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1. PISA-SPARKY: an interactive SPARKY plugin to analyze oriented solid-state NMR spectra of helical membrane proteins

   https://doi.org/10.1093/bioinformatics/btaa019  

   Weber, Daniel K; Wang, Songlin; Markley, John L; Veglia, Gianluigi; Lee, Woonghee; Elofsson, Arne (January 2020, Bioinformatics)

   Abstract Motivation Two-dimensional [15N-1H] separated local field solid-state nuclear magnetic resonance (NMR) experiments of membrane proteins aligned in lipid bilayers provide tilt and rotation angles for α-helical segments using Polar Index Slant Angle (PISA)-wheel models. No integrated software has been made available for data analysis and visualization. Results We have developed the PISA-SPARKY plugin to seamlessly integrate PISA-wheel modeling into the NMRFAM-SPARKY platform. The plugin performs basic simulations, exhaustive fitting against experimental spectra, error analysis and dipolar and chemical shift wave plotting. The plugin also supports PyMOL integration and handling of parameters that describe variable alignment and dynamic scaling encountered with magnetically aligned media, ensuring optimal fitting and generation of restraints for structure calculation. Availability and implementation PISA-SPARKY is freely available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (http://pine.nmrfam.wisc.edu/download_packages.html), the NMRbox Project (https://nmrbox.org) and to subscribers of the SBGrid (https://sbgrid.org). The pisa.py script is available and documented on GitHub (https://github.com/weberdak/pisa.py) along with a tutorial video and sample data. Supplementary information Supplementary data are available at Bioinformatics online. 

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2. Analysis of coordinated NMR chemical shifts to map allosteric regulatory networks in proteins

   https://doi.org/10.1016/j.ymeth.2022.12.002  

   Skeens, Erin; Lisi, George P. (January 2023, Methods)

   The exquisite sensitivity of the NMR chemical shift to local environment makes it an ideal probe to assess atomic level perturbations in proteins of all sizes and structural compositions. Recent advances in solution and solid-state NMR spectroscopy of biomolecules have leveraged the chemical shift to report on short- and long-range couplings between individual amino acids to establish “networks” of residues that form the basis of allosteric pathways that transmit chemical signals through the protein matrix to induce functional responses. The simple premise that thermodynamically and functionally coupled regions of a protein (i.e. active and allosteric sites) should be reciprocally sensitive to structural or dynamic perturbations has enabled NMR spectroscopy, the premier method for molecular resolution of protein structural fluctuations, to occupy a place at the forefront of investigations into protein allostery. Here, we detail several key methods of NMR chemical shift analysis to extract mechanistic information about long-range chemical signaling in a protein, focusing on practical methodological aspects and the circumstances under which a given approach would be relevant. We also detail some of the experimental considerations that should be made when applying these methods to specific protein systems. 

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3. Calculating nuclear magnetic resonance chemical shifts in solvated systems

   https://doi.org/10.1002/mrc.4994  

   Casabianca, Leah B. (February 2020, Magnetic Resonance in Chemistry)

   Abstract The nuclear magnetic resonance (NMR) chemical shift is extremely sensitive to molecular geometry, hydrogen bonding, solvent, temperature, pH, and concentration. Calculated magnetic shielding constants, converted to chemical shifts, can be valuable aids in NMR peak assignment and can also give detailed information about molecular geometry and intermolecular effects. Calculating chemical shifts in solution is complicated by the need to include solvent effects and conformational averaging. Here, we review the current state of NMR chemical shift calculations in solution, beginning with an introduction to the theory of calculating magnetic shielding in general, then covering methods for inclusion of solvent effects and conformational averaging, and finally discussing examples of applications using calculated chemical shifts to gain detailed structural information. 

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4. Predicting chemical shifts with graph neural networks

   https://doi.org/10.1039/D1SC01895G  

   Yang, Ziyue; Chakraborty, Maghesree; White, Andrew D. (January 2021, Chemical Science)

   null (Ed.)

   Inferring molecular structure from Nuclear Magnetic Resonance (NMR) measurements requires an accurate forward model that can predict chemical shifts from 3D structure. Current forward models are limited to specific molecules like proteins and state-of-the-art models are not differentiable. Thus they cannot be used with gradient methods like biased molecular dynamics. Here we use graph neural networks (GNNs) for NMR chemical shift prediction. Our GNN can model chemical shifts accurately and capture important phenomena like hydrogen bonding induced downfield shift between multiple proteins, secondary structure effects, and predict shifts of organic molecules. Previous empirical NMR models of protein NMR have relied on careful feature engineering with domain expertise. These GNNs are trained from data alone with no feature engineering yet are as accurate and can work on arbitrary molecular structures. The models are also efficient, able to compute one million chemical shifts in about 5 seconds. This work enables a new category of NMR models that have multiple interacting types of macromolecules. 

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5. POKY: a software suite for multidimensional NMR and 3D structure calculation of biomolecules

   https://doi.org/10.1093/bioinformatics/btab180  

   Lee, Woonghee; Rahimi, Mehdi; Lee, Yeongjoon; Chiu, Abigail (March 2021, Bioinformatics)

   Gorodkin, Jan (Ed.)

   Abstract Summary The need for an efficient and cost-effective method is compelling in biomolecular NMR. To tackle this problem, we have developed the Poky suite, the revolutionized platform with boundless possibilities for advancing research and technology development in signal detection, resonance assignment, structure calculation, and relaxation studies with the help of many automation and user interface tools. This software is extensible and scalable by scripting and batching as well as providing modern graphical user interfaces and a diverse range of modules right out of the box. Availability Poky is freely available to non-commercial users at https://poky.clas.ucdenver.edu. Supplementary information Supplementary data are available at Bioinformatics online. 

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