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1. Fundamental and practical aspects of machine learning for the peak picking of biomolecular NMR spectra

   https://doi.org/10.1007/s10858-022-00393-1  

   Li, Da-Wei; Hansen, Alexandar L.; Bruschweiler-Li, Lei; Yuan, Chunhua; Brüschweiler, Rafael (April 2022, Journal of Biomolecular NMR)

   Abstract Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learning approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools, standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place. 

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2. From molecular descriptions to cellular functions of intrinsically disordered protein regions

   https://doi.org/10.1063/5.0225900  

   Chen, Wei; Fraser, Olivia_A; George, Christy; Showalter, Scott_A (November 2024, Biophysics Reviews)

   Molecular descriptions of intrinsically disordered protein regions (IDRs) are fundamental to understanding their cellular functions and regulation. NMR spectroscopy has been a leading tool in characterizing IDRs at the atomic level. In this review, we highlight recent conceptual breakthroughs in the study of IDRs facilitated by NMR and discuss emerging NMR techniques that bridge molecular descriptions to cellular functions. First, we review the assemblies formed by IDRs at various scales, from one-to-one complexes to non-stoichiometric clusters and condensates, discussing how NMR characterizes their structural dynamics and molecular interactions. Next, we explore several unique interaction modes of IDRs that enable regulatory mechanisms such as selective transport and switch-like inhibition. Finally, we highlight recent progress in solid-state NMR and in-cell NMR on IDRs, discussing how these methods allow for atomic characterization of full-length IDR complexes in various phases and cellular environments. This review emphasizes recent conceptual and methodological advancements in IDR studies by NMR and offers future perspectives on bridging the gap between in vitro molecular descriptions and the cellular functions of IDRs. 

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3. NMR‐assisted protein structure prediction with MELDxMD

   https://doi.org/10.1002/prot.25788  

   Robertson, James C.; Nassar, Roy; Liu, Cong; Brini, Emiliano; Dill, Ken A.; Perez, Alberto (August 2019, Proteins: Structure, Function, and Bioinformatics)

   Abstract We describe the performance of MELD‐accelerated molecular dynamics (MELDxMD) in determining protein structures in the NMR‐data‐assisted category in CASP13. Seeded from web server predictions, MELDxMD was found best in the NMR category, over 17 targets, outperforming the next‐best groups by a factor of ~4 inz‐score. MELDxMD gives ensembles, not single structures; succeeds on a 326‐mer, near the current upper limit for NMR structures; and predicts structures that match experimental residual dipolar couplings even though the only NMR‐derived data used in the simulations was NOE‐based ambiguous atom–atom contacts and backbone dihedrals. MELD can use noisy and ambiguous experimental information to reduce the MD search space. We believe MELDxMD is a promising method for determining protein structures from NMR data. 

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4. Metal Ion Sensing by BAPTA: Investigation Using 1H NMR Spectroscopy

   https://doi.org/10.47611/jsrhs.v11i4.3515  

   Adoni, Sunayna; Mologu, Trivikram; Igumenova, Tatyana (November 2022, Journal of Student Research)

   Many  biological  macromolecules  rely  on  metal  ions  to  maintain  structural  integrity  and  control  their  regulatory  function. In biological fluids, detection and identification of metal ions requires sensitive analytical tools with clear readouts.  In  this  work,  we  sought  to  investigate  the  potential  of  solution  Nuclear  Magnetic  Resonance  (NMR)  spectroscopy to analyze metal ion solutions and mixtures. To enable 1H NMR detection, we prepared the complexes of  eight  metal  ions  with  the  chelating  agent,  1,2-bis(o-aminophenoxy)ethane-N,N,N′,N′-tetraacetic  acid  (BAPTA).  The 1H NMR spectra were collected for BAPTA samples as a function of metal ion concentrations. The analysis of NMR data revealed that all metal ions with a notable exception of Mg2+ bind BAPTA with high affinities and form complexes with 1:1 metal-to-chelator stoichiometry. Both methylene and aromatic regions of the BAPTA 1H NMR spectra  experience  significant  changes  upon  the  metal  ion  complex  formation.  We  identified  the  spectroscopic  signatures  of  trivalent  and  paramagnetic  ions  and  demonstrated  that  the  binary  Zn2+/Pb2+  metal  ion  mixture  can  be  successfully analyzed by NMR. We conclude that complexation with BAPTA followed by the 1H NMR analysis is a sensitive method to detect and identify both nutritive and xenobiotic metal ions. 

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5. Enabling materials informatics for 29Si solid-state NMR of crystalline materials

   https://doi.org/10.1038/s41524-020-0328-3  

   Sun, He; Dwaraknath, Shyam; Ling, Handong; Qu, Xiaohui; Huck, Patrick; Persson, Kristin A.; Hayes, Sophia E. (May 2020, npj Computational Materials)

   Abstract Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for obtaining precise information about the local bonding of materials, but difficult to interpret without a well-vetted dataset of reference spectra. The ability to predict NMR parameters and connect them to three-dimensional local environments is critical for understanding more complex, long-range interactions. New computational methods have revealed structural information available from²⁹Si solid-state NMR by generating computed reference spectra for solids. Such predictions are useful for the identification of new silicon-containing compounds, and serve as a starting point for determination of the local environments present in amorphous structures. In this study, we have used 42 silicon sites as a benchmarking set to compare experimentally reported²⁹Si solid-state NMR spectra with those computed by CASTEP-NMR and Vienna Ab Initio Simulation Program (VASP). Data-driven approaches enable us to identify the source of discrepancies across a range of experimental and computational results. The information from NMR (in the form of an NMR tensor) has been validated, and in some cases corrected, in an effort to catalog these for the local spectroscopy database infrastructure (LSDI), where over 10,000²⁹Si NMR tensors for crystalline materials have been computed. Knowledge of specific tensor values can serve as the basis for executing NMR experiments with precision, optimizing conditions to capture the elements accurately. The ability to predict and compare experimental observables from a wide range of structures can aid researchers in their chemical assignments and structure determination, since the computed values enables the extension beyond tables of typical chemical shift (or shielding) ranges. 

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