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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.02661 |
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| _version_ | 1866913223095091200 |
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| author | Drvodelic, Mark Gong, Mingming Webb, Andrew I. |
| author_facet | Drvodelic, Mark Gong, Mingming Webb, Andrew I. |
| contents | GraphRT is a graph based deep learning model that predicts the retention time (RT) of peptides in liquid chromatography tandem mass spectrometry (LC MSMS) experiments. Each amino acid is represented as a graph, capturing its atomic and structural properties through a graph neural network. This enables the model to understand not just the chemical composition of each amino acid, but also the intricate relationships between its atoms. The sequential context of the peptide the order and interaction of amino acids in the sequence is then encoded using recurrent neural networks. This dual approach of graph based and sequential modelling allows for a comprehensive understanding of both the individual characteristics of amino acids and their collective behaviour in a peptide sequence. GraphRT outperforms all current state of the art models and can predict retention time for peptides containing unseen modifications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02661 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GraphRT: A graph-based deep learning model for predicting the retention time of peptides Drvodelic, Mark Gong, Mingming Webb, Andrew I. Biomolecules J.3 GraphRT is a graph based deep learning model that predicts the retention time (RT) of peptides in liquid chromatography tandem mass spectrometry (LC MSMS) experiments. Each amino acid is represented as a graph, capturing its atomic and structural properties through a graph neural network. This enables the model to understand not just the chemical composition of each amino acid, but also the intricate relationships between its atoms. The sequential context of the peptide the order and interaction of amino acids in the sequence is then encoded using recurrent neural networks. This dual approach of graph based and sequential modelling allows for a comprehensive understanding of both the individual characteristics of amino acids and their collective behaviour in a peptide sequence. GraphRT outperforms all current state of the art models and can predict retention time for peptides containing unseen modifications. |
| title | GraphRT: A graph-based deep learning model for predicting the retention time of peptides |
| topic | Biomolecules J.3 |
| url | https://arxiv.org/abs/2402.02661 |