Deciphering peptide-protein interactions via composition-based prediction: a case study with survivin/BIRC5

• Published in: Machine learning: science and technology Vol. 5; no. 2; pp. 25081 - 25100
• Main Authors: Anindya, Atsarina Larasati, Olsson, Torbjörn Nur, Jensen, Maja, Garcia-Bonete, Maria-Jose, Wheatley, Sally P, Bokarewa, Maria I, Mezzasalma, Stefano A, Katona, Gergely
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.06.2024
• Subjects: Atomic physics; Biochemistry and Molecular Biology; Biokemi och molekylärbiologi; classification; Composition; composition-based prediction; feature engineering; Functional groups; multilayer perceptrons; Peptides; Periodic table; phase transition; protein interactions; Proteins; Survival; survivin
• ISSN: 2632-2153; 2632-2153
• DOI: 10.1088/2632-2153/ad5784

Abstract In the realm of atomic physics and chemistry, composition emerges as the most powerful means of describing matter. Mendeleev’s periodic table and chemical formulas, while not entirely free from ambiguities, provide robust approximations for comprehending the properties of atoms, chemicals, and their collective behaviours, which stem from the dynamic interplay of their constituents. Our study illustrates that protein-protein interactions follow a similar paradigm, wherein the composition of peptides plays a pivotal role in predicting their interactions with the protein survivin, using an elegantly simple model. An analysis of these predictions within the context of the human proteome not only confirms the known cellular locations of survivin and its interaction partners, but also introduces novel insights into biological functionality. It becomes evident that electrostatic- and primary structure-based descriptions fall short in predictive power, leading us to speculate that protein interactions are orchestrated by the collective dynamics of functional groups.