Multiple helical conformations of the helix‐turn‐helix region revealed by NOE‐restrained MD simulations of tryptophan aporepressor, TrpR

Abstract

The nature of flexibility in the helix-turn-helix region of E. coli trp aporepressor has been unexplained for a long time. The original ensemble of NMR structures showed apparent disorder, but chemical shift and relaxation measurements indicated a helical region. NOE data for a temperature-sensitive mutant showed more helical character in its helix-turn-helix region but nevertheless also led to an apparently disordered ensemble. However, conventional NMR structure determination methods require all structures in the ensemble to be consistent with each NOE simultaneously. The present work uses an alternative approach in which some structures of the ensemble are allowed to violate some NOEs to allow modelling of multiple conformational states that are in dynamic equilibrium. Newly measured NOE data for wild-type aporepressor are used as time-averaged distance restraints in MD simulations to generate an ensemble of helical conformations that is more consistent with the observed NMR data than the apparent disorder in the previously reported NMR structures. The results are consistent with the presence of alternating helical conformations that provide a better explanation for the flexibility of the helix-turn-helix region of trp aporepressor.

Crystal structures of Val58Ile tryptophan repressor in a domain-swapped array in the presence and absence ofL-tryptophan

“…The modeled iPrOH and two water molecules in the l-Trp site of the l-Trpfree structure have an average B factor of $65 Å 2 , which is slightly lower than the overall B factor of this structure (74 Å 2 ; Table 1) and is in the range of the surrounding residues ($40-70 Å 2 ). The conclusion that l-Trp is absent from the ligandbinding site of the unsoaked ds-TrpR crystals is consistent with all prior evidence from both X-ray and NMR results on apo Lawson et al, 2004;Carey et al, 2012;Harish et al, 2017). l-Trp has never been observed in crystals or NMR structures of TrpR purified as the apoprotein, and assays of purified apo TrpR always confirm its characteristic DNA-binding affinity (Carey, 1988), affirming the absence of l-Trp.…”

Representing structures of the multiple conformational states of proteins

“…Approaches have also been developed to integrate experimental NMR data with MD simulations. By incorporating time-averaged distance restraints from NOE data [19,40], MD simulations can better model conformational distributions consistent with experimental data, as in the case of the DNA-binding loops of E. coli tryptophan repressor [47]. Bayesian inference and ensemble fitting approaches, which leverage experimental data alongside MD simulations, can also generate multiple-state models [48,49].…”

“…https://doi.org/10.26434/chemrxiv-2023-wc3mx-v2 ORCID: https://orcid.org/0000-0002-9440-3059 Content not peer-reviewed by ChemRxiv. License: CC BY 4.0 [22], inhibitor-bound dengue virus NS2B/NS3 protease (two binding modes, 2m9p and 2m9q) [39], pro-islet amyloid polypeptide in detergent micelles (6ucj and 6uck) [72], E. coli tryptophan repressor (two states combined as pdb ID 5tm0) [47], and the membrane-bound SARS-CoV-2 spike protein HR1 ectodomain (two states combined as 7r95) [73]. Also shown is the three-state model of influenza hemagglutinin fusion peptide A (combined https://doi.org/10.26434/chemrxiv-2023-wc3mx-v2 ORCID: https://orcid.org/0000-0002-9440-3059 Content not peer-reviewed by ChemRxiv.…”

Representing Structures of the Multiple Conformational States of Proteins

“…Approaches have also been developed to integrate experimental NMR data with MD simulations, aiming to create more representative conformational ensembles. By incorporating timeaveraged distance restraints from NOE data [19,40], MD simulations can better model conformational distributions consistent with experimental data, as in the case of the DNA-binding loops of E. coli tryptophan repressor [47]. Bayesian inference and ensemble fitting approaches, which leverage experimental data alongside MD simulations, can also generate improved ensembles [48,49].…”