ArticleFrontiers in molecular biosciences2021
Simultaneous Assignment and Structure Determination of Proteins From Sparsely Labeled NMR Datasets.
Article in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- MELD in Action: Harnessing Data to Accelerate Molecular Dynamics.Journal of chemical information and modeling · 2025Review
- Protein Retrieval via Integrative Molecular Ensembles (PRIME) through Extended Similarity Indices.Journal of chemical theory and computation · 2024Article
- Structure Determination of Challenging Protein-Peptide Complexes Combining NMR Chemical Shift Data and Molecular Dynamics Simulations.Journal of chemical information and modeling · 2023Article
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Authors and funding
2 authors.
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Abstract
Sparsely labeled NMR samples provide opportunities to study larger biomolecular assemblies than is traditionally done by NMR. This requires new computational tools that can handle the sparsity and ambiguity in the NMR datasets. The MELD (modeling employing limited data) Bayesian approach was assessed to be the best performing in predicting structures from sparsely labeled NMR data in the 13th edition of the Critical Assessment of Structure Prediction (CASP) event-and limitations of the methodology were also noted. In this report, we evaluate the nature and difficulty in modeling unassigned sparsely labeled NMR datasets and report on an improved methodological pipeline leading to higher-accuracy predictions. We benchmark our methodology against the NMR datasets provided by CASP 13.
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