Pith. sign in

REVIEW 5 major objections 5 minor 2 cited by

A Comprehensive Review of Protein Language Models

T0 review · 5 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper provides a systematic review of protein language models across architectures, position encoding, scaling laws, datasets, benchmarks, applications, and tools.

desk verdict A useful but uneven PLM catalog whose 'systematic' label is not backed by methodology and whose factual errors are fixable—worth a round of major revision before it can serve as an entry point. read the letter →

arxiv 2502.06881 v1 pith:E77RJTJH submitted 2025-02-08 q-bio.BM

Lei Wang , Xudong Li , Han Zhang , Jinyi Wang , Dingkang Jiang , Zhidong Xue , Yan Wang This is my paper · ORCID
classification q-bio.BM
keywords proteinlanguagemodelssurveysequencerepresentationTransformerarchitecturespositionencodingscalinglawspretrainingdatasetsdownstreamapplications
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Protein language models treat amino-acid sequences as text and learn representations from large unlabeled sequence databases. The paper argues that the field's growth has outpaced its overviews, with existing reviews confined to narrow subdomains, and it sets out to supply a macro-level map. The map is organized around the factors the authors say determine model behavior: architecture (from early non-transformer embeddings through encoder-only, decoder-only, and encoder-decoder transformers), position encoding, scaling laws, and pretraining data. The same framework carries through to benchmarks, downstream applications, tools, and a curated resource collection. A sympathetic reader comes away with a structured route into the field and its open problems.

What carries the argument

The organizing machinery is a four-way architectural taxonomy of protein language models, used as the spine of the review. Around that spine the authors place three further lenses: position encoding (absolute, relative, and rotary variants), scaling laws linking model size, data, and compute to performance, and pretraining dataset choice. The taxonomy groups the field's models so that representative examples can be compared on the same axes, and the benchmark and tool sections give the reader the instruments the field actually uses to judge them.

What would settle it

An independent audit of the tables and the companion resource collection against the cited primary papers would settle it: if a substantial fraction of entries give wrong parameter counts, wrong pretraining datasets, or dead or missing code links, or if well-known models published before the cutoff are absent, the claim of a systematic and accurate review fails.

Watch

Extended reading notes

Core claim

The central claim, stated in the introduction, is that a systematic review of protein language models from a macro perspective is now possible and needed, and that this paper delivers it. The authors organize the field into non-transformer and transformer-based architectures, split the latter into encoder-only, decoder-only, and encoder-decoder families, and treat position encoding, scaling laws, and pretraining datasets as the axes along which model behavior and comparison should be understood. They further claim that evaluation is inseparable from downstream tasks, so they tie benchmarks to structure, function, mutation, and design applications. The closing argument identifies MSA-free structure prediction and multimodal sequence-structure-function models as the current mainstream directions. If the paper is right, the field now has a usable taxonomy and a curated resource repository as entry points.

Load-bearing premise

The whole overview stands on the authors having chosen the right literature and described each model, dataset, and tool correctly; if their selection is skewed or their table entries misstate the cited papers, the review loses its usefulness.

Editorial extensions

If this is right

  • A newcomer can use the paper's taxonomy and resource collection to pick models, datasets, and benchmarks for a task without reconstructing the field's history from scattered papers.
  • The juxtaposition of scaling-law evidence and claims that protein language models are often undertrained implies that larger-scale training should continue to improve performance, and that the field's largest models are not yet at the point of diminishing returns.
  • The discussion of position encodings identifies rotary and relative encodings as the choices that handle long sequences best, which gives model builders a concrete design default.
  • The benchmark survey gives standard evaluation suites such as TAPE, PEER, ProteinGym, CASP, CAMEO, FLIP, and CAFA a single reference point, making cross-paper comparisons easier to situate.
  • The identified trends toward MSA-free and multimodal models point to where near-term progress is most likely to be concentrated.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The review collects the ingredients for a controlled comparison but does not run one: a testable extension would be to train or fine-tune models from the repository on matched data and compute to isolate the effect of architecture choice from dataset choice.
  • If the paper's claim that protein language models are more prone to underfitting than NLP models is correct, then the scaling-law section implies that modest increases in training tokens may yield larger gains than increases in parameters; the paper does not itself quantify this.
  • The review's focus on language models means structure-aware models are included mainly when they fuse a language model with structural tokens; a companion review of purely geometric models would be needed to see the full protein AI landscape.
  • The data-quality-versus-data-quantity debate noted in the discussion could be sharpened into a benchmark: compare models pretrained on redundancy-clustered versus metagenome-diverse data at fixed compute.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. This manuscript claims to provide a systematic review of protein language models (PLMs) from a macro perspective. It covers model architectures (non-transformer-based and transformer-based, subdivided into encoder-only, decoder-only, and encoder-decoder), positional encoding strategies, scaling laws, pre-training and benchmark datasets, downstream applications (structure prediction, function prediction, protein design, and mutation prediction), and several widely used computational tools. The paper also presents four model tables (Tables I-IV), two dataset/benchmark tables (Tables V-VI), and a GitHub repository collecting resources related to PLMs, datasets, and tools.

Significance. The planned organization of the review—grouping models by architecture, position encoding, scaling behavior, and datasets—is reasonable, and the compiled tables and companion GitHub repository are practical resources for researchers entering the field. If the catalog entries were accurate, the paper would be a useful broad survey. However, the value of a review of this type rests entirely on the correctness of its factual and citation details, and the manuscript currently contains several errors that affect the reliability of its central deliverable. The absence of a documented selection methodology also weakens the 'systematic' claim. These issues are fixable, but they require substantive revision rather than copy-editing.

major comments (5)
  1. [Section V.A.1, Pfam paragraph] The sentence 'Models like ProteinLM[47], TAPE[103], and ESM-1b[5] have been trained on Pfam' is factually incorrect for ESM-1b, whose pretraining corpus is UniRef50 according to the cited paper [5]. Please correct this entry in the catalog.
  2. [Section II.B.2, ProtTrans description] The text states that ProtTrans 'trains various autoencoder models (e.g., BERT, ALBERT, ELECTRA)', but BERT, ALBERT, and ELECTRA are not autoencoders; they are transformer encoders trained with masked language modeling or replaced-token detection. Please revise the architecture terminology.
  3. [Section V.B.2 and References [122]/[124]] References [122] and [124] are the same work (Zhang et al., arXiv:2203.06125), yet [122] is cited in the EC paragraph and [124] in the GO paragraph. The cited work is a structure pretraining paper, not the EC or GO benchmark source; please replace these citations with the correct benchmark references and eliminate the duplicate.
  4. [Section I and Abstract] The paper labels itself a 'systematic' and 'comprehensive' review, but no review methodology is documented anywhere in the manuscript. Please add a methods subsection stating the search strategy, databases consulted, inclusion/exclusion criteria, and the date the literature search was performed, so that the 'systematic' claim is verifiable.
  5. [Section VI.C, protein design paragraph] The sentence about RoseTTAFold cites [173–175], but [173] is the ProteinMPNN paper (Dauparas et al., 2022) and is not a RoseTTAFold reference; please adjust the citation span or add a dedicated RoseTTAFold citation.
minor comments (5)
  1. [Reference list] [92] is a duplicate of [35] (both are 'Alec Radford. Improving language understanding by generative pre-training. 2018'); please remove the duplicate and renumber.
  2. [Section VI.D] The phrase 'Joshua's findings' is informal and unclear; please replace with 'the findings of Meier et al.' or similar.
  3. [Section V.B.2, GO paragraph] The sentence 'GO annotations cover multiple species and play a positive role in cross-species gene function and evolutionary research' would read better as 'GO annotations cover multiple species and support cross-species gene function and evolutionary research'.
  4. [Section III.A] In the sentence 'Learned positional encoding often leads to better downstream performance for protein language models, as adopted by numerous PLMs like ESM-1b[5], and others[46, 64, 69]', the comma after '[5]' is misplaced; consider removing it.
  5. [Table II] The ESM-C row lists Time '2024.12' and a checkmark under Code; please verify the source and date, and clarify whether the code is publicly available.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a literature review with no derivations, predictions, or fitted parameters whose outputs reduce to its inputs.

