Skip to main content

Advertisement

Correction to: Predicting protein inter-residue contacts using composite likelihood maximization and deep learning

Article metrics

The original article was published in BMC Bioinformatics 2019 20:537

Correction to: BMC Bioinformatics (2019) 20:537

https://doi.org/10.1186/s12859-019-3051-7

Following publication of the original article [1], the author explained that there are several errors in the original article;

1. The figures’ order in HTML and PDF does not match with each other.

2. The figures are incorrect order; the images do not match with the captions.

In this correction article the figures are shown correct with the correct captions.

Fig. 1
figure1

Comparison of prediction accuracy of top L/2 contacts reported by plmDCA(y-axis) and clmDCA(x-axis) with two sequence separation threshold on the PSICOV dataset. a Sequence separation >6 AA. b Sequence separation >23 AA

Fig. 2
figure2

Predicted contacts (top L/5; sequence separation >6 AA) for protein structure with PDB ID: 1ne2A by plmDCA and clmDCA. Red (green) dots indicate correct (incorrect) prediction, while grey dots indicate all true residue-residue contacts. a The comparison between clmDCA (in upper-left triangle) and plmDCA (in lower-right triangle). b The comparison between clmDCA (in upper-left triangle) and clmDCA after refining using deep residual network (in lower-right triangle)

Fig. 3
figure3

The relationship between the prediction accuracy and quality of MSA. Here the quality of MSA is measured using Neff, i.e. the number of effective homologous sequences. Dataset: PSICOV. Sequence separation: > 6 AA

Fig. 4
figure4

Native structure and predicted structures for protein structure with PDB ID: 1vmbA. a Native structure. b Structure built using contacts predicted by plmDCA (TMscore: 0.42). c Structure built using contacts predicted by clmDCA alone (TMscore: 0.55). d Structure built using contacts predicted by clmDCA together with deep learning for refinement (TMscore: 0.72)

Fig. 5
figure5

Procedure of clmDCA to predict inter-residue contacts. a For a query protein (1wlg_A as an example), we identified its homologues by running HHblits [59] against nr90 sequence database (parameter setting: j: 3, id: 90, cov: 70) and constructed multiple sequence alignment of these proteins. b The correlation among residues in MSA was disentangled using composite likelihood maximization technique, generating prediction of inter-residue contacts. c The predicted contacts were fed into a deep neural network for refinement. d The refined prediction of inter-residue contacts

Reference

  1. 1.

    Zhang H, et al. Predicting protein inter-residue contacts using composite likelihood maximization and deep learning. BMC Bioinformatics. 2019;20:537. https://doi.org/10.1186/s12859-019-3051-7.

Download references

Author information

Correspondence to Shiwei Sun or Wei-Mou Zheng or Dongbo Bu.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Zhang, Q., Ju, F. et al. Correction to: Predicting protein inter-residue contacts using composite likelihood maximization and deep learning. BMC Bioinformatics 20, 616 (2019) doi:10.1186/s12859-019-3198-2

Download citation