Uniclust databases of clustered and deeply annotated protein sequences and alignments

Basic local alignment search tool

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Announcing the worldwide Protein Data Bank

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HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

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

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Highly accurate protein structure prediction with AlphaFold

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ColabFold - Making protein folding accessible to all

Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391

Accurate prediction of protein structures and interactions using a 3-track network

Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286

Improved protein structure prediction using potentials from deep learning

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Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4

Blanco-Míguez, A., Beghini, F., Cumbo, F., McIver, L. J., Thompson, K. N., Zolfo, M., Manghi, P., Dubois, L., Huang, K. D., Thomas, A. M., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01688-w · Cited by 1,442

HH-suite3 for fast remote homology detection and deep protein annotation

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Improved prediction of protein-protein interactions using AlphaFold2

Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,022

Clustering huge protein sequence sets in linear time

Steinegger, M., & Söding, J. (2018) · Nature Communications · doi:10.1038/s41467-018-04964-5 · Cited by 875

Protein Sequence Analysis Using the MPI Bioinformatics Toolkit

Gabler, F., Nam, S., Till, S., Mirdita, M., Steinegger, M., Söding, J., Lupas, A. N., & Alva, V. (2020) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.108 · Cited by 831

ProtGPT2 is a deep unsupervised language model for protein design

Ferruz, N., Schmidt, S., & Höcker, B. (2022) · Nature Communications · doi:10.1038/s41467-022-32007-7 · Cited by 795

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

Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.07.09.450648 · Cited by 635

MMseqs2 desktop and local web server app for fast, interactive sequence searches

Mirdita, M., Steinegger, M., & Söding, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1057 · Cited by 618

The person-to-person transmission landscape of the gut and oral microbiomes

Valles-Colomer, M., Blanco-Míguez, A., Manghi, P., Asnicar, F., Dubois, L., Golzato, D., Armanini, F., Cumbo, F., Huang, K. D., Manara, S., et al. (2023) · Nature · doi:10.1038/s41586-022-05620-1 · Cited by 449

Single-sequence protein structure prediction using language models from deep learning

Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455

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

Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455

Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold

Steinegger, M., Mirdita, M., & Söding, J. (2019) · Nature Methods · doi:10.1038/s41592-019-0437-4 · Cited by 445

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

Ahdritz, G., Bouatta, N., Floristean, C., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02272-z · Cited by 439

An expanded arsenal of immune systems that protect bacteria from phages

Millman, A., Melamed, S., Leavitt, A., Doron, S., Bernheim, A., Hör, J., Garb, J., Bechon, N., Brandis, A., Lopatina, A., et al. (2022) · Cell Host & Microbe · doi:10.1016/j.chom.2022.09.017 · Cited by 428

Distance-based protein folding powered by deep learning

Xu, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821309116 · Cited by 401

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280

Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases

Ianiro, G., Punčochář, M., Karcher, N., Porcari, S., Armanini, F., Asnicar, F., Beghini, F., Blanco-Míguez, A., Cumbo, F., Manghi, P., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01964-3 · Cited by 366

MSA Transformer

Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J. F., Abbeel, P., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.02.12.430858 · Cited by 350

MetaEuk—sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics

Levy Karin, E., Mirdita, M., & Söding, J. (2020) · Microbiome · doi:10.1186/s40168-020-00808-x · Cited by 299

ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction

Tubiana, J., Schneidman-Duhovny, D., & Wolfson, H. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01490-7 · Cited by 274

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Gao, M., Nakajima An, D., Parks, J. M., & Skolnick, J. (2022) · Nature Communications · doi:10.1038/s41467-022-29394-2 · Cited by 252

SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

Stein, R. A., & Mchaourab, H. S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010483 · Cited by 258

Improved protein structure prediction by deep learning irrespective of co-evolution information

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Deep‐learning contact‐map guided protein structure prediction in CASP13

Zheng, W., Li, Y., Zhang, C., Pearce, R., Mortuza, S. M., & Zhang, Y. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25792 · Cited by 208

Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search

(2022) · doi:10.1038/s41467-022-33729-4 · Cited by 226

AFsample: improving multimer prediction with AlphaFold using massive sampling

Wallner, B. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad573 · Cited by 164

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning

Hanson, J., Paliwal, K. K., Litfin, T., & Zhou, Y. (2019) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2019.01.004 · Cited by 165

Genomic diversity and ecology of human-associated Akkermansia species in the gut microbiome revealed by extensive metagenomic assembly

Karcher, N., Nigro, E., Punčochář, M., Blanco-Míguez, A., Ciciani, M., Manghi, P., Zolfo, M., Cumbo, F., Manara, S., Golzato, D., et al. (2021) · Genome Biology · doi:10.1186/s13059-021-02427-7 · Cited by 159

Protein language model embeddings for fast, accurate, alignment-free protein structure prediction

Weissenow, K., Heinzinger, M., & Rost, B. (2021) · doi:10.1101/2021.07.31.454572 · Cited by 139

AlphaFold2-aware protein–DNA binding site prediction using graph transformer

Yuan, Q., Chen, S., Rao, J., Zheng, S., Zhao, H., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab564 · Cited by 137

Unifying the known and unknown microbial coding sequence space

Vanni, C., Schechter, M. S., Acinas, S. G., Barberán, A., Buttigieg, P. L., Casamayor, E. O., Delmont, T. O., Duarte, C. M., Eren, A. M., Finn, R. D., et al. (2022) · eLife · doi:10.7554/elife.67667 · Cited by 134

A method for multiple-sequence-alignment-free protein structure prediction using a protein language model

Fang, X., Wang, F., Liu, L., He, J., Lin, D., Xiang, Y., Zhu, K., Zhang, X., Wu, H., Li, H., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00721-6 · Cited by 132

Ensembling multiple raw coevolutionary features with deep residual neural networks for contact‐map prediction in CASP13

Li, Y., Zhang, C., Bell, E. W., Yu, D., & Zhang, Y. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25798 · Cited by 94

Structure prediction of alternative protein conformations

Bryant, P., & Noé, F. (2024) · Nature Communications · doi:10.1038/s41467-024-51507-2 · Cited by 105

Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15

Liu, J., Guo, Z., Wu, T., Roy, R. S., Quadir, F., Chen, C., & Cheng, J. (2023) · Communications Biology · doi:10.1038/s42003-023-05525-3 · Cited by 86

BFVD—a large repository of predicted viral protein structures

Kim, R. S., Levy Karin, E., Mirdita, M., Chikhi, R., & Steinegger, M. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1119 · Cited by 86

Bat genomes illuminate adaptations to viral tolerance and disease resistance

Morales, A. E., Dong, Y., Brown, T., Baid, K., Kontopoulos, D. G., Gonzalez, V., Huang, Z., Ahmed, A.-W., Bhuinya, A., Hilgers, L., et al. (2025) · Nature · doi:10.1038/s41586-024-08471-0 · Cited by 78

Structure prediction of protein-ligand complexes from sequence information with Umol

Bryant, P., Kelkar, A., Guljas, A., Clementi, C., & Noé, F. (2024) · Nature Communications · doi:10.1038/s41467-024-48837-6 · Cited by 75

Progressive assembly of multi-domain protein structures from cryo-EM density maps

Zhou, X., Li, Y., Zhang, C., Zheng, W., Zhang, G., & Zhang, Y. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00232-1 · Cited by 75

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model

Zeng, Y., Wei, Z., Yuan, Q., Chen, S., Yu, W., Lu, Y., Gao, J., & Yang, Y. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad187 · Cited by 73

Primate-conserved carbonic anhydrase IV and murine-restricted LY6C1 enable blood-brain barrier crossing by engineered viral vectors

