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License: GNU General Public License v3.0
ResNetQA: Improved protein model quality assessment by integrating sequential and pairwise features using deep learning
License: GNU General Public License v3.0
I re-generated the features by myself and made predictions for T0967 included in the Example.
After that, I compared the predicted score in this repository (data/CASP13_stage2/T0967.pkl.bz2
) with the newly predicted score, and there was a significant difference.
Could you tell me how I can reproduce the prediction score in this repository?
The following is a detailed description of the situation.
I try to generate features in two different ways.
The sequence of T0967 obtained from CASP13 website
T0967 MamB, Magnetosome protein , [Candidatus Desulfamplus magnetomortis BW-1] , 81 residues|
EDYIEAIANVLEKTPSISDVKDIIARELGQVLEFEIDLYVPPDITVTTGERIKKEVNQIIKEIVDRKSTVKVRLFAAQEEL
Using the same database as the paper (Uniclust30 Oct, 2017)
EVcouplings and Metagenome database were not used.
$ cd RaptorX-3DModeling
$ ./Server/RaptorXFolder.sh -n 0 -o output_dir T0967.fasta
sequence feature
T0967_OUT/T0967_contact/feat_T0967_uce5/T0967.inputFeatures.pkl
distance potential
T0967_OUT/DistancePred/T0967.pairPotential.DFIRE16.pkl
JOB_ID.all_IN_one
)sequence feature
JOB_ID.all_in_one/JOB_ID.inputFeatures.pkl
distance potential
JOB_ID.all_in_one/JOB_ID.pairPotential.pkl
$ cd ResNetQA/main
$ python ResNetQA.py T0967.inputFeatures.pkl T0967.pairPotential.DFIRE16.pkl ../examples/T0967_stage2/ ../examples/T0967_stage2.QA.pkl GDTTS
The input model structures were obtained from the CASP13 download page. (https://predictioncenter.org/download_area/CASP13/server_predictions/T0967.stage2.3D.srv.tar.gz)
Compare the following four scores
Original score
data/CASP13_stage2/T0967.pkl.bz2
Predicted score using features included in the repository
(examples/T0967.inputFeatures.pkl
and examples/T0967.distPotential.DFiRE16.pkl
)
Predicted score using features generated using RaptorX-3DModeling
Predicted score using features generated using webserver
The following table shows a part of the predicted scores.
model structure | original score | features included in repository | features by RaptorX-3DModeling | features by RaptorX webserver |
---|---|---|---|---|
YASARA_TS3 | 0.830 | 0.8297 | 0.4552 | 0.4219 |
Zhang-CEthreader_TS5 | 0.753 | 0.7533 | 0.4619 | 0.4020 |
QUARK_TS5 | 0.657 | 0.6573 | 0.3995 | 0.3853 |
RBO-Aleph_TS4 | 0.839 | 0.8393 | 0.4668 | 0.4255 |
Distill_TS3 | 0.828 | 0.8280 | 0.4456 | 0.4092 |
The original score and the predicted score using the features in the repository were consistent.
Therefore, it was found that there was no problem in the behavior of ResNetQA.
However, when the features were re-generated using RaptorX-3DModeling or RaptorX web server, they differed significantly from the original scores.
Could you tell me how to reproduce the original score?
Also, what kind of environment (database, etc.) did you use when you generated the features?
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