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Quantum Mechanical Study of Vicinal J Spin–Spin Coupling Constants for the Protein Backbone

We have performed densisty functional theory (DFT) calculations of vicinal J coupling constants involving the backbone torsional angle for the protein GB3 using our recently developed automatic fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) approach (Xiao He et al.<i> J. Phys. Chem. B</i> <b>2009</b>, <i>113</i>, 10380–10388). Interestingly, the calculated values based on an NMR structure are more accurate than those based on a high-resolution X-ray strucure because the NMR structure was refined using a large number of residual dipolar couplings (RDCs) whereas the hydrogen atoms were added into the X-ray structure in idealized positions, confirming that the postioning of the hydrogen atoms relative to the backbone atoms is important to the accuracy of J coupling constant prediction. By comparing three Karplus equations, our results have demonstrated that hydrogen bonding, substituent and electrostatic effects could have significant impacts on vicinal J couplings even though they depend mostly on the intervening dihedral angles. The root-mean-square deviations (RMSDs) of the calculated ³J­(H^N,H^α), ³J­(H^N,C^β), ³J­(H^N,C′) values based on the NMR structure are 0.52, 0.25, and 0.35 Hz, respectively, after taking the dynamic effect into consideration. The excellent accuracy demonstrates that our AF-QM/MM approach is a useful tool to study the relationship between J coupling constants and the structure and dynamics of proteins

Additional file 1 of Global, regional, and national burden of incidence, prevalence, and years lived with disability for facial fractures from 1990 to 2019: a systematic analysis for the Global Burden of Disease study 2019

Supplementary Material

Improving the Scoring of Protein–Ligand Binding Affinity by Including the Effects of Structural Water and Electronic Polarization

Docking programs that use scoring functions to estimate binding affinities of small molecules to biological targets are widely applied in drug design and drug screening with partial success. But accurate and efficient scoring functions for protein–ligand binding affinity still present a grand challenge to computational chemists. In this study, the polarized protein-specific charge model (PPC) is incorporated into the molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) method to rescore the binding poses of some protein–ligand complexes, for which docking programs, such as Autodock, could not predict their binding modes correctly. Different sampling techniques (single minimized conformation and multiple molecular dynamics (MD) snapshots) are used to test the performance of MM/PBSA combined with the PPC model. Our results show the availability and effectiveness of this approach in correctly ranking the binding poses. More importantly, the bridging water molecules are found to play an important role in correctly determining the protein–ligand binding modes. Explicitly including these bridging water molecules in MM/PBSA calculations improves the prediction accuracy significantly. Our study sheds light on the importance of both bridging water molecules and the electronic polarization in the development of more reliable scoring functions for predicting molecular docking and protein–ligand binding affinity

Automated Fragmentation QM/MM Calculation of Amide Proton Chemical Shifts in Proteins with Explicit Solvent Model

We have performed a density functional theory (DFT) calculation of the amide proton NMR chemical shift in proteins using a recently developed automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) approach. Systematic investigation was carried out to examine the influence of explicit solvent molecules, cooperative hydrogen bonding effects, density functionals, size of the basis sets, and the local geometry of proteins on calculated chemical shifts. Our result demonstrates that the predicted amide proton (¹H_N) NMR chemical shift in explicit solvent shows remarkable improvement over that calculated with the implicit solvation model. The cooperative hydrogen bonding effect is also shown to improve the accuracy of ¹H_N chemical shifts. Furthermore, we found that the OPBE exchange-correlation functional is the best density functional for the prediction of protein ¹H_N chemical shifts among a selective set of DFT methods (namely, B3LYP, B3PW91, M062X, M06L, mPW1PW91, OB98, OPBE), and the locally dense basis set of 6-311++G**/4-31G* is shown to be sufficient for ¹H_N chemical shift calculation. By taking ensemble averaging into account, ¹H_N chemical shifts calculated by the AF-QM/MM approach can be used to validate the performance of various force fields. Our study underscores that the electronic polarization of protein is of critical importance to stabilizing hydrogen bonding, and the AF-QM/MM method is able to describe the local chemical environment in proteins more accurately than most widely used empirical models

How Well Can the M06 Suite of Functionals Describe the Electron Densities of Ne, Ne⁶⁺, and Ne⁸⁺?

The development of better approximations to the exact exchange-correlation functional is essential to the accuracy of density functionals. A recent study suggested that functionals with few parameters provide more accurate electron densities than recently developed many-parameter functionals for light closed-shell atomic systems. In this study, we calculated electron densities, their gradients, and Laplacians of Ne, Ne⁶⁺, and Ne⁸⁺ using 19 electronic structure methods, and we compared them to the CCSD reference results. Two basis sets, namely, aug-cc-pωCV5Z and aug-cc-pV5Z, are utilized in the calculations. We found that the choice of basis set has a significant impact on the errors and rankings of some of the selected methods. The errors of electron densities, their gradients, and Laplacians calculated with the aug-cc-pV5Z basis set are substantially reduced, especially for Minnesota density functionals, as compared to the results using the aug-cc-pωCV5Z basis set (a larger basis set utilized in earlier work (Medvedev et al. <i>Science</i> <b>2017</b>, <i>355</i>, 49–52)). The rankings of the M06 suite of functionals among the 19 methods are greatly improved with the aug-cc-pV5Z basis set. In addition, the performances of the HSE06, BMK, MN12-L, and MN12-SX functionals are also improved with the aug-cc-pV5Z basis set. The M06 suite of functionals is capable of providing accurate electron densities, gradients, and Laplacians using the aug-cc-pV5Z basis set, and thus it is suitable for a wide range of applications in chemistry and physics

Predicting Mutation-Induced Stark Shifts in the Active Site of a Protein with a Polarized Force Field

The electric field inside a protein has a significant effect on the protein structure, function, and dynamics. Recent experimental developments have offered a direct approach to measure the electric field by utilizing a nitrile-containing inhibitor as a probe that can deliver a unique vibration to the specific site of interest in the protein. The observed frequency shift of the nitrile stretching vibration exhibits a linear dependence on the electric field at the nitrile site, thus providing a direct measurement of the relative electric field. In the present work, molecular dynamics simulations were carried out to compute the electric field shift in human aldose reductase (hALR2) using a polarized protein-specific charge (PPC) model derived from fragment-based quantum-chemistry calculations in implicit solvent. Calculated changes of electric field in the active site of hALR2 between the wild type and mutants were directly compared with measured vibrational frequency shifts (Stark shifts). Our study demonstrates that the Stark shifts calculated using the PPC model are in much better agreement with the experimental data than widely used nonpolarizable force fields, indicating that the electronic polarization effect is important for the accurate prediction of changes in the electric field inside proteins

Dynamically Tunable Chemiluminescence of Luminol-Functionalized Silver Nanoparticles and Its Application to Protein Sensing Arrays

It is still a great challenge to develop an array-based sensing system that can obtain only multiparameters, according to a single experiment and device. The role of conventional chemiluminescence (CL) in biosensing has been limited to a signal transducer in which a single signal (CL intensity) can be obtained for quantifying the concentrations of analytes. In this work, we have developed an dynamically tunable CL system, based on the reaction of luminol-functionalized silver nanoparticles (luminol–AgNPs) with H₂O₂, which could be tunable via adjusting various conditions such as the concentration of H₂O₂, pH value, and addition of protein. A single experiment operation could obtain multiparameters including CL intensity, the time to appear CL emission and the time to reach CL peak value. The tunable, low-background, and highly reproducible CL system based on luminol–AgNPs is applied, for the first time, as a sensing platform with trichannel properties for protein sensing arrays by principal component analysis. Identification of 35 unknowns demonstrated a success rate of >96%. The developed sensing arrays based on the luminol–AgNPs provide a new way to use nanoparticles-based CL for the fabrication of sensing arrays and hold great promise for biomedical application in the future

OCT1 and natural metabolites interactions characterized by various studies.

<p>OCT1 and natural metabolites interactions characterized by various studies.</p

Incipient Sensor Fault Diagnosis Using Moving Window Reconstruction-Based Contribution

Reconstruction-based contribution (RBC) is widely used for fault isolation and estimation in conjunction with principal component analysis (PCA)-based fault detection. Correct isolation can be guaranteed by RBC for single-sensor faults with large magnitudes. However, the incipient sensor fault diagnosis problem is not well handled by traditional PCA and RBC methods. In this paper, the limitations of traditional PCA and RBC methods for incipient sensor fault diagnosis are illustrated and analyzed. Through the introduction of a moving window, a new strategy based on the PCA model is presented for incipient fault detection. Regarding incipient fault isolation and estimation, a new contribution analysis method called moving window RBC is proposed to enhance the isolation performance and estimation accuracy. Rigorous fault detectability and isolability analyses of the proposed methods are provided. In addition, effects of the window width on fault detection, isolation, and estimation are discussed. Simulation studies on a numerical example and a continuous stirred tank reactor process are used to demonstrate the effectiveness of the proposed methods