(2023) · doi:10.1126/sciadv.adg6618 · Cited by 67

Computational design of sequence-specific DNA-binding proteins

Glasscock, C. J., Pecoraro, R. J., McHugh, R., Doyle, L. A., Chen, W., Boivin, O., Lonnquist, B., Na, E., Politanska, Y., Haddox, H. K., et al. (2025) · Nature Structural & Molecular Biology · doi:10.1038/s41594-025-01669-4 · Cited by 58

A resource for improved predictions of Trypanosoma and Leishmania protein three-dimensional structure

(2021) · doi:10.1371/journal.pone.0259871 · Cited by 54

Improved Multimer Prediction using Massive Sampling with AlphaFold in CASP15

Wallner, B. (2023) · doi:10.22541/au.168170986.60158084/v1 · Cited by 53

Improved multimer prediction using massive sampling with AlphaFold in CASP15

Wallner, B. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26562 · Cited by 53

Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein

Zeng, W., Dou, Y., Pan, L., Xu, L., & Peng, S. (2024) · Nature Communications · doi:10.1038/s41467-024-52293-7 · Cited by 49

Merizo: a rapid and accurate protein domain segmentation method using invariant point attention

Lau, A. M., Kandathil, S. M., & Jones, D. T. (2023) · Nature Communications · doi:10.1038/s41467-023-43934-4 · Cited by 48

Histones predate the split between bacteria and archaea

Alva, V., & Lupas, A. N. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1000 · Cited by 43

Identification of Iron-Sulfur (Fe-S) Cluster and Zinc (Zn) Binding Sites Within Proteomes Predicted by DeepMind’s AlphaFold2 Program Dramatically Expands the Metalloproteome

Wehrspan, Z. J., McDonnell, R. T., & Elcock, A. H. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167377 · Cited by 45

Discovery of archaeal fusexins homologous to eukaryotic HAP2/GCS1 gamete fusion proteins

(2022) · doi:10.1038/s41467-022-31564-1 · Cited by 45

Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction

Chen, C., Wu, T., Guo, Z., & Cheng, J. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26052 · Cited by 44

Protein quaternary structures in solution are a mixture of multiple forms

Marciano, S., Dey, D., Listov, D., Fleishman, S. J., Sonn-Segev, A., Mertens, H., Busch, F., Kim, Y., Harvey, S. R., Wysocki, V. H., et al. (2022) · Chemical Science · doi:10.1039/d2sc02794a · Cited by 41

Structure and activation of the human autophagy-initiating ULK1C:PI3KC3-C1 supercomplex

(2025) · doi:10.1038/s41594-025-01557-x · Cited by 40

Evolutionary gain and loss of a plant pattern-recognition receptor for HAMP recognition

Snoeck, S., Abramson, B. W., Garcia, A. G., Egan, A. N., Michael, T. P., & Steinbrenner, A. D. (2022) · eLife · doi:10.7554/elife.81050 · Cited by 37

Interpreting the molecular mechanisms of disease variants in human transmembrane proteins

Tiemann, J. K. S., Zschach, H., Lindorff-Larsen, K., & Stein, A. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2022.12.031 · Cited by 35

Structural pathway for PI3-kinase regulation by VPS15 in autophagy

Cook, A. S. I., Chen, M., Nguyen, T. N., Cabezudo, A. C., Khuu, G., Rao, S., Garcia, S. N., Yang, M., Iavarone, A. T., Ren, X., et al. (2025) · Science · doi:10.1126/science.adl3787 · Cited by 34

Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design

Moffat, L., Greener, J. G., & Jones, D. T. (2021) · doi:10.1101/2021.08.24.457549 · Cited by 33

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

Liu, J., Wu, T., Guo, Z., Hou, J., & Cheng, J. (2022) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26186 · Cited by 32

Accurate and robust protein sequence design with CarbonDesign

Ren, M., Yu, C., Bu, D., & Zhang, H. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00838-2 · Cited by 30

Functions of Essential Genes and a Scale-Free Protein Interaction Network Revealed by Structure-Based Function and Interaction Prediction for a Minimal Genome

