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Wiley, S.

Publications and source records attributed to Wiley, S..

2 recordsLinked to original sources

13C Electron Nuclear Double Resonance Spectroscopy Shows Acetyl-CoA Synthase Binds Two Substrate CO in Multiple Binding Modes and Reveals the Importance of a CO-Binding 'Alcove'

EPR and Electron Nuclear Double Resonance spectroscopies here characterize CO binding to the active-site A cluster of wild-type (WT) Acetyl-CoA Synthase (ACS) and two variants, F229W and F229A. The A-cluster binds CO to a proximal Ni (Nip) that bridges a [4Fe-4S] cluster and distal Nid. An alcove seen in the ACS crystal-structure near the A-cluster, defined by hydrophobic residues including F229, forms a cage surrounding a Xe mimic of CO and is suggested to cradle this CO. Previously, we only knew WT ACS bound a single CO in the Ared-CO intermediate, here seen as forming Nip(I)-CO with CO on-axis of the dz2 odd-electron orbital (g{perp}>g||[~]2). The two-dimensional field-frequency pattern of 2K-35 GHz 13C-ENDOR spectra collected across the Ared-CO EPR envelope now reveals a second CO bound in the dz2 orbitals equatorial plane. This WT A-cluster conformer dominates the nearly-conservative F229W variant, but 13C-ENDOR reveals a minority "A" conformation with (g||>g{perp}[~]2) characteristic of a cloverleaf (eg. dx2-y2) odd-electron orbital, and with Nip binding two, apparently in-plane CO. Disruption of the alcove through introduction of the smaller alanine residue in the F229A variant diminishes conversion to Ni(I) [~]tenfold and introduces extensive cluster flexibility. 13C-ENDOR shows the F229A cluster is mostly (60%) in the "A" conformation, but with [~]20% each of the WT conformer and an "O" state in which dz2 Nip(I) (g{perp}>g||[~]2) surprisingly lacks CO. This paper thus demonstrates the importance of an intact alcove in forming and stabilizing the Ni(I)-CO intermediate in the Wood-Ljungdahl pathway of anaerobic CO and CO2 fixation.

biochemistry↗

Inferring Reaction Networks using Perturbation Data

In this paper we examine the use of perturbation data to infer the underlying mechanistic dynamic model. The approach uses an evolutionary strategy to evolve networks based on a fitness criterion that measures the difference between the experimentally determined set of perturbation data and proposed mechanistic models. At present we only deal with reaction networks that use mass-action kinetics employing uni-uni, bi-uni, uni-bi and bi-bi reactions. The key to our approach is to split the algorithm into two phases. The first phase focuses on evolving network topologies that are consistent with the perturbation data followed by a second phase that evolves the parameter values. This results in almost an exact match between the evolved network and the original network from which the perturbation data was generated from. We test the approach on four models that include linear chain, feed-forward loop, cyclic pathway and a branched pathway. Currently the algorithm is implemented using Python and libRoadRunner but could at a later date be rewritten in a compiled language to improve performance. Future studies will focus on the impact of noise in the perturbation data on convergence and variability in the evolved parameter values and topologies. In addition we will investigate the effect of nonlinear rate laws on generating unique solutions.

systems biology↗