ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

Cited by (1,007)

Items 101–200 of 1,007

1. Democratizing protein language models with parameter-efficient fine-tuning
   Sledzieski, Samuel, Kshirsagar, Meghana, Baek, Minkyung, Dodhia, Rahul, Lavista Ferres, Juan, Berger, Bonnie · 2024 · Proceedings of the National Academy of Sciences
2. Opportunities and challenges for the computational interpretation of rare variation in clinically important genes
   McInnes, Gregory, Sharo, Andrew G., Koleske, Megan L., Brown, Julia E.H., Norstad, Matthew, Adhikari, Aashish N., Wang, Sheng, Brenner, Steven E., Halpern, Jodi, Koenig, Barbara A. et al. · 2021 · The American Journal of Human Genetics
3. pLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language models
   Kamil Kaminski, Jan Ludwiczak, Kamil Pawlicki, Vikram Alva, Stanisław Dunin-Horkawicz · 2023 · Bioinformatics
4. EquiPNAS: improved protein–nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks
   Roche, Rahmatullah, Moussad, Bernard, Shuvo, Md Hossain, Tarafder, Sumit, Bhattacharya, Debswapna · 2024 · Nucleic Acids Research
5. Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning
   Ziyi Zhou, Liang Zhang, Yuanxi Yu, Banghao Wu, Mingchen Li, Liang Hong, Pan Tan · 2024 · Nature Communications
6. Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures
   Yidong Song, Qianmu Yuan, Sheng Chen, Yuansong Zeng, Huiying Zhao, Yuedong Yang · 2024 · Nature Communications
7. SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning
   Tatiana Shashkova, Dmitriy Umerenkov, Mikhail Salnikov, П. В. Страшнов, Alina V. Konstantinova, Ivan Lebed, Д. Н. Щербинин, Marina N. Asatryan, Olga L. Kardymon, Nikita V. Ivanisenko · 2022 · Frontiers in Immunology
8. Bayesian estimation of gene constraint from an evolutionary model with gene features
   Tony Zeng, Jeffrey P. Spence, Hakhamanesh Mostafavi, Jonathan K. Pritchard · 2024 · Nature Genetics
9. Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function
   Boadu, Frimpong, Cao, Hongyuan, Cheng, Jianlin · 2023 · Bioinformatics
10. Advancing microbial production through artificial intelligence-aided biology
    Xinyu Gong, Jianli Zhang, Qi Gan, Yuxi Teng, Jixin Hou, Yanjun Lyu, Zhengliang Liu, Zihao Wu, Runpeng Dai, Yusong Zou et al. · 2024 · Biotechnology Advances
11. MOBIDB in 2025: integrating ensemble properties and function annotations for intrinsically disordered proteins
    Damiano Piovesan, Alessio Del Conte, Mahta Mehdiabadi, Maria Cristina Aspromonte, Matthias Blum, Giulio Tesei, Sören von Bülow, Kresten Lindorff‐Larsen, Silvio C. E. Tosatto · 2024 · Nucleic Acids Research
12. Codon language embeddings provide strong signals for use in protein engineering
    Carlos Outeiral, Charlotte Mary Deane · 2024 · Nature Machine Intelligence
13. Deep language models for interpretative and predictive materials science
    Yiwen Hu, Markus J. Buehler · 2023 · APL Machine Learning
14. Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning
    Yufan Liu, Boxue Tian · 2023 · Briefings in Bioinformatics
15. Predicting the subcellular location of prokaryotic proteins with DeepLocPro
    Jaime Moreno, Henrik Nielsen, Ole Winther, Felix Teufel · 2024 · Bioinformatics
16. AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning
    Milad Salem, Arash Keshavarzi Arshadi, Jiann Shiun Yuan · 2022 · BMC Bioinformatics
17. Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction
    Ken Chen, Yue Zhou, Maolin Ding, Yu Wang, Zhixiang Ren, Yuedong Yang · 2024 · Briefings in Bioinformatics
18. MMDG-DTI: Drug–target interaction prediction via multimodal feature fusion and domain generalization
    Yang Hua, Zhenhua Feng, Xiaoning Song, Xiao‐Jun Wu, Josef Kittler · 2024 · Pattern Recognition
19. Rapid discovery of high-affinity antibodies via massively parallel sequencing, ribosome display and affinity screening
    Benjamin T. Porebski, Matthew R. Balmforth, Gareth J. Browne, Aidan Riley, Kiarash Jamali, Maximilian J. L. J. Fürst, Mirko Velic, Andrew G. Buchanan, Ralph Minter, Tristan J. Vaughan et al. · 2023 · Nature Biomedical Engineering
20. Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering
    Peng Cheng, Cong Mao, Jin Tang, Sen Yang, Yu Cheng, Wuke Wang, Qiuxi Gu, Wei Han, Hao Chen, Sihan Li et al. · 2024 · Cell Research
21. Novel machine learning approaches revolutionize protein knowledge
    Bordin, Nicola, Dallago, Christian, Heinzinger, Michael, Kim, Stephanie, Littmann, Maria, Rauer, Clemens, Steinegger, Martin, Rost, Burkhard, Orengo, Christine · 2023 · Trends in Biochemical Sciences
22. Application of Transformers in Cheminformatics