Zhang, C., Zheng, W., Cheng, M., Omenn, G. S., Freddolino, P. L., & Zhang, Y. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00359 · Cited by 27

ProtMamba: a homology-aware but alignment-free protein state space model

Sgarbossa, D., Malbranke, C., & Bitbol, A.-F. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf348 · Cited by 27

Improved model quality assessment using sequence and structural information by enhanced deep neural networks

Liu, J., Zhao, K., & Zhang, G. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac507 · Cited by 25

HybridGCN for protein solubility prediction with adaptive weighting of multiple features

Chen, L., Wu, R., Zhou, F., Zhang, H., & Liu, J. K. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00788-8 · Cited by 25

Design in the DARK: Learning Deep Generative Models for De Novo Protein Design

Moffat, L., Kandathil, S. M., & Jones, D. T. (2022) · doi:10.1101/2022.01.27.478087 · Cited by 25

Assessing and enhancing foldability in designed proteins

Listov, D., Lipsh‐Sokolik, R., Rosset, S., Yang, C., Correia, B. E., & Fleishman, S. J. (2022) · Protein Science · doi:10.1002/pro.4400 · Cited by 24

Atomic resolution ensembles of intrinsically disordered proteins with Alphafold

Schnapka, V., Morozova, T. I., Sen, S., & Bonomi, M. (2026) · Nature Communications · doi:10.1038/s41467-026-69172-y · Cited by 23

Rational exploration of fold atlas for human solute carrier proteins

Xie, T., Chi, X., Huang, B., Ye, F., Zhou, Q., & Huang, J. (2022) · Structure · doi:10.1016/j.str.2022.05.015 · Cited by 23

Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?

Xie, T., & Huang, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01936 · Cited by 23

Discovery of archaeal Fusexins homologous to eukaryotic HAP2/GCS1 gamete fusion proteins

Moi, D., Nishio, S., Li, X., Valansi, C., Langleib, M., Brukman, N. G., Flyak, K., Dessimoz, C., de Sanctis, D., Tunyasuvunakool, K., et al. (2021) · doi:10.1101/2021.10.13.464100 · Cited by 23

Remote homolog detection places insect chemoreceptors in a cryptic protein superfamily spanning the tree of life

Himmel, N. J., Moi, D., & Benton, R. (2023) · Current Biology · doi:10.1016/j.cub.2023.10.008 · Cited by 23

CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks

Feng, S.-H., Xia, C.-Q., & Shen, H.-B. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab744 · Cited by 21

Structural analogue-based protein structure domain assembly assisted by deep learning

Peng, C.-X., Zhou, X.-G., Xia, Y.-H., Liu, J., Hou, M.-H., & Zhang, G.-J. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac553 · Cited by 21

Fast, accurate ranking of engineered proteins by target-binding propensity using structure modeling

Ding, X., Chen, X., Sullivan, E. E., Shay, T. F., & Gradinaru, V. (2024) · Molecular Therapy · doi:10.1016/j.ymthe.2024.04.003 · Cited by 20

Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints

Zhang, Y., Zhang, Z., Kagaya, Y., Terashi, G., Zhao, B., Xiong, Y., & Kihara, D. (2025) · Communications Biology · doi:10.1038/s42003-025-08783-5 · Cited by 19

Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins

Wojciechowski, J. W., Tekoglu, E., Gąsior-Głogowska, M., Coustou, V., Szulc, N., Szefczyk, M., Kopaczyńska, M., Saupe, S. J., & Dyrka, W. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010787 · Cited by 19

A-Prot: protein structure modeling using MSA transformer

Hong, Y., Lee, J., & Ko, J. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04628-8 · Cited by 19

Protein oligomer modeling guided by predicted interchain contacts in CASP14

Baek, M., Anishchenko, I., Park, H., Humphreys, I. R., & Baker, D. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26197 · Cited by 18

Assessing protein model quality based on deep graph coupled networks using protein language model