full rationale

This manuscript is a survey of protein language models. It makes no quantitative predictions, fits no parameters, and derives no mathematical result. Its central claim is that it 'systematically review(s) the technological advances in PLMs,' and the substance of that claim is a catalog of models, datasets, benchmarks, applications, and tools. None of these catalog entries is derived from the paper's own definitions or from a self-citation chain; each is an external, checkable fact about the cited literature. The paper does cite some of the authors' own prior work (e.g., ProtFlash), but only as one item among many in a review list, and nothing in the review's structure or conclusions depends on accepting that self-citation as an unverified premise. The skeptical concern that the 'systematic' label is not backed by a documented search methodology, and that some citations mischaracterize their sources (e.g., ESM-1b described as Pfam-trained when the primary source uses UniRef50), is a correctness and reproducibility criticism, not circular reasoning: it does not show that any claimed result is equivalent to its inputs by construction. Accordingly, the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The review rests on the accuracy and representativeness of its cited literature. It introduces no free parameters or invented entities, so those lists are empty. The key axiom is that the characterizations of the cited models and datasets are correct; several evident citation and terminology errors indicate this axiom is not fully satisfied.

assumptions (1)
  • domain assumption The cited references and their characterizations in the tables and text accurately describe the respective protein language models.
    The review's value rests on the factual accuracy of its summaries; the paper provides no verification beyond the citations, and several citation mismatches (e.g., refs [122] and [124] identical but cited for different data sources) weaken this assumption.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Comprehensive Review of Protein Language Models." pith.science (2026). https://pith.science/paper/E77RJTJH

@misc{pith2026250206881,
  author       = {Pith},
  title        = {Pith review of: A Comprehensive Review of Protein Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E77RJTJH}},
  note         = {Machine review of arXiv:2502.06881}
}
read the original abstract

At the intersection of the rapidly growing biological data landscape and advancements in Natural Language Processing (NLP), protein language models (PLMs) have emerged as a transformative force in modern research. These models have achieved remarkable progress, highlighting the need for timely and comprehensive overviews. However, much of the existing literature focuses narrowly on specific domains, often missing a broader analysis of PLMs. This study provides a systematic review of PLMs from a macro perspective, covering key historical milestones and current mainstream trends. We focus on the models themselves and their evaluation metrics, exploring aspects such as model architectures, positional encoding, scaling laws, and datasets. In the evaluation section, we discuss benchmarks and downstream applications. To further support ongoing research, we introduce relevant mainstream tools. Lastly, we critically examine the key challenges and limitations in this rapidly evolving field.

Figures

Figures reproduced from arXiv: 2502.06881 by the authors.

Figure 1
Figure 1. The conceptual similarities and hierarchical structures observed in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The framework diagram of the paper. limited to language models. Although models based on other neural networks, such as CNNs and GNNs, have also played significant roles in the protein domain, they are not considered language models. Only when these networks are combined with language models (e.g., LM-GVP[8], which is a joint model of GNNs and language models) are they included in our discussion. II. MODEL ARCHITECT… view at source ↗
Figure 3
Figure 3. The architecture of transformer. tasks. The core of the GPT model is its unidirectionality, processing sequence data from left to right, which makes it widely applicable for sequence generation tasks[38]. Fig.4 presents the architectures of BERT and GPT, show￾casing their key design elements. BERT adopts a bidirectional transformer architecture for deep contextual understanding, while GPT employs a unidirectional tr… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: The architecture of BERT and GPT. 2) Encoder-only Models: Encoder-only models, such as BERT [34], are commonly used to encode protein sequences into fixed-length vector representations, extracting features for various downstream tasks. Notable examples include Meta’s E…
Figure 5
Figure 5. Figure 5: The evoformer module of AlphaFold2, which has now been replaced [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Applications of protein language models. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Variable-Length Generative Protein Design via Generalized Poisson Flow

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Generalized Poisson Flow learns variable protein length via an inhomogeneous Poisson rate plus within-length flow matching, with KL bounds and gains on structure, sequence, motif, and peptide tasks.

  2. Constrained Sliced Wasserstein Embedding

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Adding SWGG dissimilarity constraints to sliced Wasserstein embedding, trained via primal-dual optimization with a softsort relaxation, improves pooling accuracy on image, point cloud, and protein-sequence benchmarks.

Reference graph

Works this paper leans on

186 extracted references · 44 canonical work pages · cited by 2 Pith papers

  1. [124]

    Protein representation learning by geometric structure pretraining

    Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chen- thamarakshan, Aurelie Lozano, Payel Das, and Jian Tang. Protein representation learning by geometric structure pretraining. arXiv preprint arXiv:2203.06125, 2022

    arXiv 2022

  2. [92]

    Improving language understanding by generative pre-training

    Alec Radford. Improving language understanding by generative pre-training. 2018

    2018

  3. [47]

    Modeling protein using large-scale pre- train language model

    Yijia Xiao, Jiezhong Qiu, Ziang Li, Chang-Yu Hsieh, and Jie Tang. Modeling protein using large-scale pre- train language model. arXiv preprint arXiv:2108.07435, 2021

    arXiv 2021

  4. [103]

    Evaluating protein transfer learning with tape

    Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, and Yun Song. Evaluating protein transfer learning with tape. Advances in neural information processing systems , 32, 2019

    2019

  5. [5]

    Biolog- ical structure and function emerge from scaling un- supervised learning to 250 million protein sequences

    Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C Lawrence Zitnick, Jerry Ma, et al. Biolog- ical structure and function emerge from scaling un- supervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences , 118(15):e2016239118, 2021

    2021

  6. [173]

    Robust deep learning–based protein sequence design using proteinmpnn

    Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al. Robust deep learning–based protein sequence design using proteinmpnn. Science, 378(6615):49–56, 2022

    2022

  7. [1]

    Attention is all you need

    A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems , 2017

    2017

  8. [2]

    Nomenclature and symbolism for amino acids and pep- tides

    H BIELKA GDR, N Sharon, and EW Australia. Nomenclature and symbolism for amino acids and pep- tides. Pure and Applied Chemistry , 56:595–624, 1984

    1984

Show all 186 references
  1. [3]

    Principles that govern the folding of protein chains

    Christian B Anfinsen. Principles that govern the folding of protein chains. Science, 181(4096):223–230, 1973

    1973

  2. [4]

    Learning the pro- tein language: Evolution, structure, and function

    Tristan Bepler and Bonnie Berger. Learning the pro- tein language: Evolution, structure, and function. Cell systems, 12(6):654–669, 2021

    2021

  3. [6]

    Prottrans: Toward understanding the language of life through self-supervised learning

    Ahmed Elnaggar, Michael Heinzinger, Christian Dal- lago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, et al. Prottrans: Toward understanding the language of life through self-supervised learning. IEEE trans- actions on patt...

    2021

  4. [7]

    Fine-tuning protein language models boosts pre- dictions across diverse tasks

    Robert Schmirler, Michael Heinzinger, and Burkhard Rost. Fine-tuning protein language models boosts pre- dictions across diverse tasks. Nature Communications, 15(1):7407, 2024

    2024

  5. [8]

    Lm-gvp: an extensible sequence and structure informed deep learning frame- work for protein property prediction

    Zichen Wang, Steven A Combs, Ryan Brand, Miguel Romero Calvo, Panpan Xu, George Price, Na- taliya Golovach, Emmanuel O Salawu, Colby J Wise, Sri Priya Ponnapalli, et al. Lm-gvp: an extensible sequence and structure informed deep learning frame- work for protein property predic...

    2022

  6. [9]

    Deep learning in bioinformatics: Introduction, application, and perspective in the big data era

    Yu Li, Chao Huang, Lizhong Ding, Zhongxiao Li, Yijie Pan, and Xin Gao. Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods, 166:4–21, 2019

    2019

  7. [10]

    Efficient estimation of word represen- tations in vector space

    Tomas Mikolov. Efficient estimation of word represen- tations in vector space. arXiv preprint arXiv:1301.3781, 2013

    2013 arXiv

  8. [11]

    Con- tinuous distributed representation of biological se- quences for deep proteomics and genomics

    Ehsaneddin Asgari and Mohammad RK Mofrad. Con- tinuous distributed representation of biological se- quences for deep proteomics and genomics. PloS one, 10(11):e0141287, 2015

    2015

  9. [12]

    Uniprotkb/swiss-prot, the manually annotated section of the uniprot knowledgebase: how to use the entry view

    Emmanuel Boutet, Damien Lieberherr, Michael Tog- nolli, Michel Schneider, Parit Bansal, Alan J Bridge, Sylvain Poux, Lydie Bougueleret, and Ioannis Xenarios. Uniprotkb/swiss-prot, the manually annotated section of the uniprot knowledgebase: how to use the entry view. Plant bio...

    2016

  10. [13]

    Probabilistic variable-length segmen- tation of protein sequences for discriminative motif dis- JOURNAL OF LATEX CLASS FILES, VOL

    Ehsaneddin Asgari, Alice C McHardy, and Moham- mad RK Mofrad. Probabilistic variable-length segmen- tation of protein sequences for discriminative motif dis- JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, DECEMBER 2024 12 covery (dimotif) and sequence embedding (protvecx). Scie...

