Dual-Modality Transformer-Based Approach for Viral Protein Classification Integrating Protein Language Models and 3Di FASTA Representations

Viruses significantly impact ecosystems by influencing microbial diversity and facilitating genetic exchange, but their genomes remain poorly annotated. Accurate viral genome annotation is challenging due to limited viral protein representation in databases and rapid sequence divergence. We present...

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Autores:: Puentes Mozo, Juanita

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2024

Institución:: Universidad de los Andes

Repositorio:: Séneca: repositorio Uniandes

Idioma:: eng

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dc.title.eng.fl_str_mv	Dual-Modality Transformer-Based Approach for Viral Protein Classification Integrating Protein Language Models and 3Di FASTA Representations
title	Dual-Modality Transformer-Based Approach for Viral Protein Classification Integrating Protein Language Models and 3Di FASTA Representations
spellingShingle	Dual-Modality Transformer-Based Approach for Viral Protein Classification Integrating Protein Language Models and 3Di FASTA Representations Phage protein classification Multi-modality approach Viral proteins Transformer models Deep learning Artificial intelligence PHROGs database PHROG-function prediction Transfer learning in virology Microbiología
title_short	Dual-Modality Transformer-Based Approach for Viral Protein Classification Integrating Protein Language Models and 3Di FASTA Representations
title_full	Dual-Modality Transformer-Based Approach for Viral Protein Classification Integrating Protein Language Models and 3Di FASTA Representations
title_fullStr	Dual-Modality Transformer-Based Approach for Viral Protein Classification Integrating Protein Language Models and 3Di FASTA Representations
title_full_unstemmed	Dual-Modality Transformer-Based Approach for Viral Protein Classification Integrating Protein Language Models and 3Di FASTA Representations
title_sort	Dual-Modality Transformer-Based Approach for Viral Protein Classification Integrating Protein Language Models and 3Di FASTA Representations
dc.creator.fl_str_mv	Puentes Mozo, Juanita
dc.contributor.advisor.none.fl_str_mv	García Botero, Camilo Reyes Muñoz, Alejandro
dc.contributor.author.none.fl_str_mv	Puentes Mozo, Juanita
dc.contributor.researchgroup.none.fl_str_mv	Facultad de Ciencias::Biología Computacional y Ecología Microbiana
dc.subject.keyword.eng.fl_str_mv	Phage protein classification Multi-modality approach Viral proteins Transformer models Deep learning Artificial intelligence PHROGs database PHROG-function prediction Transfer learning in virology
topic	Phage protein classification Multi-modality approach Viral proteins Transformer models Deep learning Artificial intelligence PHROGs database PHROG-function prediction Transfer learning in virology Microbiología
dc.subject.themes.none.fl_str_mv	Microbiología
description	Viruses significantly impact ecosystems by influencing microbial diversity and facilitating genetic exchange, but their genomes remain poorly annotated. Accurate viral genome annotation is challenging due to limited viral protein representation in databases and rapid sequence divergence. We present a novel approach for viral protein classification by integrating text embeddings from protein language models (pLMs) and visual features from 3Di FASTA representations using transformer models. Leveraging pre-trained models such as ProteinBERT, ProteinBFD, and ESM, we performed a series of viral protein classification experiments at two levels: category level (9 classes) and PHROGs family level (1159 classes). Our model achieved superior results with PHROGs labels, attaining precision, recall, and F-score values of 0.784, 0.789, and 0.786, respectively, at the category level. The integration of 3Di image features with FASTA sequences further improved classification accuracy, enhancing true positive rates across most classes. These findings highlight the importance of accurate functional annotations and demonstrate the potential of transformer-based models in viral protein classification. The results also suggest that homology-based labels, such as those used by Pharokka, may introduce inconsistencies, warranting further investigation. Our dual-modality approach provides a robust framework for future research, promoting more precise and comprehensive protein classification methodologies.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-08-02T18:46:45Z
dc.date.available.none.fl_str_mv	2024-08-02T18:46:45Z
dc.date.issued.none.fl_str_mv	2024-08-02
dc.type.none.fl_str_mv	Trabajo de grado - Pregrado
