DomPep--a general method for predicting modular domain-mediated protein-protein interactions

Abstract

Protein-protein interactions (PPIs) are frequently mediated by the binding of a modular domain in one protein to a short, linear peptide motif in its partner. The advent of proteomic methods such as peptide and protein arrays has led to the accumulation of a wealth of interaction data for modular interaction domains. Although several computational programs have been developed to predict modular domain-mediated PPI events, they are often restricted to a given domain type. We describe DomPep, a method that can potentially be used to predict PPIs mediated by any modular domains. DomPep combines proteomic data with sequence information to achieve high accuracy and high coverage in PPI prediction. Proteomic binding data were employed to determine a simple yet novel parameter Ligand-Binding Similarity which, in turn, is used to calibrate Domain Sequence Identity and Position-Weighted-Matrix distance, two parameters that are used in constructing prediction models. Moreover, DomPep can be used to predict PPIs for both domains with experimental binding data and those without. Using the PDZ and SH2 domain families as test cases, we show that DomPep can predict PPIs with accuracies superior to existing methods. To evaluate DomPep as a discovery tool, we deployed DomPep to identify interactions mediated by three human PDZ domains. Subsequent in-solution binding assays validated the high accuracy of DomPep in predicting authentic PPIs at the proteome scale. Because DomPep makes use of only interaction data and the primary sequence of a domain, it can be readily expanded to include other types of modular domains.

Figure 1. A schematic representation of the DomPep methodology.

Figure 2. Correlation of LBS, DSI and PWM distance with each other and with the specificity of the PDZ domain.

(A) The relationship between ligand binding similarity (LBS) and domain sequence identity (DSI) for PDZ domain pairs based on domain-peptide array binding data. DSI values were calculated according to the ClustalW2 program . (B) The relationship between PWM distance and LBS for PDZ domain pairs in different DSI groups. LBS appears more closely related to PWM distance than to DSI. (C) The relationship of PWM distance to DSI. The percentage of PDZ pairs within a given DSI group was plotted against the corresponding PWM distance. (D) A strategy for the identification of specificity-similar domains for a PDZ domain. This strategy takes advantage of results generated from (A) and (B).

Figure 3. Correlation of LBS, DSI and PWM distance with each other and with the specificity of the SH2 domain.

(A) Correlation of DSI and LBS in SH2 domain pairs based on domain-peptide array data. (B) Correlation of PWM distance and LBS for SH2 domain pairs within different DSI groups. It is apparent that LBS is related to both PWM distance and DSI. (C) The distribution of SH2 domain pairs along PWM distance for different DSI groups. (D) A strategy for the identification of similar domains for an SH2 domain.

Figure 4. Application of DomPep to the prediction of PDZ-binding ligands. ROC curves of DomPep prediction for the three Scrib PDZ-1/2/3 domains using 56 peptides selected from the human protein database as the test set.

The respective affinities of the three domains for the 56 peptides were measured by fluorescence polarization (Table 1).

Figure 5. Framework of the DomPep program.

DomPep contains three components- a Graphical user interface (GUI), a local BLAST server and a set of embedded domain-peptide binding predictors. The GUI allows a user to input protein sequences and choose predictors for specific domains. If the query domain does not have an embedded predictor, a substitute query domain with a known predictor can be identified from domains that exhibit the highest DSI from a BLAST search. The output of a DomPep prediction consists of a list of peptides with prediction scores arranged in a descending order from top to bottom. The corresponding proteins are listed in a separate column.