    2024

  11. [14]

    seq2vec: Analyzing sequential data using multi-rank embedding vectors

    Hwa Jong Kim, Seong Eun Hong, and Kyung Jin Cha. seq2vec: Analyzing sequential data using multi-rank embedding vectors. Electronic Commerce Research and Applications, 43:101003, 2020

    2020

  12. [15]

    Distributed represen- tations of sentences and documents

    Quoc Le and Tomas Mikolov. Distributed represen- tations of sentences and documents. In International conference on machine learning , pages 1188–1196. PMLR, 2014

    2014

  13. [16]

    Masked inverse folding with sequence transfer for protein representation learning

    Kevin K Yang, Niccol `o Zanichelli, and Hugh Yeh. Masked inverse folding with sequence transfer for protein representation learning. Protein Engineering, Design and Selection , 36:gzad015, 2023

    2023

  14. [17]

    Gradient-based learning applied to document recognition

    Yann LeCun, L ´eon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE , 86(11):2278– 2324, 1998

    1998

  15. [18]

    The graph neural network model

    Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE transactions on neural networks, 20(1):61–80, 2008

    2008

  16. [19]

    Convo- lutions are competitive with transformers for protein sequence pretraining

    Kevin K Yang, Nicolo Fusi, and Alex X Lu. Convo- lutions are competitive with transformers for protein sequence pretraining. Cell Systems , 15(3):286–294, 2024

    2024

  17. [20]

    Recurrent neural network regular- ization

    Wojciech Zaremba. Recurrent neural network regular- ization. arXiv preprint arXiv:1409.2329 , 2014

    2014 arXiv

  18. [21]

    Long short-term mem- ory

    Alex Graves and Alex Graves. Long short-term mem- ory. Supervised sequence labelling with recurrent neu- ral networks, pages 37–45, 2012

    2012

  19. [22]

    Unified rational protein engineering with sequence-based deep representation learning

    Ethan C Alley, Grigory Khimulya, Surojit Biswas, Mo- hammed AlQuraishi, and George M Church. Unified rational protein engineering with sequence-based deep representation learning. Nature methods, 16(12):1315– 1322, 2019

    2019

  20. [23]

    Multiplicative lstm for sequence modelling

    Ben Krause, Liang Lu, Iain Murray, and Steve Renals. Multiplicative lstm for sequence modelling. arXiv preprint arXiv:1609.07959, 2016

    2016 arXiv

  21. [24]

    Uniref: comprehensive and non-redundant uniprot reference clusters

    Baris E Suzek, Hongzhan Huang, Peter McGarvey, Raja Mazumder, and Cathy H Wu. Uniref: comprehensive and non-redundant uniprot reference clusters. Bioinfor- matics, 23(10):1282–1288, 2007

    2007

  22. [25]

    Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches

    Baris E Suzek, Yuqi Wang, Hongzhan Huang, Pe- ter B McGarvey, Cathy H Wu, and UniProt Consor- tium. Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics, 31(6):926–932, 2015

    2015

  23. [26]

    Udsmprot: universal deep sequence models for protein classification

    Nils Strodthoff, Patrick Wagner, Markus Wenzel, and Wojciech Samek. Udsmprot: universal deep sequence models for protein classification. Bioinformatics, 36(8):2401–2409, 2020

    2020

  24. [27]

    Regularizing and optimizing lstm language models

    Stephen Merity, Nitish Shirish Keskar, and Richard Socher. Regularizing and optimizing lstm language models. arXiv preprint arXiv:1708.02182 , 2017

    2017 arXiv

  25. [28]

    Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer

    Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations. CoRR, abs/1802.05365, 2018

    2018 arXiv

  26. [29]

    Modeling aspects of the language of life through transfer-learning protein sequences

    Michael Heinzinger, Ahmed Elnaggar, Yu Wang, Chris- tian Dallago, Dmitrii Nechaev, Florian Matthes, and Burkhard Rost. Modeling aspects of the language of life through transfer-learning protein sequences. BMC bioinformatics, 20:1–17, 2019

    2019

  27. [30]

    Faster and smaller n-gram language models

    Adam Pauls and Dan Klein. Faster and smaller n-gram language models. In Proceedings of the 49th annual meeting of the Association for Computational Linguis- tics: Human Language Technologies , pages 258–267, 2011

    2011

  28. [31]

    Optimizing multi-gpu parallelization strategies for deep learning training

    Saptadeep Pal, Eiman Ebrahimi, Arslan Zulfiqar, Yaosheng Fu, Victor Zhang, Szymon Migacz, David Nellans, and Puneet Gupta. Optimizing multi-gpu parallelization strategies for deep learning training. Ieee Micro, 39(5):91–101, 2019

    2019

  29. [32]

    Transformer-based deep learning for predicting protein properties in the life sciences

    Abel Chandra, Laura T ¨unnermann, Tommy L ¨ofstedt, and Regina Gratz. Transformer-based deep learning for predicting protein properties in the life sciences. Elife, 12:e82819, 2023

    2023

  30. [33]

    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, and Pan Tan. Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning. Nature Com- munications, 15(1):5566, 2024

    2024

  31. [34]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018

    2018 arXiv

  32. [36]

    A structured self-attentive sentence embedding

    Zhouhan Lin, Minwei Feng, Cicero Nogueira dos San- tos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130 , 2017

    2017 arXiv

  33. [37]

    Deciphering the protein landscape with protflash, a lightweight language model

    Lei Wang, Hui Zhang, Wei Xu, Zhidong Xue, and Yan Wang. Deciphering the protein landscape with protflash, a lightweight language model. Cell Reports Physical Science, 4(10), 2023

    2023

  34. [38]

    Unified language model pre-training for nat- ural language understanding and generation

    Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao- Wuen Hon. Unified language model pre-training for nat- ural language understanding and generation. Advances in neural information processing systems , 32, 2019

    2019

  35. [39]

    Language models en- able zero-shot prediction of the effects of mutations on protein function

    Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, and Alex Rives. Language models en- able zero-shot prediction of the effects of mutations on protein function. Advances in neural information processing systems, 34:29287–29303, 2021

    2021

  36. [41]

    Simulating 500 million years of evolution with a language model

    Tomas Hayes, Roshan Rao, Halil Akin, Nicholas J Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q Tran, Jonathan Deaton, Marius Wiggert, et al. Simulating 500 million years of evolution with a language model. bioRxiv, pages 2024–07, 2024. JOURNAL OF LATEX CLASS FIL...

    2024

  37. [42]

    Esm cambrian: Revealing the mysteries of proteins with unsupervised learning

    ESM Team. Esm cambrian: Revealing the mysteries of proteins with unsupervised learning. EvolutionaryScale Website, 2024. December 4, 2024

    2024

  38. [43]

    Audio albert: A lite bert for self-supervised learning of audio representation

    Po-Han Chi, Pei-Hung Chung, Tsung-Han Wu, Chun- Cheng Hsieh, Yen-Hao Chen, Shang-Wen Li, and Hung- yi Lee. Audio albert: A lite bert for self-supervised learning of audio representation. In 2021 IEEE Spoken Language Technology Workshop (SLT), pages 344–350. IEEE, 2021

    2021

  39. [44]

    Electra: Pre-training text encoders as dis- criminators rather than generators

    K Clark. Electra: Pre-training text encoders as dis- criminators rather than generators. arXiv preprint arXiv:2003.10555, 2020

    2003 arXiv

  40. [45]

    Protein-level assembly increases protein sequence re- covery from metagenomic samples manyfold

    Martin Steinegger, Milot Mirdita, and Johannes S ¨oding. Protein-level assembly increases protein sequence re- covery from metagenomic samples manyfold. Nature methods, 16(7):603–606, 2019

    2019

  41. [46]

    Distilprot- bert: a distilled protein language model used to distin- guish between real proteins and their randomly shuffled counterparts

    Yaron Geffen, Yanay Ofran, and Ron Unger. Distilprot- bert: a distilled protein language model used to distin- guish between real proteins and their randomly shuffled counterparts. Bioinformatics, 38(Supplement 2):ii95– ii98, 2022

    2022

  42. [48]

    Transforming the language of life: transformer neural networks for protein prediction tasks

    Ananthan Nambiar, Maeve Heflin, Simon Liu, Sergei Maslov, Mark Hopkins, and Anna Ritz. Transforming the language of life: transformer neural networks for protein prediction tasks. In Proceedings of the 11th ACM international conference on bioinformatics, com- putational biolog...