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dc.relation.references.none.fl_str_mv	Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373(6557), 871–876. Bebis, G., & Georgiopoulos, M. (1994). Feed-forward neural networks. Ieee Potentials, 13(4), 27–31. Bouras, G., Nepal, R., Houtak, G., Psaltis, A. J., Wormald, P.-J., & Vreugde, S. (2023). Pharokka: A fast scalable bacteriophage annotation tool. Bioinformatics, 39(1), btac776. Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., & Linial, M. (2022). Proteinbert: A universal deep-learning model of protein sequence and function. Bioinformatics, 38(8), 2102–2110. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877–1901. Câmara, G. B., Coutinho, M. G., Silva, L. M. d., Gadelha, W. V. d. N., Torquato, M. F., Barbosa, R. d. M., & Fernandes, M. A. (2022). Convolutional neural network applied to sars-cov-2 sequence classification. Sensors, 22(15), 5730. Choi, S. R., & Lee, M. (2023). Transformer architecture and attention mechanisms in genome data analysis: A comprehensive review. Biology, 12(7), 1033. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. Elnaggar, A., Heinzinger, M., Dallago, C., et al. (2020). Prottrans: Towards cracking the language of life’s code through 500 self-supervised deep learning and high performance computing [j]. IEEE Trans, 685. Fang, Z., Feng, T., Zhou, H., & Chen, M. (2022). Deepvp: Identification and classification of phage virion proteins using deep learning. Gigascience, 11, giac076. Fang, Z., & Zhou, H. (2021). Virionfinder: Identification of complete and partial prokaryote virus virion protein from virome data using the sequence and biochemical properties of amino acids. Frontiers in microbiology, 12, 615711. Flamholz, Z. N., Biller, S. J., & Kelly, L. (2024). Large language models improve annotation of prokaryotic viral proteins. Nature Microbiology, 9(2), 537–549. Hatfull, G. F., & Hendrix, R. W. (2011). Bacteriophages and their genomes. Current opinion in virology, 1(4), 298–303. Heinzinger, M., Weissenow, K., Sanchez, J. G., Henkel, A., Mirdita, M., Steinegger, M., & Rost, B. (2023). Bilingual language model for protein sequence and structure. bioRxiv, 2023–07. Jain, P., & Hirst, J. D. (2010). Automatic structure classification of small proteins using random forest. BMC bioinformatics, 11, 1–14. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021). Highly accurate protein structure prediction with alphafold. nature, 596(7873), 583–589. Li, L. H., Yatskar, M., Yin, D., Hsieh, C.-J., & Chang, K.-W. (2019). Visualbert: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557. McNair, K., Zhou, C., Dinsdale, E. A., Souza, B., & Edwards, R. A. (2019). Phanotate: A novel approach to gene identification in phage genomes. Bioinformatics, 35(22), 4537–4542. Modak, S., Mehta, S., Sehgal, D., & Valadi, J. (2019). Application of support vector machines in viral biology. Global Virology III: Virology in the 21st Century, 361–403. Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences, 118(15), e2016239118. Shen, Y., Chen, Z., Mamalakis, M., He, L., Xia, H., Li, T., Su, Y., He, J., & Wang, Y. G. (2024). A fine-tuning dataset and benchmark for large language models for protein understanding. arXiv preprint arXiv:2406.05540. Smug, B. J., Szczepaniak, K., Rocha, E. P., Dunin-Horkawicz, S., & Mostowy, R. J. (2023). Ongoing shuffling of protein fragments diversifies core viral functions linked to interactions with bacterial hosts. Nature Communications, 14(1), 7460. Terzian, P., Olo Ndela, E., Galiez, C., Lossouarn, J., Pérez Bucio, R. E., Mom, R., Toussaint, A., Petit, M.-A., & Enault, F. (2021). Phrog: Families of prokaryotic virus proteins clustered using remote homology. NAR Genomics and Bioinformatics, 3(3), lqab067. Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288. Van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L., Söding, J., & Steinegger, M. (2024). Fast and accurate protein structure search with foldseek. Nature biotechnology, 42(2), 243–246. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
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spelling	García Botero, CamiloReyes Muñoz, Alejandrovirtual::19647-1Puentes Mozo, JuanitaFacultad de Ciencias::Biología Computacional y Ecología Microbiana2024-08-02T18:46:45Z2024-08-02T18:46:45Z2024-08-02https://hdl.handle.net/1992/74920instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Viruses significantly impact ecosystems by influencing microbial diversity and facilitating genetic exchange, but their genomes remain poorly annotated. Accurate viral genome annotation is challenging due to limited viral protein representation in databases and rapid sequence divergence. We present a novel approach for viral protein classification by integrating text embeddings from protein language models (pLMs) and visual features from 3Di FASTA representations using transformer models. Leveraging pre-trained models such as ProteinBERT, ProteinBFD, and ESM, we performed a series of viral protein classification experiments at two levels: category level (9 classes) and PHROGs family level (1159 classes). Our model achieved superior results with PHROGs labels, attaining precision, recall, and F-score values of 0.784, 0.789, and 0.786, respectively, at the category level. The integration of 3Di image features with FASTA sequences further improved classification accuracy, enhancing true positive rates across most classes. These findings highlight the importance of accurate functional annotations and demonstrate the potential of transformer-based models in viral protein