Cited by 335Machine Learning in BioinformaticsGenomics and Phylogenetic StudiesProtein Structure and Dynamics

The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.

Using deep learning to annotate the protein universe

Open access

Bileschi, Maxwell L., Belanger, David, Bryant, Drew H., Sanderson, Theo, Carter, Brandon, Sculley, D., Bateman, Alex, DePristo, Mark A., Colwell, Lucy J.·2022·Nature Biotechnology

DOI 10.1038/s41587-021-01179-w

Published version of a preprint

Open this paper in CoChat — free account Open PDF

Abstract

Understanding the relationship between amino acid sequence and protein function is a long-standing challenge with far-reaching scientific and translational implications. State-of-the-art alignment-based techniques cannot predict function for one-third of microbial protein sequences, hampering our ability to exploit data from diverse organisms. Here, we train deep learning models to accurately predict functional annotations for unaligned amino acid sequences across rigorous benchmark assessments built from the 17,929 families of the protein families database Pfam. The models infer known patterns of evolutionary substitutions and learn representations that accurately cluster sequences from unseen families. Combining deep models with existing methods significantly improves remote homology detection, suggesting that the deep models learn complementary information. This approach extends the coverage of Pfam by >9.5%, exceeding additions made over the last decade, and predicts function for 360 human reference proteome proteins with no previous Pfam annotation. These results suggest that deep learning models will be a core component of future protein annotation tools.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/s41587-021-01179-w

Cites

66 of 66 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Long Short-Term Memory
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Fast and sensitive protein alignment using DIAMOND
Buchfink, B., Xie, C., & Huson, D. H. (2015) · Nature Methods · doi:10.1038/nmeth.3176 · Cited by 14,201
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
UniProt: the Universal Protein knowledgebase
Apweiler, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh131 · Cited by 7,593

Show all 59 references

Research works printed but not linked: 1

El-Gebali, S., Richardson, L. & Finn, R. Repeats in Pfam. EMBL-EBI Training https://doi.org/10.6019/TOL.Pfam_repeats-t.2018.00001.1 (2018). · DOI

