Coarse-grained (CG) structures of SARS-CoV-2 related proteins

This is a data repository of Coarse-grained structures of SARS-CoV-2/COVID-19 related biomolecules. These CG structutres are generated from DSGPM neural network model.

Please note that non-bonded protein chains/segments are coarse-grained separately. Each structure contain multiple mappings with different resolutions. Furthermore, HETAM entries of the original PDB files are removed.

Available Structures

PDB ID	Segments	No. of mappings
1r42	Whole structure	20
1r4l	Whole structure	20
2ajf	SPIKE	18
2fe8	CHAIN A	24
	CHAIN B	23
	CHAIN C	24
4ow0	Whole structure	12
6lu7	Whole structure	15
6lxt	CHAIN A	15
	CHAIN B	15
	CHAIN C	15
6m03	Whole structure	7
6m17	CHAIN A	16
	CHAIN C	16
	CHAIN E	12
	CHAIN F	12
6m71	NSP7	10
	NSP8	15
6vw1	RBD	20
6w41	Whole structure	17
6y2f	Whole structure	9
6y2g	Whole structure	17
7bv1	NSP7	10
	NSP8	19
7bv2	NSP7	10
	NSP8	17
	PRIMER	6
	TEMPLETE	6

Funding

These structures are generated as a milestone of the research project proposed for the MOLSSI Seed-Fellowship (Fellowship agreement No.480388) supported by the National Science Foundation.

For Citations

If you use these models or our model to generate CG mappings, please cite our paper

@Article{D0SC02458A,
author ="Li, Zhiheng and Wellawatte, Geemi P. and Chakraborty, Maghesree and Gandhi, Heta A. and Xu, Chenliang and White, Andrew D.",
title  ="Graph neural network based coarse-grained mapping prediction",
journal  ="Chem. Sci.",
year  ="2020",
pages  ="-",
publisher  ="The Royal Society of Chemistry",
doi  ="10.1039/D0SC02458A",
url  ="http://dx.doi.org/10.1039/D0SC02458A",
abstract  ="The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice and there is a need for theory. The current state-of-the art method is mapping operators manually selected by experts. In this work{,} we demonstrate an automated approach by viewing this problem as supervised learning where we seek to reproduce the mapping operators produced by experts. We present a graph neural network based CG mapping predictor called Deep Supervised Graph Partitioning Model (DSGPM) that treats mapping operators as a graph segmentation problem. DSGPM is trained on a novel dataset{,} Human-annotated Mappings (HAM){,} consisting of 1180 molecules with expert annotated mapping operators. HAM can be used to facilitate further research in this area. Our model uses a novel metric learning objective to produce high-quality atomic features that are used in spectral clustering. The results show that the DSGPM outperforms state-of-the-art methods in the field of graph segmentation. Finally{,} we find that predicted CG mapping operators indeed result in good CG MD models when used in simulation."}