A lightweight classification of adaptor proteins using transformer networks

BMC Bioinformatics

Research

Sylwan Rahardja¹  Pasi Fränti¹  Mou Wang²  Susanto Rahardja³  Binh P. Nguyen⁴ 

[1] School of Computing, University of Eastern Finland, Joensuu, Finland;School of Marine Science and Technology, Northwestern Polytechnical University, 710072, Xi’an, China;School of Marine Science and Technology, Northwestern Polytechnical University, 710072, Xi’an, China;Singapore Institute of Technology, 138683, Singapore, Singapore;School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand;

关键词: Adaptor protein;    Protein classification;    Deep learning;    Transformer;   

DOI  :  10.1186/s12859-022-05000-6

 received in 2022-04-03, accepted in 2022-09-13,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundAdaptor proteins play a key role in intercellular signal transduction, and dysfunctional adaptor proteins result in diseases. Understanding its structure is the first step to tackling the associated conditions, spurring ongoing interest in research into adaptor proteins with bioinformatics and computational biology. Our study aims to introduce a small, new, and superior model for protein classification, pushing the boundaries with new machine learning algorithms.ResultsWe propose a novel transformer based model which includes convolutional block and fully connected layer. We input protein sequences from a database, extract PSSM features, then process it via our deep learning model. The proposed model is efficient and highly compact, achieving state-of-the-art performance in terms of area under the receiver operating characteristic curve, Matthew’s Correlation Coefficient and Receiver Operating Characteristics curve. Despite merely 20 hidden nodes translating to approximately 1% of the complexity of previous best known methods, the proposed model is still superior in results and computational efficiency.ConclusionsThe proposed model is the first transformer model used for recognizing adaptor protein, and outperforms all existing methods, having PSSM profiles as inputs that comprises convolutional blocks, transformer and fully connected layers for the use of classifying adaptor proteins.

【 授权许可】

CC BY   
© The Author(s) 2022. corrected publication 2023

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