TY - JOUR

T1 - A generic deep convolutional neural network framework for prediction of receptor–ligand interactions - NetPhosPan

T2 - application to kinase phosphorylation prediction

AU - Fenoy, Emilio

AU - Izarzugaza, Jose M.G.

AU - Jurtz, Vanessa

AU - Brunak, Søren

AU - Nielsen, Morten

PY - 2019

Y1 - 2019

N2 - Motivation: 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. Results: 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 data sets allowing predictions of the receptor specificity for all members of a given protein family including those described by limited or no ligand data. The approach was applied to the protein kinase superfamily, leading to the method NetPhosPan. The method was extensively validated and benchmarked against state-of-the-art prediction methods and was found to have unprecedented performance in particularly for kinase domains characterized by limited or no experimental data.Availability and Implementation: The method is freely available to non-commercial users and can be downloaded at https://www.cbs.dtu.dk/services/NetPhospan-1.0.Supplementary information: Supplementary data are available at Bioinformatics online.

AB - Motivation: 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. Results: 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 data sets allowing predictions of the receptor specificity for all members of a given protein family including those described by limited or no ligand data. The approach was applied to the protein kinase superfamily, leading to the method NetPhosPan. The method was extensively validated and benchmarked against state-of-the-art prediction methods and was found to have unprecedented performance in particularly for kinase domains characterized by limited or no experimental data.Availability and Implementation: The method is freely available to non-commercial users and can be downloaded at https://www.cbs.dtu.dk/services/NetPhospan-1.0.Supplementary information: Supplementary data are available at Bioinformatics online.

U2 - 10.1093/bioinformatics/bty715

DO - 10.1093/bioinformatics/bty715

M3 - Journal article

C2 - 30169744

VL - 35

SP - 1098

EP - 1107

JO - Computer Applications in the Biosciences

JF - Computer Applications in the Biosciences

SN - 1471-2105

IS - 7

ER -