Covalent attachment of active enzymes to upconversion phosphors allows ratiometric detection of substrates

Upconverting phosphors (UCPs) convert multiple low energy photons into higher energy emission via the process of photon upconversion and offer an attractive alternative to organic fluorophores for use as luminescent probes. Here, UCPs were capped with functionalized silica in order to provide a surface to covalently conjugate proteins with surface?accessible cysteines. Variants of green fluorescent protein (GFP) and the flavoenzyme pentaerythritol tetranitrate reductase (PETNR) were then attached via maleimide?thiol coupling in order to allow energy transfer from the UCP to the GFP or flavin cofactor of PETNR, respectively. PETNR retains its activity when coupled to the UCPs, which allows reversible detection of enzyme substrates via ratiometric sensing of the enzyme redox state

Predicting new protein conformations from molecular dynamics simulation conformational landscapes and machine learning

From Wiley via Jisc Publications RouterHistory: received 2020-08-05, rev-recd 2021-01-21, accepted 2021-02-23, pub-electronic 2021-03-03, pub-print 2021-08Article version: VoRPublication status: PublishedFunder: Biotechnology and Biological Sciences Research Council; Id: http://dx.doi.org/10.13039/501100000268; Grant(s): BB/M017702/1Abstract: Molecular dynamics (MD) simulations are a popular method of studying protein structure and function, but are unable to reliably sample all relevant conformational space in reasonable computational timescales. A range of enhanced sampling methods are available that can improve conformational sampling, but these do not offer a complete solution. We present here a proof‐of‐principle method of combining MD simulation with machine learning to explore protein conformational space. An autoencoder is used to map snapshots from MD simulations onto a user‐defined conformational landscape defined by principal components analysis or specific structural features, and we show that we can predict, with useful accuracy, conformations that are not present in the training data. This method offers a new approach to the prediction of new low energy/physically realistic structures of conformationally dynamic proteins and allows an alternative approach to enhanced sampling of MD simulations

Convergence of Theory and Experiment on the Role of Preorganization, Quantum Tunneling, and Enzyme Motions into Flavoenzyme-Catalyzed Hydride Transfer

Hydride transfer is one of the most common reactions catalyzed by enzymatic systems, and it has become an object of study because of possible significant quantum tunneling effects. In the present work, we provide a combination of theoretical QM/MM simulations and experimental measurements of the rate constants and kinetic isotopic effects (KIEs) for the hydride transfer reaction catalyzed by morphinone reductase, MR. Quantum mechanical tunneling coefficients, computed in the framework of variational transition-state theory, play a significant role in this reaction, reaching values of 23.8 ± 5.5 for the lightest isotopologue—one of the largest values reported for enzymatic systems. This prediction is supported by the agreement between the theoretically predicted rate constants and the corresponding experimental values. Simulations indicate that the role of protein motions can be satisfactorily described as equilibrium fluctuations along the reaction coordinate, in line with a high degree of preorganization displayed by this enzyme.V.M. is grateful to the University of Bath for the award of a David Parkin Visiting Professorship. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the “Centre de Calcul de la Universitat de València” through the use of Multivac and Tirant

Combined pulsed electron double resonance EPR and molecular dynamics investigations of calmodulin suggest effects of crowding agents on protein structures

A.M.S. received Early Stage Research Funding from the European Union’s Seventh Framework Programme FP-7-PEOPLE-2013-ITN through the “MAGnetic Innovation in Catalysis” (MAGIC) Initial Training Network (grant agreement no. 606831). Part of this work was also supported by BBSRC grant: BB/M007065/1. J.L. thanks the Royal Society for a University Research Fellowship, the Carnegie Trust (RIG007510), and the Wellcome Trust for a Multi-User Equipment grant (099149/Z/12/Z).Calmodulin (CaM) is a highly dynamic Ca2+-binding protein that exhibits large conformational changes upon binding Ca2+ and target proteins. Although it is accepted that CaM exists in an equilibrium of conformational states in the absence of target protein, the physiological relevance of an elongated helical linker region in the Ca2+-replete form has been highly debated. In this study, we use PELDOR (pulsed electron–electron double resonance) EPR measurements of a doubly spin-labeled CaM variant to assess the conformational states of CaM in the apo-, Ca2+-bound, and Ca2+ plus target peptide-bound states. Our findings are consistent with a three-state conformational model of CaM, showing a semi-open apo-state, a highly extended Ca2+-replete state, and a compact target protein-bound state. Molecular dynamics simulations suggest that the presence of glycerol, and potentially other molecular crowding agents, has a profound effect on the relative stability of the different conformational states. Differing experimental conditions may explain the discrepancies in the literature regarding the observed conformational state(s) of CaM, and our PELDOR measurements show good evidence for an extended conformation of Ca2+-replete CaM similar to the one observed in early X-ray crystal structures.Publisher PDFPeer reviewe

Real-time feedback improves imagined 3D primitive object classification from EEG

Brain-computer interfaces (BCI) enable movement-independent information transfer from humans to computers. Decoding imagined 3D objects from electroencephalography (EEG) may improve design ideation in engineering design or image reconstruction from EEG for application in brain-computer interfaces, neuro-prosthetics, and cognitive neuroscience research. Object-imagery decoding studies, to date, predominantly employ functional magnetic resonance imaging (fMRI) and do not provide real-time feedback. We present four linked studies in a study series to investigate: (1) whether five imagined 3D primitive objects (sphere, cone, pyramid, cylinder, and cube) could be decoded from EEG; and (2) the influence of real-time feedback on decoding accuracy. Studies 1 (N=10) and 2 (N=3) involved a single-session and a multi-session design, respectively, without real-time feedback. Studies 3 (N=2) and 4 (N=4) involved multiple sessions, without and with real-time feedback. The four studies involved 69 sessions in total of which 26 sessions were online with real-time feedback (15,480 trials for offline and at least 6,840 trials for online sessions in total). We demonstrate that decoding accuracy over multiple sessions improves significantly with biased feedback (p=0.004), compared to performance without feedback. This is the first study to show the effect of real-time feedback on the performance of primitive object-imagery BCI