    2020

  43. [49]

    Roberta: A robustly optimized bert pre- training approach

    Yinhan Liu. Roberta: A robustly optimized bert pre- training approach. arXiv preprint arXiv:1907.11692 , 2019

    1907 arXiv

  44. [50]

    Large batch optimization for deep learning: Training bert in 76 minutes

    Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh. Large batch optimization for deep learning: Training bert in 76 minutes. arXiv preprint arXiv:1904.00962 , 2019

    1904 arXiv

  45. [51]

    Pre-training co-evolutionary protein representation via a pairwise masked language model

    Liang He, Shizhuo Zhang, Lijun Wu, Huanhuan Xia, Fusong Ju, He Zhang, Siyuan Liu, Yingce Xia, Jianwei Zhu, Pan Deng, et al. Pre-training co-evolutionary protein representation via a pairwise masked language model. arXiv preprint arXiv:2110.15527 , 2021

    arXiv

  46. [52]

    Proteinbert: a universal deep-learning model of protein sequence and function

    Nadav Brandes, Dan Ofer, Yam Peleg, Nadav Rap- poport, and Michal Linial. Proteinbert: a universal deep-learning model of protein sequence and function. Bioinformatics, 38(8):2102–2110, 2022

    2022

  47. [53]

    Single-sequence protein structure prediction using a language model and deep learning

    Ratul Chowdhury, Nazim Bouatta, Surojit Biswas, Christina Floristean, Anant Kharkar, Koushik Roy, Charlotte Rochereau, Gustaf Ahdritz, Joanna Zhang, George M Church, et al. Single-sequence protein structure prediction using a language model and deep learning. Nature Biotechnol...

    2022

  48. [54]

    Multi- level protein structure pre-training via prompt learning

    Zeyuan Wang, Qiang Zhang, HU Shuang-Wei, Haoran Yu, Xurui Jin, Zhichen Gong, and Huajun Chen. Multi- level protein structure pre-training via prompt learning. In The Eleventh International Conference on Learning Representations, 2022

    2022

  49. [55]

    Esm all-atom: Multi-scale protein lan- guage model for unified molecular modeling

    Kangjie Zheng, Siyu Long, Tianyu Lu, Junwei Yang, Xinyu Dai, Ming Zhang, Zaiqing Nie, Wei-Ying Ma, and Hao Zhou. Esm all-atom: Multi-scale protein lan- guage model for unified molecular modeling. In Forty- first International Conference on Machine Learning

  50. [56]

    Tcr-bert: learning the grammar of t-cell receptors for flexible antigen-binding analyses

    Kevin E Wu, Kathryn Yost, Bence Daniel, Julia Belk, Yu Xia, Takeshi Egawa, Ansuman Satpathy, Howard Chang, and James Zou. Tcr-bert: learning the grammar of t-cell receptors for flexible antigen-binding analyses. In Machine Learning in Computational Biology , pages 194–229. PMLR, 2024

    2024

  51. [57]

    Deciphering the language of antibodies using self-supervised learning

    Jinwoo Leem, Laura S Mitchell, James HR Farmery, Justin Barton, and Jacob D Galson. Deciphering the language of antibodies using self-supervised learning. Patterns, 3(7), 2022

    2022

  52. [58]

    Deciphering antibody affinity maturation with language models and weakly supervised learning

    Jeffrey A Ruffolo, Jeffrey J Gray, and Jeremias Sulam. Deciphering antibody affinity maturation with language models and weakly supervised learning. arXiv preprint arXiv:2112.07782, 2021

    2021 arXiv

  53. [60]

    Accurate prediction of antibody func- tion and structure using bio-inspired antibody language model

    Hongtai Jing, Zhengtao Gao, Sheng Xu, Tao Shen, Zhangzhi Peng, Shwai He, Tao You, Shuang Ye, Wei Lin, and Siqi Sun. Accurate prediction of antibody func- tion and structure using bio-inspired antibody language model. Briefings in Bioinformatics , 25(4):bbae245, 2024

    2024

  54. [61]

    Large scale paired antibody language models

    Henry Kenlay, Fr ´ed´eric A Dreyer, Aleksandr Kovaltsuk, Dom Miketa, Douglas Pires, and Charlotte M Deane. Large scale paired antibody language models. PLOS Computational Biology, 20(12):e1012646, 2024

    2024

  55. [62]

    Saprot: Protein language modeling with structure-aware vocabulary

    Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, and Fajie Yuan. Saprot: Protein language modeling with structure-aware vocabulary. bioRxiv, pages 2023–10, 2023

    2023

  56. [63]

    Petribert: Augmenting bert with tridimensional encoding for inverse protein folding and design

    Baldwin Dumortier, Antoine Liutkus, Cl ´ement Carr ´e, and Gabriel Krouk. Petribert: Augmenting bert with tridimensional encoding for inverse protein folding and design. BioRxiv, pages 2022–08, 2022

    2022

  57. [64]

    Msa transformer

    Roshan M Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, and Alexander Rives. Msa transformer. In International Conference on Machine Learning , pages 8844–8856. PMLR, 2021

    2021

  58. [65]

    Enhancing the protein tertiary structure prediction by multiple sequence alignment generation

    Le Zhang, Jiayang Chen, Tao Shen, Yu Li, and Siqi Sun. Enhancing the protein tertiary structure prediction by multiple sequence alignment generation. arXiv preprint arXiv:2306.01824, 2023

    2023 arXiv

  59. [66]

    Progen: Language modeling for protein generation

    Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R Eguchi, Po-Ssu Huang, and Richard Socher. Progen: Language modeling for protein generation. arXiv preprint JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, DECEMBER 2024 14 arXiv:2004.03497, 2020

    2024 arXiv

  60. [67]

    Progen2: exploring the boundaries of protein language models

    Erik Nijkamp, Jeffrey A Ruffolo, Eli N Weinstein, Nikhil Naik, and Ali Madani. Progen2: exploring the boundaries of protein language models. Cell systems , 14(11):968–978, 2023

    2023

  61. [68]

    Rita: a study on scaling up generative protein sequence models

    Daniel Hesslow, Niccol ´o Zanichelli, Pascal Notin, Ia- copo Poli, and Debora Marks. Rita: a study on scaling up generative protein sequence models. arXiv preprint arXiv:2205.05789, 2022

    2022 arXiv

  62. [69]

    Prot- gpt2 is a deep unsupervised language model for protein design

    Noelia Ferruz, Steffen Schmidt, and Birte H ¨ocker. Prot- gpt2 is a deep unsupervised language model for protein design. Nature communications, 13(1):4348, 2022

    2022

  63. [70]

    Poet: A generative model of protein families as sequences-of- sequences

    Timothy Truong Jr and Tristan Bepler. Poet: A generative model of protein families as sequences-of- sequences. Advances in Neural Information Processing Systems, 36:77379–77415, 2023

    2023

  64. [71]

    Generative language modeling for antibody design

    Richard W Shuai, Jeffrey A Ruffolo, and Jeffrey J Gray. Generative language modeling for antibody design. BioRxiv, pages 2021–12, 2021

    2021

  65. [72]

    Zymctrl: a conditional language model for the controllable generation of artificial enzymes

    Geraldene Munsamy, Sebastian Lindner, Philipp Lorenz, and Noelia Ferruz. Zymctrl: a conditional language model for the controllable generation of artificial enzymes. In NeurIPS Machine Learning in Structural Biology Workshop, 2022

    2022

  66. [73]

    Prollama: A protein large language model for multi-task protein language processing

    Liuzhenghao Lv, Zongying Lin, Hao Li, Yuyang Liu, Ji- axi Cui, Calvin Yu-Chian Chen, Li Yuan, and Yonghong Tian. Prollama: A protein large language model for multi-task protein language processing. arXiv preprint arXiv:2402.16445, 2024

    2024 arXiv

  67. [74]

    Generative anti- body design for complementary chain pairing sequences through encoder-decoder language model

    Simon KS Chu and Kathy Y Wei. Generative anti- body design for complementary chain pairing sequences through encoder-decoder language model. arXiv preprint arXiv:2301.02748, 2023

    2023 arXiv

  68. [75]

    Exploring the limits of transfer learning with a unified text-to-text transformer

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research , 21(140):1–67, 2020

    2020

  69. [76]

    xtrimopglm: unified 100b-scale pre-trained transformer for deciphering the language of protein

    Bo Chen, Xingyi Cheng, Pan Li, Yangli-ao Geng, Jing Gong, Shen Li, Zhilei Bei, Xu Tan, Boyan Wang, Xin Zeng, et al. xtrimopglm: unified 100b-scale pre-trained transformer for deciphering the language of protein. arXiv preprint arXiv:2401.06199 , 2024

