Prediction and Modeling of Protein-Protein Interactions Using "Spotted" Peptides with a Template-Based Approach

Abstract

Protein-peptide interactions (PpIs) are a subset of the overall protein-protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., "PepThreader", to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, "spotting" the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein-peptide complexes that were collected from existing databases of experimentally determined protein-peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization.

Keywords: PepThreader; protein–peptide interactions; protein–protein interactions; template-based modeling.

Figure 1

Workflow of the PepThreader algorithm. PepThreader receives as input a protein sequence or a library of peptides in FASTA format and a template protein–peptide complex in PDB format. The sequence is divided into multiple peptides and the latter are ranked according to an alignment matrix score. The best ranking peptides are then modeled on the template protein and 3D structure models are generated for these peptides. A second structure-based ranking is then calculated by energy scoring functions. The output consists of a series of peptides in complex with their receptor, ranked by sequence-based and energy-based scores.

Figure 2

Distribution of the query sequence length and query peptides length: (a) n = 292, mean = 742, std = 738; (b) n = 292, mean = 9.0, std = 2.3.

Figure 3

Distribution of sequence identity and RMSD in complex pairs: (a) Sequence identity in protein–peptide pairs. As shown in the histogram, the identity goes from 0.0% to a maximum value of 77.8% (n = 146, mean = 18.2%, std = 19.6%); (b) distribution of the Cα RMSD between the peptides in the protein–peptide complex pairs, expressed in Angstroms (Å). Values are between 0.4 Å and 6.8 Å (n = 146, mean = 1.8 Å, std = 1.3 Å).

Figure 4

Results of PepThreader usage for spotting a TSG101 peptide binder. (a) 3D model of the complex between the top-ranked peptide of HRS and TSG101 (peptide “PTPSAPVPL” highlighted in magenta), superimposed to the 3D structure used as a template (PDB code: 3OBX, peptide “PEATAPPEE” highlighted in light blue); (b) the same 3D model of (a) superimposed to the corresponding crystal structure (PDB code: 3OBQ).

Figure 5

Results of PepThreader usage for spotting the M1 peptide binder of Bora to Aurora-A. The structural superposition of Bora (cyan) motif M1 with TPX2 motif (white) responsible for binding to Aurora-A (gray surface) is shown. The essential residues in TPX2 responsible for binding Aurora-A and the corresponding residue numbers in Bora are highlighted.