Getting Insights into Structural and Energetic Properties of Reciprocal Peptide–Protein Interactions

Peptide–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.

多肽-蛋白相互作用在众多涉及癌症和神经退行性等广泛传播疾病的细胞和代谢过程中发挥着至关重要的作用。尽管在多肽-蛋白界面结构表征方面取得了进展，但深入了解其相互作用背后的分子细节仍然是一项艰巨的任务。在本研究中，我们针对多肽和蛋白两个角度，开展了针对多肽结合位点的首次全面虚拟形态和能量研究。以PixelDB数据库为基础，该数据库收录了高质量3D晶体结构的多肽-蛋白复合物非冗余基准集合，我们对其最具代表性的类别进行了基于每个共结晶多肽性质的分类分析。从多肽和目标蛋白两方面计算了若干可解释的几何和能量描述符，旨在揭示物理化学和结构因果关联。最终，我们调查了结合界面上最常见的多肽-蛋白残基对，并基于GRID MIFs进行了广泛能量分析，旨在研究多肽亲和力增强的相互作用，以期进一步在理性药物设计策略中得以利用。