Modeling the language of life – Deep Learning Protein Sequences

Cited by (599)

Items 101–200 of 599

1. Pre-Training of Deep Bidirectional Protein Sequence Representations With Structural Information
   Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon · 2021 · IEEE Access
2. nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning
   Yongzi Chen, Zhuozhi Wang, Yanan Wang, Guoguang Ying, Zhen Chen, Jiangning Song · 2021 · Briefings in Bioinformatics
3. Deep learning methods for protein function prediction
   Boadu, Frimpong, Lee, Ahhyun, Cheng, Jianlin · 2025 · PROTEOMICS
4. The generative capacity of probabilistic protein sequence models
   Francisco A. McGee, Sandro Hauri, Quentin Novinger, Slobodan Vučetić, Ronald M. Levy, Vincenzo Carnevale, Allan Haldane · 2021 · Nature Communications
5. Deep learning for protein secondary structure prediction: Pre and post-AlphaFold
   Dewi Pramudi Ismi, Reza Pulungan, Afiahayati · 2022 · Computational and Structural Biotechnology Journal
6. Revolutionizing protein–protein interaction prediction with deep learning
   Jing Zhang, Jesse Durham, Qian Cong · 2024 · Current Opinion in Structural Biology
7. Protein subcellular localization prediction tools
   Maryam Gillani, Gianluca Pollastri · 2024 · Computational and Structural Biotechnology Journal
8. PANDA2: protein function prediction using graph neural networks
   Chenguang Zhao, Tong Liu, Zheng Wang · 2022 · NAR Genomics and Bioinformatics
9. Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning
   Alex Xijie Lu, Amy X. Lu, Iva Pritišanac, Taraneh Zarin, Julie Deborah Forman-Kay, Alan M Moses · 2022 · PLoS Computational Biology
10. SETH predicts nuances of residue disorder from protein embeddings
    Dagmar Ilzhöfer, Michael Heinzinger, Burkhard Rost · 2022 · Frontiers in Bioinformatics
11. Are protein language models the new universal key?
    Konstantin Weißenow, Burkhard Rost · 2025 · Current Opinion in Structural Biology
12. pNPs-CapsNet: Predicting Neuropeptides Using Protein Language Models and FastText Encoding-Based Weighted Multi-View Feature Integration with Deep Capsule Neural Network
    Shahid Akbar, Ali Raza, Hamid Hussain Awan, Quan Zou, Wajdi Alghamdi, Aamir Saeed · 2025 · ACS Omega
13. SonicParanoid2: fast, accurate, and comprehensive orthology inference with machine learning and language models
    2024
14. Deep learning for protein complex structure prediction
    Patrick Bryant · 2023 · Current Opinion in Structural Biology
15. ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein–DNA binding site prediction
    Yi-Heng Zhu, Zi Liu, Yan Liu, Zhi-Wei Ji, Dong-Jun Yu · 2024 · Briefings Bioinform.
16. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy
    Alla Bulashevska, Zsófia Nacsa, Franziska Lang, Markus Braun, Martin Machyna, Mustafa Diken, Liam Harold Childs, Renate König · 2024 · Frontiers in Immunology
17. SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model
    Jaspreet Singh, Thomas Litfin, Jaswinder Singh, Kuldip K. Paliwal, Yaoqi Zhou · 2022 · Bioinformatics
18. Factorbook: an updated catalog of transcription factor motifs and candidate regulatory motif sites
    Henry Pratt, Gregory Andrews, Nishigandha N. Phalke, Jack D. Huey, Michael J. Purcaro, Arjan van der Velde, Jill E. Moore, Zhiping Weng · 2021 · Nucleic Acids Research
