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Download fileGetting Insights into Structural and Energetic Properties of Reciprocal Peptide–Protein Interactions
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posted on 2022-02-11, 21:05 authored by Daniela Trisciuzzi, Lydia Siragusa, Massimo Baroni, Ida Autiero, Orazio Nicolotti, Gabriele CrucianiPeptide–protein
interactions play a key role for many cellular
and metabolic processes involved in the onset of largely spread diseases
such as cancer and neurodegenerative pathologies. Despite the progress
in the structural characterization of peptide–protein interfaces,
the in-depth knowledge of the molecular details behind their interactions
is still a daunting task. Here, we present the first comprehensive in silico morphological and energetic study of peptide binding
sites by focusing on both peptide and protein standpoints. Starting
from the PixelDB database, a nonredundant benchmark collection of
high-quality 3D crystallographic structures of peptide–protein
complexes, a classification analysis of the most representative categories
based on the nature of each cocrystallized peptide has been carried
out. Several interpretable geometrical and energetic descriptors have
been computed both from peptide and target protein sides in the attempt
to unveil physicochemical and structural causative correlations. Finally,
we investigated the most frequent peptide–protein residue pairs
at the binding interface and made extensive energetic analyses, based
on GRID MIFs, with the aim to study the peptide affinity-enhancing
interactions to be further exploited in rational drug design strategies.
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several interpretable geometricalnonredundant benchmark collectionmolecular details behindmetabolic processes involvedlargely spread diseasestarget protein sidesstructural causative correlationsrepresentative categories basedpeptide binding sitesprotein standpointsbinding interfaceunveil physicochemicalstructural characterizationsilico pixeldb databasepeptide affinityneurodegenerative pathologiesmany cellularkey rolegrid mifsgetting insightsfirst comprehensiveenergetic propertiesenergetic descriptorsdepth knowledgedaunting taskcocrystallized peptideclassification analysis