Sorted by citations

Items 201–300 of 335

bHLHDB: A next generation database of basic helix loop helix transcription factors based on deep learning model
Öncül, A. B., Çelik, Y., Ünel, N. M., & Baloğlu, M. C. (2022) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720022500147 · Cited by 4
Organizing the bacterial annotation space with amino acid sequence embeddings
Grigson, S. R., McKerral, J. C., Mitchell, J. G., & Edwards, R. A. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04930-5 · Cited by 4
“other sequence embedding techniques including convolutional and long-term shortterm memory neural networks which have either mirrored the BLOSUM62 matrix or learned similarities between amino acids with chemically similar sidechains [44, 45].”
Background
Protein Functional Family Classification Based on Multilevel Feature Information
Zhou, G., & Chen, W. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9994943 · Cited by 4
Towards mechanistic models of mutational effects: Deep learning on Alzheimer’s Aβ peptide
Wang, B., Razavi, S., & Gamazon, E. R. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.03.051 · Cited by 5
PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information
Saha, G., Sawmya, S., Saha, A., Akil, A., Tasnim, S., Rahman, M. S., & Rahman, M. S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae218 · Cited by 4
Predicting enzyme substrate chemical structure with protein language modelsPreprint
Jinich, A., Nazia, S. Z., Tellez, A. V., Rappoport, D., AlQuraishi, M., & Rhee, K. Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.28.509940 · Cited by 4
“The parametrized protein or amino-acid embeddings obtained from self-supervised training can then be used for downstream machine learning tasks 19,25–27 .”
Method
What the protein!? Computational methods for predicting microbial protein functionsPreprint
Grigson, S. R., & Edwards, R. P. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/jhmta · Cited by 4
Unsupervised machine learning leads to an abiotic picomolar peptide ligandPreprint
Brown, J. S., Mohapatra, S., Lee, M. A., Misteli, R., Tseo, Y., Grob, N. M., Quartararo, A. J., Loas, A., Gómez‐Bombarelli, R., & Pentelute, B. L. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-tws4n · Cited by 4
CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters k cat , K m and K iPreprint
Boorla, V. S., & Maranas, C. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.10.584340 · Cited by 4
“To meet this challenge, artificial intelligence (AI) algorithms have emerged as promising alternatives for the automated assignment of functions of uncharacterized proteins 3 .”
Background
Protein family annotation for the Unified Human Gastrointestinal Proteome by DPCfam clustering
Barone, F., Russo, E. T., Garcia, E. N. V., Punta, M., Cozzini, S., Ansuini, A., & Cazzaniga, A. (2024) · Scientific Data · doi:10.1038/s41597-024-03131-4 · Cited by 3
“Future plans include further optimization of the clustering procedure allowing for faster processing of newly deposited metagenomic data, and combining automatic classification with functional annotation leveraging deep learning [3, 31] for even more comprehensive annotation of UHGP.”
Method
Artificial Intelligence Learns Protein PredictionPreprint
Heinzinger, M., & Rost, B. (2024) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041458 · Cited by 4
Pre-trained language models for protein and molecular design
Zhang, E., Pan, Z., Yao, Z., Dong, T., Chen, G., Deng, T., Chen, S., & Chen, C. Y. (2025) · Physical Chemistry Chemical Physics · doi:10.1039/d5cp00785b · Cited by 3
AFPDeepPred: A Deep Learning Framework for Accurate Identification of Antifreeze Proteins
Lin, X., Guan, J., Wang, F., Xie, P., Zhao, Y., Zhao, Z., Xiang, M., Wei, L., Liu, X., Lee, T.-Y., et al. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02024 · Cited by 3
Deep learning‐based annotation of plant abiotic stress resistance genes for crops
Zhang, H., Liu, X., Liu, W., Wang, S., Li, Y., Xiang, W., Yang, Q., Zhang, A., Wang, G., Li, Y., et al. (2025) · The Plant Journal · doi:10.1111/tpj.70556 · Cited by 4
Deep learning revolutionizes protein research: Advances in structure prediction, functional annotation, and engineered design
Zhang, J., Wang, S., Gao, L., & Li, Q. (2026) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2026.03.012 · Cited by 4
Applications of machine learning in plant biotechnology
Ogra, I. O., Silué, Y., Fawole, O. A., Aremu, A. O., & Opara, U. L. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01166-7 · Cited by 3
AI-assisted design of next-generation antibiotics against multidrug-resistant Staphylococcus aureus
Zheng, Y., Jin, F., Chang, Q., Yu, J., Zheng, Y., & Li, Y. (2026) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2026.109092 · Cited by 3
