general method for identifying important residues in allosteric mechanisms would be very useful, but remains elusive due to the complexity of long-range phenomena. Here, we introduce an NMR method to identify residues involved in allosteric coupling between two ligand-binding sites in a protein, which we call chemical shift detection of allostery participants (CAP). Networks of functional groups responding to each ligand are defined through correlated NMR perturbations. In this process, we also identify allostery participants, groups that respond to both binding events and likely play a role in the coupling between the binding sites. Such residues exhibit multiple functional states with distinct NMR chemical shifts, depending on binding status at both binding sites. Such a strategy was applied to the prototypical ion channel KcsA. We had previously shown that the potassium affinity at the extracellular selectivity filter is strongly dependent on proton binding at the intracellular pH sensor. Here, we analyzed proton and potassium binding networks and identified groups that depend on both proton and potassium binding (allostery participants). These groups are viewed as candidates for transmitting information between functional units. The vital role of one such identified amino acid was validated through site-specific mutagenesis, electrophysiology functional studies, and NMR-detected thermodynamic analysis of allosteric coupling. This strategy for identifying allostery participants is likely to have applications for many other systems.

Conformational changes upon gating of KirBac1.1 into an open-activated state revealed by solid-state NMR and functional assays

“…We reconstituted U-15 N, 13 C-KirBac1.1 into POPC and 3:2 POPC:POPG proteoliposomes with a 1:1 lipid-to-protein ratio by mass. This is the same ratio we used previously (58) and is consistent with multiple previous studies of KcsA (53)(54)(55)(59)(60)(61)(62)(63)(64). The sensitivity of these samples allowed us to acquire 2 twodimensional (2D) dipole-assisted rotational resonance (DARR) (65) 13 C-13 C correlation spectra with 12 ms and 25 ms of mixing, an NCA SPECIFIC-CP (66) 2D, and NcaCX and NcoCX 2Ds with 25 ms and 35 ms of DARR following SPECIFIC CP, respectively.…”

“…In contrast, only the conductive state of the selectivity filter is observed in POPC bilayers, with much greater overall signal intensity. The observed pattern of chemical shift perturbations suggests a novel allosteric pathway distinct from C-type inactivation (51)(52)(53)(54)(55). In our hypothetical model, the minor conformer is a shallowly inactivated state of the selectivity filter, which is similar to the Mg 2+ -bound (and thus rectified) conformation observed in the X-ray crystal structure (11).…”

Probing Allosteric Coupling of a Constitutively Open Mutant of the Ion Channel KcsA using Solid State NMR

“…triple mutant E71A/ H25R/E118A [30]. 192 We recently identified several participants in allosteric coupling in KcsA using 193 NMR [43] and the residues identified by NMR are in good agreement with other 194 molecular dynamics and electrophysiology studies [22,43]. These studies indicate that 195 T74 is a key allosteric participant and a mutant T74S exhibits a strong reduction in 196 allosteric coupling in KcsA.…”

Integrated Computational Approaches and Tools for Allosteric Drug Discovery

“…NMR chemical exchange saturation transfer (CEST) experiments [68][69][70] can identify invisible hidden states and characterize slow-to-intermediate conformational exchanges. NMR chemical-shift covariance (CHESCA) and projection (CHESPA) analyzes can identify residue interaction networks that show correlated changes in chemical shifts due to allosteric perturbations caused by ligand binding or mutations [71][72][73]. NMR chemical-shift perturbations have also been combined with Markov modelling and network analysis to reveal the dynamic flow of communication between allosteric communities in proteins [74].…”