Academic literature on the topic 'Protein language models'
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Journal articles on the topic "Protein language models"
Tang, Lin. "Protein language models using convolutions." Nature Methods 21, no. 4 (April 2024): 550. http://dx.doi.org/10.1038/s41592-024-02252-3.
Full textAli, Sarwan, Prakash Chourasia, and Murray Patterson. "When Protein Structure Embedding Meets Large Language Models." Genes 15, no. 1 (December 23, 2023): 25. http://dx.doi.org/10.3390/genes15010025.
Full textFerruz, Noelia, and Birte Höcker. "Controllable protein design with language models." Nature Machine Intelligence 4, no. 6 (June 2022): 521–32. http://dx.doi.org/10.1038/s42256-022-00499-z.
Full textLi, Xiang, Zhuoyu Wei, Yueran Hu, and Xiaolei Zhu. "GraphNABP: Identifying nucleic acid-binding proteins with protein graphs and protein language models." International Journal of Biological Macromolecules 280 (November 2024): 135599. http://dx.doi.org/10.1016/j.ijbiomac.2024.135599.
Full textSingh, Arunima. "Protein language models guide directed antibody evolution." Nature Methods 20, no. 6 (June 2023): 785. http://dx.doi.org/10.1038/s41592-023-01924-w.
Full textTran, Chau, Siddharth Khadkikar, and Aleksey Porollo. "Survey of Protein Sequence Embedding Models." International Journal of Molecular Sciences 24, no. 4 (February 14, 2023): 3775. http://dx.doi.org/10.3390/ijms24043775.
Full textPokharel, Suresh, Pawel Pratyush, Hamid D. Ismail, Junfeng Ma, and Dukka B. KC. "Integrating Embeddings from Multiple Protein Language Models to Improve Protein O-GlcNAc Site Prediction." International Journal of Molecular Sciences 24, no. 21 (November 6, 2023): 16000. http://dx.doi.org/10.3390/ijms242116000.
Full textWeissenow, Konstantin, and Burkhard Rost. "Are protein language models the new universal key?" Current Opinion in Structural Biology 91 (April 2025): 102997. https://doi.org/10.1016/j.sbi.2025.102997.
Full textWang, Wenkai, Zhenling Peng, and Jianyi Yang. "Single-sequence protein structure prediction using supervised transformer protein language models." Nature Computational Science 2, no. 12 (December 19, 2022): 804–14. http://dx.doi.org/10.1038/s43588-022-00373-3.
Full textKenlay, Henry, Frédéric A. Dreyer, Aleksandr Kovaltsuk, Dom Miketa, Douglas Pires, and Charlotte M. Deane. "Large scale paired antibody language models." PLOS Computational Biology 20, no. 12 (December 6, 2024): e1012646. https://doi.org/10.1371/journal.pcbi.1012646.
Full textDissertations / Theses on the topic "Protein language models"
Meynard, Barthélémy. "Language Models towards Conditional Generative Modelsof Proteins Sequences." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS195.
