PAIRpred: partner-specific prediction of interacting residues from sequence and structure

Abstract

We present a novel partner-specific protein-protein interaction site prediction method called PAIRpred. Unlike most existing machine learning binding site prediction methods, PAIRpred uses information from both proteins in a protein complex to predict pairs of interacting residues from the two proteins. PAIRpred captures sequence and structure information about residue pairs through pairwise kernels that are used for training a support vector machine classifier. As a result, PAIRpred presents a more detailed model of protein binding, and offers state of the art accuracy in predicting binding sites at the protein level as well as inter-protein residue contacts at the complex level. We demonstrate PAIRpred's performance on Docking Benchmark 4.0 and recent CAPRI targets. We present a detailed performance analysis outlining the contribution of different sequence and structure features, together with a comparison to a variety of existing interface prediction techniques. We have also studied the impact of binding-associated conformational change on prediction accuracy and found PAIRpred to be more robust to such structural changes than existing schemes. As an illustration of the potential applications of PAIRpred, we provide a case study in which PAIRpred is used to analyze the nature and specificity of the interface in the interaction of human ISG15 protein with NS1 protein from influenza A virus. Python code for PAIRpred is available at http://combi.cs.colostate.edu/supplements/pairpred/.

Keywords: protein binding site prediction; protein interface prediction.

Figure 1

Residue-level feature extraction in PAIRpred. The feature representation for residue a is denoted by x_a. Different components of the feature representation are denoted by the superscript (e.g., $x_a^{rASA}$ indicates the relative accessible surface area for residue a). Each box also indicates the program used to extract a given set of features.

Figure 2

Overview of PAIRpred. (i) Extract residue-level features from sequence and unbound structures (see Figure 1 for details). (ii) Construct pairwise kernel from the residue-level kernel K_r(a_i, a_j). (iii) Use the pairwise kernel to train the SVM and classify each residue pair in the query proteins.

Figure 3

Selecting the optimal sequence/structure representation by comparing ROC curves for different kernel designs. Shown are the averaged ROC curves computed using 5-fold cross-validation over complexes in DBD 3.0. The inset shows the true positive rate (TPR) vs. false positive rate (FPR) for up to first 10 % false positives. The legend shows the AUC scores for the different kernels used. (a) Results for different residue kernels K_r using the pairwise kernel K_pw = K_mlpk + K_tppk + K_sum. The curves illustrate the increase in performance as additional structural information is added to the sequence-based kernel. Recall that K_profile is the PSI-BLAST profile kernel; K_prASA uses predicted rASA; K_exp is the residue exposure kernel; K_HSAAC is the half-sphere exposure kernel; K_CX uses protrusion-index features. (b) Results for different pairwise kernels K_pw with residue kernel K_r = K_profile + K_exp + K_HSAAC + K_CX.

Figure 4

Comparison between PAIRpred, PPiPP [9] and vanilla SVM at the complex and protein level predictions on DBD 3.0. PAIRpred-seq refers to PAIRpred based only on sequence features.

Figure 5

The maximum AUC from top N ZDOCK predictions in comparison to PAIRpred and PPiPP [9].

Figure 6

Effect of conformational change on PAIRpred performance. (a) AUC vs. RMSD for each complex in DBD 3.0 and the 53 new complexes in DBD 4.0 using leave-one-complex-out cross-validation. The legend shows mean AUC and standard deviation (within paranthesis) of complexes in each category for each data set. (b) Relationship between AUC and the change in rASA (ΔrASA). Residue pairs were binned into groups based on their ΔrASA and the AUC score was computed for all the residues within each bin using our 5-fold cross-validation scheme on DBD 3.0 complexes.

Figure 7

(a) PAIRpred predictions for human ISG15 and influenza B NS1 mapped onto the 3D structure of the complex (PDB ID: 3SDL). The red dotted lines indicate the true positives in the top predictions with the width of the line proportional to the prediction score. The circled area is expanded in (b). (b) Results of in silico mutagenesis. The blue residues were changed to alanines. Notice the change in the prediction score (indicated by the width of the orange dotted lines) for the mutated residues between the wild-type (left) and the mutant (right).