4D-CHAINS is a software for fully automated protein backbone N-H and sidechain aliphatic C & H chemical shift assignment from 2 NMR spectra: the 4D HCNH TOCSY and the 4D HCNH NOESY. Read our paper in Nature Communications for more details.
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Abstract from the paper
Automated methods for NMR structure determination of proteins are continuously becoming more robust. However, current methods addressing larger, more complex targets rely on analyzing 6–10 complementary spectra, suggesting the need for alternative approaches. Here, we describe 4D-CHAINS/autoNOE-Rosetta, a complete pipeline for NOE-driven structure determination of medium- to larger-sized proteins. The 4D-CHAINS algorithm analyzes two 4D spectra recorded using a single, fully protonated protein sample in an iterative ansatz where common NOEs between different spin systems supplement conventional through-bond connectivities to establish assignments of sidechain and backbone resonances at high levels of completeness and with a minimum error rate. The 4D-CHAINS assignments are then used to guide automated assignment of long-range NOEs and structure refinement in autoNOE-Rosetta. Our results on four targets ranging in size from 15.5 to 27.3 kDa illustrate that the structures of proteins can be determined accurately and in an unsupervised manner in a matter of days.
Link to the paper |
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4D-CHAINS software for protein NMR assignment is a property of Masaryk university and the authors are Thomas Evangelidis and Konstantinos Tripsianes. The code is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY- NC-ND 4.0). You are free to:
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Under the following terms:
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Below is reported the success rate of automatic chemical shift assignment by 4D-CHAINS in our set of 12 proteins. Manual assignment was first conducted, and then comparison with 4D-CHAINS automatic assignment using the 4D HC(CC-TOCSY(CO))NH and 4D HMQC-NOESY-HSQC (HCNH) spectra. 4D-CHAINS was able to assign in average 95.5% of the peaks to the correct atom type, namely with less than 5% error rate.
| Protein | CORRECT ASSIGNMENTS |
|---|---|
| 3NIK | 97.635 % |
| aLP | 93.935 % |
| CHAP | 96.706 % |
| EH11 | 93.534 % |
| EH12 | 96.388 % |
| FD3A | 96.524 % |
| hIL9 | 95.172 % |
| MS6282 | 97.192 % |
| Nab3 | 92.932 % |
| nEIt | 95.458 % |
| RTT | 92.475 % |
| TUDOR | 97.119 % |
- For large proteins (>180 residues) it is recommended to have >=32 GB RAM and >=64 GB SWAP memory.
- Pyhon3.x: I recommend installing Anaconda Python Distribution from https://docs.continuum.io/anaconda/install that is platform independent.
Download the code (clone the repositoty) from github:
git clone https://github.com/tevang/4D-CHAINS.git
Or to update an existing repository:
git checkout master
git pull origin master
Create a dedicated Python environment:
conda env create -f 4dchains.yml
Activate the Python environment whenever you wish to run 4D-CHAINS:
conda activate 4dchains
conda deactivate # to exit the environment when you finish
Add 4D-CHAINS to your PATH and PYTHONPATH environment variables to call 4D-CHAINS executables from every directory. E.g. place the following lines in your .bashrc:
export PATH=<path to 4D-CHAINS/>/bin:$PATH
export PYTHONPATH=<path to 4D-CHAINS/>:$PYTHONPATH
To run 4D-CHAINS use the 4Dchains.py script. You can do all operations you wish with this script as long as you provide the appropriate protocol file. E.g.
4Dchains.py -p protocol.txt
You can generate a template protocol file like this:
4Dchains.py -writeprotocol
For information about all the available directives in the protocol file, please refer to the Documentation.
You can find tutorials for TUDOR (60 residues) and nEIt (248 residues) proteins under the tutorials/ directory.
In each folder you will find a protocol file and all the required input files. There is also the
correct_output_files/ folder, which contains the output files you should get if you run successfully the tutorial.
Just note that you might find out that the shifts of some equivalent protons (e.g. VAL QG1, QG2) are swapped, which is
absolutely fine.
Below are the contents of the protocol file:
The directives you must modify are highlighted in yellow, while the optional directives in orange. The rest are just for advanced users, and it is recommended to leave them unaltered. For a detailed description of each directive, please refer to the Documentation.
fasta: the FASTA sequence file of the protein construct.
HSQC: the H-N HSQC spectrum as a 3-column (spin system's label, N, HN) Sparky list file.
4DTOCSY: the 4D HCNH TOCSY as a 5-column (spin system's label, H, C, N, HN) Sparky list file.
4DNOESY: the 4D HCNH NOESY as a 6-column (spin system's label, H, C, N, HN, peak's intensity) Sparky list file.
doNHmapping: whether to do NH-mapping (1st step)
doassign4DTOCSY: whether to do chemical shift assignment in the TOCSY (2nd step)
doassign4DNOESY: whether to do chemical shift assignment of the remaining unassigned peaks in the NOESY (3rd step)
user_4DTOCSY_assignedall: same as 4DTOCSY but with manually assigned peak labels. If this file is provided then its labels will be used to proof-read the assigned TOCSY peaks in the output file.
user_4DNOESY_assignedall: same as 4DNOESY but with manually assigned peak labels. If this file is provided then its labels will be used to proof-read the assigned NOESY peaks in the output file.
rstart: the residue ID of the first amino acid in the FASTA sequence you have provided. If your HSQC file has labels, and rstart is left blank, then the first residue ID will be guessed from the labels. If your HSQC file does not have labels, then the spin systems will be indexed by their order of occurrence, e.g. 'X1', 'X2', 'X3', and so on.
In the same folder are included *.curated Sparky list files, which contain the manually assigned labels of
the spin systems. If your input HSQC file doesn't contain any labels (e.g. HSQC=TUDOR_HSQC.list),
then 4D-CHAINS will index the spin systems by the order they occur in that file. If you set
HSQC=TUDOR_HSQC.list.curated then the labels you have set will be passed to the TOCSY and NOESY files
during chemical shift assignment. If, in addition, you provide curated TOCSY and NOESY files, to directives
user_4DTOCSY_assignedall and user_4DNOESY_assignedall, respectively, then the final .xeasy file will contain
comments like , , which indicate at which peak the automatic assignment has succeeded or has failed.
To run full chemical shift assignment of the TUDOR, do:
4Dchains.py -p TUDOR_protocol.txt
Briefly, the N-H resonances are mapped to the protein sequence. These N-H
resonances are used to assist the assignment of 4D-TOCSY peaks to aliphatic carbons. Finally, the assignments
from 4D-TOCSY are transferred to the respective 4D-NOESY peaks, and the rest of them are assigned de novo.
At the end you will find several output files and a directory named 4DCHAINS_workdir, which contains all the
intermediate files for error backtracking. The most important file is 4DNOESY_assignedall.proofread.xeasy,
which will be passed as the input chemical shift file to autNOE-Rosetta for protein structure prediction.
To see what these output files mean, do:
4Dchains.py -h
The assignment of nEIt protein's peaks by 4D-CHAINS requires >=32 GB RAM and >= 64 GB SWAP due to its large size. To launch it, do:
4Dchains.py -p nEIt_protocol.txt
For detailed description of all directives and executables refer to file doc/4D-CHAINS_Manual.pdf.