Sensitive remote homology search by local alignment of small positional embeddings from protein language models
(2024) · doi:10.7554/elife.91415.3 · Cited by 3
Unsupervised Domain Classification of AlphaFold2-Predicted Protein Structures
Barone, F., Laio, A., Punta, M., Cozzini, S., Ansuini, A., & Cazzaniga, A. (2025) · PRX Life · doi:10.1103/prxlife.3.023009 · Cited by 3
HiPHD: Hierarchical Classification for Protein Remote Homology Detection by Incorporating Protein Sequential and Structural Information
Qu, F., Zhao, Y., Huang, T., Wang, X., Zhang, J., & Chen, J. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3604441 · Cited by 4
Revisiting Protein–Protein Docking: A Systematic Evaluation Framework
Jiang, L., Zhang, K., Zhu, K., Wang, Y., Kang, Y., & Hou, T. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01399 · Cited by 3
Prot-GO: A Parallel Transformer Encoder-Based Fusion Model for Accurately Predicting Gene Ontology (GO) Terms from Full-Scale Protein Sequences
Tamir, A., & Yuan, J. S. (2025) · Electronics · doi:10.3390/electronics14193944 · Cited by 6
Differential proteomics of bacteria grown in vitro and in planta reveals functions used during growth on maize roots
Garrell, A.-K., Cheadle, J. B., Crook, N., Pal, G., Septer, A. N., Wagner, M. R., Beck, A. E., & Kleiner, M. (2026) · mSystems · doi:10.1128/msystems.00371-26 · Cited by 5
Multidimensional Scaling for Gene Sequence Data with Autoencoders
Wickramasinghe, P., & Fox, G. (2021) · 2021 2nd International Conference on Computing and Data Science (CDS) · doi:10.1109/cds52072.2021.00095 · Cited by 3
Visualizing and Annotating Protein Sequences using A Deep Neural Network
Zhao, Z., & ROSEN, G. L. (2020) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf51394.2020.9443364 · Cited by 3
“The function prediction performance 506 978-0-7381-3126-9/20/$31.00 ©2020 IEEE Asilomar 2020 are further improved by deep learning based models in recent years [2], [12], [13].”
BackgroundInfluential
CNN-BLSTM based deep learning framework for eukaryotic kinome classification: An explainability based approach
John, C., Sahoo, J., Sajan, I. K., Madhavan, M., & Mathew, O. K. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108169 · Cited by 4
Intelligent Information Processing Technology for Digital Libraries Based on Deep Learning
Wang, L., & Yu, X. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.10.056 · Cited by 6
Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools
Prabakaran, R., & Bromberg, Y. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf035 · Cited by 5
GLAMOUR: Graph learning over macromolecule representations
Mohapatra, S., An, J., & Gómez‐Bombarelli, R. (2021) · doi:10.1021/scimeetings.1c01022 · Cited by 3
“Apart from sequence alignment in linear macromolecules, edit distances (Riesen & Bunke, 2009; Zhang et al., 2010), linear kernels (Leslie et al., 2001; Jaakkola et al., 2000) and deep learning methods (Bileschi et al., 2019; Seo et al., 2018) have been proposed to compute similarity.”
Background
AnnoPRO: an Innovative Strategy for Protein Function Annotation based on Image-like Protein Representation and Multimodal Deep LearningPreprint
Zheng, L., Shi, S., Fang, P., Zhang, H., Pan, Z., Huang, S., Xia, W., Li, H., Zeng, Z., Zhang, S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.13.540619 · Cited by 3
“existing methods/tools to improve the prediction accuracies for families in Tail Label Levels, and 1 the “long-tail problem” in protein functional annotation remains unsolved [41-43].”
Method
Deciphering enzymatic potential in metagenomic reads through DNA language models
Prabakaran, R., & Bromberg, Y. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf836 · Cited by 4
“Recent developments in deep learning models, and particularly language models (LMs), have succeeded in predicting protein structures and made significant advances in protein domain and function annotations [ 25–28 ].”
Protein multi‐level structure feature‐integrated deep learning method for mutational effect prediction
Pang, A., Luo, Y., Zhou, J., Cai, X., Huang, L., Zhang, B., Liu, Z., & Zheng, Y. (2024) · Biotechnology Journal · doi:10.1002/biot.202400203 · Cited by 3
“[10,11] A range of deep learning algorithms have been developed to model the protein fitness landscape, thereby facilitating the rapid identification of advantageous mutants.”
Predicting Protein Function in the AI and Big Data Era
Percudani, R., & Rito, C. D. (2025) · Biochemistry · doi:10.1021/acs.biochem.5c00186 · Cited by 3
Harnessing Machine Learning for Agnostic Biodetection
Sandholtz, S. H., Valdes, C., Mulakken, N., Torres, M. W., Avila-Herrera, A., Drocco, J. A., Martí, J. M., Allen, J., Tipnis, U., Jaing, C. J., et al. (2025) · Health Security · doi:10.1089/hs.2024.0075 · Cited by 2
Socioscientific issues based on local wisdom in science education: A systematic review