19. SPOT-1D-Single: improving the single-sequence-based prediction of protein secondary structure, backbone angles, solvent accessibility and half-sphere exposures using a large training set and ensembled deep learning
    Jaspreet Singh, Thomas Litfin, Kuldip K. Paliwal, Jaswinder Singh, Anil Kumar Hanumanthappa, Yaoqi Zhou · 2021 · Bioinformatics
20. An analysis of protein language model embeddings for fold prediction
    Villegas-Morcillo, Amelia, Gomez, Angel M, Sanchez, Victoria · 2022 · Briefings in Bioinformatics
21. E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants
    Matteo Manfredi, Castrense Savojardo, Pier Luigi Martelli, Rita Casadio · 2022 · Bioinformatics
22. How to approach machine learning-based prediction of drug/compound–target interactions
    Heval Ataş, Tunca Doğan · 2023 · Journal of Cheminformatics
23. DeepPTM: Protein Post-translational Modification Prediction from Protein Sequences by Combining Deep Protein Language Model with Vision Transformers
    Necla Nisa Soylu, Emre Sefer · 2024 · Current Bioinformatics
24. Practical Applications of Language Models in Protein Sorting Prediction: SignalP 6.0, DeepLoc 2.1, and DeepLocPro 1.0
    Henrik Nielsen · 2025 · Methods in molecular biology
25. Association Analysis: Basic Concepts and Algorithms
    Qingfeng Chen · 2024 · Springer eBooks
26. Transfer learning in proteins: evaluating novel protein learned representations for bioinformatics tasks
    Emilio Fenoy, Alejando A Edera, Georgina S. Stegmayer · 2022 · Briefings in Bioinformatics
27. BERT2OME: Prediction of 2′-O-Methylation Modifications From RNA Sequence by Transformer Architecture Based on BERT
    Necla Nisa Soylu, Emre Sefer · 2023 · IEEE Transactions on Computational Biology and Bioinformatics
28. Protein Design with Deep Learning
    Marianne Defresne, Sophie Barbe, Thomas Schiex · 2021 · International Journal of Molecular Sciences
29. Fast, sensitive detection of protein homologs using deep dense retrieval
    Liang Hong, Zhigang Hu, Siqi Sun, Xiangru Tang, Jiuming Wang, Qingxiong Tan, Liangzhen Zheng, Sheng Wang, Sheng Yong Xu, Irwin King et al. · 2024 · Nature Biotechnology
30. Computational methods for protein localization prediction
    Yuexu Jiang, Duolin Wang, Weiwei Wang, Dong He Xu · 2021 · Computational and Structural Biotechnology Journal
31. Synthetic molecular evolution of antimicrobial peptides
    Charles H. Chen, Tristan Bepler, Karen Pepper, Debbie Fu, Timothy K. Lu · 2022 · Current Opinion in Biotechnology
32. Context-aware sentiment analysis with attention-enhanced features from bidirectional transformers
    Surindernath Sivakumar, R. Rajalakshmi · 2022 · Social Network Analysis and Mining
33. Medium-sized protein language models perform well at transfer learning on realistic datasets
    Vieira, Luiz C., Handojo, Morgan L., Wilke, Claus O. · 2025 · Scientific Reports
34. Learning deep representations of enzyme thermal adaptation
    Gang Li, Filip Buric, Jan Zrimec, Sandra Viknander, Jens Nielsen, Aleksej Zelezniak, Martin K. M. Engqvist · 2022 · Protein Science
35. Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data
    Feifei Cui, Zilong Zhang, Chen Cao, Quan Zou, Dong Chen, Xi Su · 2022 · PROTEOMICS
36. Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics
    An-Qi Lin, Junpu Ye, Chang Qi, Lingxuan Zhu, Wei-Ming Mou, Wen-Yi Gan, Dong-Qiang Zeng, Bu-Fu Tang, Ming-Jia Xiao, G. Chu et al. · 2025 · Briefings Bioinform.
