Artificial Intelligence Transforming Post-Translational Modification Research
Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolut...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/1/26 |
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| author | Doo Nam Kim Tianzhixi Yin Tong Zhang Alexandria K. Im John R. Cort Jordan C. Rozum David Pollock Wei-Jun Qian Song Feng |
| author_facet | Doo Nam Kim Tianzhixi Yin Tong Zhang Alexandria K. Im John R. Cort Jordan C. Rozum David Pollock Wei-Jun Qian Song Feng |
| author_sort | Doo Nam Kim |
| collection | DOAJ |
| description | Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering. |
| format | Article |
| id | doaj-art-315cd72964f1492d9f308b7ca9d911ab |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-315cd72964f1492d9f308b7ca9d911ab2025-01-24T13:23:00ZengMDPI AGBioengineering2306-53542024-12-011212610.3390/bioengineering12010026Artificial Intelligence Transforming Post-Translational Modification ResearchDoo Nam Kim0Tianzhixi Yin1Tong Zhang2Alexandria K. Im3John R. Cort4Jordan C. Rozum5David Pollock6Wei-Jun Qian7Song Feng8Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USANational Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USABiological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USABiological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USABiological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USABiological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USABiological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USABiological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USABiological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USAPost-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering.https://www.mdpi.com/2306-5354/12/1/26artificial intelligencedeep learningmachine learningPost-Translational Modification |
| spellingShingle | Doo Nam Kim Tianzhixi Yin Tong Zhang Alexandria K. Im John R. Cort Jordan C. Rozum David Pollock Wei-Jun Qian Song Feng Artificial Intelligence Transforming Post-Translational Modification Research Bioengineering artificial intelligence deep learning machine learning Post-Translational Modification |
| title | Artificial Intelligence Transforming Post-Translational Modification Research |
| title_full | Artificial Intelligence Transforming Post-Translational Modification Research |
| title_fullStr | Artificial Intelligence Transforming Post-Translational Modification Research |
| title_full_unstemmed | Artificial Intelligence Transforming Post-Translational Modification Research |
| title_short | Artificial Intelligence Transforming Post-Translational Modification Research |
| title_sort | artificial intelligence transforming post translational modification research |
| topic | artificial intelligence deep learning machine learning Post-Translational Modification |
| url | https://www.mdpi.com/2306-5354/12/1/26 |
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