Dewi, R. K., Rahayu, S. L., Sumarni, W., Muntholib, M., & Alsulami, N. M. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2026206 · Cited by 2
From waste to wealth: AI-driven enzyme innovation for straw transformation
Sun, X., Zhou, L., Li, Y., Shen, Q., Wang, Y., Jia, B., Yao, Y., & Pu, Z. (2026) · Bioresource Technology · doi:10.1016/j.biortech.2026.134875 · Cited by 3
ROSes-FINDER: a multi-task deep learning framework for accurate prediction of microorganism reactive oxygen species scavenging enzymes
Yan, Y., Shi, Z., & Wei, H. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1245805 · Cited by 2
“While a multitude of bioinformatics tools and databases exist for protein annotation, such as DEEPARG and ARG_SHINE, these resources still have their own set of limitations (Bileschi et al., 2022), the identification of ROSes still poses a significant challenge (Ho Thanh Lam et al.”
Background
Clustering and machine learning framework for medical time series classification
Ruipérez-Campillo, S., Reiß, M., Ramírez, E., Cebrián, A., Millet, J., & Castells, F. (2024) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2024.07.005 · Cited by 2
Investigation of protein family relationships with deep learning
Ponamareva, I., Andreeva, A. E., Bileschi, M. L., Colwell, L. J., & Bateman, A. G. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae132 · Cited by 2
Self-citation
Helicobacter cappadocius sp. nov., from lizards: The first psychrotrophic Helicobacter species
Aydın, F., Tarhane, S., Karakaya, E., Abay, S., Kayman, T., Güran, Ö., Bozkurt, E., Üzüm, N., Avcı, A., Olgun, K., et al. (2024) · Systematic and Applied Microbiology · doi:10.1016/j.syapm.2024.126557 · Cited by 2
“Gene expression analyses, enzyme activity assays, and pathway reconstructions should be employed to confirm the roles of these protein families, particularly those involved in xenobiotic degradation, amino acid metabolism, and energy production (Bileschi et al., 2022).”
PROBind: A Web Server for Prediction, Analysis, and Visualization of Protein-Protein and Protein-Nucleic Acid Binding Residues
Wu, C., Zhang, F., Jia, P., Zhu, J., Zeng, M., Hu, G., Wang, K., Kurgan, L., & Li, M. (2026) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzag032 · Cited by 2
TMvisDB: resource for transmembrane protein annotation and 3D visualizationPreprint
Marquet, C., Grekova, A., Houri, L., Bernhofer, M., Soto, L. F. J., Karl, T., Heinzinger, M., Dallago, C., & Rost, B. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.30.518551 · Cited by 2
“The knowledge acquired during pre-training on raw sequences can readily be transferred to downstream predictions of per-residue [10, 23-26], and per-protein phenotypes [27-30].”
Background
Ultra-Accurate Classification and Discovery of Functional Protein-Coding Genes from Microbiomes Using FunGeneTyper: An Expandable Deep Learning-Based FrameworkPreprint
Zhang, G., Wang, H., Zhang, Z., Zhang, L., Guo, G., Yang, J., Yuan, F., & Ju, F. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.28.522150 · Cited by 2
Many Families of Lids for TonB-dependent Transporters in BacteroidesPreprint
Price, M. N., Deutschbauer, A. M., & Arkin, A. P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.17.533168 · Cited by 2
“XusB belongs to DUF4374 (PFam PF14298); Echvi_4215 is not assigned to DUF4374 by PFam’s hidden markov model, but some BLAST-level homologs are, and Echvi_4215 is assigned to this family by a deep learning approach (PFam-N, (Bileschi et al. 2022)).”
Background
Enzyme promiscuous profiles for protein sequence and reaction annotationPreprint
MohammadiPeyhani, H., Sveshnikova, A. N., Mišković, L., & Vassily Hatzimanikatis, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.13.557547 · Cited by 2
“In recent studies, machine learning (ML) has been successfully used in all stages of protein annotation, from structural prediction to functional analysis 5 .”
Sensitive remote homology search by local alignment of small positional embeddings from protein language models
Johnson, S. R., Peshwa, M., & Sun, Z. (2024) · doi:10.7554/elife.91415.2 · Cited by 2
Prediction and design of transcriptional repressor domains with large-scale mutational scans and deep learningPreprint
Valbuena, R., Nigam, A., Tycko, J., Suzuki, P., Spees, K., Aradhana, Arana, S. M., Du, P. P., Patel, R. A., Bintu, L., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.21.614253 · Cited by 2
PathoGraph: A Graph-Based Method for Standardized Representation of Pathology Knowledge
Lou, P., Dong, Y., Ding, C., Wang, C., Guo, R. R., Wang, C., Wang, C., & Li, C. (2025) · Scientific Data · doi:10.1038/s41597-025-04906-z · Cited by 2
Evolutionary discovery and characterization of fungal transcriptional activators using active learningPreprint
