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1. ssPINE: Probabilistic Algorithm for Automated Chemical Shift Assignment of Solid-State NMR Data from Complex Protein Systems

   https://doi.org/10.3390/membranes12090834  

   Dwarasala, Adilakshmi; Rahimi, Mehdi; Markley, John L.; Lee, Woonghee (September 2022, Membranes)

   The heightened dipolar interactions in solids render solid-state NMR (ssNMR) spectra more difficult to interpret than solution NMR spectra. On the other hand, ssNMR does not suffer from severe molecular weight limitations like solution NMR. In recent years, ssNMR has undergone rapid technological developments that have enabled structure–function studies of increasingly larger biomolecules, including membrane proteins. Current methodology includes stable isotope labeling schemes, non-uniform sampling with spectral reconstruction, faster magic angle spinning, and innovative pulse sequences that capture different types of interactions among spins. However, computational tools for the analysis of complex ssNMR data from membrane proteins and other challenging protein systems have lagged behind those for solution NMR. Before a structure can be determined, thousands of signals from individual types of multidimensional ssNMR spectra of samples, which may have differing isotopic composition, must be recognized, correlated, categorized, and eventually assigned to atoms in the chemical structure. To address these tedious steps, we have developed an automated algorithm for ssNMR spectra called “ssPINE”. The ssPINE software accepts the sequence of the protein plus peak lists from a variety of ssNMR experiments as inputs and offers automated backbone and side-chain assignments. The alpha version of ssPINE, which we describe here, is freely available through a web submission form. 

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2. Elucidations of Structure and Molecular Dynamics of Complex Polymers by State-of-the-Art Solid-State NMR Spectroscopy

   https://doi.org/10.1021/acs.macromol.3c02535  

   Zhang, Rongchun; Chen, Wei; Miyoshi, Toshikazu (March 2024, Macromolecules)

   Solid-state nuclear magnetic resonance (ssNMR) has been playing an indispensable role in revealing the interplay of structure and molecular dynamics in polymers at different states. In this Perspective, we first provide an overview about the fundamental spin interactions in ssNMR and then highlight some recent progress on sensitivity-enhanced ssNMR spectroscopy and in situ NMR. Moreover, we highlight ssNMR applications in the field of polymer crystallization, molecular dynamics, chemical reactions, supramolecular polymers, energy materials, and so on. Finally, our personal perspective is given on the future development at the crossroad of ssNMR and polymer science. 

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3. Broadband adiabatic inversion cross‐polarization to integer‐spin nuclei with application to deuterium NMR

   https://doi.org/10.1002/mrc.5145  

   Altenhof, Adam_R; Wi, Sungsool; Schurko, Robert_W (March 2021, Magnetic Resonance in Chemistry)

   Abstract Solid‐state NMR (SSNMR) spectroscopy of integer‐spin quadrupolar nuclei is important for the molecular‐level characterization of a variety of materials and biological solids; of the integer spins,²H (S = 1) is by far the most widely studied, due to its usefulness in probing dynamical motions. SSNMR spectra of integer‐spin nuclei often feature very broad powder patterns that arise largely from the effects of the first‐order quadrupolar interaction; as such, the acquisition of high‐quality spectra continues to remain a challenge. The broadband adiabatic inversion cross‐polarization (BRAIN‐CP) pulse sequence, which is capable of cross‐polarization (CP) enhancement over large bandwidths, has found success for the acquisition of SSNMR spectra of integer‐spin nuclei, including¹⁴N (S = 1), especially when coupled with Carr–Purcell/Meiboom–Gill pulse sequences featuring frequency‐swept WURST pulses (WURST‐CPMG) forT₂‐based signal enhancement. However, to date, there has not been a systematic investigation of the spin dynamics underlying BRAIN‐CP, nor any concrete theoretical models to aid in its parameterization for applications to integer‐spin nuclei. In addition, the BRAIN‐CP/WURST‐CPMG scheme has not been demonstrated for generalized application to wideline or ultra‐wideline (UW)²H SSNMR. Herein, we provide a theoretical description of the BRAIN‐CP pulse sequence for spin‐1/2 → spin‐1 CP under static conditions, featuring a set of analytical equations describing Hartmann–Hahn matching conditions and numerical simulations that elucidate a CP mechanism involving polarization transfer, coherence exchange, and adiabatic inversion. Several experimental examples are presented for comparison with theoretical models and previously developed integer‐spin CP methods, demonstrating rapid acquisition of²H NMR spectra from efficient broadband CP. 

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4. Charting the solid‐state NMR signals of polysaccharides: A database‐driven roadmap

   https://doi.org/10.1002/mrc.5397  

   Zhao, Wancheng; Debnath, Debkumar; Gautam, Isha; Fernando, Liyanage_D; Wang, Tuo (September 2023, Magnetic Resonance in Chemistry)

   Abstract Solid‐state nuclear magnetic resonance (ssNMR) measurements of intact cell walls and cellular samples often generate spectra that are difficult to interpret due to the presence of many coexisting glycans and the structural polymorphism observed in native conditions. To overcome this analytical challenge, we present a statistical approach for analyzing carbohydrate signals using high‐resolution ssNMR data indexed in a carbohydrate database. We generate simulated spectra to demonstrate the chemical shift dispersion and compare this with experimental data to facilitate the identification of important fungal and plant polysaccharides, such as chitin and glucans in fungi and cellulose, hemicellulose, and pectic polymers in plants. We also demonstrate that chemically distinct carbohydrates from different organisms may produce almost identical signals, highlighting the need for high‐resolution spectra and validation of resonance assignments. Our study provides a means to differentiate the characteristic signals of major carbohydrates and allows us to summarize currently undetected polysaccharides in plants and fungi, which may inspire future investigations. 

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5. LinguaPhylo: A probabilistic model specification language for reproducible phylogenetic analyses

   https://doi.org/10.1371/journal.pcbi.1011226  

   Drummond, Alexei J.; Chen, Kylie; Mendes, Fábio K.; Xie, Dong (July 2023, PLOS Computational Biology)

   Kumar, Sudhir (Ed.)

   Phylogenetic models have become increasingly complex, and phylogenetic data sets have expanded in both size and richness. However, current inference tools lack a model specification language that can concisely describe a complete phylogenetic analysis while remaining independent of implementation details. We introduce a new lightweight and concise model specification language, ‘LPhy’, which is designed to be both human and machine-readable. A graphical user interface accompanies ‘LPhy’, allowing users to build models, simulate data, and create natural language narratives describing the models. These narratives can serve as the foundation for manuscript method sections. Additionally, we present a command-line interface for converting LPhy-specified models into analysis specification files (in XML format) compatible with the BEAST2 software platform. Collectively, these tools aim to enhance the clarity of descriptions and reporting of probabilistic models in phylogenetic studies, ultimately promoting reproducibility of results. 

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