    2024 arXiv

  70. [77]

    Glm: General language model pretraining with autoregressive blank infilling

    Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. Glm: General language model pretraining with autoregressive blank infilling. arXiv preprint arXiv:2103.10360 , 2021

    2021 arXiv

  71. [78]

    Efficient and accurate sequence generation with small-scale protein language models

    Yaiza Serrano, Sergi Roda, Victor Guallar, and Alexis Molina. Efficient and accurate sequence generation with small-scale protein language models. bioRxiv, pages 2023–08, 2023

    2023

  72. [79]

    Ankh: Optimized protein language model unlocks general-purpose mod- elling

    Ahmed Elnaggar, Hazem Essam, Wafaa Salah-Eldin, Walid Moustafa, Mohamed Elkerdawy, Charlotte Rochereau, and Burkhard Rost. Ankh: Optimized protein language model unlocks general-purpose mod- elling. arXiv preprint arXiv:2301.06568 , 2023

    2023 arXiv

  73. [80]

    Bilingual language model for protein sequence and structure

    Michael Heinzinger, Konstantin Weissenow, Joaquin Gomez Sanchez, Adrian Henkel, Milot Mirdita, Martin Steinegger, and Burkhard Rost. Bilingual language model for protein sequence and structure. bioRxiv, pages 2023–07, 2023

    2023

  74. [81]

    Prosst: Protein language modeling with quantized structure and disentangled attention

    Mingchen Li, Yang Tan, Xinzhu Ma, Bozitao Zhong, Huiqun Yu, Ziyi Zhou, Wanli Ouyang, Bingxin Zhou, Liang Hong, and Pan Tan. Prosst: Protein language modeling with quantized structure and disentangled attention. bioRxiv, pages 2024–04, 2024

    2024

  75. [82]

    Structure-informed language models are protein designers

    Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, and Quanquan Gu. Structure-informed language models are protein designers. In International confer- ence on machine learning, pages 42317–42338. PMLR, 2023

    2023

  76. [83]

    Self- attention with relative position representations

    Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. Self- attention with relative position representations. arXiv preprint arXiv:1803.02155, 2018

    2018 arXiv

  77. [84]

    The curious case of absolute position embeddings

    Koustuv Sinha, Amirhossein Kazemnejad, Siva Reddy, Joelle Pineau, Dieuwke Hupkes, and Adina Williams. The curious case of absolute position embeddings. arXiv preprint arXiv:2210.12574 , 2022

    2022 arXiv

  78. [85]

    Roformer: Enhanced trans- former with rotary position embedding

    Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced trans- former with rotary position embedding. Neurocomput- ing, 568:127063, 2024

    2024

  79. [86]

    Transformer-xl: Attentive language mod- els beyond a fixed-length context

    Zihang Dai. Transformer-xl: Attentive language mod- els beyond a fixed-length context. arXiv preprint arXiv:1901.02860, 2019

    1901 arXiv

  80. [87]

    Deberta: Decoding-enhanced bert with disentangled attention

    Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. Deberta: Decoding-enhanced bert with disentangled attention. arXiv preprint arXiv:2006.03654, 2020

    2006 arXiv

  81. [88]

    Train short, test long: Attention with linear biases enables input length extrapolation

    Ofir Press, Noah A Smith, and Mike Lewis. Train short, test long: Attention with linear biases enables input length extrapolation. arXiv preprint arXiv:2108.12409 , 2021

    2021 arXiv

  82. [89]

    Multilingual elmo and the effects of corpus sampling

    Vinit Ravishankar, Andrey Kutuzov, Lilja Øvrelid, and Erik Velldal. Multilingual elmo and the effects of corpus sampling. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa) , pages 378– 384, 2021

    2021

  83. [90]

    Protein language models: Is scaling necessary? bioRxiv, pages 2024–09, 2024

    Quentin Fournier, Robert M Vernon, Almer van der Sloot, Benjamin Schulz, Sarath Chandar, and Christo- pher James Langmead. Protein language models: Is scaling necessary? bioRxiv, pages 2024–09, 2024

    2024

  84. [91]

    Scal- ing laws for neural language models

    Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scal- ing laws for neural language models. arXiv preprint arXiv:2001.08361, 2020

    2001 arXiv

  85. [93]

    Language models are unsupervised multitask learners

    Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog , 1(8):9, 2019

    2019

  86. [94]

    Language models are few-shot learners

    Tom B Brown. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 , 2020

    2005 arXiv

  87. [95]

    JOURNAL OF LATEX CLASS FILES, VOL

    Uniprot: the universal protein knowledgebase in 2023. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, DECEMBER 2024 15 Nucleic acids research, 51(D1):D523–D531, 2023

    2023

  88. [96]

    Uniprot: a worldwide hub of pro- tein knowledge

    UniProt Consortium. Uniprot: a worldwide hub of pro- tein knowledge. Nucleic acids research, 47(D1):D506– D515, 2019

    2019

  89. [97]

    Edittotrembl: a distributed approach to high- quality automated protein sequence annotation

    S M ¨oller, Ulf Leser, Wolfgang Fleischmann, and Rolf Apweiler. Edittotrembl: a distributed approach to high- quality automated protein sequence annotation. Bioin- formatics (Oxford, England) , 15(3):219–227, 1999

    1999

  90. [98]

    Uniclust databases of clustered and deeply anno- tated protein sequences and alignments

    Milot Mirdita, Lars V on Den Driesch, Clovis Galiez, Maria J Martin, Johannes S ¨oding, and Martin Steineg- ger. Uniclust databases of clustered and deeply anno- tated protein sequences and alignments. Nucleic acids research, 45(D1):D170–D176, 2017

    2017

  91. [99]

    Highly accurate protein structure prediction with alphafold

    John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin ˇZ´ıdek, Anna Potapenko, et al. Highly accurate protein structure prediction with alphafold. nature, 596(7873):583–589, 2021

    2021

  92. [100]

    Uniprot archive

    Rasko Leinonen, Federico Garcia Diez, David Binns, Wolfgang Fleischmann, Rodrigo Lopez, and Rolf Ap- weiler. Uniprot archive. Bioinformatics, 20(17):3236– 3237, 2004

    2004

  93. [101]

    Pfam: The protein families database in 2021

    Jaina Mistry, Sara Chuguransky, Lowri Williams, Mat- loob Qureshi, Gustavo A Salazar, Erik LL Sonnhammer, Silvio CE Tosatto, Lisanna Paladin, Shriya Raj, Lorna J Richardson, et al. Pfam: The protein families database in 2021. Nucleic acids research , 49(D1):D412–D419, 2021

    2021

  94. [102]

    Pfam: the protein families database

    Robert D Finn, Alex Bateman, Jody Clements, Penelope Coggill, Ruth Y Eberhardt, Sean R Eddy, Andreas Heger, Kirstie Hetherington, Liisa Holm, Jaina Mistry, et al. Pfam: the protein families database. Nucleic acids research, 42(D1):D222–D230, 2014

    2014

  95. [104]

    Mgnify: the microbiome sequence data analysis resource in 2023

    Lorna Richardson, Ben Allen, Germana Baldi, Mar- tin Beracochea, Maxwell L Bileschi, Tony Burdett, Josephine Burgin, Juan Caballero-P´erez, Guy Cochrane, Lucy J Colwell, et al. Mgnify: the microbiome sequence data analysis resource in 2023. Nucleic Acids Research, 51(D1):D753–...