Full textThis thesis explores the intersection of artificial intelligence (AI) and biology, focusing on how generative models can innovate in protein sequence design. Our research unfolds in three distinct yet interconnected stages, each building upon the insights of the previous to enhance the model's applicability and performance in protein engineering.We begin by examining what makes a generative model effective for protein sequences. In our first study, "Interpretable Pairwise Distillations for Generative Protein Sequence Models," we compare complex neural network models to simpler, pairwise distribution models. This comparison highlights that deep learning strategy mainly model second order interaction, highlighting their fundamental role in modeling proteins family.In a second part, we try to expand this principle of using second order interaction to inverse folding. We explore structure conditioning in "Uncovering Sequence Diversity from a Known Protein Structure" Here, we present InvMSAFold, a method that produces diverse protein sequences designed to fold into a specific structure. This approach tries to combines two different tradition of proteins modeling: the MSA based models that try to capture the entire fitness landscape and the inverse folding types of model that focus on recovering one specific sequence. This is a first step towards the possibility of conditioning the fitness landscape by considering the protein's final structure in the design process, enabling the generation of sequences that are not only diverse but also maintain their intended structural integrity. Finally, we delve into sequence conditioning with "Generating Interacting Protein Sequences using Domain-to-Domain Translation." This study introduces a novel approach to generate protein sequences that can interact with specific other proteins. By treating this as a translation problem, similar to methods used in language processing, we create sequences with intended functionalities. Furthermore, we address the critical challenge of T-cell receptor (TCR) and epitope interaction prediction in "TULIP—a Transformer based Unsupervised Language model for Interacting Peptides and T-cell receptors." This study introduces an unsupervised learning approach to accurately predict TCR-epitope bindings, overcoming limitations in data quality and training bias inherent in previous models. These advancements underline the potential of sequence conditioning in creating functionally specific and interaction-aware protein designs
Vander, Meersche Yann. "Étude de la flexibilité des protéines : analyse à grande échelle de simulations de dynamique moléculaire et prédiction par apprentissage profond." Electronic Thesis or Diss., Université Paris Cité, 2024. http://www.theses.fr/2024UNIP5147.
Full textProteins are essential to biological processes. Understanding their dynamics is crucial for elucidating their biological functions and interactions. However, experimentally measuring protein flexibility remains challenging due to technical limitations and associated costs. This thesis aims to deepen the understanding of protein dynamic properties and to propose computational methods for predicting their flexibility directly from their sequence. This work is organised in four main contributions: 1) Protein flexibility prediction in terms of B-factors. We have developed MEDUSA, a flexibility prediction method based on deep learning, which leverages the physicochemical and evolutionary information of amino acids to predict experimental flexibility classes from protein sequences. MEDUSA has outperformed previously available tools but shows limitations due to the variability of experimental data. 2) Large-scale analysis of in silico protein dynamics. We have released ATLAS, a database of standardised all-atom molecular dynamics simulations providing detailed information on protein flexibility for over 1.5k representative protein structures. ATLAS enables interactive analysis of protein dynamics at different levels and offers valuable insights into proteins exhibiting atypical dynamical behaviour, such as dual personality fragments. 3) An in-depth analysis of AlphaFold 2's pLDDT score and its relation to protein flexibility. We have assessed pLDDT correlation with different flexibility descriptors derived from molecular dynamics simulations and from NMR ensembles and demonstrated that confidence in 3D structure prediction does not necessarily reflect expected flexibility of the protein region, in particular, for protein fragments involved in molecular interaction. 4) Prediction of MD-derived flexibility descriptors using protein language embeddings. We introduce PEGASUS, a novel flexibility prediction tool developed using ATLAS database. Using protein sequence encoding by protein language models and a simple deep learning model, PEGASUS provides precise predictions of flexibility metrics and effectively captures the impact of mutations on protein dynamics. The perspectives of this work include enriching simulations with varied environments and integrating membrane proteins to enhance PEGASUS and enable new analyses. We also highlight the emergence of methods capable of predicting conformational ensembles, offering promising advances for better capturing protein dynamics. This thesis offers new perspectives for the prediction and analysis of protein flexibility, paving the way for advances in areas such as biomedical research, mutation studies, and drug design
Hladiš, Matej. "Réseaux de neurones en graphes et modèle de langage des protéines pour révéler le code combinatoire de l'olfaction." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5024.