    Kha-Dinh Luong, Ambuj K. Singh · 2024 · Journal of Chemical Information and Modeling
23. Leveraging transformers‐based language models in proteome bioinformatics
    Nguyen Quoc Khanh Le · 2023 · PROTEOMICS
24. Self-play reinforcement learning guides protein engineering
    Yi Wang, Hui Tang, Lichao Huang, Lulu Pan, Lixiang Yang, Huanming Yang, Feng Mu, Meng Yang · 2023 · Nature Machine Intelligence
25. Deep learning for protein structure prediction and design—progress and applications
    Jürgen Jänes, Pedro Beltrão · 2024 · Molecular Systems Biology
26. De novo design of peptide binders to conformationally diverse targets with contrastive language modeling
    Suhaas Bhat, Kalyan Palepu, Lauren Hong, Yiwei Mao, Tianzheng Ye, Rema Iyer, Lin Zhao, Tianlai Chen, Sophia Vincoff, Rio Watson et al. · 2025 · Science Advances
27. PTM-Mamba: a PTM-aware protein language model with bidirectional gated Mamba blocks
    Fred Zhangzhi Peng, Chen-Tong Wang, Tong Chen, Benjamin Schussheim, Sophia Vincoff, Pranam Chatterjee · 2025 · Nature Methods
28. A survey of geometric graph neural networks: data structures, models and applications
    Jiaqi Han, Jiacheng Cen, Liming Wu, Zongzhao Li, Xiangzhe Kong, Rui Jiao, Ziyang Yu, Tingyang Xu, Fandi Wu, Zihe Wang et al. · 2025 · Frontiers of Computer Science
29. Biophysics-based protein language models for protein engineering
    Sam Gelman, Bryce Johnson, Chase R. Freschlin, Arnav Sharma, Sameer D’Costa, John Peters, Anthony Gitter, Philip A. Romero · 2025 · Nature Methods
30. Prediction of RNA–protein interactions using a nucleotide language model
    Keisuke Yamada, Michiaki Hamada · 2022 · Bioinformatics Advances
31. Fine-tuning of BERT Model to Accurately Predict Drug–Target Interactions
    Hyeunseok Kang, Sungwoo Goo, Hyunjung Lee, Jung‐woo Chae, Hwi‐yeol Yun, Sangkeun Jung · 2022 · Pharmaceutics
32. Addressing the antibody germline bias and its effect on language models for improved antibody design
    Tobias Hegelund Olsen, Iain H. Moal, Charlotte Mary Deane · 2024 · Bioinformatics
33. Target sequence-conditioned design of peptide binders using masked language modeling
    Tianlai Chen, Zachary Quinn, Madeleine Dumas, Christina Yi Peng, Lauren Hong, Moises Lopez-Gonzalez, Alexander A. Mestre, Rio Watson, Sophia Vincoff, Lin Zhao et al. · 2025 · Nature Biotechnology
34. Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning
    Qianmu Yuan, Sheng Chen, Yu Wang, Huiying Zhao, Yuedong Yang · 2022 · Briefings in Bioinformatics
35. Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction
    Yiheng Zhu, Chengxin Zhang, Dong‐Jun Yu, Yang Zhang · 2022 · PLoS Computational Biology
36. Theoretical and Data-Driven Approaches for Biomolecular Condensates
    Kadi Liis Saar, Daoyuan Qian, Lydia L. Good, Alexey S. Morgunov, Rosana Collepardo‐Guevara, Robert B. Best, Tuomas P. J. Knowles · 2023 · Chemical Reviews
37. AI Methods for Antimicrobial Peptides: Progress and Challenges
    Carlos A. Brizuela, Gary Liu, Jonathan M. Stokes, César de la Fuente‐Núñez · 2025 · Microbial Biotechnology
38. Protein language models are biased by unequal sequence sampling across the tree of life
    Ding, Frances, Steinhardt, Jacob · 2024
39. Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments
    Ruyun Hu, Lihao Fu, Yongcan Chen, Junyu Chen, Yu Qiao, Tong Si · 2022 · Briefings in Bioinformatics
40. Machine learning prediction of enzyme optimum pH
    Japheth E. Gado, Matthew Knotts, Ada Y. Shaw, Debora S. Marks, Nicholas Paul Gauthier, Chris Sander, Gregg T. Beckham · 2025 · Nature Machine Intelligence
41. Evidential deep learning-based drug-target interaction prediction
    Yanpeng Zhao, Yuting Xing, Yixin Zhang, Yifei Wang, Mengxuan Wan, Duoyun Yi, Chengkun Wu, Shangze Li, Huiyan Xu, Hongyang Zhang et al. · 2025 · Nature Communications
42. Structure-informed Language Models Are Protein Designers
    Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, Quanquan Gu · 2023 · bioRxiv (Cold Spring Harbor Laboratory)
43. TemStaPro: protein thermostability prediction using sequence representations from protein language models
    Ieva Pudžiuvelytė, Kliment Olechnovič, Eglė Godliauskaitė, Kristupas Sermokas, Tomas Urbaitis, Giedrius Gasiūnas, Darius Kazlauskas · 2024 · Bioinformatics
44. DDMut-PPI: predicting effects of mutations on protein–protein interactions using graph-based deep learning
    Yunzhuo Zhou, Yoochan Myung, Carlos H. M. Rodrigues, David Benjamin Ascher · 2024 · Nucleic Acids Research
45. CellSAM: A Foundation Model for Cell Segmentation
