Graph neural network for predicting NMR chemical shifts

This library is the code and a pre-trained model to predict NMR chemical shifts from protein structures and organic molecules. It relies on the nmrdata package which includes embeddings and NMR parameters.

Install

To install from this github repo directly:

pip install nmrgnn@git+git://github.com/ur-whitelab/nmrgnn.git

Command Line Usage

Available commands are

nmrgnn eval-struct to predict chemical shifts of structure via MDAnalysis library as coordinate reader
nmrgnn train to train a model
nmrgnn hper to tune hyperparameters
nmrgnn eval-tfrecords to evaluate model on records in format from nmrdata package

Predict NMR Chemical Shfits

Note: This model is trained on models with no solvent, so remove that before use. For small molecules, the model was trained mostly on water solutions. You should only expect agreement in relative chemical shifts between atoms depending on your solvent and reference.

To predict NMR chemical shifts via the MDAnalysis library as a reader:

nmrgnn eval-struct [struct-file] [output-csv]

where struct-file could be a pdb file or equivalent. Example:

nmrgnn eval-struct 108M.pdb 108M-predicted.csv

Library Usage

Available functions are

load_model to load the included pre-trained model or specify a path to a trained model
universe2graph to convert an MDAnalysis universe into a tuple of atoms, neighbor list, edges, inverse_degree.
check_peaks to estimate validity of predicted peaks

The example below predicts peaks and estimates (True/False) if the peaks are valid. Examples of why peaks are not valid are that the elements are not inlcuded in training data (e.g., oxygen shifts) or unusual chemistries or you forgot to remove solvent.

import MDAnalysis as md
import nmrgnn

model = nmrgnn.load_model()
u = md.Universe('108M.pdb')
g = nmrgnn.universe2graph(u)
peaks = model(g)
# check_peaks only uses first element of tuple (atom identities)
confident = nmrgnn.check_peaks(g[0], peaks)

You should not trust peaks coming from model without checking

Citation

Please cite Predicting Chemical Shifts with Graph Neural Networks

@article{yang2020predicting,
  title     = {Predicting Chemical Shifts with Graph Neural Networks},
  author    = {Yang, Ziyue and Chakraborty, Maghesree and White, Andrew D},
  journal   = {bioRxiv},
  year      = {2020},
  publisher = {Cold Spring Harbor Laboratory}
}

Model Performance

Here is the included model performance on proteins (P prefix) and organic molecules (Mol prefix). r is correlation coefficient and rmsd is root mean square deviation. These results vary from paper values because they are evaluated on whole proteins instead of 256 atom fragments.

