UniProt: a worldwide hub of protein knowledge

UniProt: the Universal Protein knowledgebase

Apweiler, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh131 · Cited by 7,593

UniProt: the universal protein knowledgebase

The UniProt Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1099 · Cited by 4,436

A METTL3–METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation

Liu, J., Yue, Y., Han, D., Wang, X., Fu, Y., Zhang, L., Jia, G., Yu, M., Lu, Z., Deng, X., et al. (2014) · Nature Chemical Biology · doi:10.1038/nchembio.1432 · Cited by 3,433

Ensembl 2018

Zerbino, D. R., Achuthan, P., Akanni, W., Amode, M. R., Barrell, D., Bhai, J., Billis, K., Cummins, C., Gall, A., Girón, C. G., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1098 · Cited by 2,605

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

Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., Wu, C. H., & the UniProt Consortium (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu739 · Cited by 1,928

Structural Basis for Cooperative Function of Mettl3 and Mettl14 Methyltransferases

Wang, P., Doxtader, K. A., & Nam, Y. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.05.041 · Cited by 1,265

Structural basis of N6-adenosine methylation by the METTL3–METTL14 complex

Wang, X., Feng, J. Q., Xue, Y. C., Guan, Z., Zhang, D., Liu, Z., Gong, Z., Wang, Q., Huang, J. B., Tang, C., et al. (2016) · Nature · doi:10.1038/nature18298 · Cited by 1,226

ChEBI in 2016: Improved services and an expanding collection of metabolites

Hastings, J., Owen, G., Dekker, A., Ennis, M., Kale, N., Muthukrishnan, V., Turner, S., Swainston, N., Mendes, P., & Steinbeck, C. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1031 · Cited by 1,083

VIRMA mediates preferential m6A mRNA methylation in 3′UTR and near stop codon and associates with alternative polyadenylation

Yue, Y., Liu, J., Cui, X., Cao, J., Luo, G., Zhang, Z., Cheng, T., Gao, M., Shu, X., Ma, H., et al. (2018) · Cell Discovery · doi:10.1038/s41421-018-0019-0 · Cited by 960

The Gene Ontology project in 2008

The Gene Ontology Consortium (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm883 · Cited by 776

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

The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

Szklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., Doncheva, N. T., Legeay, M., Fang, T., Bork, P., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1074 · Cited by 8,056

UniProt: the universal protein knowledgebase in 2021

The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1100 · Cited by 6,372

g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)

Raudvere, U., Kolberg, L., Kuzmin, I., Arak, T., Adler, P., Peterson, H., & Vilo, J. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz369 · Cited by 5,045

PubChem in 2021: new data content and improved web interfaces

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa971 · Cited by 3,953

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

Beghini, F., McIver, L. J., Blanco-Míguez, A., Dubois, L., Asnicar, F., Maharjan, S., Mailyan, A., Manghi, P., Scholz, M., Thomas, A. M., et al. (2021) · eLife · doi:10.7554/elife.65088 · Cited by 2,303

ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329

RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences

Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Christie, C. H., Dalenberg, K., Di Costanzo, L., Duarte, J. M., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1038 · Cited by 1,581

GENCODE 2021

Frankish, A., Diekhans, M., Jungreis, I., Lagarde, J., Loveland, J. E., Mudge, J. M., Sisu, C., Wright, J. C., Armstrong, J., Barnes, I., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1087 · Cited by 1,334

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse

Koopmans, F., van Nierop, P., Andres-Alonso, M., Byrnes, A., Cijsouw, T., Coba, M. P., Cornelisse, L. N., Farrell, R. J., Goldschmidt, H. L., Howrigan, D. P., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.05.002 · Cited by 938

Using machine learning approaches for multi-omics data analysis: A review

Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903

NINJ1 mediates plasma membrane rupture during lytic cell death

Kayagaki, N., Kornfeld, O. S., Lee, B. L., Stowe, I. B., O’Rourke, K., Li, Q., Sandoval, W., Yan, D., Kang, J., Xu, M., et al. (2021) · Nature · doi:10.1038/s41586-021-03218-7 · Cited by 857