    Uriah Israel, Markus Marks, Rohit Dilip, Qilin Li, Changhua Yu, Emily Laubscher, Ahamed Iqbal, Elora Pradhan, Ada Ates, Martin Abt et al. · 2023 · bioRxiv (Cold Spring Harbor Laboratory)
46. DTI-LM: language model powered drug–target interaction prediction
    Khandakar Tanvir Ahmed, Md Istiaq Ansari, Wei Zhang · 2024 · Bioinformatics
47. TPpred-SC: multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning
    Ke Yan, Hongwu Lv, Jiangyi Shao, Shutao Chen, Bin Liu · 2024 · Science China Information Sciences
48. Industrial Internet of Things With Large Language Models (LLMs): An Intelligence-Based Reinforcement Learning Approach
    Yuzheng Ren, Haijun Zhang, Fei Richard Yu, Wei Li, Pincan Zhao, Ying He · 2024 · IEEE Transactions on Mobile Computing
49. Large scale paired antibody language models
    Henry Kenlay, Frédéric A. Dreyer, Aleksandr Kovaltsuk, Dom Miketa, Douglas E. V. Pires, Charlotte Mary Deane · 2024 · PLoS Computational Biology
50. A review of deep learning methods for ligand based drug virtual screening
    Hongjie Wu, Junkai Liu, Runhua Zhang, Yaoyao Lu, Guozeng Cui, Zhiming Cui, Yijie Ding · 2024 · Fundamental Research
51. BIONIC: biological network integration using convolutions
    Duncan T. Forster, Sheena Claire Li, Yoko Yashiroda, Mami Yoshimura, Zhijian Li, Luis Alberto Vega Isuhuaylas, Kaori Itto‐Nakama, Daisuke Yamanaka, Yoshikazu Ohya, Hiroyuki Osada et al. · 2022 · Nature Methods
52. Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning
    Zilong Hou, Yuning Yang, Zhiqiang Ma, Ka‐Chun Wong, Xiangtao Li · 2023 · Communications Biology
53. Progress and opportunities of foundation models in bioinformatics
    Qing Li, Zhihang Hu, Yixuan Wang, Lei Li, Yimin Fan, Irwin King, Gengjie Jia, Sheng Wang, Le Song, Yu Li · 2024 · Briefings in Bioinformatics
54. Evaluating the representational power of pre-trained DNA language models for regulatory genomics
    Ziqi Tang, Nirali Somia, Yiyang Yu, Peter K. Koo · 2025 · Genome biology
55. Persistent spectral theory-guided protein engineering
    2023
56. AI for targeted polypharmacology: The next frontier in drug discovery
    Anna Cichońska, Balaguru Ravikumar, Rayees Rahman · 2024 · Current Opinion in Structural Biology
57. DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning
    Jonghyun Lee, Dae Won Jun, Ildae Song, Yun Kim · 2024 · Journal of Cheminformatics
58. A comprehensive review and comparison of existing computational methods for protein function prediction
    Baohui Lin, Xiaoling Luo, Yumeng Liu, Xiaopeng Jin · 2024 · Briefings in Bioinformatics
59. Integration of pre-trained protein language models into geometric deep learning networks
    Fang Wu, Lirong Wu, Dragomir Radev, Jinbo Xu, Stan Ziqing Li · 2023 · Communications Biology
60. MolPROP: Molecular Property prediction with multimodal language and graph fusion
    Zachary A. Rollins, Alan C. Cheng, Essam Metwally · 2024 · Journal of Cheminformatics
61. From large language models to multimodal AI: a scoping review on the potential of generative AI in medicine
    Lukas Buess, Matthias Keicher, Nassir Navab, Andreas Maier, Soroosh Tayebi Arasteh · 2025 · Biomedical Engineering Letters
62. Deciphering microbial gene function using natural language processing
    Danielle Miller, Adi Stern, David Burstein · 2022 · Nature Communications
63. In vitro continuous protein evolution empowered by machine learning and automation
    Tianhao Yu, Aashutosh Girish Boob, Nilmani Singh, Yufeng Su, Huimin Zhao · 2023 · Cell Systems
64. Generative models for protein sequence modeling: recent advances and future directions
    Mehrsa Mardikoraem, Zirui Wang, Nathaniel Pascual, Daniel Woldring · 2023 · Briefings in Bioinformatics
65. Embedding-based alignment: combining protein language models with dynamic programming alignment to detect structural similarities in the twilight-zone
    Pantolini, Lorenzo, Studer, Gabriel, Pereira, Joana, Durairaj, Janani, Tauriello, Gerardo, Schwede, Torsten · 2024 · Bioinformatics
66. Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts
    Mohammed Majid Abdulrazzaq, Nehad T. A. Ramaha, Alaa Ali Hameed, Mohammad Salman, Dong Keon Yon, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee · 2024 · Mathematics
67. xTrimoPGLM: unified 100-billion-parameter pretrained transformer for deciphering the language of proteins
    Bo Chen, Xingyi Cheng, Pan Li, Yangli‐ao Geng, Jing Gong, Shen Li, Zhilei Bei, Xu Tan, Boyan Wang, Xin Zeng et al. · 2025 · Nature Methods
68. BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
    Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, Rui Yan · 2023 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
