xCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model
Abstract Background Predicting protein–protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have been proposed, the utilization of sequence embeddings from protein language models, which contain diverse information, including...
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Format: | Article |
Language: | English |
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BMC
2024-03-01
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Series: | BMC Bioinformatics |
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Online Access: | https://doi.org/10.1186/s12859-024-05725-6 |
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author | Thanh Hai Dang Tien Anh Vu |
author_facet | Thanh Hai Dang Tien Anh Vu |
author_sort | Thanh Hai Dang |
collection | DOAJ |
description | Abstract Background Predicting protein–protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have been proposed, the utilization of sequence embeddings from protein language models, which contain diverse information, including structural, evolutionary, and functional aspects, has not been fully exploited. Additionally, there is a significant need for a comprehensive neural network capable of efficiently extracting these multifaceted representations. Results Addressing this gap, we propose xCAPT5, a novel hybrid classifier that uniquely leverages the T5-XL-UniRef50 protein large language model for generating rich amino acid embeddings from protein sequences. The core of xCAPT5 is a multi-kernel deep convolutional siamese neural network, which effectively captures intricate interaction features at both micro and macro levels, integrated with the XGBoost algorithm, enhancing PPIs classification performance. By concatenating max and average pooling features in a depth-wise manner, xCAPT5 effectively learns crucial features with low computational cost. Conclusion This study represents one of the initial efforts to extract informative amino acid embeddings from a large protein language model using a deep and wide convolutional network. Experimental results show that xCAPT5 outperforms recent state-of-the-art methods in binary PPI prediction, excelling in cross-validation on several benchmark datasets and demonstrating robust generalization across intra-species, cross-species, inter-species, and stringent similarity contexts. |
first_indexed | 2024-04-25T01:02:55Z |
format | Article |
id | doaj.art-943133c482684dadae75a41970fb6555 |
institution | Directory Open Access Journal |
issn | 1471-2105 |
language | English |
last_indexed | 2024-04-25T01:02:55Z |
publishDate | 2024-03-01 |
publisher | BMC |
record_format | Article |
series | BMC Bioinformatics |
spelling | doaj.art-943133c482684dadae75a41970fb65552024-03-10T12:23:19ZengBMCBMC Bioinformatics1471-21052024-03-0125112010.1186/s12859-024-05725-6xCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language modelThanh Hai Dang0Tien Anh Vu1Faculty of Information Technology, VNU University of Engineering and TechnologyFaculty of Biology, VNU University of ScienceAbstract Background Predicting protein–protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have been proposed, the utilization of sequence embeddings from protein language models, which contain diverse information, including structural, evolutionary, and functional aspects, has not been fully exploited. Additionally, there is a significant need for a comprehensive neural network capable of efficiently extracting these multifaceted representations. Results Addressing this gap, we propose xCAPT5, a novel hybrid classifier that uniquely leverages the T5-XL-UniRef50 protein large language model for generating rich amino acid embeddings from protein sequences. The core of xCAPT5 is a multi-kernel deep convolutional siamese neural network, which effectively captures intricate interaction features at both micro and macro levels, integrated with the XGBoost algorithm, enhancing PPIs classification performance. By concatenating max and average pooling features in a depth-wise manner, xCAPT5 effectively learns crucial features with low computational cost. Conclusion This study represents one of the initial efforts to extract informative amino acid embeddings from a large protein language model using a deep and wide convolutional network. Experimental results show that xCAPT5 outperforms recent state-of-the-art methods in binary PPI prediction, excelling in cross-validation on several benchmark datasets and demonstrating robust generalization across intra-species, cross-species, inter-species, and stringent similarity contexts.https://doi.org/10.1186/s12859-024-05725-6Protein–protein interactionsConvolutional neural networksProtein language models |
spellingShingle | Thanh Hai Dang Tien Anh Vu xCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model BMC Bioinformatics Protein–protein interactions Convolutional neural networks Protein language models |
title | xCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model |
title_full | xCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model |
title_fullStr | xCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model |
title_full_unstemmed | xCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model |
title_short | xCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model |
title_sort | xcapt5 protein protein interaction prediction using deep and wide multi kernel pooling convolutional neural networks with protein language model |
topic | Protein–protein interactions Convolutional neural networks Protein language models |
url | https://doi.org/10.1186/s12859-024-05725-6 |
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