Nuclear magnetic resonance (NMR) spectroscopy yields rich residue-level information on biomolecular dynamics and chemical environments, two frontiers for quantitative predictive methods in biochemistry. Decades of data are publicly archived in the Biological Magnetic Resonance Data Bank (BMRB)1, yet in practice, this information remains difficult to access and interpret at scale and within computational workflows. Here we present makeshift, an open-source Python package for accessing, curating, and analyzing NMR datasets. Users can readily retrieve and parse BMRB entries and perform essential analyses such as chemical shift re-referencing, secondary structure propensity prediction, and interpretation of relaxation datasets for dynamics. We re-implemented several widely-used NMR data calculations which were not open-source or available in Python and validated our implementations against the original implementations. By integrating data access, processing, and analysis into a single Python interface, makeshift lowers the barrier for reproducible, scalable analysis and machine learning applications using biomolecular NMR data.
A quantitative scoring framework for comparing experimental and back-calculated observables is introduced and combined with regularized ensemble selection and Monte Carlo simulated annealing to provide direct inference of protein ensembles within a flexible ensemble-selection architecture incorporating multiple classes...
Key dynamics directly linked to biological function, including enzyme catalysis and ligand binding, are particularly well predicted by Dyna-1, which parallels the findings that residues experiencing µs-ms exchange are more conserved.
Hannah K. Wayment-Steele, Gina El Nesr, Ramith Hettiarachchi et al.· Nature· 1 citation
SPINDLE, an ensemble of deep neural networks trained on a large synthetic set of NMR relaxation data predicts both fast and slow timescale dynamics parameters from a single set of three relaxation datasets using the ensemble for error estimation, and demonstrates a strong correlation to ground truth dynamics parameters...
Olivia E. Krise, Michael P. Latham· bioRxiv· 0 citations
The resulting MLIP/ShiftML3 workflow reduces computational costs by at least three orders of magnitude, making high-accuracy NMR crystallography accessible without HPC infrastructure, and opening the door to fast molecular dynamics simulations at the hybrid DFT level.
Shubha S. Gunaga, Rob Schurko, Sean T. Holmes et al.· Chemical Science· 3 citations
PALI (principal component analysis for ligand interactions), an objective and PCA-based framework designed to standardize multivariate NMR analysis, is introduced, bridging the gap between advanced multivariate statistics and routine structural biology workflows.
Min June Yang, Joonhyeok Choi, Hyeonjun Lee et al.· Analytical Chemistry· 0 citations
Ruler is a local, browser-based tool that uses inter- residue distance measurements to quickly quantify atomic displacements and map changes in internal geometry across ensembles of related protein structures, enabling rapid identification of regions undergoing coordinated motion, local rearrangement, or large-scale co...
Timothy H. Baker, M. Ohi, W. Salmen· bioRxiv· 1 citation
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