Waldburger, L. M., Nisonoff, H. M., Zintel, M. A., Kirkpatrick, L. D., Lam, A., Lanclos, N., Keasling, J. D., Staller, M. V., & Shih, P. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.12.675635 · Cited by 2
“While small-scale experiments have elucidated gene-level principles, large-scale approaches enable researchers to uncover genome-level features governing biological functions [3, 4].”
Genos-m: a foundation model for human-associated microbial genomesPreprint
Fang, C., Yang, F., Hou, H., Ren, H., Zhong, H., Xu, H., Zhang, J., Su, J., Cai, J., Yuan, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.21.726868 · Cited by 3
“These approaches remain essential for interpretable taxonomic and functional compositions, but they are constrained by predefined features and reference coverage [6, 8, 9].”
Machine learning-based prediction of proteins’ architecture using sequences of amino acids and structural alphabets
Abbass, J., & Parisi, C. (2024) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2024.2328736 · Cited by 1
“One way to help annotate those structures has been to employ machine learning (ML) and/or deep learning (DL) algorithms (Bileschi et al., 2022; Nallapareddy et al., 2023).”
Synergistic Distributed CNN Model for Protein Classification With a Collaborative BSP Synchronization Based on LSTM Prediction
Assali, T., Ayoub, Z. T., & Ouni, S. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70025 · Cited by 1
“Besides, we implemented the ProtCNN model [33] and compared it with our distributed CNN in terms of training time.”
Integrating genomics and artificial intelligent tools to unlock the safety and uricolytic activities of promising purinolytic Lactobacillus plantarum L123
Wang, L., Yue, S., Liao, J., & Wang, C. (2025) · Journal of Future Foods · doi:10.1016/j.jfutfo.2025.04.016 · Cited by 2
KAN-Mixer: Kolmogorov-Arnold Networks for Gene Expression Prediction in Plant Species
Gao, J., Zhao, J., Li, K., & Wang, D. Y. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92387-6_9 · Cited by 1
AI-empowered chemical biology
Yang, L., Gao, Y. Q., Huang, Y., & Yang, J. (2025) · Scientia Sinica Chimica · doi:10.1360/ssc-2025-0169 · Cited by 1
Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding
LeBlanc, C. J., Agarwal, P., Demaray, J., Hu, G. F., Zintel, M. A., Lam, A., Hernandez, J. E. C., & Staller, M. V. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101701 · Cited by 2
Embedding-Based Alignments Capture Structural and Sequence Domains of Distantly Related Multifunctional Human Proteins
Vazzana, G., Manfredi, M., Savojardo, C., Martelli, P. L., & Casadio, R. (2026) · Computation · doi:10.3390/computation14010025 · Cited by 1
Identifying microbial protease allergens through protein language model-guided homology
Thurimella, K., Wu, E., Li, C., Graham, D. B., Owens, R. M., Plichta, D. R., Sokol, C. L., Xavier, R. J., & Bacallado, S. (2026) · Cell Systems · doi:10.1016/j.cels.2025.101510 · Cited by 1
ET-Pfam: ensemble transfer learning for protein family prediction
Duarte, S., Vitale, R. G., Escudero, S., Fenoy, E., Bugnon, L. A., Milone, D. H., & Stegmayer, G. S. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag121 · Cited by 1
Context-informed subgraph foundation models enable interpretable protein-function prediction
Zhou, Z., Rao, J., Zhang, Z., Ma, R., Yang, J., & Zheng, S. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101535 · Cited by 1
DeepOTG: An effective deep learning framework for identifying human protein O-linked threonine glycosylation sites via attention-based information bottleneck
Ye, Y., Xie, P., Chung, C., Wang, F., Shi, B., Lin, X., Chiu, Y.-P., Lee, T.-Y., Liu, X., Guan, J., et al. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.152152 · Cited by 1
Emerging technologies for the discovery of biosynthetic genes in plants
Jaczkowski, A., Bültemeier, A., Seligmann, B., Pucker, B., & Franke, J. (2026) · Natural Product Reports · doi:10.1039/d6np00011h · Cited by 1
Advances and challenges in alternative proteins: From biotechnology to sustainable food production
Zhang, C., Wu, X., He, Q., Chen, J., & Zhou, J. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108958 · Cited by 1
Gut Microbiomes of Rainbow Trout and Atlantic Salmon: Nutritional Modulation, Mucosal Immunity, and Resistome Risk
Jiang, Z., Chen, J., Ren, Y., Lin, T., Li, S., Shen, F., Qin, B., Li, L., Li, C., Ying, N., et al. (2026) · Biology · doi:10.3390/biology15131066 · Cited by 1
“At a broader level of functional interpretation, deep learning protein-annotation frameworks and DeepFRI can improve inference efficiency from sequence or structure to function, potentially alleviating the problem of excessive unknown functional fragments in salmonid gut metagenomes [148,149].”
ProFAB—open protein functional annotation benchmark