37. Nearest neighbor search on embeddings rapidly identifies distant protein relations
    Konstantin Schütze, Michael Heinzinger, Martin Steinegger, Burkhard Rost · 2022 · Frontiers in Bioinformatics
38. DeepHomo2.0: improved protein–protein contact prediction of homodimers by transformer-enhanced deep learning
    Peicong Lin, Yumeng Yan, Sheng‐You Huang · 2022 · Briefings in Bioinformatics
39. Therapeutic enzyme engineering using a generative neural network
    Andrew J. Giessel, Athanasios D. Dousis, Kanchana R. Ravichandran, Kevin Smith, Sreyoshi Sur, Iain J. McFadyen, Wei Zheng, Stuart Licht · 2022 · Scientific Reports
40. Domain loss enabled evolution of novel functions in the snake three-finger toxin gene superfamily
    Ivan Koludarov, Tobias Senoner, Timothy Jackson, Daniel Dashevsky, Michael Heinzinger, Steven Douglas Aird, Burkhard Rost · 2023 · Nature Communications
41. Accelerating the discovery of antifungal peptides using deep temporal convolutional networks
    Vishakha Singh, Sameer Shrivastava, Sanjay Kumar Singh, Abhinav Kumar, Sonal Saxena · 2022 · Briefings in Bioinformatics
42. Deep Learning for Antimicrobial Peptides: Computational Models and Databases
    Xiangrun Zhou, Guixia Liu, Shuyuan Cao, Ji Lv · 2025 · Journal of Chemical Information and Modeling
43. Omics Data and Data Representations for Deep Learning-Based Predictive Modeling
    Stefanos Tsimenidis, Εleni Vrochidou, George A. Papakostas · 2022 · International Journal of Molecular Sciences
44. Pretraining model for biological sequence data
    Bosheng Song, Zimeng Li, Xuan Lin, Jianmin Wang, Tian Wang, Xiangzheng Fu · 2021 · Briefings in Functional Genomics
45. Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings
    Sumit Madan, Victoria Demina, Marcus Stapf, Oliver Ernst, Holger Fröhlich · 2022 · Patterns
46. Automatic Gene Function Prediction in the 2020’s
    Stavros Makrodimitris, Roeland C. H. J. van Ham, Marcel J. T. Reinders · 2020 · Genes
47. PyPEF—An Integrated Framework for Data-Driven Protein Engineering
    Niklas E. Siedhoff, Alexander-Maurice Illig, Ulrich Schwaneberg, Mehdi D. Davari · 2021 · Journal of Chemical Information and Modeling
48. MM-StackEns: A new deep multimodal stacked generalization approach for protein–protein interaction prediction
    Alexandra-Ioana Albu, Maria-Iuliana Bocicor, Gabriela Czibula · 2023 · Computers in Biology and Medicine
49. Variant effect predictions capture some aspects of deep mutational scanning experiments
    Jonas Reeb, Theresa Wirth, Burkhard Rost · 2020 · BMC Bioinformatics
50. Accurate and efficient protein sequence design through learning concise local environment of residues
    Huang, Bin, Fan, Tingwen, Wang, Kaiyue, Zhang, Haicang, Yu, Chungong, Nie, Shuyu, Qi, Yangshuo, Zheng, Wei-Mou, Han, Jian, Fan, Zheng et al. · 2023 · Bioinformatics
51. Natural Language Processing Methods for the Study of Protein–Ligand Interactions
    James Michels, Ramya Bandarupalli, Amin Ahangar Akbari, Thai Le, Hong Yu Xiao, Jing Li, Erik F. Y. Hom · 2025 · Journal of Chemical Information and Modeling
52. Deepro-Glu: combination of convolutional neural network and Bi-LSTM models using ProtBert and handcrafted features to identify lysine glutarylation sites
    Xiao Rui Wang, Zhaoyuan Ding, Rong Wang, Xi Lin · 2023 · Briefings in Bioinformatics
53. TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation
    Nanjun Chen, Jixiang Yu, Zhe Liu, Fuzhou Wang, Xiangtao Li, Ka‐Chun Wong · 2024 · Briefings in Bioinformatics
54. From Code to Life: The AI‐Driven Revolution in Genome Editing
    Zhidong Li, Wasi Ullah Khan, Genxiang Bai, Dong Chao, Jungang Wang, Youpeng Zhang, Chong Wang, Hongbin Zhang, Wenyi Wang, Ming Luo et al. · 2025 · Advanced Science
55. Beyond sequence: Structure-based machine learning
    Janani Durairaj, Dick de Ridder, Aalt D. J. van Dijk · 2022 · Computational and Structural Biotechnology Journal
56. HNetGO: protein function prediction via heterogeneous network transformer
    Xiaoshuai Zhang, Huannan Guo, Fan Zhang, Xuan Wang, Kaitao Wu, Shizheng Qiu, Bo Liu, Yadong Wang, Yang Hu, Junyi Li · 2021 · Briefings in Bioinformatics
57. Domain-PFP allows protein function prediction using function-aware domain embedding representations
    Nabil Ibtehaz, Yuki Kagaya, Daisuke Kihara · 2023 · Communications Biology
58. FANTASIA leverages language models to decode the functional dark proteome across the animal tree of life
    Gemma I. Martinez Redondo, Francisco M Pérez-Canales, Belén Carbonetto, José M. Fernández, Israel Barrios-Núñez, Marçal Vázquez-Valls, Ildefonso Cases, Ana Maria Rojas, Rosa Fernández · 2025 · Communications Biology
59. In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins
    Marco Anteghini, Vítor A. P. Martins dos Santos, Edoardo Saccenti · 2021 · International Journal of Molecular Sciences
60. ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning
    Nhat Truong Pham, Annie Terrina Terrance, Young-Jun Jeon, Rajan Rakkiyappan, Balachandran Manavalan · 2024 · Molecular Therapy — Nucleic Acids
61. Structure-informed protein language models are robust predictors for variant effects
    2024
62. Finding functional motifs in protein sequences with deep learning and natural language models
    Castrense Savojardo, Pier Luigi Martelli, Rita Casadio · 2023 · Current Opinion in Structural Biology
63. Approaching Optimal pH Enzyme Prediction with Large Language Models
    Mark Zaretckii, Pavel Buslaev, Igor Kozlovskii, Alexander Morozov, Petr A. Popov · 2024 · ACS Synthetic Biology
64. ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers
    Pascal Notin, Ruben Weitzman, Debora S. Marks, Yarin Gal · 2023 · neural information processing systems
65. OUP accepted manuscript
    Ryan Feehan, Daniel Montezano, Joanna S.G. Slusky · 2021 · Protein Engineering Design and Selection
66. GOProFormer: A Multi-Modal Transformer Method for Gene Ontology Protein Function Prediction
    Anowarul Kabir, Amarda Shehu · 2022 · Biomolecules
67. Survey of Protein Sequence Embedding Models
    Chau Thi Thuy Tran, Siddharth Khadkikar, Aleksey Porollo · 2023 · International Journal of Molecular Sciences
68. Protein embeddings improve phage-host interaction prediction
    Mark Edward M. Gonzales, Jennifer C. Ureta, Anish Man Singh Shrestha · 2023 · PLoS ONE
69. Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep Learning
    Despoina P. Kiouri, Georgios Batsis, Christos T. Chasapis · 2025 · Biomolecules
70. Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data
    Quintin Pope, Rohan Varma, Christine A. Tataru, Maude M. David, Xiaoli Z. Fern · 2025 · PLoS Computational Biology
71. Deep Learning Concepts and Applications for Synthetic Biology
    William Beardall, Guy-Bart V. Stan, Mary J. Dunlop · 2022 · GEN Biotechnology
72. Modeling Protein Using Large-scale Pretrain Language Model
    Yijia Xiao, Jiezhong Qiu, Ziang Li, Chang‐Yu Hsieh, Jie Tang · 2021 · arXiv (Cornell University)
73. Ultra-fast protein structure prediction to capture effects of sequence variation in mutation movies
    Konstantin Weißenow, Michael Heinzinger, Martin Steinegger, Burkhard Rost · 2022 · bioRxiv (Cold Spring Harbor Laboratory)
74. Protein embeddings predict binding residues in disordered regions
    2024
75. Clustering FunFams using sequence embeddings improves EC purity
    Maria Littmann, Nicola Bordin, Michael Heinzinger, Konstantin Schütze, Christian Dallago, Christine Anne Orengo, Burkhard Rost · 2021 · Bioinformatics
76. HemoNet: Predicting hemolytic activity of peptides with integrated feature learning
    Adiba Yaseen, Sadaf Gull, Naeem Akhtar, Imran Ul Amin, Fayyaz Minhas · 2021 · Journal of Bioinformatics and Computational Biology
77. Predicting synergistic anticancer drug combination based on low-rank global attention mechanism and bilinear predictor
    Yanglan Gan, Xingyu Huang, Wenjing Guo, Cairong Yan, Guobing Zou · 2023 · Bioinformatics
78. Seq-InSite: sequence supersedes structure for protein interaction site prediction
    Hosseini, SeyedMohsen, Golding, G Brian, Ilie, Lucian · 2024 · Bioinformatics
79. Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning
    Shijie Xu, Akira Onoda · 2023 · Journal of Chemical Information and Modeling
80. Protein Condensate Atlas from predictive models of heteromolecular condensate composition
