Time Evolution of the Millisecond Allosteric Activation of Imidazole Glycerol Phosphate Synthase

Abstract

Deciphering the molecular mechanisms of enzymatic allosteric regulation requires the structural characterization of functional states and also their time evolution toward the formation of the allosterically activated ternary complex. The transient nature and usually slow millisecond time scale interconversion between these functional states hamper their experimental and computational characterization. Here, we combine extensive molecular dynamics simulations, enhanced sampling techniques, and dynamical networks to describe the allosteric activation of imidazole glycerol phosphate synthase (IGPS) from the substrate-free form to the active ternary complex. IGPS is a heterodimeric bienzyme complex whose HisH subunit is responsible for hydrolyzing glutamine and delivering ammonia for the cyclase activity in HisF. Despite significant advances in understanding the underlying allosteric mechanism, essential molecular details of the long-range millisecond allosteric activation of IGPS remain hidden. Without using a priori information of the active state, our simulations uncover how IGPS, with the allosteric effector bound in HisF, spontaneously captures glutamine in a catalytically inactive HisH conformation, subsequently attains a closed HisF:HisH interface, and finally forms the oxyanion hole in HisH for efficient glutamine hydrolysis. We show that the combined effector and substrate binding dramatically decreases the conformational barrier associated with oxyanion hole formation, in line with the experimentally observed 4500-fold activity increase in glutamine hydrolysis. The allosteric activation is controlled by correlated time-evolving dynamic networks connecting the effector and substrate binding sites. This computational strategy tailored to describe millisecond events can be used to rationalize the effect of mutations on the allosteric regulation and guide IGPS engineering efforts.

Dynamic allosteric networks drive adenosine A1receptor activation and G-protein coupling

“…A complete understanding of allosteric modulation involves the decoding of the communication pathways that dynamically couples distinct protein sites. Despite the difficulties, network theory has been successfully applied to uncover the allosteric communication pathways in protein complexes [23, 26], including GPCRs [27, 28]. In order to trace down the allosteric pathways in A 1 R-ADO, we relied on the protein energy networks (PEN) approach.…”

“…In contrast, when starting from the inactive structure, the receptor mostly samples inactive conformations exhibiting a low probability to progress towards the intermediate state, thus suggesting a higher inactive-to-active transition time scale. To estimate a reliable energy landscape of the A1R-ADO activation and check the importance of the intermediate state, we relied on metadynamics simulations, a powerful method that has been successfully used to study complex conformational transitions in proteins, [23][24][25] including GPCRs. [12,15] In particular, we performed well-tempered metadynamics (WT-MetaD) simulations using the walkers approach (see Materials and methods).…”

Changes in protonation states of in-pathway residues can alter ligand binding pathways obtained from spontaneous binding molecular dynamics simulations

“…dBen-Asp189 below 5 Å and dBen-His57 around 10 Å, see B in Figure 3). Benzamidine also accumulates in both cases in a region of the FEL defined by a range of dBen-Asp189 [15,20] Å and dben-His57 [10,15] Å, which corresponds to trypsin hydrophobic S3 pocket composed by Trp210 and surrounding residues. Interestingly, the FEL displays significant differences in the binding patterns of HID57 and HIP57 when benzamidine approaches the S1 pocket (dBen-Asp189 within [5,15] Å).…”

“…7,8 Unconstrained enhanced sampling methods were used to simulate binding of allosteric modulators into G-protein coupled receptors 9 or substrate binding in allosterically regulated enzymes. 10 The predictive power of spontaneous binding simulations relies on being able to access the timescale required to sample the binding event and critically depends on the accurate description of the simulated system. Around 60% of protein-ligand binding events involve changes in protonation states.…”

Changes in Protonation States of In-Pathway Residues can Alter Ligand Binding Pathways Obtained From Spontaneous Binding Molecular Dynamics Simulations

“…Markov-State Models were used to completely reconstruct ligand binding and unbinding pathways and the associated kinetics of enzyme-inhibitor complexes ( Buch et al, 2011 ; Plattner and Noé, 2015 ). Unconstrained enhanced sampling methods were used to simulate binding of allosteric modulators into G-protein coupled receptors ( Miao and McCammon, 2016 ) or substrate binding in allosterically regulated enzymes ( Calvó-Tusell et al, 2022 ). The predictive power of spontaneous binding simulations relies on being able to access the timescale required to sample the binding event and critically depends on the accurate description of the simulated system.…”