69. Large language models for science and medicine
    Amalio Telenti, Michael Auli, Brian Hie, Cyrus Maher, Suchi Saria, John P. A. Ioannidis · 2024 · European Journal of Clinical Investigation
70. MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction
    Jingjing Wang, Zhijiang Yang, Chang Chen, Yao Ge, Xiukun Wan, Shaoheng Bao, Junjie Ding, Liangliang Wang, Hui Jiang · 2024 · Briefings in Bioinformatics
71. ProT‐Diff: A Modularized and Efficient Strategy for De Novo Generation of Antimicrobial Peptide Sequences by Integrating Protein Language and Diffusion Models
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72. Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools
    Varun Dewaker, Vivek Kumar Morya, Yoo Hee Kim, Sung Taek Park, Hyeong Su Kim, Young Ho Koh · 2025 · Biomarker Research
73. Generalized biological foundation model with unified nucleic acid and protein language
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74. Generative power of a protein language model trained on multiple sequence alignments
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75. Linguistically inspired roadmap for building biologically reliable protein language models
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76. Large language models for drug discovery and development
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77. ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design
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78. DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis
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79. SETH predicts nuances of residue disorder from protein embeddings
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80. Leveraging machine learning models for peptide–protein interaction prediction
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81. Computationally restoring the potency of a clinical antibody against Omicron
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82. Deep learning methods for protein function prediction
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83. A text-guided protein design framework
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84. Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14
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85. Using machine learning to predict the effects and consequences of mutations in proteins
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86. Antibody design using deep learning: from sequence and structure design to affinity maturation
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87. Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review
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88. Protein subcellular localization prediction tools
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89. Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models
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90. An analysis of protein language model embeddings for fold prediction
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91. Interpretable feature extraction and dimensionality reduction in ESM2 for protein localization prediction
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92. PepNet: an interpretable neural network for anti-inflammatory and antimicrobial peptides prediction using a pre-trained protein language model
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93. pNPs-CapsNet: Predicting Neuropeptides Using Protein Language Models and FastText Encoding-Based Weighted Multi-View Feature Integration with Deep Capsule Neural Network
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94. A trimodal protein language model enables advanced protein searches
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95. Understanding mutation hotspots for the SARS-CoV-2 spike protein using Shannon Entropy and K-means clustering
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96. Harnessing the power of artificial intelligence to advance cell therapy
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97. Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein
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98. pLM4Alg: Protein Language Model-Based Predictors for Allergenic Proteins and Peptides
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99. Deep self-supervised learning for biosynthetic gene cluster detection and product classification
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100. Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins
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