Peptide Sequencing Via Protein Language Models
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911975128170496 |
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| author | Pham, Thuong Le Hoai Saurav, Jillur Rahman Omere, Aisosa A. Heyl, Calvin J. Nasr, Mohammad Sadegh Reynolds, Cody Tyler Veerla, Jai Prakash Yadav Shang, Helen H Jaworski, Justyn Ravenscraft, Alison Buonomo, Joseph Anthony Luber, Jacob M. |
| author_facet | Pham, Thuong Le Hoai Saurav, Jillur Rahman Omere, Aisosa A. Heyl, Calvin J. Nasr, Mohammad Sadegh Reynolds, Cody Tyler Veerla, Jai Prakash Yadav Shang, Helen H Jaworski, Justyn Ravenscraft, Alison Buonomo, Joseph Anthony Luber, Jacob M. |
| contents | We introduce a protein language model for determining the complete sequence of a peptide based on measurement of a limited set of amino acids. To date, protein sequencing relies on mass spectrometry, with some novel edman degregation based platforms able to sequence non-native peptides. Current protein sequencing techniques face limitations in accurately identifying all amino acids, hindering comprehensive proteome analysis. Our method simulates partial sequencing data by selectively masking amino acids that are experimentally difficult to identify in protein sequences from the UniRef database. This targeted masking mimics real-world sequencing limitations. We then modify and finetune a ProtBert derived transformer-based model, for a new downstream task predicting these masked residues, providing an approximation of the complete sequence. Evaluating on three bacterial Escherichia species, we achieve per-amino-acid accuracy up to 90.5% when only four amino acids ([KCYM]) are known. Structural assessment using AlphaFold and TM-score validates the biological relevance of our predictions. The model also demonstrates potential for evolutionary analysis through cross-species performance. This integration of simulated experimental constraints with computational predictions offers a promising avenue for enhancing protein sequence analysis, potentially accelerating advancements in proteomics and structural biology by providing a probabilistic reconstruction of the complete protein sequence from limited experimental data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00892 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Peptide Sequencing Via Protein Language Models Pham, Thuong Le Hoai Saurav, Jillur Rahman Omere, Aisosa A. Heyl, Calvin J. Nasr, Mohammad Sadegh Reynolds, Cody Tyler Veerla, Jai Prakash Yadav Shang, Helen H Jaworski, Justyn Ravenscraft, Alison Buonomo, Joseph Anthony Luber, Jacob M. Biomolecules Machine Learning We introduce a protein language model for determining the complete sequence of a peptide based on measurement of a limited set of amino acids. To date, protein sequencing relies on mass spectrometry, with some novel edman degregation based platforms able to sequence non-native peptides. Current protein sequencing techniques face limitations in accurately identifying all amino acids, hindering comprehensive proteome analysis. Our method simulates partial sequencing data by selectively masking amino acids that are experimentally difficult to identify in protein sequences from the UniRef database. This targeted masking mimics real-world sequencing limitations. We then modify and finetune a ProtBert derived transformer-based model, for a new downstream task predicting these masked residues, providing an approximation of the complete sequence. Evaluating on three bacterial Escherichia species, we achieve per-amino-acid accuracy up to 90.5% when only four amino acids ([KCYM]) are known. Structural assessment using AlphaFold and TM-score validates the biological relevance of our predictions. The model also demonstrates potential for evolutionary analysis through cross-species performance. This integration of simulated experimental constraints with computational predictions offers a promising avenue for enhancing protein sequence analysis, potentially accelerating advancements in proteomics and structural biology by providing a probabilistic reconstruction of the complete protein sequence from limited experimental data. |
| title | Peptide Sequencing Via Protein Language Models |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2408.00892 |