    Kadi Liis Saar, Rob M. Scrutton, Kotryna Bloznelyte, Alexey S. Morgunov, Lydia L. Good, Alpha A. Lee, Sarah Amalia Teichmann, Tuomas P. J. Knowles · 2024 · Nature Communications
81. Comprehensive benchmarking of large language models for RNA secondary structure prediction
    Luciano I Zablocki, Leandro A. Bugnon, Matias F. Gerard, Leandro Ezequiel Di Persia, Georgina S. Stegmayer, Diego Humberto Milone · 2025 · Briefings in Bioinformatics
82. Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
    Fernando Lobo, Maily Selena González, Alicia Boto, José Manuel Pérez de la Lastra · 2023 · International Journal of Molecular Sciences
83. PROTSOLM: Protein Solubility Prediction with Multi-modal Features
    Yang Tan, Jia Zheng, Hong Liang, Bozitao Zhong · 2024 · IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
84. Artificial Intelligence-Based Model for Predicting the Minimum Inhibitory Concentration of Antibacterial Peptides Against ESKAPEE Pathogens
    Ritesh Sharma, Sameer Shrivastava, Sanjay Kumar Singh, Abhinav Kumar, Amit Kumar Singh, Sonal Saxena · 2023 · IEEE Journal of Biomedical and Health Informatics
85. Using protein language models for protein interaction hot spot prediction with limited data
    K. Sargsyan, C. Lim · 2024 · BMC Bioinformatics
86. Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants
    Yi‐Yan Yang, Keith Dufault‐Thompson, Wei Yan, Tian Cai, Lei Xie, Xiaofang Jiang · 2024 · GigaScience
87. Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network
    Vikas Upadhyay, Veda Sheersh Boorla, Costas D. Maranas · 2023 · Metabolic Engineering
88. Deep learning in preclinical antibody drug discovery and development
    Yuwei Zhou, Ziru Huang, Wenzhen Li, Jinyi Wei, Qianhu Jiang, Wei Yang, Jian Hua Huang · 2023 · Methods
89. Best practices for machine learning in antibody discovery and development
    Leonard Wossnig, Norbert Furtmann, Andrew G. Buchanan, Sandeep Kumar, Victor Greiff · 2024 · Drug Discovery Today
90. HybridGCN for protein solubility prediction with adaptive weighting of multiple features
    Chen, Long, Wu, Rining, Zhou, Feixiang, Zhang, Huifeng, Liu, Jian K. · 2023 · Journal of Cheminformatics
91. A unified evolution-driven deep learning framework for virus variation driver prediction
    Zhiwei Nie, Xudong Liu, Jie Chen, Zhennan Wang, Yutian Liu, Hao-Rui Si, Tianyi Dong, Fan Xu, Guoli Song, Yu Wang et al. · 2025 · Nature Machine Intelligence
92. Teaching AI to speak protein
    Michael Heinzinger, Burkhard Rost · 2025 · Current Opinion in Structural Biology
93. RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins
    Xinxin Peng, Xiaoyu Wang, Yuming Guo, Zongyuan Ge, Fuyi Li, Xin Gao, Jiangning Song · 2022 · Briefings in Bioinformatics
94. Benchmarking machine learning robustness in Covid-19 genome sequence classification
    Sarwan Ali, Bikram Sahoo, Alex Zelikovsky, Pin‐Yu Chen, Murray Patterson · 2023 · Scientific Reports
95. A Review for Artificial Intelligence Based Protein Subcellular Localization
    Hanyu Xiao, Yijin Zou, Jieqiong Wang, Shibiao Wan · 2024 · Biomolecules
96. PHIStruct: improving phage–host interaction prediction at low sequence similarity settings using structure-aware protein embeddings
    Gonzales, Mark Edward M, Ureta, Jennifer C, Shrestha, Anish M S · 2024 · Bioinformatics
97. Computational drug design in the artificial intelligence era: A systematic review of molecular representations, generative architectures, and performance assessment
    Karim Abbasi, Parvin Razzaghi, Ali Gharizadeh, Amin Ghareyazi, Abbas Dehnad, Hamid Reza Rabiee, Mohammad R. K. Mofrad · 2025 · Pharmacological Reviews
98. Machine learning methods for predicting protein structure from single sequences
    Shaun M. Kandathil, Andy M C Lau, David T. Jones · 2023 · Current Opinion in Structural Biology
99. Hybrid protein–ligand binding residue prediction with protein language models: does the structure matter?
    Gamouh, Hamza, Novotný, Marian, Hoksza, David · 2025 · Bioinformatics
100. Deep Local Analysis deconstructs protein–protein interfaces and accurately estimates binding affinity changes upon mutation
     Mohseni Behbahani, Yasser, Laine, Elodie, Carbone, Alessandra · 2023 · Bioinformatics