classification. The results also suggest that homology-based labels, such as those used by Pharokka, may introduce inconsistencies, warranting further investigation. Our dual-modality approach provides a robust framework for future research, promoting more precise and comprehensive protein classification methodologies.PregradoBiología Computacional22 páginasapplication/pdfengUniversidad de los AndesMicrobiologíaFacultad de CienciasDepartamento de Ciencias BiológicasAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Dual-Modality Transformer-Based Approach for Viral Protein Classification Integrating Protein Language Models and 3Di FASTA RepresentationsTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPPhage protein classificationMulti-modality approachViral proteinsTransformer modelsDeep learningArtificial intelligencePHROGs databasePHROG-function predictionTransfer learning in virologyMicrobiologíaBaek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373(6557), 871–876. Bebis, G., & Georgiopoulos, M. (1994). Feed-forward neural networks. Ieee Potentials, 13(4), 27–31. Bouras, G., Nepal, R., Houtak, G., Psaltis, A. J., Wormald, P.-J., & Vreugde, S. (2023). Pharokka: A fast scalable bacteriophage annotation tool. Bioinformatics, 39(1), btac776. Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., & Linial, M. (2022). Proteinbert: A universal deep-learning model of protein sequence and function. Bioinformatics, 38(8), 2102–2110. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877–1901. Câmara, G. B., Coutinho, M. G., Silva, L. M. d., Gadelha, W. V. d. N., Torquato, M. F., Barbosa, R. d. M., & Fernandes, M. A. (2022). Convolutional neural network applied to sars-cov-2 sequence classification. Sensors, 22(15), 5730. Choi, S. R., & Lee, M. (2023). Transformer architecture and attention mechanisms in genome data analysis: A comprehensive review. Biology, 12(7), 1033. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. Elnaggar, A., Heinzinger, M., Dallago, C., et al. (2020). Prottrans: Towards cracking the language of life’s code through 500 self-supervised deep learning and high performance computing [j]. IEEE Trans, 685. Fang, Z., Feng, T., Zhou, H., & Chen, M. (2022). Deepvp: Identification and classification of phage virion proteins using deep learning. Gigascience, 11, giac076. Fang, Z., & Zhou, H. (2021). Virionfinder: Identification of complete and partial prokaryote virus virion protein from virome data using the sequence and biochemical properties of amino acids. Frontiers in microbiology, 12, 615711. Flamholz, Z. N., Biller, S. J., & Kelly, L. (2024). Large language models improve annotation of prokaryotic viral proteins. Nature Microbiology, 9(2), 537–549. Hatfull, G. F., & Hendrix, R. W. (2011). Bacteriophages and their genomes. Current opinion in virology, 1(4), 298–303. Heinzinger, M., Weissenow, K., Sanchez, J. G., Henkel, A., Mirdita, M., Steinegger, M., & Rost, B. (2023). Bilingual language model for protein sequence and structure. bioRxiv, 2023–07. Jain, P., & Hirst, J. D. (2010). Automatic structure classification of small proteins using random forest. BMC bioinformatics, 11, 1–14. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021). Highly accurate protein structure prediction with alphafold. nature, 596(7873), 583–589. Li, L. H., Yatskar, M., Yin, D., Hsieh, C.-J., & Chang, K.-W. (2019). Visualbert: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557. McNair, K., Zhou, C., Dinsdale, E. A., Souza, B., & Edwards, R. A. (2019). Phanotate: A novel approach to gene identification in phage genomes. Bioinformatics, 35(22), 4537–4542. Modak, S., Mehta, S., Sehgal, D., & Valadi, J. (2019). Application of support vector machines in viral biology. Global Virology III: Virology in the 21st Century, 361–403. Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences, 118(15), e2016239118. Shen, Y., Chen, Z., Mamalakis, M., He, L., Xia, H., Li, T., Su, Y., He, J., & Wang, Y. G. (2024). A fine-tuning dataset and benchmark for large language models for protein understanding. arXiv preprint arXiv:2406.05540. Smug, B. J., Szczepaniak, K., Rocha, E. P., Dunin-Horkawicz, S., & Mostowy, R. J. (2023). Ongoing shuffling of protein fragments diversifies core viral functions linked to interactions with bacterial hosts. Nature Communications, 14(1), 7460. Terzian, P., Olo Ndela, E., Galiez, C., Lossouarn, J., Pérez Bucio, R. E., Mom, R., Toussaint, A., Petit, M.-A., & Enault, F. (2021). Phrog: Families of prokaryotic virus proteins clustered using remote homology. NAR Genomics and Bioinformatics, 3(3), lqab067. Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288. Van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L., Söding, J., & Steinegger, M. (2024). Fast and accurate protein structure search with foldseek. Nature biotechnology, 42(2), 243–246. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. 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Dual-Modality Transformer-Based Approach for Viral Protein Classification Integrating Protein Language Models and 3Di FASTA Representations

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