Analysis of conformational motions and residue fluctuations for Escherichia coli ribose-binding protein revealed with elastic network models

Ribose-Binding Protein Mutants With Improved Interaction Towards the Non-natural Ligand 1,3-Cyclohexanediol

Bioreporters consist of genetically modified living organisms that respond to the presence of target chemical compounds by production of an easily measurable signal. The central element in a bioreporter is a sensory protein or aptamer, which, upon ligand binding, modifies expression of the reporter signal protein. A variety of naturally occurring or modified versions of sensory elements has been exploited, but it has proven to be challenging to generate elements that recognize non-natural ligands. Bacterial periplasmic binding proteins have been proposed as a general scaffold to design receptor proteins for non-natural ligands, but despite various efforts, with only limited success. Here, we show how combinations of randomized mutagenesis and reporter screening improved the performance of a set of mutants in the ribose binding protein (RbsB) of Escherichia coli, which had been designed based on computational simulations to bind the non-natural ligand 1,3-cyclohexanediol (13CHD). Randomized mutant libraries were constructed that used the initially designed mutants as scaffolds, which were cloned in an appropriate E. coli bioreporter system and screened for improved induction of the GFPmut2 reporter fluorescence in presence of 1,3-cyclohexanediol. Multiple rounds of library screening, sorting, renewed mutagenesis and screening resulted in 4.5-fold improvement of the response to 1,3-cyclohexanediol and a lower detection limit of 0.25 mM. All observed mutations except one were located outside the direct ligand-binding pocket, suggesting they were compensatory and helping protein folding or functional behavior other than interaction with the ligand. Our results thus demonstrate that combinations of ligand-binding-pocket redesign and randomized mutagenesis can indeed lead to the selection and recovery of periplasmic-binding protein mutants with non-natural compound recognition. However, current lack of understanding of the intermolecular movement and ligand-binding in periplasmic binding proteins such as RbsB are limiting the rational production of further and better sensory mutants.

Ligand-induced structural changes analysis of ribose-binding protein as studied by molecular dynamics simulations

BACKGROUND

The ribose-binding protein (RBP) from Escherichia coli is one of the representative structures of periplasmic binding proteins. Binding of ribose at the cleft between two domains causes a conformational change corresponding to a closure of two domains around the ligand. The RBP has been crystallized in the open and closed conformations.

OBJECTIVE

With the complex trajectory as a control, our goal was to study the conformation changes induced by the detachment of the ligand, and the results have been revealed from two computational tools, MD simulations and elastic network models.

METHODS

Molecular dynamics (MD) simulations were performed to study the conformation changes of RBP starting from the open-apo, closed-holo and closed-apo conformations.

RESULTS

The evolution of the domain opening angle θ clearly indicates large structural changes. The simulations indicate that the closed states in the absence of ribose are inclined to transition to the open states and that ribose-free RBP exists in a wide range of conformations. The first three dominant principal motions derived from the closed-apo trajectories, consisting of rotating, bending and twisting motions, account for the major rearrangement of the domains from the closed to the open conformation.

CONCLUSIONS

The motions showed a strong one-to-one correspondence with the slowest modes from our previous study of RBP with the anisotropic network model (ANM). The results obtained for RBP contribute to the generalization of robustness for protein domain motion studies using either the ANM or PCA for trajectories obtained from MD.

Computational redesign of the Escherichia coli ribose-binding protein ligand binding pocket for 1,3-cyclohexanediol and cyclohexanol

Bacterial periplasmic-binding proteins have been acclaimed as general biosensing platform, but their range of natural ligands is too limited for optimal development of chemical compound detection. Computational redesign of the ligand-binding pocket of periplasmic-binding proteins may yield variants with new properties, but, despite earlier claims, genuine changes of specificity to non-natural ligands have so far not been achieved. In order to better understand the reasons of such limited success, we revisited here the Escherichia coli RbsB ribose-binding protein, aiming to achieve perceptible transition from ribose to structurally related chemical ligands 1,3-cyclohexanediol and cyclohexanol. Combinations of mutations were computationally predicted for nine residues in the RbsB binding pocket, then synthesized and tested in an E. coli reporter chassis. Two million variants were screened in a microcolony-in-bead fluorescence-assisted sorting procedure, which yielded six mutants no longer responsive to ribose but with 1.2-1.5 times induction in presence of 1 mM 1,3-cyclohexanediol, one of which responded to cyclohexanol as well. Isothermal microcalorimetry confirmed 1,3-cyclohexanediol binding, although only two mutant proteins were sufficiently stable upon purification. Circular dichroism spectroscopy indicated discernable structural differences between these two mutant proteins and wild-type RbsB. This and further quantification of periplasmic-space abundance suggested most mutants to be prone to misfolding and/or with defects in translocation compared to wild-type. Our results thus affirm that computational design and library screening can yield RbsB mutants with recognition of non-natural but structurally similar ligands. The inherent arisal of protein instability or misfolding concomitant with designed altered ligand-binding pockets should be overcome by new experimental strategies or by improved future protein design algorithms.

An efficient perturbation method to predict the functionally key sites of glutamine binding protein

Glutamine-Binding Protein (GlnBP) of Escherichia coli, an important member of the periplasmic binding protein family, is responsible for the first step in the active transport of glutamine across the cytoplasmic membrane. In this work, the functionally key regulation sites of GlnBP were identified by utilizing a perturbation method proposed by our group, in which the residues whose perturbations markedly change the binding free energy between GlnBP and glutamine are considered to be functionally key residues. The results show that besides the substrate binding sites, some other residues distant from the binding pocket, including the ones in the hinge regions between the two domains, the front- and back- door channels and the exposed region, are important for the function of glutamine binding and transport. The predicted results are well consistent with the theoretical and experimental data, which indicates that our method is an effective approach to identify the key residues important for both ligand binding and long-range allosteric signal transmission. This work can provide some insights into the function performance of GlnBP and the physical mechanism of its allosteric regulation.

Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM)

Gaussian network model (GNM) and anisotropic network model (ANM) are some of the most popular methods for the study of protein flexibility and related functions. In this work, we propose generalized GNM (gGNM) and ANM methods and show that the GNM Kirchhoff matrix can be built from the ideal low-pass filter, which is a special case of a wide class of correlation functions underpinning the linear scaling flexibility-rigidity index (FRI) method. Based on the mathematical structure of correlation functions, we propose a unified framework to construct generalized Kirchhoff matrices whose matrix inverse leads to gGNMs, whereas, the direct inverse of its diagonal elements gives rise to FRI method. With this connection, we further introduce two multiscale elastic network models, namely, multiscale GNM (mGNM) and multiscale ANM (mANM), which are able to incorporate different scales into the generalized Kirchhoff matrices or generalized Hessian matrices. We validate our new multiscale methods with extensive numerical experiments. We illustrate that gGNMs outperform the original GNM method in the B-factor prediction of a set of 364 proteins. We demonstrate that for a given correlation function, FRI and gGNM methods provide essentially identical B-factor predictions when the scale value in the correlation function is sufficiently large. More importantly, we reveal intrinsic multiscale behavior in protein structures. The proposed mGNM and mANM are able to capture this multiscale behavior and thus give rise to a significant improvement of more than 11% in B-factor predictions over the original GNM and ANM methods. We further demonstrate the benefits of our mGNM through the B-factor predictions of many proteins that fail the original GNM method. We show that the proposed mGNM can also be used to analyze protein domain separations. Finally, we showcase the ability of our mANM for the analysis of protein collective motions.