Protein structure refinement using a quantum mechanics-based chemical shielding predictor

Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning

Theoretical predictions of NMR chemical shifts from first-principles can greatly facilitate experimental interpretation and structure identification of molecules in gas, solution, and solid-state phases. However, accurate prediction of chemical shifts using the gold-standard coupled cluster with singles, doubles, and perturbative triple excitations [CCSD(T)] method with a complete basis set (CBS) can be prohibitively expensive. By contrast, machine learning (ML) methods offer inexpensive alternatives for chemical shift predictions but are hampered by generalization to molecules outside the original training set. Here, we propose several new ideas in ML of the chemical shift prediction for H, C, N, and O that first introduce a novel feature representation, based on the atomic chemical shielding tensors within a molecular environment using an inexpensive quantum mechanics (QM) method, and train it to predict NMR chemical shieldings of a high-level composite theory that approaches the accuracy of CCSD(T)/CBS. In addition, we train the ML model through a new progressive active learning workflow that reduces the total number of expensive high-level composite calculations required while allowing the model to continuously improve on unseen data. Furthermore, the algorithm provides an error estimation, signaling potential unreliability in predictions if the error is large. Finally, we introduce a novel approach to keep the rotational invariance of the features using tensor environment vectors (TEVs) that yields a ML model with the highest accuracy compared to a similar model using data augmentation. We illustrate the predictive capacity of the resulting inexpensive shift machine learning (iShiftML) models across several benchmarks, including unseen molecules in the NS372 data set, gas-phase experimental chemical shifts for small organic molecules, and much larger and more complex natural products in which we can accurately differentiate between subtle diastereomers based on chemical shift assignments.

Near-Infrared Bodipy-Based Molecular Rotors for β-Amyloid Imaging In Vivo

β-amyloid (Aβ) is one of the important biomarkers for diagnosing Alzheimer's disease (AD). Many near-infrared probes based on the donor-π-acceptor structure have been developed to detect Aβ. Most reported Aβ probes are based on the N,N-dimethylamino group as the ideal donor, which is a widely accepted binding unit. As such, the development of fluorescent probes with improved binding units to detect Aβ is urgently required. Therefore, with this research three anchoring molecular rotor electron donors consisting of cyclic amines of different ring sizes are developed, namely five-membered ring (TPyr), six-membered ring (TPip), and seven-membered ring (THAI). These new anchored molecular rotors are connected to a 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene (BODIPY) and named TPyrBDP, TPipBDP, and THAIBDP. These probes exhibit high affinities (from 28 to 54 nm) for Aβ1-42 aggregates. The six-membered ring dye TPipBDP exhibits the highest signal-to-noise (75.5-fold) and higher affinity (28.30 ± 5.94 nm). TPipBDP can cross the blood-brain barrier and exhibits higher fluorescence enhancement with APP/PS1 (AD) double transgenic (Tg) mice than with wild-type (WT) mice.

DELTA50: A Highly Accurate Database of Experimental 1H and 13C NMR Chemical Shifts Applied to DFT Benchmarking

Density functional theory (DFT) benchmark studies of ¹H and ¹³C NMR chemical shifts often yield differing conclusions, likely due to non-optimal test molecules and non-standardized data acquisition. To address this issue, we carefully selected and measured ¹H and ¹³C NMR chemical shifts for 50 structurally diverse small organic molecules containing atoms from only the first two rows of the periodic table. Our NMR dataset, DELTA50, was used to calculate linear scaling factors and to evaluate the accuracy of 73 density functionals, 40 basis sets, 3 solvent models, and 3 gauge-referencing schemes. The best performing DFT methodologies for ¹H and ¹³C NMR chemical shift predictions were WP04/6-311++G(2d,p) and ωB97X-D/def2-SVP, respectively, when combined with the polarizable continuum solvent model (PCM) and gauge-independent atomic orbital (GIAO) method. Geometries should be optimized at the B3LYP-D3/6-311G(d,p) level including the PCM solvent model for the best accuracy. Predictions of 20 organic compounds and natural products from a separate probe set had root-mean-square deviations (RMSD) of 0.07 to 0.19 for ¹H and 0.5 to 2.9 for ¹³C. Maximum deviations were less than 0.5 and 6.5 ppm for ¹H and ¹³C, respectively.