Full textMammals identify and interpret a myriad of olfactory stimuli using a complex coding mechanism involving interactions between odorant molecules and hundreds of olfactory receptors (ORs). These interactions generate unique combinations of activated receptors, called the combinatorial code, which the human brain interprets as the sensation we call smell. Until now, the vast number of possible receptor-molecule combinations have prevented a large-scale experimental study of this code and its link to odor perception. Therefore, revealing this code is crucial to answering the long-term question of how we perceive our intricate chemical environment. ORs belong to the class A of G protein-coupled receptors (GPCRs) and constitute the largest known multigene family. To systematically study olfactory coding, we develop M2OR, a comprehensive database compiling the last 25 years of OR bioassays. Using this dataset, a tailored deep learning model is designed and trained. It combines the [CLS] token embedding from a protein language model with graph neural networks and multi-head attention. This model predicts the activation of ORs by odorants and reveals the resulting combinatorial code for any odorous molecule. This approach is refined by developing a novel model capable of predicting the activity of an odorant at a specific concentration, subsequently allowing the estimation of the EC50 value for any OR-odorant pair. Finally, the combinatorial codes derived from both models are used to predict the odor perception of molecules. By incorporating inductive biases inspired by olfactory coding theory, a machine learning model based on these codes outperforms the current state-of-the-art in smell prediction. To the best of our knowledge, this is the most comprehensive and successful application of combinatorial coding to odor quality prediction. Overall, this work provides a link between the complex molecule-receptor interactions and human perception
Books on the topic "Protein language models"
Beatriz, Solís Leree, ed. La Ley televisa y la lucha por el poder en México. México, D.F: Universidad Autónoma Metropolitana, Unidad Xochimilco, 2009.
Find full textYoshikawa, Saeko. William Wordsworth and Modern Travel. Liverpool University Press, 2020. http://dx.doi.org/10.3828/liverpool/9781789621181.001.0001.
Full textHardiman, David. The Nonviolent Struggle for Indian Freedom, 1905-19. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190920678.001.0001.
Full textMcNally, Michael D. Defend the Sacred. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691190907.001.0001.
Full textMeddings, Jennifer, Vineet Chopra, and Sanjay Saint. Preventing Hospital Infections. 2nd ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780197509159.001.0001.
Full textHalvorsen, Tar, and Peter Vale. One World, Many Knowledges: Regional experiences and cross-regional links in higher education. African Minds, 2016. http://dx.doi.org/10.47622/978-0-620-55789-4.
Full textBook chapters on the topic "Protein language models"
Xu, Yaoyao, Xinjian Zhao, Xiaozhuang Song, Benyou Wang, and Tianshu Yu. "Boosting Protein Language Models with Negative Sample Mining." In Lecture Notes in Computer Science, 199–214. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70381-2_13.
Full textZhao, Junming, Chao Zhang, and Yunan Luo. "Contrastive Fitness Learning: Reprogramming Protein Language Models for Low-N Learning of Protein Fitness Landscape." In Lecture Notes in Computer Science, 470–74. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-1-0716-3989-4_55.
Full textPratyush, Pawel, Suresh Pokharel, Hamid D. Ismail, Soufia Bahmani, and Dukka B. KC. "LMPTMSite: A Platform for PTM Site Prediction in Proteins Leveraging Transformer-Based Protein Language Models." In Methods in Molecular Biology, 261–97. New York, NY: Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-4196-5_16.
Full textGhazikhani, Hamed, and Gregory Butler. "A Study on the Application of Protein Language Models in the Analysis of Membrane Proteins." In Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference, 147–52. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23210-7_14.
Full textWu, Tianqi, Weihang Cheng, and Jianlin Cheng. "Improving Protein Secondary Structure Prediction by Deep Language Models and Transformer Networks." In Methods in Molecular Biology, 43–53. New York, NY: Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-4196-5_3.
Full textZeng, Shuai, Duolin Wang, Lei Jiang, and Dong Xu. "Prompt-Based Learning on Large Protein Language Models Improves Signal Peptide Prediction." In Lecture Notes in Computer Science, 400–405. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-1-0716-3989-4_40.
Full textFernández, Diego, Álvaro Olivera-Nappa, Roberto Uribe-Paredes, and David Medina-Ortiz. "Exploring Machine Learning Algorithms and Protein Language Models Strategies to Develop Enzyme Classification Systems." In Bioinformatics and Biomedical Engineering, 307–19. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34953-9_24.
Full textPaaß, Gerhard, and Sven Giesselbach. "Foundation Models for Speech, Images, Videos, and Control." In Artificial Intelligence: Foundations, Theory, and Algorithms, 313–82. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23190-2_7.