    2023

  96. [105]

    Brenda in 2017: new perspectives and new tools in brenda

    Sandra Placzek, Ida Schomburg, Antje Chang, Lisa Jeske, Marcus Ulbrich, Jana Tillack, and Dietmar Schomburg. Brenda in 2017: new perspectives and new tools in brenda. Nucleic acids research , page gkw952, 2016

    2017

  97. [106]

    Observed antibody space: a resource for data mining next-generation sequencing of antibody repertoires

    Aleksandr Kovaltsuk, Jinwoo Leem, Sebastian Kelm, James Snowden, Charlotte M Deane, and Konrad Krawczyk. Observed antibody space: a resource for data mining next-generation sequencing of antibody repertoires. The Journal of Immunology , 201(8):2502– 2509, 2018

    2018

  98. [107]

    Ontoprotein: Pro- tein pretraining with gene ontology embedding

    Ningyu Zhang, Zhen Bi, Xiaozhuan Liang, Siyuan Cheng, Haosen Hong, Shumin Deng, Jiazhang Lian, Qiang Zhang, and Huajun Chen. Ontoprotein: Pro- tein pretraining with gene ontology embedding. arXiv preprint arXiv:2201.11147, 2022

    2022 arXiv

  99. [108]

    Pird: pan immune repertoire database

    Wei Zhang, Longlong Wang, Ke Liu, Xiaofeng Wei, Kai Yang, Wensi Du, Shiyu Wang, Nannan Guo, Chuanchuan Ma, Lihua Luo, et al. Pird: pan immune repertoire database. Bioinformatics, 36(3):897–903, 2020

    2020

  100. [109]

    Tcrdb: a comprehensive database for t-cell receptor sequences with powerful search function

    Si-Yi Chen, Tao Yue, Qian Lei, and An-Yuan Guo. Tcrdb: a comprehensive database for t-cell receptor sequences with powerful search function. Nucleic acids research, 49(D1):D468–D474, 2021

    2021

  101. [110]

    Protein data bank (pdb): the single global macromolecular structure archive

    Stephen K Burley, Helen M Berman, Gerard J Kley- wegt, John L Markley, Haruki Nakamura, and Sameer Velankar. Protein data bank (pdb): the single global macromolecular structure archive. Protein crystallog- raphy: methods and protocols , pages 627–641, 2017

    2017

  102. [111]

    Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

    Mihaly Varadi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, Oana Stroe, Gemma Wood, Agata Laydon, et al. Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-a...

    2022

  103. [112]

    Evolutionary- scale prediction of atomic-level protein structure with a language model

    Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, et al. Evolutionary- scale prediction of atomic-level protein structure with a language model. Science, 379(6637):1123–1130, 2023

    2023

  104. [113]

    Critical as- sessment of methods of protein structure prediction (casp)—round xiv

    Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, and John Moult. Critical as- sessment of methods of protein structure prediction (casp)—round xiv. Proteins: Structure, Function, and Bioinformatics, 89(12):1607–1617, 2021

    2021

  105. [114]

    Continuous automated model evaluation (cameo)—perspectives on the future of fully automated evaluation of structure prediction methods

    Xavier Robin, Juergen Haas, Rafal Gumienny, Anna Smolinski, Gerardo Tauriello, and Torsten Schwede. Continuous automated model evaluation (cameo)—perspectives on the future of fully automated evaluation of structure prediction methods. Proteins: Structure, Function, and Bioinf...

    1977

  106. [115]

    Continuous automated model evaluation (cameo) com- plementing the critical assessment of structure predic- tion in casp12

    J ¨urgen Haas, Alessandro Barbato, Dario Behringer, Gabriel Studer, Steven Roth, Martino Bertoni, Khaled Mostaguir, Rafal Gumienny, and Torsten Schwede. Continuous automated model evaluation (cameo) com- plementing the critical assessment of structure predic- tion in casp12. P...

    2018

  107. [116]

    Helixfold-single: Msa-free protein structure prediction by using protein language model as an alternative

    Xiaomin Fang, Fan Wang, Lihang Liu, Jingzhou He, Dayong Lin, Yingfei Xiang, Xiaonan Zhang, Hua Wu, Hui Li, and Le Song. Helixfold-single: Msa-free protein structure prediction by using protein language model as an alternative. arXiv preprint arXiv:2207.13921 , 2022

    2022 arXiv

  108. [117]

    Scope: Structural classification of pro- teins—extended, integrating scop and astral data and classification of new structures

    Naomi K Fox, Steven E Brenner, and John-Marc Chandonia. Scope: Structural classification of pro- teins—extended, integrating scop and astral data and classification of new structures. Nucleic acids research, 42(D1):D304–D309, 2014. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8,...

    2014

  109. [118]

    Scop: a structural classification of proteins database for the investigation of sequences and structures

    Alexey G Murzin, Steven E Brenner, Tim Hubbard, and Cyrus Chothia. Scop: a structural classification of proteins database for the investigation of sequences and structures. Journal of molecular biology , 247(4):536– 540, 1995

    1995

  110. [119]

    Sys- tematic comparison of scop and cath: a new gold standard for protein structure analysis

    Gergely Csaba, Fabian Birzele, and Ralf Zimmer. Sys- tematic comparison of scop and cath: a new gold standard for protein structure analysis. BMC structural biology, 9:1–11, 2009

    2009

  111. [120]

    Cath–a hierarchic classification of protein domain structures

    Christine A Orengo, Alex D Michie, Susan Jones, David T Jones, Mark B Swindells, and Janet M Thorn- ton. Cath–a hierarchic classification of protein domain structures. Structure, 5(8):1093–1109, 1997

    1997

  112. [121]

    Explorenz: the primary source of the iubmb enzyme list

    Andrew G McDonald, Sin ´ead Boyce, and Keith F Tipton. Explorenz: the primary source of the iubmb enzyme list. Nucleic acids research, 37(suppl 1):D593– D597, 2009

    2009

  113. [123]

    Enzyme function prediction using contrastive learning

    Tianhao Yu, Haiyang Cui, Jianan Canal Li, Yunan Luo, Guangde Jiang, and Huimin Zhao. Enzyme function prediction using contrastive learning. Science, 379(6639):1358–1363, 2023

    2023

  114. [125]

    The gene ontology re- source: 20 years and still going strong

    Gene Ontology Consortium. The gene ontology re- source: 20 years and still going strong. Nucleic acids research, 47(D1):D330–D338, 2019

    2019

  115. [126]

    Flip: Bench- mark tasks in fitness landscape inference for proteins

    Christian Dallago, Jody Mou, Kadina E Johnston, Bruce J Wittmann, Nicholas Bhattacharya, Samuel Goldman, Ali Madani, and Kevin K Yang. Flip: Bench- mark tasks in fitness landscape inference for proteins. bioRxiv, pages 2021–11, 2021

    2021

  116. [127]

    The cafa challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

    Naihui Zhou, Yuxiang Jiang, Timothy R Bergquist, Alexandra J Lee, Balint Z Kacsoh, Alex W Crocker, Kimberley A Lewis, George Georghiou, Huy N Nguyen, Md Nafiz Hamid, et al. The cafa challenge reports improved protein function prediction and new functional annotations for hundr...

    2019

  117. [128]

    An expanded evaluation of protein function prediction methods shows an improve- ment in accuracy

    Yuxiang Jiang, Tal Ronnen Oron, Wyatt T Clark, Asma R Bankapur, Daniel D’Andrea, Rosalba Lepore, Christopher S Funk, Indika Kahanda, Karin M Ver- spoor, Asa Ben-Hur, et al. An expanded evaluation of protein function prediction methods shows an improve- ment in accuracy. Genome...

    2016

  118. [129]

    Integrating unsupervised language model with triplet neural networks for protein gene ontology predic- tion

    Yi-Heng Zhu, Chengxin Zhang, Dong-Jun Yu, and Yang Zhang. Integrating unsupervised language model with triplet neural networks for protein gene ontology predic- tion. PLOS Computational Biology , 18(12):e1010793, 2022

    2022

  119. [130]

    Fast and accurate protein function prediction from sequence through pretrained language model and homology-based label diffusion

    Qianmu Yuan, Junjie Xie, Jiancong Xie, Huiying Zhao, and Yuedong Yang. Fast and accurate protein function prediction from sequence through pretrained language model and homology-based label diffusion. Briefings in bioinformatics, 24(3):bbad117, 2023

    2023

  120. [131]

    Structure-based protein function prediction using graph convolutional networks

    Vladimir Gligorijevi ´c, P Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C Taylor, Ian M Fisk, Hera Vlamakis, et al. Structure-based protein function prediction using graph convolutional networks. Nature communica...

    2021

  121. [132]

    Accurate prediction of protein function using statistics-informed graph networks

    Yaan J Jang, Qi-Qi Qin, Si-Yu Huang, Arun T John Peter, Xue-Ming Ding, and Benoˆıt Kornmann. Accurate prediction of protein function using statistics-informed graph networks. Nature Communications, 15(1):6601, 2024

    2024

  122. [133]

    Peer: a comprehensive and multi-task benchmark for protein sequence understanding

    Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Ma Chang, Runcheng Liu, and Jian Tang. Peer: a comprehensive and multi-task benchmark for protein sequence understanding. Advances in Neu- ral Information Processing Systems , 35:35156–35173, 2022

    2022

  123. [134]

    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. Proteingym: Large-scale benchmarks for protein fitness prediction and design. Advances in Neural Information Processing S...

    2024

  124. [135]

    Tranception: protein fitness predic- tion with autoregressive transformers and inference- time retrieval

    Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N Gomez, Debora Marks, and Yarin Gal. Tranception: protein fitness predic- tion with autoregressive transformers and inference- time retrieval. In International Conference on Machine Learning, pages 16...

