Localising individual atoms of tryptophan side chains in the metallo-<i>β</i>-lactamase IMP-1 by pseudocontact shifts from paramagnetic lanthanoid tags at multiple sites

The metallo-β-lactamase IMP-1 features a flexible loop near the active site that assumes different conformations in single crystal structures, which may assist in substrate binding and enzymatic activity. To probe the position of this loop, we labelled the tryptophan residues of IMP-1 with 7-13C-indole and the protein with lanthanoid tags at three different sites. The magnetic susceptibility anisotropy (Δχ) tensors were determined by measuring pseudocontact shifts (PCSs) of backbone amide protons. The Δχ tensors were subsequently used to identify the atomic coordinates of the tryptophan side chains in the protein. The PCSs were sufficient to determine the location of Trp28, which is in the active site loop targeted by our experiments, with high accuracy. Its average atomic coordinates showed barely significant changes in response to the inhibitor captopril. It was found that localisation spaces could be defined with better accuracy by including only the PCSs of a single paramagnetic lanthanoid ion for each tag and tagging site. The effect was attributed to the shallow angle with which PCS isosurfaces tend to intersect if generated by tags and tagging sites that are identical except for the paramagnetic lanthanoid ion.

Accurate Prediction of Voltage of Battery Electrode Materials Using Attention Based Graph Neural Networks

Performing first principle calculations to discover electrodes’ properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials’ spatial information and only use pre-defined features: based only on chemical compositions. We propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed method, which combines both atomic composition and atomic coordinates in 3D-space, improves the accuracy in voltage prediction by 17% when compared to composition based ML models. The first model directly learns the chemical reaction of electrodes and metal-ions to predict their average voltage, whereas the second model combines electrodes’ ML predicted formation energy (Eform) to compute their average voltage. Our models demonstrates improved accuracy in transferability from our subset of learned metal-ions to other metal-ions.

Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

Molecular Dynamics Study of Escherichia coli Thymidine Phosphorylase in a Complex with 3'-Azidothymidine Inhibitor and Phosphate

Abstract— Using a molecular dynamics method, the state of the dimeric thymidine phosphorylase molecule from Escherichia coli in a complex with noncompetitive enzyme inhibitor 3'-azidothymidine and phosphate ion was studied on a trajectory of 50 ns. Previously obtained atomic coordinates of a complex of thymidine phosphorylase with azidothymidine and sulfate at a resolution of 1.52 Å were used as a starting model. It was demonstrated that both subunits of a dimeric enzyme molecule function asynchronously in a given time interval; moreover, each subunit maintains an open conformation. It was found that the nature of ligand at the nucleoside center affects the binding strength of phosphate in the phosphate center. In a complex with an inhibitor, both ligands over the entire time interval remain bound to the enzyme, while the release of phosphate from the active center is observed when simulating the behavior of thymidine phosphorylase in the presence of phosphate and thymidine substrate. The stabilizing effect of azidothymidine on phosphate binding is consistent with the behavior of azidothymidine as a noncompetitive inhibitor of thymidine phosphorylase.

Localising individual atoms of tryptophan side chains in the metallo-β-lactamase IMP-1 by pseudocontact shifts from paramagnetic lanthanoid tags at multiple sites

The metallo-β-lactamase IMP-1 features a flexible loop near the active site that assumes different conformations in single crystal structures, which may assist in substrate binding and enzymatic activity. To probe the position of this loop, we labelled the tryptophan residues of IMP-1 with 7-13C-indole and the protein with lanthanoid tags at three different sites. The magnetic susceptibility anisotropy (Δχ) tensors were determined by measuring pseudocontact shifts (PCS) of backbone amide protons. The Δχ tensors were subsequently used to identify the atomic coordinates of the tryptophan side chains in the protein. The PCSs were sufficient to determine the location of Trp28, which is located in the active site loop targeted by our experiments, with high accuracy. Its average atomic coordinates showed barely significant changes in response to the inhibitor captopril. It was found that localisation spaces could be defined with better accuracy by including only the PCSs of a single paramagnetic lanthanoid ion for each tag and tagging site. The effect was attributed to the shallow angle with which PCS isosurfaces tend to intersect if generated by tags and tagging sites that are identical except for the paramagnetic lanthanoid ion.

Structural Modeling of the TMPRSS Subfamily of Host Cell Proteases Reveals Potential Binding Sites

The transmembrane protease serine subfamily (TMPRSS) has at least eight members with known protein sequence: TMPRSS2, TMPRRS3, TMPRSS4, TMPRSS5, TMPRSS6, TMPRSS7, TMPRSS9, TMPRSS11, TMPRSS12 and TMPRSS13. A majority of these TMPRSS proteins have key roles in human hemostasis as well as promoting certain pathologies, including several types of cancer. In addition, TMPRSS proteins have been shown to facilitate the entrance of respiratory viruses into human cells, most notably TMPRSS2 and TMPRSS4 activate the spike protein of the SARS-CoV-2 virus. Despite the wide range of functions that these proteins have in the human body, none of them have been successfully crystallized. The lack of structural data has significantly hindered any efforts to identify potential drug candidates with high selectivity to these proteins. In this study, we present homology models for all members of the TMPRSS family including any known isoform (the homology model of TMPRSS2 is not included in this study as it has been previously published). The atomic coordinates for all homology models have been refined and equilibrated through molecular dynamic simulations. The structural data revealed potential binding sites for all TMPRSS as well as key amino acids that can be targeted for drug selectivity.

Patterns in Protein Flexibility: A Comparison of NMR “Ensembles”, MD Trajectories, and Crystallographic B-Factors

Proteins are molecular machines requiring flexibility to function. Crystallographic B-factors and Molecular Dynamics (MD) simulations both provide insights into protein flexibility on an atomic scale. Nuclear Magnetic Resonance (NMR) lacks a universally accepted analog of the B-factor. However, a lack of convergence in atomic coordinates in an NMR-based structure calculation also suggests atomic mobility. This paper describes a pattern in the coordinate uncertainties of backbone heavy atoms in NMR-derived structural “ensembles” first noted in the development of FindCore2 (previously called Expanded FindCore: DA Snyder, J Grullon, YJ Huang, R Tejero, GT Montelione, Proteins: Structure, Function, and Bioinformatics 82 (S2), 219–230) and demonstrates that this pattern exists in coordinate variances across MD trajectories but not in crystallographic B-factors. This either suggests that MD trajectories and NMR “ensembles” capture motional behavior of peptide bond units not captured by B-factors or indicates a deficiency common to force fields used in both NMR and MD calculations.

Crystal structure of pimecrolimus Form B, C43H68ClNO11

The crystal structure of pimecrolimus Form B has been solved and refined using synchrotron X-ray powder diffraction data and optimized using density functional techniques. Pimecrolimus crystallizes in the space group P21 (#4) with a = 15.28864(7), b = 13.31111(4), c = 10.95529(5) Å, β = 96.1542(3)°, V = 2216.649(9) Å3, and Z = 2. Although there are an intramolecular six-ring hydrogen bond and some larger chain and ring patterns, the crystal structure is dominated by van der Waals interactions. There is a significant difference between the conformation of the Rietveld-refined and the DFT-optimized structures in one portion of the macrocyclic ring. Although weak, intermolecular interactions are apparently important in determining the solid-state conformation. The powder pattern is included in the Powder Diffraction File™ (PDF®) as entry 00-066-1619. This study provides the atomic coordinates to be added to the PDF entry.