Özdilek, A. S., Atakan, A. K., Özsarı, G., Acar, A. C., Atalay, V., Doğan, T., & Rifaioğlu, A. S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac627 · Cited by 1
Sequence-Structure Embeddings via Protein Language Models Improve on Prediction Tasks
Kabir, A., & Shehu, A. (2022) · IEEE International Conference on Knowledge Graph (ICKG) · doi:10.1109/ickg55886.2022.00021 · Cited by 1
“This gap, along with the potential that function characterization holds for therapeutic applications, continues to motivate computational research on function prediction [15].”
Background
Exploring Post-Training Quantization of Protein Language Models
Peng, S., Yang, F., Sun, N., Chen, S., Jiang, Y., & Pan, A. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385775 · Cited by 2
“…[12] have been successfully applied for various protein-related tasks, including secondary structure prediction [24], [26], contact prediction [25], [26], 3D structure prediction [1], [16], annotation prediction [4], protein-protein interaction prediction [10], and fitness prediction [9], [14].”
The globins of cyanobacteria and green algae: An update
Lecomte, J. T. J., & Johnson, E. A. (2024) · Advances in microbial physiology/Advances in Microbial Physiology · doi:10.1016/bs.ampbs.2024.04.004 · Cited by 1
Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors
Pais, D. A. M., Mayer, J.-P. A., Felderer, K., Batalha, M. B., Eichner, T., Santos, S. T., Kumar, R., da Silva, S. R. A., & Kaufmann, H. (2024) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-024-00569-x · Cited by 1
“…development [17], prediction of protein function including antigen–antibody binding from structure based models or primary amino acid sequence [31, 32] and prediction of biophysical properties such as melting temperature or methionine oxidation from antibody amino acid composition or protein…”
Exploring protein natural diversity in environmental microbiomes with DeepMetagenome
Li, X., Zhang, J., Ma, D., Fan, X., Zheng, X. S., & Liu, Y. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100896 · Cited by 1
Enhancing Transcription Factor Prediction via Domain Knowledge Integration With Logic Tensor Networks
Gao, L., Sun, L., & Sheng, V. S. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3617864 · Cited by 1
Data intelligence for molecular science
Li, Y., Jiang, J., & Luo, Y. (2023) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2022-1152 · Cited by 1
Drug design and repurposing with a sequence-to-drug paradigmPreprint
Chen, L., Fan, Z., Chang, J., Yang, R., Guo, H., Zhang, Y., Yang, T., Zhou, C., Chen, Z., Zheng, C., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.26.485909 · Cited by 1
Opinion piece/Book chapter: Artificial Intelligence learns protein predictionPreprint
Heinzinger, M., & Rost, B. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/n7wfp · Cited by 1
Functional profiling of the sequence stockpile: a review and assessment of in silico prediction toolsPreprint
Prabakaran, R., & Bromberg, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.12.548726 · Cited by 1
“…based on the type of protein annotation into four categories: GO – DeepFri 58 , DeepGOPlus 59 , GoPredSim 42 , GOProFormer 60 , and NetGO 61 , Pfam – HMMER 62 , InterProScan 63 , ProtENN & ProtCNN 64 and ProteInfer 65 , Orthologs – GhostKOALA 66 , KofamKOALA 67 and E.C. – ECPred 68 and Mantis 69 .”
Influential
Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gutPreprint
Schmitz, M. A., Dimonaco, N. J., Clavel, T., & Hitch, T. C. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.29.596415 · Cited by 1
Protein sequence domain annotation using a language modelPreprint
Sarkar, A., Krishnan, K., & Eddy, S. R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.04.596712 · Cited by 2
“ProtCNN and ProtENN use deep convolutional models to assign Pfam family labels to whole sequences [16]; ProtTNN frames the task with transformer-based protein language models (pLMs) [17]; and ProtEx augments a transformer with database retrieval capabilities to improve sequence-level functional…”
Computational methods for prokaryotic (meta)genomic annotation and function prediction using horizontal information transfer
Ruoshi, Z. (2024) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-10594 · Cited by 1
Characterization of microbial primary and secondary metabolism in the marine realm
Geller‐McGrath, D. (2024) · Open Access Server of the Woods Hole Scientific Community (Woods Hole Scientific Community) · doi:10.1575/1912/70396 · Cited by 1
Editorial: Machine Learning for Structural Biology
Corso, G., Nesr, G. E., & Wayment-Steele, H. K. (2024) · PRX Life · doi:10.1103/prxlife.2.040001 · Cited by 1
Deep functional profiling of gene sets using Large Language Models: A blueprint for tailored, context-aware functional annotationPreprint
Khan, T., Yurieva, M., Kabeer, B. S. A., Toufiq, M., Rinchai, D., Palucka, K., & Chaussabel, D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.12.628275 · Cited by 1
