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

Anindya, Atsarina Larasati; Olsson, Torbjörn Nur; Jensen, Maja; Garcia-Bonete, Maria-Jose; Wheatley, Sally P; Bokarewa, Maria I; Mezzasalma, Stefano A.; Katona, Gergely (2024) Deciphering peptide-protein interactions via composition-based prediction: a case study with survivin/BIRC5. Machine Learning: Science and Technology, 5 (2). ISSN 2632-2153

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Official URL: http://doi.org/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.

Item Type:

Article

Uncontrolled Keywords:

peptide-protein interactions; composition-based prediction; multilayer perceptrons; classification; feature engineering; protein interactions; survivin; phase transition

Subjects:

NATURAL SCIENCES > Chemistry

Divisions:

Division of Materials Physics

Projects: