Bioinspired Cu(II) Defect Sites in ZIF-8 for Selective Methane Oxidation.

Activating the C-H bonds of alkanes without further oxidation to more thermodynamically stable products, CO and CO2, is a long-sought goal of catalytic chemistry. Inspired by the monocopper active site of methane monooxygenase, we synthesized a Cu-doped ZIF-8 metal-organic framework with 25% Cu and 75% Zn in the nodes and activated it by heating to 200 °C and dosing in a stepwise fashion with O2, methane, and steam. We found that it does oxidize methane to methanol and formaldehyde. The catalysis persists through at least five cycles, and beyond the third cycle, the selectivity improves to the extent that no CO2 can be detected. Experimental characterization and analysis were carried out by PXRD, DRUV-vis, SEM, and XAS (XANES and EXAFS). The reaction is postulated to proceed at open-coordination copper sites generated by defects, and the mechanism of methanol production was explicated by density functional calculations with the revMO6-L exchange-correlation functional. The calculations reveal a catalytic cycle of oxygen-activated CuI involving the conversion of two molecules of CH4 to two molecules of CH3OH by a sequence of hydrogen atom transfer reactions and rebound steps. For most steps in the cycle, the reaction is more favored by singlet species than by triplets.

Parametrically Managed Activation Function for Fitting a Neural Network Potential with Physical Behavior Enforced by a Low-Dimensional Potential.

Machine-learned representations of potential energy surfaces generated in the output layer of a feedforward neural network are becoming increasingly popular. One difficulty with neural network output is that it is often unreliable in regions where training data is missing or sparse. Human-designed potentials often build in proper extrapolation behavior by choice of functional form. Because machine learning is very efficient, it is desirable to learn how to add human intelligence to machine-learned potentials in a convenient way. One example is the well-understood feature of interaction potentials that they vanish when subsystems are too far separated to interact. In this article, we present a way to add a new kind of activation function to a neural network to enforce low-dimensional constraints. In particular, the activation function depends parametrically on all of the input variables. We illustrate the use of this step by showing how it can force an interaction potential to go to zero at large subsystem separations without either inputting a specific functional form for the potential or adding data to the training set in the asymptotic region of geometries where the subsystems are separated. In the process of illustrating this, we present an improved set of potential energy surfaces for the 14 lowest 3A' states of O3. The method is more general than this example, and it may be used to add other low-dimensional knowledge or lower-level knowledge to machine-learned potentials. In addition to the O3 example, we present a greater-generality method called parametrically managed diabatization by deep neural network (PM-DDNN) that is an improvement on our previously presented permutationally restrained diabatization by deep neural network (PR-DDNN).

Electron transfer dissociation of synthetic and natural peptides containing lanthionine/methyllanthionine bridges.

RATIONALE: The modes of cleavage of lanthionine/methyllanthionine bridges under electron transfer dissociation (ETD) were investigated using synthetic and natural lantipeptides. Knowledge of the mass spectrometric fragmentation of lanthionine/methyllanthionine bridges may assist in the development of analytical methods for the rapid discovery of new lantibiotics. The present study strengthens the advantage of ETD in the characterization of posttranslational modifications of peptides and proteins. METHODS: Synthetic and natural lantipeptides were obtained by desulfurization of peptide disulfides and cyanogen bromide digestion of the lantibiotic nisin, respectively. These peptides were subjected to electrospray ionization collision-induced dissociation tandem mass spectrometry (CID-MS/MS) and ETD-MS/MS using an HCT ultra ETDII ion trap mass spectrometer. MS3 CID was performed on the desired product ions to prove cleavage of the lanthionine/methyllanthionine bridge during ETD-MS/MS. RESULTS: ETD has advantages over CID in the cleavage of the side chain of lanthionine/methyllanthionine bridges. The cleavage of the N-Cα backbone peptide bond followed by C-terminal side chain of the lanthionine bridge results in formation of c•+ and z+ ions. Cleavage at the preceding peptide bond to the C-terminal side chain of lanthionine/methyllanthionine bridges yields specific fragments with the cysteine/methylcysteine thiyl radical and dehydroalanine. CONCLUSIONS: ETD successfully cleaves the lanthionine/methyllanthionine bridges of synthetic and natural lantipeptides. Diagnostic fragment ions of ETD cleavage of lanthionine/methyllanthionine bridges are the N-terminal cysteine/methylcysteine thiyl radical and C-terminal dehydroalanine. Detection of the cysteine/methylcysteine thiyl radical and dehydroalanine in combined ETD-CID-MS may be used for the rapid identification of lantipeptide natural products.

Noncovalent Carbon-Bonding Interactions in Proteins.

Carbon bonds (C-bonds) are the highly directional noncovalent interactions between carbonyl-oxygen acceptors and sp3 -hybridized-carbon σ-hole donors through n→σ* electron delocalization. We have shown the ubiquitous existence of C-bonds in proteins with the help of careful protein structure analysis and quantum calculations, and have precisely determined C-bond energies. The importance of conventional noncovalent interactions such as hydrogen bond (H-bonds) and halogen bond (X-bonds) in the structure and function of biological molecules are well established, while carbon bonds C-bonds have still to be recognized. We have shown that C-bonds are present in proteins, contribute enthalpically to the overall hydrophobic interaction and play a significant role in the photodissociation mechanism of myoglobin and the binding of nucleobases to proteins.

Nonadiabatic Dynamics of 1,3-Cyclohexadiene by Curvature-Driven Coherent Switching with Decay of Mixing.

The photoinduced ring-opening reaction of 1,3-cyclohexadiene to produce 1,3,5-hexatriene is a classic electrocyclic reaction and is also a prototype for many reactions of biological and synthetic importance. Here, we simulate the ultrafast nonadiabatic dynamics of the reaction in the manifold of the three lowest valence electronic states by using extended multistate complete-active-space second-order perturbation theory (XMS-CASPT2) combined with the curvature-driven coherent switching with decay of mixing (κCSDM) dynamical method. We obtain an excited-state lifetime of 79 fs, and a product quantum yield of 40% from the 500 trajectories initiated in the S1 excited state. The obtained lifetime and quantum yield values are very close to previously reported experimental and computed values, showing the capability of performing a reasonable nonadiabatic ring-opening dynamics with the κCSDM method that does not require nonadiabatic coupling vectors, time derivatives, or diabatization. In addition, we study the ring-opening reaction by initiating the trajectories in the dark state S2. We also optimize the S0/S1 and S1/S2 minimum-energy conical intersections (MECIs) by XMS-CASPT2; for S1/S2, we optimized both an inner and an outer local-minimum-energy conical intersections (LMECIs). We provide the potential energy profile along the ring-opening coordinate by joining selected critical points via linear synchronous transit paths. We find the inner S1/S2 LMECI to be more crucial than the outer one.