AstraROLE2 & AstraSUIT2: Multi-Task Annotation Models for Functional Profiling of ProteinsPreprint
Bozkurt, Ç., Vasilyeva, A., & Goteti, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.21.660734 · Cited by 1
Fixed-Length Protein Embeddings using Contextual LensesPreprint
Shanehsazzadeh, A., Belanger, D., & Dohan, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.15065
“Bileschi et al. [4] develops ProtCNN, a deep, residual, dilated CNN trained for an analogous Pfam family classiﬁcation task.”
InfluentialSelf-citation
[The influence of hydrogene ion concentrations on peritoneal macrophage plasma membrane's photosensitivity].
Pirutin, S. K., Туровецкий, В. Б., & Kudriashov, I. B. (2009) · PubMed
Recurrent Neural Network Encoding Decoding Translator based Prediction Protein Function and Functional Annotation
Hossain, M. N., Islam, M. M., & Islam, M. K. B. (2021) · International Conference on Science & Contemporary Technologies (ICSCT) · doi:10.1109/icsct53883.2021.9642535
Coronavirus Genome Sequence Similarity and Protein Sequence Classification
Mukherjee, P. P., Badr, Y., Karvekar, S. N., & Viswanathan, S. (2021) · Journal of Digital Science · doi:10.33847/2686-8296.3.2_1
Functional domain annotation of protein sequences with deep metric learning
(2022) · International Multiconference on Bioinformatics of Genome Regulation and Structure / Systems Biology (BGRS/SB) · doi:10.18699/sbb-2022-668
Deep Learning Methods For Prediction of HLA-Peptide Interactions in IDB
Ameen, Z. S., Ibrahim, A. U., Mubarak, A. S., Al-Turjman, F. M., & Ghali, U. M. (2022) · International Conference on Artificial Intelligence in Everything (AIE) · doi:10.1109/aie57029.2022.00021
gGN: learning to represent graph nodes as low-rank Gaussian distributionsPreprint
Edera, A., Stegmayer, G. S., & Milone, D. H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.15.516704
“This task is fundamental for comparing the functional relatedness of gene products based on their annotated GO terms, and lies at the heart of automating annotation of protein functions [47, 48, 49].”
Background
Brassica Bitki Türlerinde Transkripsiyon Faktörü DNA'sının Derin Öğrenme ile Sınıflandırılması
Öncül, A. B. (2022) · European Journal of Science and Technology · doi:10.31590/ejosat.1200680
Evaluating Tuning Strategies for Sequence Generation with Protein Language ModelsPreprint
Nathansen, A., Klein, K., Renard, B. Y., Nowicka, M., & Bartoszewicz, J. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.28.530492 · Cited by 1
“We further evaluate the properties of generated sequences by predicting family membership with ProtCNN (Bileschi et al., 2022) and HMMER and applying protein activity filters (Johnson et al., 2023).”
BackgroundMethodResultInfluential
Classification of Family Domain of Amino Acid Sequences Using CNN-LSTM
Mahalakshmi, G. S., Sunilkumar, G., Gilbert, S. F., & Sendhilkumar, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9228-5_55
Challenges and opportunities of machine chemists
Jiang, J., Li, Y., Shen, X., & Gao, F. (2023) · Scientia Sinica Chimica · doi:10.1360/ssc-2023-0044
Sensitive remote homology search by local alignment of small positional embeddings from protein language models
Johnson, S. R., Peshwa, M., & Sun, Z. (2023) · doi:10.1101/2023.07.26.550718
Ekstraksi Fitur Rantai Markov untuk Klasifikasi Famili Protein
Haryanto, T., Kurniawan, R. P., Muhammad, S., Kustiyo, A., & Giri, E. P. (2023) · Jurnal Ilmiah SINUS · doi:10.30646/sinus.v21i2.748
“Beberapa metode baru berbasis deep learning juga telah diusulkan, seperti DeepFam (Seo et al., 2018) dan ProtCNN (Bileschi et al., 2019) untuk mengidentifikasi dan menganotasi protein.”
Background
Sensitive remote homology search by local alignment of small positional embeddings from protein language modelsPreprint
Johnson, S. R., Peshwa, M., & Sun, Z. (2023) · eLife · doi:10.7554/elife.91415.1
T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification
Tayebi, Z., Ali, S., Chourasia, P., Murad, T., & Patterson, M. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8141-0_17
The Democratization and Development of Cell-Free Protein Synthesis
Levine, M. Z. (2019) · doi:10.15368/theses.2019.141
Using Deep Learning to Classify Full-Length Transcriptome Sequences
Li, W., Ma, J., Li, C., Yu, T., & Cui, X. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385824
“Deep learning models have achieved state-of-the-art performance in the protein domain on problems ranging from annotating protein Universe [18] and protein structure prediction [19].”
Background
Additional file 1 of Protein language models can capture protein quaternary state
Avraham, O., Tsaban, T., Ben-Aharon, Z., Tsaban, L., & Schueler‐Furman, O. (2023) · Figshare · doi:10.6084/m9.figshare.24564069

