You can now predict DSSP structural classes from chemical shifts using at the server here: https://st-protein.chem.au.dk/chespi
You can download CheSPI python source code for random coil chemical shift prediction at github: https://github.com/protein-nmr/CheSPI
CheSPI: Chemical shift Secondary structure Population Inference (CheSPI)
Nielsen JT, Mulder FAA.
J Biomol NMR. 2021 Jul;75(6-7):273-291. doi: 10.1007/s10858-021-00374-w.
You can predict your 'random coil chemical shifts' with POTENCI at the server here: https://st-protein02.chem.au.dk/potenci/
You can download POTENCI python source code for random coil chemical shift prediction at github: https://github.com/protein-nmr/POTENCI
POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins. (POTENCI)
Nielsen JT, Mulder FAA.
J Biomol NMR. 2018 Mar;70(3):141-165. doi: 10.1007/s10858-018-0166-5
You can download the CheZOD database used for testing disorder predictors at these links:
http://www.protein-nmr.org/CheZOD.tar.gz and http://www.protein-nmr.org/chezodseqs.txt for the "117" protein dataset (used in Nielsen JT & Mulder FAA, Front Mol Biosci. 2016)
http://www.protein-nmr.org/CheZOD1325.tar.gz and http://www.protein-nmr.org/allseqs1325.fasta for the "1325" protein dataset used for ODiNPred (Dass R, Mulder FAA, Nielsen JT., Sci Rep. 2020)
There is Diversity in Disorder-"In all Chaos there is a Cosmos, in all Disorder a Secret Order". (CheZOD database)
Nielsen JT, Mulder FAA.
Front Mol Biosci. 2016 Feb 11;3:4. doi: 10.3389/fmolb.2016.00004
Predict your protein's CheZOD disorder score from assigned NMR chemical shifts at the server: https://st-protein.chem.au.dk/chezod
Quantitative Protein Disorder Assessment using NMR Chemical Shifts (CheZOD predictor)
Nielsen JT, Mulder FAA.
Methods Mol Biol. 2020;2141:303-317. doi: 10.1007/978-1-0716-0524-0_15.
You can predict the order/disorder profile for a protein from sequence here: https://st-protein.chem.au.dk/odinpred
Quality and bias of protein disorder predictors.
Nielsen JT, Mulder FAA.
Sci Rep. 2019 Mar 26;9(1):5137. doi: 10.1038/s41598-019-41644-w
ODiNPred: comprehensive prediction of protein order and disorder.
Dass R, Mulder FAA, Nielsen JT.
Sci Rep. 2020 Sep 8;10(1):14780. doi: 10.1038/s41598-020-71716-1.
You can predict peptide/IDP titration curves (and compute pKa constants, etc.) at the server here: https://st-protein02.chem.au.dk/pepkalc/
pepKalc: scalable and comprehensive calculation of electrostatic interactions in random coil polypeptides. (pepKalc)
Tamiola K, Scheek RM, van der Meulen P, Mulder FAA.
Bioinformatics. 2018 Jun 15;34(12):2053-2060. doi: 10.1093/bioinformatics/bty033.
You can predict your 'random coil chemical shifts' with ncIDP (2010) at the server here: https://st-protein02.chem.au.dk/ncIDP/
Sequence-specific random coil chemical shifts of intrinsically disordered proteins. (ncIDP)
Tamiola K, Acar B, Mulder FAA.
J Am Chem Soc. 2010 Dec 29;132(51):18000-3. doi: 10.1021/ja105656t
The ncSPC program for assessing structural propensities based on ncIDP is found here (for the moment): https://st-protein02.chem.au.dk/ncSPC/
Using NMR chemical shifts to calculate the propensity for structural order and disorder in proteins. (ncSPC)
Tamiola K, Mulder FAA.
Biochem Soc Trans. 2012 Oct;40(5):1014-20
Don't know what that peak is in your spectrum? Query it here against a database of common impurities based chemical shift and coupling pattern: common impurities
(remove extension .txt when placing in PP directory)
3D HNCOCO - CO/N/H version (Yoshimura et al., JBNMR 2015): hncocogp3d1
3D HNCOCO - N/N/H version (Yoshimura et al., JBNMR 2015): hncocogp3d2
Arg head group CN experiment (Yoshimura et al., Angewandte Chemie 2017): arginine_head_group.yy
Paris-DECOR - fast backbone HX by HA(CACO)N (Dass et al., ChemPhysChem 2019): hcacongp3d-hdx
Paris-DECOR - fast backbone HX by CON (Dass et al., Methods Mol Biol 2020): c_con_sq_bshd_hd_v3
