+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

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

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

Bioinformatics 35(7): 1098-1107

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 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. The method is freely available to non-commercial users and can be downloaded at http://www.cbs.dtu.dk/services/NetPhospan-1.0. Supplementary data are available at Bioinformatics online.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 065682524

Download citation: RISBibTeXText

PMID: 30169744

DOI: 10.1093/bioinformatics/bty715

Related references

Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method. Journal of Theoretical Biology 461: 230-238, 2019

A Multi-modal Convolutional Neural Network Framework for the Prediction of Alzheimer's Disease. Conference Proceedings 2018: 1271-1274, 2018

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors 17(4):, 2017

Prediction of CRISPR sgRNA Activity Using a Deep Convolutional Neural Network. Journal of Chemical Information and Modeling 59(1): 615-624, 2019

Chromatin accessibility prediction via a hybrid deep convolutional neural network. Bioinformatics 34(5): 732-738, 2018

K DEEP : Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks. Journal of Chemical Information and Modeling 58(2): 287-296, 2018

A Cascaded Deep Convolutional Neural Network for Joint Segmentation and Genotype Prediction of Brainstem Gliomas. IEEE Transactions on Bio-Medical Engineering 65(9): 1943-1952, 2018

Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network. Physics in Medicine and Biology 64(12): 125017, 2019

A simple convolutional neural network for prediction of enhancer-promoter interactions with DNA sequence data. Bioinformatics 35(17): 2899-2906, 2019

Dose distribution prediction in isodose feature-preserving voxelization domain using deep convolutional neural network. Medical Physics 46(7): 2978-2987, 2019

Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. Journal of Periodontal and Implant Science 48(2): 114-123, 2018

Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Physics in Medicine and Biology 62(21): 8246-8263, 2017

Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Computers in Biology and Medicine 103: 220-231, 2018

Generic artificial neural network framework for habitat assessment and prediction of Australian stream systems. Pages 813-818 2003, 2003

MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction. Bioinformatics 33(24): 3909-3916, 2017