Authors: Bileschi, M. L., Belanger, D., Bryant, D. H., Sanderson, T., Carter, B., Sculley, D., Bateman, A., DePristo, M. A., & Colwell, L. J.
Affiliations: Google (United States) (US)
MIT Computer Science and Artificial Intelligence Laboratory (US)
European Bioinformatics Institute (GB)
Corresponding author: Bileschi, Maxwell L.
Colwell, Lucy J.
ORCID: Bileschi, Maxwell L.
Belanger, David
Bryant, Drew H.
Sanderson, Theo
Carter, Brandon
Bateman, Alex
DePristo, Mark A.
Colwell, Lucy J.
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2022-02-21
Volume: 40
Issue: 6
Pages: 932-937
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/s41587-021-01179-w
OpenAlex: W4213112325
Cited by: 332
References: 59
Citations by year: 2026: 39 · 2025: 66 · 2024: 70 · 2023: 73 · 2022: 45
FWCI: 20.90
Type: journal article
Language: en
Open access: green
doi.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Biotechnology (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
PubMed (publishedVersion)
Funders: Wellcome Trust (210918/Z/18/Z)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords: protein function prediction, deep learning, Pfam, sequence annotation, remote homology detection, sequence clustering, microbial protein, human proteome, protein families
MeSH terms: Deep Learning, Amino Acid Sequence, Humans, Proteome, Databases, Protein, Proteomics, Molecular Sequence Annotation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Using deep learning to annotate the protein universe

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks