Using molecular dynamics trajectories to predict nuclear spin relaxation behaviour in large spin systems

AssignSLP_GUI, a software tool exploiting AI for NMR resonance assignment of sparsely labeled proteins

Not all proteins are amenable to uniform isotopic labeling with 13C and 15N, something needed for the widely used, and largely deductive, triple resonance assignment process. Among them are proteins expressed in mammalian cell culture where native glycosylation can be maintained, and proper formation of disulfide bonds facilitated. Uniform labeling in mammalian cells is prohibitively expensive, but sparse labeling with one or a few isotopically enriched amino acid types is an option for these proteins. However, assignment then relies on accessing the best match between a variety of measured NMR parameters and predictions based on 3D structure, often from X-ray crystallography. Finding this match is a challenging process that has benefitted from many computational tools, including trained neural nets for chemical shift prediction, genetic algorithms for searches through a myriad of assignment possibilities, and now AI-based prediction of high-quality structures for protein targets. AssignSLP_GUI, a new version of a software package for assignment of resonances from sparsely-labeled proteins, uses many of these tools. These tools and new additions to the package are highlighted in an application to a sparsely-labeled domain from a glycoprotein, CEACAM1.

Advances in the exact nuclear Overhauser effect 2018-2022

The introduction of the exact nuclear Overhauser enhancement (eNOE) methodology to solution-state nuclear magnetic resonance (NMR) spectroscopy results in tighter distance restraints from NOEs than in convention analysis. These improved restraints allow for higher resolution in structure calculation and even the disentanglement of different conformations of macromolecules. While initial work primarily focused on technical development of the eNOE, structural studies aimed at the elucidation of spatial sampling in proteins and nucleic acids were published in parallel prior to 2018. The period of 2018-2022 saw a continued series of technical innovation, but also major applications addressing biological questions. Here, we review both aspects, covering topics from the implementation of non-uniform sampling of NOESY buildups, novel pulse sequences, adaption of the eNOE to solid-state NMR, advances in eNOE data analysis, and innovations in structural ensemble calculation, to applications to protein, RNA, and DNA structure elucidation.

Stochastic Modelling of 13C NMR Spin Relaxation Experiments in Oligosaccharides

A framework for the stochastic description of relaxation processes in flexible macromolecules including dissipative effects has been recently introduced, starting from an atomistic view, describing the joint relaxation of internal coordinates and global degrees of freedom, and depending on parameters recoverable from classic force fields (energetics) and medium modelling at the continuum level (friction tensors). The new approach provides a rational context for the interpretation of magnetic resonance relaxation experiments. In its simplest formulation, the semi-flexible Brownian (SFB) model has been until now shown to reproduce correctly correlation functions and spectral densities related to orientational properties obtained by direct molecular dynamics simulations of peptides. Here, for the first time, we applied directly the SFB approach to the practical evaluation of high-quality ¹³C nuclear magnetic resonance relaxation parameters, T1 and T2, and the heteronuclear NOE of several oligosaccharides, which were previously interpreted on the basis of refined ad hoc modelling. The calculated NMR relaxation parameters were in agreement with the experimental data, showing that this general approach can be applied to diverse classes of molecular systems, with the minimal usage of adjustable parameters.