ProBAN: Neural network algorithm for predicting binding affinity in protein–protein complexes

• Published in: Proteins, structure, function, and bioinformatics Vol. 92; no. 9; pp. 1127 - 1136
• Main Authors: Bogdanova, Elizaveta Alexandrovna, Novoseletsky, Valery Nikolaevich
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.09.2024; Wiley Subscription Services, Inc
• Subjects: Affinity; Algorithms; Artificial neural networks; Binding; Binding Sites; Computational Biology - methods; Databases, Protein; Datasets; Energy of dissociation; Free energy; Gibbs free energy; Impact prediction; interaction energy; multiprotein complexes; Neural networks; Neural Networks, Computer; Peptides; Predictions; Protein Binding; Protein Interaction Mapping - methods; Proteins; Proteins - chemistry; Proteins - metabolism; Source code; structure‐based features; Tensors; Thermodynamics; interaction energy; multiprotein complexes; protein binding; neural networks; structure‐based features
• ISSN: 0887-3585; 1097-0134; 1097-0134
• DOI: 10.1002/prot.26700

Determining binding affinities in protein–protein and protein–peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein–protein complexes based on a deep convolutional neural network. Prediction is carried out for the spatial structures of complexes, presented in the format of a 4D tensor, which includes information about the location of atoms and their abilities to participate in various types of interactions realized in protein–protein and protein–peptide complexes. The effectiveness of the model was assessed both on an internal test data set containing complexes consisting of three or more molecules, as well as on an external test for the PPI‐Affinity service. As a result, we managed to achieve the best prediction quality on these data sets among all the analyzed models: on the internal test, Pearson correlation R = 0.6, MAE = 1.60, on the external test, R = 0.55, MAE = 1.75. The open‐source code, the trained ProBAN model, and the collected dataset are freely available at the following link https://github.com/EABogdanova/ProBAN.