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Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design.
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The difficulty of protein structure alignment under the rmsd
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lddt: a local superposition-free score for comparing protein structures and models using distance difference tests
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A large-scale evaluation of computational protein function prediction
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Thomas J Magliery. 2015 · 2015
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Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches
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Uniprotkb/swiss-prot, the manually annotated section of the uniprot knowledgebase: how to use the entry view
Emmanuel Boutet, Damien Lieberherr, Michael Tognolli, Michel Schneider, Parit Bansal, Alan J Bridge, Sylvain Poux, Lydie Bougueleret, and Ioannis Xenarios. 2016 · 2016
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Protein–sol: a web tool for predicting protein solubility from sequence
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Clustering huge protein sequence sets in linear time
Martin Steinegger and Johannes Söding. 2018 · 2018
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Unified rational protein engineering with sequence-based deep representation learning
Ethan C Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M Church. 2019 · 2019
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Learning protein sequence embeddings using information from structure
Tristan Bepler and Bonnie Berger. 2019 · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Using deep neural networks to reconstruct non-uniformly sampled nmr spectra
D Flemming Hansen. 2019 · 2019
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Critical assessment of methods of protein structure prediction (casp)—round xiii
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Advances in protein structure prediction and design
Brian Kuhlman and Philip Bradley. 2019 · 2019
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Challenges and opportunities in cryo-em single-particle analysis
Dmitry Lyumkis. 2019 · 2019
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Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, and Yun Song. 2019 · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
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Dali and the persistence of protein shape
Liisa Holm. 2020 · 2020
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Fast reconstruction of non-uniform sampling multidimensional nmr spectroscopy via a deep neural network
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Mutation effect estimation on protein–protein interactions using deep contextualized representation learning
Guangyu Zhou, Muhao Chen, Chelsea JT Ju, Zheng Wang, Jyun-Yu Jiang, and Wei Wang. 2020 · 2020
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Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N Kinch, R Dustin Schaeffer, et al. 2021 · 2021
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Artificial intelligence to solve the x-ray crystallography phase problem: a case study report
Irène Barbarin-Bocahu and Marc Graille. 2021 · 2021
Cited alongside, same era.
Fold2seq: A joint sequence (1d)-fold (3d) embedding-based generative model for protein design
Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, and Yang Shen. 2021 · 2021
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Deep learning-based mixed-dimensional gaussian mixture model for characterizing variability in cryo-em
Muyuan Chen and Steven J Ludtke. 2021 · 2021
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Prottrans: Toward understanding the language of life through self-supervised learning
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, et al. 2021 · 2021
Cited alongside, same era.
Structure-based protein function prediction using graph convolutional networks
Vladimir Gligorijević, P Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C Taylor, Ian M Fisk, Hera Vlamakis, et al. 2021 · 2021
Zero-shot mutation effect prediction on protein stability and function using rosettafold
Sanaa Mansoor, Minkyung Baek, David Juergens, Joseph L Watson, and David Baker. 2023 · 2023
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Leveraging protein language models for accurate multiple sequence alignments
Claire D McWhite, Isabel Armour-Garb, and Mona Singh. 2023 · 2023
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Progen2: exploring the boundaries of protein language models
Erik Nijkamp, Jeffrey A Ruffolo, Eli N Weinstein, Nikhil Naik, and Ali Madani. 2023 · 2023
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Proteingym: Large-scale benchmarks for protein fitness prediction and design
Pascal Notin, Aaron Kollasch, Daniel Ritter, Lood Van Niekerk, Steffanie Paul, Han Spinner, Nathan Rollins, Ada Shaw, Rose Orenbuch, Ruben Weitzman, et al. 2023 · 2023
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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, and Rui Yan. 2023 · 2023
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Cited alongside, same era.
Cryogan: A new reconstruction paradigm for single-particle cryo-em via deep adversarial learning
Harshit Gupta, Michael T McCann, Laurene Donati, and Michael Unser. 2021 · 2021
Cited alongside, same era.
Msa-conditioned generative protein language models for fitness landscape modelling and design
Alex Hawkins-Hooker, David T Jones, and Brooks Paige. 2021 · 2021
Cited alongside, same era.
Learning the language of viral evolution and escape
Brian Hie, Ellen D Zhong, Bonnie Berger, and Bryan Bryson. 2021 · 2021
Cited alongside, same era.
Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael JL Townshend, and Ron Dror. 2021 · 2021
Cited alongside, same era.
Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al. 2021 · 2021
Cited alongside, same era.
Fid-net: A versatile deep neural network architecture for nmr spectral reconstruction and virtual decoupling
Gogulan Karunanithy and D Flemming Hansen. 2021 · 2021
Cited alongside, same era.
The generative capacity of probabilistic protein sequence models
Francisco McGee, Sandro Hauri, Quentin Novinger, Slobodan Vucetic, Ronald M Levy, Vincenzo Carnevale, and Allan Haldane. 2021 · 2021
Cited alongside, same era.
3dflex: determining structure and motion of flexible proteins from cryo-em
Ali Punjani and David J Fleet. 2023 · 2023
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Iglm: Infilling language modeling for antibody sequence design
Richard W Shuai, Jeffrey A Ruffolo, and Jeffrey J Gray. 2023 · 2023
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Biomolecular nmr spectroscopy in the era of artificial intelligence
Vaibhav Kumar Shukla, Gabriella T Heller, and D Flemming Hansen. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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Poet: A generative model of protein families as sequences-of-sequences
Timothy Truong Jr and Tristan Bepler. 2023 · 2023
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De novo design of protein structure and function with rfdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al. 2023 · 2023
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Integration of pre-trained protein language models into geometric deep learning networks
Fang Wu, Lirong Wu, Dragomir Radev, Jinbo Xu, and Stan Z Li. 2023 · 2023
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Enzyme function prediction using contrastive learning
Tianhao Yu, Haiyang Cui, Jianan Canal Li, Yunan Luo, Guangde Jiang, and Huimin Zhao. 2023 · 2023
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Structure-informed language models are protein designers
Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, and Quanquan Gu. 2023 · 2023
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Protein representation learning via knowledge enhanced primary structure modeling
Hong-Yu Zhou, Yunxiang Fu, Zhicheng Zhang, Cheng Bian, and Yizhou Yu. 2023 · 2023
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Prot2text: Multimodal protein’s function generation with gnns and transformers
Hadi Abdine, Michail Chatzianastasis, Costas Bouyioukos, and Michalis Vazirgiannis. 2024 · 2024
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Generative models in protein engineering: A comprehensive survey
Xinhui Chen, Yiwen Yuan, Joseph Liu, Chak Tou Leong, Xiaoye Zhu, and Jiaqi Chen. 2024c · 2024
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Toward de novo protein design from natural language
Fengyuan Dai, Yuliang Fan, Jin Su, Chentong Wang, Chenchen Han, Xibin Zhou, Jianming Liu, Hui Qian, Shunzhi Wang, Anping Zeng, et al. 2024 · 2024
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Exploring the application of sitemap and site finder for focused cryptic pocket identification
Yunhui Ge, Vineet Pande, Mark J Seierstad, and Kelly L Damm-Ganamet. 2024 · 2024
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Protagents: protein discovery via large language model multi-agent collaborations combining physics and machine learning
Alireza Ghafarollahi and Markus J Buehler. 2024 · 2024
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Protein remote homology detection and structural alignment using deep learning
Tymor Hamamsy, James T Morton, Robert Blackwell, Daniel Berenberg, Nicholas Carriero, Vladimir Gligorijevic, Charlie EM Strauss, Julia Koehler Leman, Kyunghyun Cho, and Richard Bonneau. 2024 · 2024
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De novo generation of sars-cov-2 antibody cdrh3 with a pre-trained generative large language model
Haohuai He, Bing He, Lei Guan, Yu Zhao, Feng Jiang, Guanxing Chen, Qingge Zhu, Calvin Yu-Chian Chen, Ting Li, and Jianhua Yao. 2024 · 2024
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Bilingual language model for protein sequence and structure
Michael Heinzinger, Konstantin Weissenow, Joaquin Gomez Sanchez, Adrian Henkel, Milot Mirdita, Martin Steinegger, and Burkhard Rost. 2024 · 2024
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Trustllm: Trustworthiness in large language models
Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, et al. 2024 · 2024
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Prollm: protein chain-of-thoughts enhanced llm for protein-protein interaction prediction
Mingyu Jin, Haochen Xue, Zhenting Wang, Boming Kang, Ruosong Ye, Kaixiong Zhou, Mengnan Du, and Yongfeng Zhang. 2024 · 2024
