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.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

“…The availability of 3D protein structures, principally protein-protein complexes, is crucial to developing peptides targeting PPIs [1,2]. In this framework, Nuclear Magnetic Resonance (NMR), X-ray crystallography, and cryo-Electron Microscopy (cryo-EM) are the most frequently applied techniques to obtain the structures of single interactors or their complexes [1,2].…”

“…The availability of 3D protein structures, principally protein-protein complexes, is crucial to developing peptides targeting PPIs [1,2]. In this framework, Nuclear Magnetic Resonance (NMR), X-ray crystallography, and cryo-Electron Microscopy (cryo-EM) are the most frequently applied techniques to obtain the structures of single interactors or their complexes [1,2]. More precisely, X-ray crystallography is rather suitable for retrieving data for large globular domains (alone or associated with each other) whereas, NMR can also be exploited to study proteins involved in the formation of transient and weak complexes and obtain dynamic information [1,2].…”

“…In this framework, Nuclear Magnetic Resonance (NMR), X-ray crystallography, and cryo-Electron Microscopy (cryo-EM) are the most frequently applied techniques to obtain the structures of single interactors or their complexes [1,2]. More precisely, X-ray crystallography is rather suitable for retrieving data for large globular domains (alone or associated with each other) whereas, NMR can also be exploited to study proteins involved in the formation of transient and weak complexes and obtain dynamic information [1,2]. Moreover, structures with near-atomic resolutions can be determined by combining low-resolution structural techniques [e.g., cryo-Electron Microscopy (cryo-EM) and Small-Angle X-ray Scattering (SAXS)] with X-ray crystallography, NMR, Förster Resonance Energy Transfer (FRET), or Mass Spectroscopy (MS) techniques, along with in silico approaches [2].…”

“…Protein functions are primarily dictated by interactions with other proteins or biomolecules [1]. Interestingly, an ensemble of hundreds of thousands of protein-protein interactions (PPIs) composes the human interactome, and the aberrant modulation, in particular inhibition, of key PPIs within this ensemble can be associated with different diseases, ranging from cancer to neurodegenerative pathologies [2,3].…”

“…Peptides with a small molecular weight, high flexibility, and minimal toxicity can potentially constitute a new class of biopharmaceuticals [3]. On the other hand, peptide-based drug development, which requires analysis by high-throughput (HT) approaches of peptide-protein interactions, represents an expensive and time-consuming process that can benefit from in silico methods [1]. Therefore, this review is focused on the cutting-edge in silico approaches that could be implemented to discover peptide modulators of PPIs with potential therapeutic effects.…”