The advent of accurate methods to predict the fold of proteins initiated by AlphaFold2 is rapidly changing our understanding of proteins and helping their design. However, these methods are mainly trained on protein structures determined with X-ray diffraction, where the protein is packed in crystals at often cryogenic temperatures. They can therefore only reliably cover well-folded parts of proteins that experience few, if any, conformational changes. Experimentally, solution nuclear magnetic...
[NMR paper] Improving Geometric Validation Metrics and Ensuring Consistency with Experimental Data through TrioSA: An NMR Refinement Protocol
Improving Geometric Validation Metrics and Ensuring Consistency with Experimental Data through TrioSA: An NMR Refinement Protocol
Protein model refinement a the crucial step in improving the quality of a predicted protein model. This study presents an NMR refinement protocol called TrioSA (torsion-angle and implicit-solvation-optimized simulated annealing) that improves the accuracy of backbone/side-chain conformations and the overall structural quality of proteins. TrioSA was applied to a subset of 3752 solution NMR protein structures accompanied by experimental NMR data: distance and...
nmrlearner
Journal club
0
09-10-2023 02:39 AM
[NMR paper] Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data
Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data
Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been...
...
nmrlearner
Journal club
0
06-01-2023 05:37 PM
[NMR paper] Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data
Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data
Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been...
...
[ASAP] Dissecting the Protein Dynamics Coupled Ligand Binding with Kinetic Models and Single-Molecule FRET
Dissecting the Protein Dynamics Coupled Ligand Binding with Kinetic Models and Single-Molecule FRET
https://pubs.acs.org/na101/home/literatum/publisher/achs/journals/content/bichaw/0/bichaw.ahead-of-print/acs.biochem.1c00771/20220228/images/medium/bi1c00771_0006.gif
Biochemistry
DOI: 10.1021/acs.biochem.1c00771
More...
nmrlearner
Journal club
0
03-02-2022 01:13 PM
[NMR paper] Predicting 19 F NMR chemical shifts: A combined computational and experimental study of a trypasonomal oxidoreductase-inhibitor complex.
Predicting 19 F NMR chemical shifts: A combined computational and experimental study of a trypasonomal oxidoreductase-inhibitor complex.
http://www.bionmr.com//www.ncbi.nlm.nih.gov/corehtml/query/egifs/http:--media.wiley.com-assets-7388-69-wiley-full-text.png Related Articles Predicting 19 F NMR chemical shifts: A combined computational and experimental study of a trypasonomal oxidoreductase-inhibitor complex.
Angew Chem Int Ed Engl. 2020 Apr 02;:
Authors: Dietschreit J, Wagner A, Le TA, Klein P, Schindelin H, Opatz T, Engels B, Hellmich U,...
nmrlearner
Journal club
0
04-03-2020 09:41 PM
[CNS Yahoo group] postdoctoral positions available for experimental and computational
postdoctoral positions available for experimental and computational
Postdoctoral Positions Available for Experimental and Computational Methods Developments for XFEL-based Crystallography Faculty and staff of the SLAC
More...
nmrlearner
News from other NMR forums
0
07-19-2012 10:00 AM
A Large-scale Comparison of Computational Models on the Residue Flexibility for NMR-derived Proteinss.
A Large-scale Comparison of Computational Models on the Residue Flexibility for NMR-derived Proteinss.
A Large-scale Comparison of Computational Models on the Residue Flexibility for NMR-derived Proteinss.
Protein Pept Lett. 2011 Sep 20;
Authors: Zhang H, Shi H, Hanlon M
Abstract
As an alternative to X-ray crystallography, nuclear magnetic resonance (NMR) has also emerged as the method of choice for studying both protein structure and dynamics in solution. However, little work using computational models such as Gaussian network model...
Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics
Linear Mode