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Examining imbalance effects on performance and demographic fairness of clinical language models
Precious Jones, Weisi Liu, I-Chan Huang, and Xiaolei Huang. 2024 · 2024
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Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data
Huan Yee Koh, Anh TN Nguyen, Shirui Pan, Lauren T May, and Geoffrey I Webb. 2024 · 2024
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From words to molecules: A survey of large language models in chemistry
Chang Liao, Yemin Yu, Yu Mei, and Ying Wei. 2024 · 2024
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Prollama: A protein large language model for multi-task protein language processing
Liuzhenghao Lv, Zongying Lin, Hao Li, Yuyang Liu, Jiaxi Cui, Calvin Yu-Chian Chen, Li Yuan, and Yonghong Tian. 2024 · 2024
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Integration of alphafold with molecular dynamics for efficient conformational sampling of transporter protein nark
Jun Ohnuki and Kei-ichi Okazaki. 2024 · 2024
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Biot5+: Towards generalized biological understanding with iupac integration and multi-task tuning
Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, and Rui Yan. 2024 · 2024
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Toursynbio: A multi-modal large model and agent framework to bridge text and protein sequences for protein engineering
Yiqing Shen, Zan Chen, Michail Mamalakis, Yungeng Liu, Tianbin Li, Yanzhou Su, Junjun He, Pietro Liò, and Yu Guang Wang. 2024b · 2024
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Interplm: Discovering interpretable features in protein language models via sparse autoencoders
Elana Simon and James Zou. 2024 · 2024
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Saprot: Protein language modeling with structure-aware vocabulary
Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, and Fajie Yuan. 2024 · 2024
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Structure-informed protein language models are robust predictors for variant effects
Yuanfei Sun and Yang Shen. 2024 · 2024
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Simple, efficient, and scalable structure-aware adapter boosts protein language models
Yang Tan, Mingchen Li, Bingxin Zhou, Bozitao Zhong, Lirong Zheng, Pan Tan, Ziyi Zhou, Huiqun Yu, Guisheng Fan, and Liang Hong. 2024 · 2024
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Fast and accurate protein structure search with foldseek
Michel Van Kempen, Stephanie S Kim, Charlotte Tumescheit, Milot Mirdita, Jeongjae Lee, Cameron LM Gilchrist, Johannes Söding, and Martin Steinegger. 2024 · 2024
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Recent advances in interpretable machine learning using structure-based protein representations
Luiz Felipe Vecchietti, Minji Lee, Begench Hangeldiyev, Hyunkyu Jung, Hahnbeom Park, Tae-Kyun Kim, Meeyoung Cha, and Ho Min Kim. 2024 · 2024
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Proteinclip: enhancing protein language models with natural language
Kevin E Wu, Howard Chang, and James Zou. 2024a · 2024
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Annotation-guided protein design with multi-level domain alignment
Chaohao Yuan, Songyou Li, Geyan Ye, Yikun Zhang, Long-Kai Huang, Wenbing Huang, Wei Liu, Jianhua Yao, and Yu Rong. 2024 · 2024
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Structure-informed protein language model
Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie Lozano, Payel Das, and Jian Tang. 2024 · 2024
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Ccpl: Cross-modal contrastive protein learning
Jiangbin Zheng and Stan Z Li. 2024 · 2024
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Protllm: An interleaved protein-language llm with protein-as-word pre-training
Le Zhuo, Zewen Chi, Minghao Xu, Heyan Huang, Heqi Zheng, Conghui He, Xian-Ling Mao, and Wentao Zhang. 2024 · 2024
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Simulating 500 million years of evolution with a language model
Thomas Hayes, Roshan Rao, Halil Akin, Nicholas J Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q Tran, Jonathan Deaton, Marius Wiggert, et al. 2025 · 2025
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S-plm: Structure-aware protein language model via contrastive learning between sequence and structure
Duolin Wang, Mahdi Pourmirzaei, Usman L Abbas, Shuai Zeng, Negin Manshour, Farzaneh Esmaili, Biplab Poudel, Yuexu Jiang, Qing Shao, Jin Chen, et al. 2025 · 2025
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Integrating protein language models and automatic biofoundry for enhanced protein evolution
Qiang Zhang, Wanyi Chen, Ming Qin, Yuhao Wang, Zhongji Pu, Keyan Ding, Yuyue Liu, Qunfeng Zhang, Dongfang Li, Xinjia Li, et al. 2025 · 2025
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