A generic deep convolutional neural network framework for prediction of receptor-ligand interactions-NetPhosPan: application to kinase phosphorylation prediction

Bioinformatics
Emilio FenoyMorten Nielsen

Abstract

Understanding the specificity of protein receptor-ligand interactions is pivotal for our comprehension of biological mechanisms and systems. Receptor protein families often have a certain level of sequence diversity that converges into fewer conserved protein structures, allowing the exertion of well-defined functions. T and B cell receptors of the immune system and protein kinases that control the dynamic behaviour and decision processes in eukaryotic cells by catalysing phosphorylation represent prime examples. Driven by the large sequence diversity, the receptors within such protein families are often found to share specificities although divergent at the sequence level. This observation has led to the notion that prediction models of such systems are most effectively handled in a receptor-specific manner. We show that this approach in many cases is suboptimal, and describe an alternative improved framework for generating models with pan-receptor-predictive power for receptor protein families. The framework is based on deep artificial neural networks and integrates information from individual receptors into a single pan-receptor model, leveraging information across multiple receptor-specific datasets allowing predictions of ...Continue Reading

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Citations

Nov 11, 2020·Nature Reviews. Genetics·Erick ArmingolNathan E Lewis
Jul 31, 2020·Computational and Structural Biotechnology Journal·Gonzalo Pérez-MejíasIrene Díaz-Moreno
Sep 18, 2020·Proteomics·Bo WenBing Zhang
Aug 14, 2021·Computational Biology and Chemistry·Md Khaled Ben IslamShamim Ahmad
Sep 12, 2021·Communications Biology·Alessandro MontemurroMorten Nielsen

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