    2022

  125. [136]

    Proteinnpt: Improving protein property prediction and design with non-parametric transformers

    Pascal Notin, Ruben Weitzman, Debora Marks, and Yarin Gal. Proteinnpt: Improving protein property prediction and design with non-parametric transformers. Advances in Neural Information Processing Systems , 36:33529–33563, 2023

    2023

  126. [137]

    Machine learning in protein structure prediction

    Mohammed AlQuraishi. Machine learning in protein structure prediction. Current opinion in chemical biol- ogy, 65:1–8, 2021

    2021

  127. [138]

    Retrieved sequence augmentation for protein representation learning

    Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qin- tong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, and Lingpeng Kong. Retrieved sequence augmentation for protein representation learning. bioRxiv, pages 2023– 02, 2023

    2023

  128. [139]

    Recent advances and challenges in protein structure predic- tion

    Chun-Xiang Peng, Fang Liang, Yu-Hao Xia, Kai-Long Zhao, Ming-Hua Hou, and Gui-Jun Zhang. Recent advances and challenges in protein structure predic- tion. Journal of Chemical Information and Modeling , 64(1):76–95, 2023

    2023

  129. [140]

    High-resolution de novo structure prediction from primary sequence

    Ruidong Wu, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su, Zuofan Wu, Qi Xie, Bonnie Berger, et al. High-resolution de novo structure prediction from primary sequence. BioRxiv, pages 2022–07, 2022

    2022

  130. [141]

    Single- sequence protein structure prediction using supervised JOURNAL OF LATEX CLASS FILES, VOL

    Wenkai Wang, Zhenling Peng, and Jianyi Yang. Single- sequence protein structure prediction using supervised JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, DECEMBER 2024 17 transformer protein language models. Nature Compu- tational Science, 2(12):804–814, 2022

    2024

  131. [142]

    Ultra-fast protein struc- ture prediction to capture effects of sequence variation in mutation movies

    Konstantin Weissenow, Michael Heinzinger, Martin Steinegger, and Burkhard Rost. Ultra-fast protein struc- ture prediction to capture effects of sequence variation in mutation movies. bioRxiv, pages 2022–11, 2022

    2022

  132. [143]

    Fast, sensitive de- tection of protein homologs using deep dense retrieval

    Liang Hong, Zhihang Hu, Siqi Sun, Xiangru Tang, Ji- uming Wang, Qingxiong Tan, Liangzhen Zheng, Sheng Wang, Sheng Xu, Irwin King, et al. Fast, sensitive de- tection of protein homologs using deep dense retrieval. Nature Biotechnology, pages 1–13, 2024

    2024

  133. [144]

    Bernardes

    Juliana S. Bernardes. A review of protein function prediction under machine learning perspective. Recent patents on biotechnology , 7(2):122–141, 2013

    2013

  134. [145]

    Gpsfun: geometry-aware protein sequence function pre- dictions with language models

    Qianmu Yuan, Chong Tian, Yidong Song, Peihua Ou, Mingming Zhu, Huiying Zhao, and Yuedong Yang. Gpsfun: geometry-aware protein sequence function pre- dictions with language models. Nucleic Acids Research, page gkae381, 2024

    2024

  135. [146]

    De novo protein design, a retrospective

    Ivan V Korendovych and William F DeGrado. De novo protein design, a retrospective. Quarterly reviews of biophysics, 53:e3, 2020

    2020

  136. [147]

    The coming of age of de novo protein design

    Po-Ssu Huang, Scott E Boyken, and David Baker. The coming of age of de novo protein design. Nature, 537(7620):320–327, 2016

    2016

  137. [148]

    Recent advances in de novo protein design: Principles, methods, and applications

    Xingjie Pan and Tanja Kortemme. Recent advances in de novo protein design: Principles, methods, and applications. Journal of Biological Chemistry , 296, 2021

    2021

  138. [149]

    Antibody complementarity-determining region sequence design using alphafold2 and binding affinity prediction model

    Takafumi Ueki and Masahito Ohue. Antibody complementarity-determining region sequence design using alphafold2 and binding affinity prediction model. In 2023 Congress in Computer Science, Computer En- gineering, & Applied Computing (CSCE) , pages 2133–

    2023

  139. [150]

    Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes

    Lior Zimmerman, Noga Alon, Itay Levin, Anna Ko- ganitsky, Nufar Shpigel, Chen Brestel, and Gideon D Lapidoth. Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes. Proceedings of the National Academy ...

    2024

  140. [151]

    Estimating conformational het- erogeneity of tryptophan synthase with a template-based alphafold2 approach

    Guillem Casadevall, Cristina Duran, Miquel Est ´evez- Gay, and S´ılvia Osuna. Estimating conformational het- erogeneity of tryptophan synthase with a template-based alphafold2 approach. Protein Science , 31(10):e4426, 2022

    2022

  141. [152]

    Computational design of sol- uble analogues of integral membrane protein structures

    Casper A Goverde, Martin Pacesa, Lars J Dornfeld, Sandrine Georgeon, St ´ephane Rosset, Justas Dauparas, Christian Schellhaas, Simon Kozlov, David Baker, Sergey Ovchinnikov, et al. Computational design of sol- uble analogues of integral membrane protein structures. may 2023. d...

    2023 doi

  142. [153]

    De novo protein design by inversion of the alphafold structure prediction network

    Casper A Goverde, Benedict Wolf, Hamed Khakzad, St´ephane Rosset, and Bruno E Correia. De novo protein design by inversion of the alphafold structure prediction network. Protein Science, 32(6):e4653, 2023

    2023

  143. [154]

    Alphadesign: A de novo protein design framework based on alphafold

    Michael Jendrusch, Jan O Korbel, and S Kashif Sadiq. Alphadesign: A de novo protein design framework based on alphafold. Biorxiv, pages 2021–10, 2021

    2021

  144. [155]

    Design of a specific peptide against phenolic glycolipid-1 from mycobac- terium leprae and its implications in leprosy bacilli en- try

    Nelson Enrique Arenas, Gilles Pieffet, Cristian Rocha- Roa, and Martha In ´ırida Guerrero. Design of a specific peptide against phenolic glycolipid-1 from mycobac- terium leprae and its implications in leprosy bacilli en- try. Mem´orias do Instituto Oswaldo Cruz, 117:e220025, 2022

    2022

  145. [156]

    Identify- ing well-folded de novo proteins in the new era of accurate structure prediction

    Daniel Pe ˜nas-Utrilla and Enrique Marcos. Identify- ing well-folded de novo proteins in the new era of accurate structure prediction. Frontiers in Molecular Biosciences, 9:991380, 2022

    2022

  146. [157]

    Assessing and enhancing foldability in designed proteins

    Dina Listov, Rosalie Lipsh-Sokolik, St ´ephane Rosset, Che Yang, Bruno E Correia, and Sarel Jacob Fleish- man. Assessing and enhancing foldability in designed proteins. Protein Science, 31(9):e4400, 2022

    2022

  147. [158]

    Efficient evolution of human antibodies from general protein language models

    Brian L Hie, Varun R Shanker, Duo Xu, Theodora UJ Bruun, Payton A Weidenbacher, Shaogeng Tang, Wes- ley Wu, John E Pak, and Peter S Kim. Efficient evolution of human antibodies from general protein language models. Nature Biotechnology , 42(2):275– 283, 2024

    2024

  148. [159]

    Language models generalize beyond natural proteins

    Robert Verkuil, Ori Kabeli, Yilun Du, Basile IM Wicky, Lukas F Milles, Justas Dauparas, David Baker, Sergey Ovchinnikov, Tom Sercu, and Alexander Rives. Language models generalize beyond natural proteins. BioRxiv, pages 2022–12, 2022

    2022

  149. [160]

    Protein language model supervised precise and efficient protein backbone design method

    Bo Zhang, Kexin Liu, Zhuoqi Zheng, Yunfeiyang Liu, Junxi Mu, Ting Wei, and Haifeng Chen. Protein language model supervised precise and efficient protein backbone design method. bioRxiv, pages 2023–10, 2023

    2023

  150. [161]

    Exploring latent space for gen- erating peptide analogs using protein language models

    Po-Yu Liang, Xueting Huang, Tibo Duran, Andrew J Wiemer, and Jun Bai. Exploring latent space for gen- erating peptide analogs using protein language models. arXiv preprint arXiv:2408.08341 , 2024

    2024 arXiv

  151. [162]

    Unsupervised evolution of protein and antibody complexes with a structure-informed language model

    Varun R Shanker, Theodora UJ Bruun, Brian L Hie, and Peter S Kim. Unsupervised evolution of protein and antibody complexes with a structure-informed language model. Science, 385(6704):46–53, 2024

    2024

  152. [163]

    Computational scoring and experimental evalua- tion of enzymes generated by neural networks

    Sean R Johnson, Xiaozhi Fu, Sandra Viknander, Clara Goldin, Sarah Monaco, Aleksej Zelezniak, and Kevin K Yang. Computational scoring and experimental evalua- tion of enzymes generated by neural networks. Nature biotechnology, pages 1–10, 2024

    2024

  153. [164]

    Highly accurate and robust protein sequence design with carbondesign

    Milong Ren, Chungong Yu, Dongbo Bu, and Haicang Zhang. Highly accurate and robust protein sequence design with carbondesign. bioRxiv, pages 2023–08, 2023

    2023

  154. [165]

    Large language models generate functional protein sequences across diverse families

    Ali Madani, Ben Krause, Eric R Greene, Subu Subrama- nian, Benjamin P Mohr, James M Holton, Jose Luis Ol- mos, Caiming Xiong, Zachary Z Sun, Richard Socher, et al. Large language models generate functional protein sequences across diverse families. Nature Biotechnol- ogy, 41(8...

