Identification of Protein–Protein Interactions by Detecting Correlated Mutation at the Interface

Protein–protein interactions play key roles in a multitude of biological processes, such as de novo drug design, immune response, and enzymatic activity. It is of great interest to understand how proteins in a complex interact with each other. Here, we present a novel method for identifying protein–protein interactions, based on typical co-evolutionary information. Correlated mutation analysis can be used to predict interface residues. In this paper, we propose a non-redundant database to detect correlated mutation at the interface. First, we construct structure alignments for one input protein, based on all aligned proteins in the database. Evolutionary distance matrices, one for each input protein, can be calculated through geometric similarity and evolutionary information. Then, we use evolutionary distance matrices to estimate correlation coefficient between each pair of fragments from two input proteins. Finally, we extract interacting residues with high values of correlation coefficient, which can be grouped as interacting patches. Experiments illustrate that our method achieves better results than some existing co-evolution-based methods. Applied to SK/RR interaction between sensor kinase and response regulator proteins, our method has accuracy and coverage values of 53% and 45%, which improves upon accuracy and coverage values of 50% and 30% for DCA method. We evaluate interface prediction on four protein families, and our method has overall accuracy and coverage values of 34% and 30%, which improves upon overall accuracy and coverage values of 27% and 21% for PIFPAM. Our method has overall accuracy and coverage values of 59% and 63% on Benchmark v4.0, and 50% and 49% on CAPRI targets. Comparing to existing methods, our method improves overall accuracy value by at least 2%

Original values of six physicochemical properties of 20 amino acid types.

<p>Original values of six physicochemical properties of 20 amino acid types.</p

The AUROC comparison of seven feature combinations through Jackknife cross-validation on PDB1075 dataset.

<p>The AUROC comparison of seven feature combinations through Jackknife cross-validation on PDB1075 dataset.</p

The computational time of feature extraction and jackknife test evaluation on PDB1075.

<p>The computational time of feature extraction and jackknife test evaluation on PDB1075.</p

The feature score through SVM-RFE+CBR on the dataset of PDB1075.

<p>The x-axis represents the feature index.</p

Schematic diagram of a 4-level DWT.

<p>Schematic diagram of a 4-level DWT.</p

The performance of different features on PDB1075 dataset (Jackknife test evaluation).

<p>The performance of different features on PDB1075 dataset (Jackknife test evaluation).</p

The performance of our method and other existing methods on PDB186 dataset.

<p>The performance of our method and other existing methods on PDB186 dataset.</p

The accuracy of different lg values on PDB1075 (Five-fold cross validation).

<p>The accuracy of different lg values on PDB1075 (Five-fold cross validation).</p

The accuracy of different m values on PDB1075 (Five-fold cross validation).

<p>The accuracy of different m values on PDB1075 (Five-fold cross validation).</p