Full textShan, Kaixuan, Xiankun Zhang, and Chen Song. "Prediction of Protein-DNA Binding Sites Based on Protein Language Model and Deep Learning." In Advanced Intelligent Computing in Bioinformatics, 314–25. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5692-6_28.
Full textMatsiunova, Antonina. "Semantic opposition of US versus THEM in late 2020 Russian-language Belarusian discourse." In Protest in Late Modern Societies, 42–55. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003270065-4.
Full textConference papers on the topic "Protein language models"
Amjad, Maheera, Ayesha Munir, Usman Zia, and Rehan Zafar Paracha. "Pre-trained Language Models for Decoding Protein Language: a Survey." In 2024 4th International Conference on Digital Futures and Transformative Technologies (ICoDT2), 1–12. IEEE, 2024. http://dx.doi.org/10.1109/icodt262145.2024.10740205.
Full textLiu, Xu, Yiming Li, Fuhao Zhang, Ruiqing Zheng, Fei Guo, Min Li, and Min Zeng. "ComLMEss: Combining multiple protein language models enables accurate essential protein prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 67–72. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822205.
Full textSun, Xin, and Yuhao Wu. "Combinative Bio-feature Proteins Generation via Pre-trained Protein Large Language Models." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 99–104. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821839.
Full textLiang, Po-Yu, Xueting Huang, Tibo Duran, Andrew J. Wiemer, and Jun Bai. "Exploring Latent Space for Generating Peptide Analogs Using Protein Language Models." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 842–47. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821777.
Full textJiang, Yanfeng, Ning Sun, Zhengxian Lu, Shuang Peng, Yi Zhang, Fei Yang, and Tao Li. "MEFold: Memory-Efficient Optimization for Protein Language Models via Chunk and Quantization." In 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651470.
Full textKim, Yunsoo. "Foundation Model for Biomedical Graphs: Integrating Knowledge Graphs and Protein Structures to Large Language Models." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), 346–55. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-srw.30.
Full textShen, Yiqing, Zan Chen, Michail Mamalakis, Luhan He, Haiyang Xia, Tianbin Li, Yanzhou Su, Junjun He, and Yu Guang Wang. "A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2390–95. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821894.
Full textZhang, Jun, Zhiqiang Yan, Hao Zeng, and Zexuan Zhu. "PAIR: protein-aptamer interaction prediction based on language models and contrastive learning framework." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 5426–32. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822859.
Full textEngel, Ryan, and Gilchan Park. "Evaluating Large Language Models for Predicting Protein Behavior under Radiation Exposure and Disease Conditions." In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, 427–39. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.bionlp-1.34.
Full textTsutaoka, Takuya, Noriji Kato, Toru Nishino, Yuanzhong Li, and Masahito Ohue. "Predicting Antibody Stability pH Values from Amino Acid Sequences: Leveraging Protein Language Models for Formulation Optimization." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 240–43. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822009.
Full textReports on the topic "Protein language models"
Wu, Jyun-Jie. Improving Predictive Efficiency and Literature Quality Assessment for Lung Cancer Complications Post-Proton Therapy Through Large Language Models and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2024. http://dx.doi.org/10.37766/inplasy2024.8.0103.
Full textShani, Uri, Lynn Dudley, Alon Ben-Gal, Menachem Moshelion, and Yajun Wu. Root Conductance, Root-soil Interface Water Potential, Water and Ion Channel Function, and Tissue Expression Profile as Affected by Environmental Conditions. United States Department of Agriculture, October 2007. http://dx.doi.org/10.32747/2007.7592119.bard.
Full textMelnyk, Iurii. JUSTIFICATION OF OCCUPATION IN GERMAN (1938) AND RUSSIAN (2014) MEDIA: SUBSTITUTION OF AGGRESSOR AND VICTIM. Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11101.
Full textYatsymirska, Mariya. KEY IMPRESSIONS OF 2020 IN JOURNALISTIC TEXTS. Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11107.
Full textOr, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7587232.bard.
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