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Enzyme-Substrate-Cofactor Dynamical Networks Revealed by High-Resolution Field Cycling Relaxometry.

ABSTRACT: The remarkable power and specificity of enzyme catalysis rely on the dynamic alignment of the enzyme, substrates, and cofactors, yet the role of dynamics has usually been approached from the perspective of the protein. We have been using an underappreciated NMR technique, subtesla high-resolution field cycling ³¹P NMR relaxometry, to investigate the dynamics of the enzyme-bound substrates and cofactor on guanosine-5'-monophosphate reductase (GMPR). GMPR forms two dead end, yet catalytically competent, complexes that mimic distinct steps in the catalytic cycle: E·IMP·NADP⁺ undergoes a partial hydride transfer reaction, while E·GMP·NADP⁺ undergoes a partial deamination reaction. A different cofactor conformation is required for each partial reaction. Here we report the effects of mutations designed to perturb cofactor conformation and ammonia binding with the goal of identifying the structural features that contribute to the distinct dynamic signatures of the hydride transfer and deamination complexes. These experiments suggest that Asp129 is a central cog in a dynamic network required for both hydride transfer and deamination. In contrast, Lys77 modulates the conformation and mobility of substrates and cofactors in a reaction-specific manner. Thr105 and Tyr318 are part of a deamination-specific dynamic network that includes the 2'-OH of GMP. These residues have comparatively little effect on the dynamic properties of the hydride transfer complex. These results further illustrate the potential of high-resolution field cycling NMR relaxometry for the investigation of ligand dynamics. In addition, exchange experiments indicate that NH₃/NH₄⁺ has a high affinity for the deamination complex but a low affinity for the hydride transfer complex, suggesting that the movement of ammonia may gate the cofactor conformational change. Collectively, these experiments reinforce the view that the enzyme, substrates, and cofactor are linked in intricate, reaction-specific, dynamic networks and demonstrate that distal portions of the substrates and cofactors are critical features in these networks.

SUBMITTER: Rosenberg MM

PROVIDER: S-EPMC8364753 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The representation of the natural-density, heterogeneous connectivity of neuronal network models at relevant spatial scales remains a challenge for Computational Neuroscience and Neuromorphic Computing. In particular, the memory demands imposed by the vast number of synapses in brain-scale network simulations constitute a major obstacle. Limiting the number resolution of synaptic weights appears to be a natural strategy to reduce memory and compute load. In this study, we investigate the effects of a limited synaptic-weight resolution on the dynamics of recurrent spiking neuronal networks resembling local cortical circuits and develop strategies for minimizing deviations from the dynamics of networks with high-resolution synaptic weights. We mimic the effect of a limited synaptic weight resolution by replacing normally distributed synaptic weights with weights drawn from a discrete distribution, and compare the resulting statistics characterizing firing rates, spike-train irregularity, and correlation coefficients with the reference solution. We show that a naive discretization of synaptic weights generally leads to a distortion of the spike-train statistics. If the weights are discretized such that the mean and the variance of the total synaptic input currents are preserved, the firing statistics remain unaffected for the types of networks considered in this study. For networks with sufficiently heterogeneous in-degrees, the firing statistics can be preserved even if all synaptic weights are replaced by the mean of the weight distribution. We conclude that even for simple networks with non-plastic neurons and synapses, a discretization of synaptic weights can lead to substantial deviations in the firing statistics unless the discretization is performed with care and guided by a rigorous validation process. For the network model used in this study, the synaptic weights can be replaced by low-resolution weights without affecting its macroscopic dynamical characteristics, thereby saving substantial amounts of memory.

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Enzyme-Substrate-Cofactor Dynamical Networks Revealed by High-Resolution Field Cycling Relaxometry.

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