	N	baseline
Mol-H-r	307	0.9591749434360993
Mol-H-rmsd	307	0.39710393617916234
P-C-r	6701	0.864163
P-H-r	7747	0.72265
P-N-r	7640	0.890842
P-CA-r	8305	0.97374
P-CB-r	6827	0.990706
P-CD-r	739	0.996123
P-CD1-r	961	0.999515
P-CD2-r	609	0.999223
P-CE-r	340	0.991736
P-CE1-r	261	0.958121
P-CE2-r	173	0.943739
P-CE3-r	37	-0.215088
P-CG-r	1674	0.998763
P-CG1-r	589	0.93124
P-CG2-r	839	0.829016
P-CH2-r	43	0.158363
P-CZ-r	125	0.984575
P-CZ2-r	45	0.311805
P-CZ3-r	37	0.164961
P-HA-r	5565	0.839377
P-HA2-r	462	0.495514
P-HA3-r	449	0.262298
P-HB-r	960	0.958713
P-HB2-r	3427	0.901358
P-HB3-r	3255	0.901234
P-HD1-r	383	0.44733
P-HD11-r	753	0.615756
P-HD12-r	753	0.585852
P-HD13-r	753	0.609181
P-HD2-r	1043	0.988991
P-HD21-r	428	0.617599
P-HD22-r	428	0.651927
P-HD23-r	428	0.605888
P-HD3-r	637	0.95089
P-HE-r	93	0.396258
P-HE1-r	413	0.879142
P-HE2-r	561	0.98963
P-HE3-r	293	0.985685
P-HG-r	389	0.810401
P-HG1-r	11	0.0653286
P-HG11-r	350	0.572609
P-HG12-r	350	0.498696
P-HG13-r	350	0.558426
P-HG2-r	1317	0.867619
P-HG21-r	936	0.689592
P-HG22-r	936	0.674086
P-HG23-r	936	0.662057
P-HG3-r	1200	0.856177
P-HH-r	1	nan
P-HH2-r	51	0.217372
P-HZ-r	134	0.407285
P-HZ2-r	54	0.419415
P-HZ3-r	45	0.318577
P-ND1-r	9	0.184443
P-ND2-r	173	0.320299
P-NE-r	88	0.0135033
P-NE1-r	64	0.0998792
P-NE2-r	149	0.972614
P-NH1-r	3	-0.914066
P-NH2-r	3	-0.276087
P-NZ-r	1	nan
P-C-rmsd	6701	1.22819
P-H-rmsd	7747	0.279766
P-N-rmsd	7640	6.65505
P-CA-rmsd	8305	1.3298
P-CB-rmsd	6827	3.10571
P-CD-rmsd	739	10.3192
P-CD1-rmsd	961	2.74597
P-CD2-rmsd	609	4.35399
P-CE-rmsd	340	1.14623
P-CE1-rmsd	261	4.69154
P-CE2-rmsd	173	4.82229
P-CE3-rmsd	37	3.0327
P-CG-rmsd	1674	1.63828
P-CG1-rmsd	589	1.558
P-CG2-rmsd	839	1.87753
P-CH2-rmsd	43	1.95861
P-CZ-rmsd	125	4.32496
P-CZ2-rmsd	45	1.22984
P-CZ3-rmsd	37	1.99567
P-HA-rmsd	5565	0.0903255
P-HA2-rmsd	462	0.119584
P-HA3-rmsd	449	0.234069
P-HB-rmsd	960	0.103812
P-HB2-rmsd	3427	0.10552
P-HB3-rmsd	3255	0.117287
P-HD1-rmsd	383	0.114696
P-HD11-rmsd	753	0.0699893
P-HD12-rmsd	753	0.0744762
P-HD13-rmsd	753	0.0711484
P-HD2-rmsd	1043	0.105893
P-HD21-rmsd	428	0.0737762
P-HD22-rmsd	428	0.0689306
P-HD23-rmsd	428	0.0764191
P-HD3-rmsd	637	0.0869007
P-HE-rmsd	93	0.422132
P-HE1-rmsd	413	0.376196
P-HE2-rmsd	561	0.0861489
P-HE3-rmsd	293	0.0855213
P-HG-rmsd	389	0.118694
P-HG1-rmsd	11	10.3704
P-HG11-rmsd	350	0.0504736
P-HG12-rmsd	350	0.0552385
P-HG13-rmsd	350	0.0516929
P-HG2-rmsd	1317	0.0654069
P-HG21-rmsd	936	0.0634577
P-HG22-rmsd	936	0.0650697
P-HG23-rmsd	936	0.0679991
P-HG3-rmsd	1200	0.0775636
P-HH-rmsd	1	4.07231
P-HH2-rmsd	51	0.0862706
P-HZ-rmsd	134	0.147387
P-HZ2-rmsd	54	0.13507
P-HZ3-rmsd	45	0.083249
P-ND1-rmsd	9	1576.13
P-ND2-rmsd	173	6.56618
P-NE-rmsd	88	231.589
P-NE1-rmsd	64	4.51713
P-NE2-rmsd	149	13.9975
P-NH1-rmsd	3	5.76985
P-NH2-rmsd	3	0.91028
P-NZ-rmsd	1	165.069