(2023) · doi:10.1093/bib/bbad420 · Cited by 18

PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects

Tekpinar, M., David, L., Henry, T., & Carbone, A. (2025) · Genome Biology · doi:10.1186/s13059-025-03581-y · Cited by 18

Molecular insights into the activation of Mre11-Rad50 endonuclease activity by Sae2/CtIP

Nicolas, Y., Bret, H., Cannavo, E., Acharya, A., Cejka, P., Borde, V., & Guerois, R. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.05.019 · Cited by 12

A paired sequence language model for protein-protein interaction modeling

Liu, J., Chen, H., & Zhang, Y. (2026) · Nature Communications · doi:10.1038/s41467-026-70457-5 · Cited by 8

Identification and characterization of substrate- and product-selective nylon hydrolases

Drufva, E. E., Cahill, J. F., Saint-Vincent, P. M. B., Williams, A. N., Bocharova, V., Capra, N., Meilleur, F., Carper, D. L., Bourgery, C., Miyazaki, K., et al. (2025) · Chem Catalysis · doi:10.1016/j.checat.2025.101418 · Cited by 7

Comprehensive computational analysis of the SRK–SP11 molecular interaction underlying self-incompatibility in Brassicaceae using improved structure prediction for cysteine-rich proteins

Sawa, T., Moriwaki, Y., Jiang, H., Murase, K., Takayama, S., Shimizu, K., & Terada, T. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.10.026 · Cited by 6

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning

(2023) · doi:10.1021/acs.jcim.3c01387 · Cited by 6

Construct a variable-length fragment library for de novo protein structure prediction

Feng, Q., Hou, M., Liu, J., Zhao, K., & Zhang, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac086 · Cited by 5

The C-terminal tail of CSNAP attenuates the CSN complex

Füzesi-Levi, M. G., Ben-Nissan, G., Listov, D., Fridmann Sirkis, Y., Hayouka, Z., Fleishman, S., & Sharon, M. (2023) · Life Science Alliance · doi:10.26508/lsa.202201634 · Cited by 5

Protein language model identifies disordered, conserved motifs implicated in phase separation

Zhang, Y., Zheng, J., & Zhang, B. (2025) · eLife · doi:10.7554/elife.105309 · Cited by 4

Protein Quaternary Structures in Solution are a Mixture of Multiple forms

Marciano, S., Dey, D., Listov, D., Fleishman, S. J., Sonn-Segev, A., Mertens, H., Busch, F., Kim, Y., Harvey, S. R., Wysocki, V. H., et al. (2022) · doi:10.1101/2022.03.30.486392 · Cited by 3

RAG-ESM: Improving Pretrained Protein Language Models via Sequence Retrieval

Sgarbossa, D., & Bitbol, A.-F. (2025) · PRX Life · doi:10.1103/db1b-hy16 · Cited by 3

Improved prediction of site‐rates from structure with averaging across homologs

Norn, C., Oliveira, F., & André, I. (2024) · Protein Science · doi:10.1002/pro.5086 · Cited by 3

DeepAssembly2: A Web Server for Protein Complex Structure Assembly Based on Domain-Domain Interactions

Xia, Y., Pu, Y., Wang, S., Zhuang, J., Liu, D., Hou, M., & Zhang, G. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169128 · Cited by 2

Lipophilic modification of salirasib modulates the antiproliferative and antimigratory activity

Ballari, M. S., O. J. Porta, E., Zalazar, E. A., Etichetti, C. M. B., Padrón, J. M., Girardini, J. E., & Labadie, G. R. (2023) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2023.117417 · Cited by 2

Genome-scale annotation of protein binding sites via language model and geometric deep learning

(2023) · doi:10.1101/2023.11.02.565344 · Cited by 2

E(Q)AGNN-PPIS: Attention Enhanced Equivariant Graph Neural Network for Protein-Protein Interaction Site Prediction

(2025) · doi:10.1109/tsipn.2025.3587396 · Cited by 1

Caspase-1 activates gasdermin A in non-mammals

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