Informing NMR experiments with molecular dynamics simulations to characterize the dominant activated state of the KcsA ion channel

As the first potassium channel with an x-ray structure determined, and given its homology to eukaryotic channels, the pH-gated prokaryotic channel KcsA has been extensively studied. Nevertheless, questions related, in particular, to the allosteric coupling between its gates remain open. The many currently available x-ray crystallography structures appear to correspond to various stages of activation and inactivation, offering insights into the molecular basis of these mechanisms. Since these studies have required mutations, complexation with antibodies, and substitution of detergents in place of lipids, examining the channel under more native conditions is desirable. Solid-state nuclear magnetic resonance (SSNMR) can be used to study the wild-type protein under activating conditions (low pH), at room temperature, and in bacteriomimetic liposomes. In this work, we sought to structurally assign the activated state present in SSNMR experiments. We used a combination of molecular dynamics (MD) simulations, chemical shift prediction algorithms, and Bayesian inference techniques to determine which of the most plausible x-ray structures resolved to date best represents the activated state captured in SSNMR. We first identified specific nuclei with simulated NMR chemical shifts that differed significantly when comparing partially open vs fully open ensembles from MD simulations. The simulated NMR chemical shifts for those specific nuclei were then compared to experimental ones, revealing that the simulation of the partially open state was in good agreement with the SSNMR data. Nuclei that discriminate effectively between partially and fully open states belong to residues spread over the sequence and provide a molecular level description of the conformational change.

Combining Experimental Data and Computational Methods for the Non-Computer Specialist

Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.

Screening anti-TMV agents targeting tobacco mosaic virus helicase protein

Tobacco mosaic virus helicase (TMV-Hel) plays important roles in viral multiplication. TMV-Hel is a potential target of anti-TMV agents. Our previous studies expressed and purified TMV-Hel as target protein for cytosinpeptidemycin. In this study, we preform molecular docking to study the binding sites of commercial antiviral agents with TMV-Hel. Then we verify the interactions between the potential anti-TMV agents and TMV-Hel in vitro using Microscale Thermophoresis experiment and study the inhibiting expression of TMV-Hel with the potential anti-TMV agents in vivo using Western-blot (WB) method. The results showed that ribavirin bound to TMV-Hel with a dissociation constant of 1.55 μM by direct interaction with eight binding sites, which was consistent with the docking studies. Ribavirin inhibited the expression of TMV-Hel in Nicotiana benthamiana. Docking studies combined Microscale Thermophoresis and WB experiment provided a new method to screen anti-TMV agents targeting TMV-Hel.

Automated Fragmentation QM/MM Calculation of NMR Chemical Shifts for Protein-Ligand Complexes

In this study, the automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) method was applied for NMR chemical shift calculations of protein-ligand complexes. In the AF-QM/MM approach, the protein binding pocket is automatically divided into capped fragments (within ~200 atoms) for density functional theory (DFT) calculations of NMR chemical shifts. Meanwhile, the solvent effect was also included using the Poission-Boltzmann (PB) model, which properly accounts for the electrostatic polarization effect from the solvent for protein-ligand complexes. The NMR chemical shifts of neocarzinostatin (NCS)-chromophore binding complex calculated by AF-QM/MM accurately reproduce the large-sized system results. The 1H chemical shift perturbations (CSP) between apo-NCS and holo-NCS predicted by AF-QM/MM are also in excellent agreement with experimental results. Furthermore, the DFT calculated chemical shifts of the chromophore and residues in the NCS binding pocket can be utilized as molecular probes to identify the correct ligand binding conformation. By combining the CSP of the atoms in the binding pocket with the Glide scoring function, the new scoring function can accurately distinguish the native ligand pose from decoy structures. Therefore, the AF-QM/MM approach provides an accurate and efficient platform for protein-ligand binding structure prediction based on NMR derived information.

Binding studies between cytosinpeptidemycin and the superfamily 1 helicase protein of tobacco mosaic virus

Tobacco mosaic virus (TMV) helicases play important roles in viral multiplication and interactions with host organisms. They can also be targeted by antiviral agents. Cytosinpeptidemycin has a good control effect against TMV. However, the mechanism of action is unclear. In this study, we expressed and purified TMV superfamily 1 helicase (TMV-Hel) and analyzed its three-dimensional structure. Furthermore, the binding interactions of TMV-Hel and cytosinpeptidemycin were studied. Microscale thermophoresis and isothermal titration calorimetry experiments showed that cytosinpeptidemycin bound to TMV-Hel with a dissociation constant of 0.24–0.44 μM. Docking studies provided further insights into the interaction of cytosinpeptidemycin with the His375 of TMV-Hel. Mutational and Microscale thermophoresis analyses showed that cytosinpeptidemycin bound to a TMV-Hel mutant (H375A) with a dissociation constant of 14.5 μM. Thus, His375 may be the important binding site for cytosinpeptidemycin. The data are important for designing and synthesizing new effective antiphytoviral agents.

Tobacco mosaic virus (TMV) helicases play important roles in viral multiplication and interactions with host organisms.