ComplexContact: a web server for inter-protein contact prediction using deep learning - PubMed (original) (raw)

ComplexContact: a web server for inter-protein contact prediction using deep learning

Hong Zeng et al. Nucleic Acids Res. 2018.

Abstract

ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

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Figures

Figure 1.

Figure 1.

Illustration of ComplexContact workflow. Given a pair of putative interacting proteins A and B, ComplexContact first uses HHblits (38) to search for sequence homologs and build an MSA for each protein. Then ComplexContact constructs two paired MSAs using genome and phylogeny information. Finally, ComplexContact applies deep learning to predict two inter-protein contact maps from the two paired MSAs and calculates their average as the final contact prediction. The top half of this figure is inspired by Fig. S2 in (40).

Figure 2.

Figure 2.

The top-50 prediction accuracy by ComplexContact and Gremlin on the protein pairs extracted from 3DComplex. Each dot represents one protein pair and is colored by its species. A dot below the diagonal line indicates that ComplexContact has a better accuracy.

Figure 3.

Figure 3.

Quality assessment of the top 50 predicted interfacial contacts for 4479 heterodimers extracted from 3Dcomplex. (A) and (B) show the precision-recall (in red) and ROC (in blue) curves generated by Gremlin and ComplexContact, respectively. AUC: Area Under the ROC curve; AUPRC: Area Under the precision-recall curve.

Figure 4.

Figure 4.

ComplexContact server job submission. (A) Users may submit a job by a web interface, which has fields for job name (1), optional user email address (2), and a pair of sequences (or multiple sequence alignments) (3). The sequences shall be in FASTA format and can also be submitted in a file. (B) Users may also submit a job by a publicly available program Curl without using the web interface. In this command, Job name and Email address are optional. A job URL will be returned on screen after submission. Curl allows users to submit a large number of jobs quickly.

Figure 5.

Figure 5.

ComplexContact server result page. The left part shows the predicted complex contact map (1), where the predicted probability is displayed in greyscale, with a darker color indicating a larger value. The middle part shows three panels. The first one is used to zoom and drag contact images (2). The second panel is for downloading the predicted contact map (3), and the third panel is for downloading the detailed prediction results (4). The right part shows two paired MSAs generated by genome-based method (5) and phylogeny-based method (6).

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