    2023

  155. [166]

    Petribert: Augmenting bert with JOURNAL OF LATEX CLASS FILES, VOL

    Baldwin Dumortier, Antoine Liutkus, Cl ´ement Carr ´e, and Gabriel Krouk. Petribert: Augmenting bert with JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, DECEMBER 2024 18 tridimensional encoding for inverse protein folding and design. BioRxiv, pages 2022–08, 2022

    2024

  156. [167]

    Biophi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning

    David Prihoda, Jad Maamary, Andrew Waight, Veronica Juan, Laurence Fayadat-Dilman, Daniel Svozil, and Danny A Bitton. Biophi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning. In MAbs, volume 14, pag...

    2022

  157. [168]

    Ablang: an antibody language model for complet- ing antibody sequences

    Tobias H Olsen, Iain H Moal, and Charlotte M Deane. Ablang: an antibody language model for complet- ing antibody sequences. Bioinformatics Advances , 2(1):vbac046, 2022

    2022

  158. [169]

    Addressing the antibody germline bias and its effect on language models for improved antibody design

    Tobias H Olsen, Iain H Moal, and Charlotte M Deane. Addressing the antibody germline bias and its effect on language models for improved antibody design. Bioinformatics, 40(11):btae618, 2024

    2024

  159. [170]

    Transformer-based protein generation with regularized latent space optimization

    Egbert Castro, Abhinav Godavarthi, Julian Rubinfien, Kevin Givechian, Dhananjay Bhaskar, and Smita Kr- ishnaswamy. Transformer-based protein generation with regularized latent space optimization. Nature Machine Intelligence, 4(10):840–851, 2022

    2022

  160. [171]

    Design in the dark: learning deep generative models for de novo protein design

    Lewis Moffat, Shaun M Kandathil, and David T Jones. Design in the dark: learning deep generative models for de novo protein design. bioRxiv, pages 2022–01, 2022

    2022

  161. [172]

    Low-n protein engineering with data-efficient deep learning

    Surojit Biswas, Grigory Khimulya, Ethan C Alley, Kevin M Esvelt, and George M Church. Low-n protein engineering with data-efficient deep learning. Nature methods, 18(4):389–396, 2021

    2021

  162. [174]

    Humphreys, Jimin Pei, Minkyung Baek, Aditya Krishnakumar, Ivan Anishchenko, Sergey Ovchinnikov, Jing Zhang, Travis J

    Ian R. Humphreys, Jimin Pei, Minkyung Baek, Aditya Krishnakumar, Ivan Anishchenko, Sergey Ovchinnikov, Jing Zhang, Travis J. Ness, Sudeep Banjade, Saket R. Bagde, Viktoriya G. Stancheva, Xiao-Han Li, Kaix- ian Liu, Zhi Zheng, Daniel J. Barrero, Upasana Roy, Jochen Kuper, Israe...

    2021

  163. [175]

    Scaffolding protein functional sites using deep learning

    Jue Wang, Sidney Lisanza, David Juergens, Doug Tis- cher, Joseph L Watson, Karla M Castro, Robert Ragotte, Amijai Saragovi, Lukas F Milles, Minkyung Baek, et al. Scaffolding protein functional sites using deep learning. Science, 377(6604):387–394, 2022

    2022

  164. [176]

    Protein language model predicts mutation pathogenicity and clinical prognosis

    Xiangling Liu, Xinyu Yang, Linkun Ouyang, Guibing Guo, Jin Su, Ruibin Xi, Ke Yuan, and Fajie Yuan. Protein language model predicts mutation pathogenicity and clinical prognosis. bioRxiv, pages 2022–09, 2022

    2022

  165. [177]

    Disease variant prediction with deep generative models of evolutionary data

    Jonathan Frazer, Pascal Notin, Mafalda Dias, Aidan Gomez, Joseph K Min, Kelly Brock, Yarin Gal, and Debora S Marks. Disease variant prediction with deep generative models of evolutionary data. Nature, 599(7883):91–95, 2021

    2021

  166. [178]

    Accurate proteome-wide missense variant effect prediction with alphamissense

    Jun Cheng, Guido Novati, Joshua Pan, Clare By- croft, Akvil ˙e ˇZemgulyt˙e, Taylor Applebaum, Alexan- der Pritzel, Lai Hong Wong, Michal Zielinski, Tobias Sargeant, et al. Accurate proteome-wide missense variant effect prediction with alphamissense. Science, 381(6664):eadg7492, 2023

    2023

  167. [179]

    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. Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering. Cell Research, 34(9):630–647, 2024

    2024

  168. [180]

    Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

    Martin Steinegger and Johannes S ¨oding. Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature biotechnology , 35(11):1026–1028, 2017

    2017

  169. [181]

    Hhblits: lightning-fast iterative protein sequence searching by hmm-hmm alignment

    Michael Remmert, Andreas Biegert, Andreas Hauser, and Johannes S ¨oding. Hhblits: lightning-fast iterative protein sequence searching by hmm-hmm alignment. Nature methods, 9(2):173–175, 2012

    2012

  170. [182]

    Accelerated profile hmm searches

    Sean R Eddy. Accelerated profile hmm searches. PLoS computational biology, 7(10):e1002195, 2011

    2011

  171. [183]

    Pymol: An open-source molec- ular graphics tool

    Warren L DeLano et al. Pymol: An open-source molec- ular graphics tool. CCP4 Newsl. Protein Crystallogr , 40(1):82–92, 2002

    2002

  172. [184]

    Boosting the full potential of pymol with structural biology plugins

    Serena Rosignoli and Alessandro Paiardini. Boosting the full potential of pymol with structural biology plugins. Biomolecules, 12(12):1764, 2022

    2022

  173. [185]

    Tm-align: a protein structure alignment algorithm based on the tm-score

    Yang Zhang and Jeffrey Skolnick. Tm-align: a protein structure alignment algorithm based on the tm-score. Nucleic acids research, 33(7):2302–2309, 2005

    2005

  174. [186]

    Fast and accurate protein structure search with foldseek

    Michel Van Kempen, Stephanie S Kim, Charlotte Tumescheit, Milot Mirdita, Jeongjae Lee, Cameron LM Gilchrist, Johannes S ¨oding, and Martin Steinegger. Fast and accurate protein structure search with foldseek. Nature biotechnology, 42(2):243–246, 2024

    2024

  175. [187]

    Umap: Uniform manifold approximation and pro- jection for dimension reduction

    Leland McInnes, John Healy, and James Melville. Umap: Uniform manifold approximation and pro- jection for dimension reduction. arXiv preprint arXiv:1802.03426, 2018

    2018 arXiv

  176. [188]

    Visu- alizing data using t-sne

    Laurens Van der Maaten and Geoffrey Hinton. Visu- alizing data using t-sne. Journal of machine learning research, 9(11), 2008

    2008

  177. [189]

    Improving deep learning protein monomer and complex structure pre- diction using deepmsa2 with huge metagenomics data

    Wei Zheng, Qiqige Wuyun, Yang Li, Chengxin Zhang, P Lydia Freddolino, and Yang Zhang. Improving deep learning protein monomer and complex structure pre- diction using deepmsa2 with huge metagenomics data. Nature Methods, 21(2):279–289, 2024

    2024

  178. [190]

    Accurate structure prediction of biomolecular interactions with alphafold 3

    Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, et al. Accurate structure prediction of biomolecular interactions with alphafold 3. Nature, pages 1–3, 2024

    2024

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.