BRENDA, the ELIXIR core data resource in 2021: new developments and updates

Chang, A., Jeske, L., Ulbrich, S., Hofmann, J., Koblitz, J., Schomburg, I., Neumann-Schaal, M., Jahn, D., & Schomburg, D. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1025 · Cited by 789

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

Disease variant prediction with deep generative models of evolutionary data

Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J. K., Brock, K., Gal, Y., & Marks, D. S. (2021) · Nature · doi:10.1038/s41586-021-04043-8 · Cited by 791

Evaluating Protein Transfer Learning with TAPE

Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., & Song, Y. S. (2019) · doi:10.1101/676825 · Cited by 719

Rethinking Attention with Performers

Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J., Mohiuddin, A., Kaiser, L., et al. (2020) · arXiv · doi:10.48550/arxiv.2009.14794 · Cited by 695

Isolation of an archaeon at the prokaryote–eukaryote interface

Imachi, H., Nobu, M. K., Nakahara, N., Morono, Y., Ogawara, M., Takaki, Y., Takano, Y., Uematsu, K., Ikuta, T., Ito, M., et al. (2020) · Nature · doi:10.1038/s41586-019-1916-6 · Cited by 624

NAD+ homeostasis in health and disease

Katsyuba, E., Romani, M., Hofer, D., & Auwerx, J. (2020) · Nature Metabolism · doi:10.1038/s42255-019-0161-5 · Cited by 627

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection

Messner, C. B., Demichev, V., Wendisch, D., Michalick, L., White, M., Freiwald, A., Textoris-Taube, K., Vernardis, S. I., Egger, A.-S., Kreidl, M., et al. (2020) · Cell Systems · doi:10.1016/j.cels.2020.05.012 · Cited by 561

Proteogenomic Characterization of Endometrial Carcinoma

Dou, Y., Kawaler, E. A., Cui Zhou, D., Gritsenko, M. A., Huang, C., Blumenberg, L., Karpova, A., Petyuk, V. A., Savage, S. R., Satpathy, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.026 · Cited by 485

CATH: increased structural coverage of functional space

Sillitoe, I., Bordin, N., Dawson, N., Waman, V. P., Ashford, P., Scholes, H. M., Pang, C. S. M., Woodridge, L., Rauer, C., Sen, N., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1079 · Cited by 509

Learning the protein language: Evolution, structure, and function

Bepler, T., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.017 · Cited by 483

ProGen2: Exploring the boundaries of protein language models

Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., & Madani, A. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.002 · Cited by 480

Identification of bacteria-derived HLA-bound peptides in melanoma

Kalaora, S., Nagler, A., Nejman, D., Alon, M., Barbolin, C., Barnea, E., Ketelaars, S. L. C., Cheng, K., Vervier, K., Shental, N., et al. (2021) · Nature · doi:10.1038/s41586-021-03368-8 · Cited by 457

Protein posttranslational modifications in health and diseases: Functions, regulatory mechanisms, and therapeutic implications

Zhong, Q., Xiao, X., Qiu, Y., Xu, Z., Chen, C., Chong, B., Zhao, X., Hai, S., Li, S., An, Z., et al. (2023) · MedComm · doi:10.1002/mco2.261 · Cited by 442

Autism genes converge on asynchronous development of shared neuron classes

Paulsen, B., Velasco, S., Kedaigle, A. J., Pigoni, M., Quadrato, G., Deo, A. J., Adiconis, X., Uzquiano, A., Sartore, R., Yang, S. M., et al. (2022) · Nature · doi:10.1038/s41586-021-04358-6 · Cited by 439

Senescence atlas reveals an aged-like inflamed niche that blunts muscle regeneration

Moiseeva, V., Cisneros, A., Sica, V., Deryagin, O., Lai, Y., Jung, S., Andrés, E., An, J., Segalés, J., Ortet, L., et al. (2023) · Nature · doi:10.1038/s41586-022-05535-x · Cited by 423

Expanding functional protein sequence spaces using generative adversarial networks

Repecka, D., Jauniskis, V., Karpus, L., Rembeza, E., Rokaitis, I., Zrimec, J., Poviloniene, S., Laurynenas, A., Viknander, S., Abuajwa, W., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00310-5 · Cited by 388

Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19

Shu, T., Ning, W., Wu, D., Xu, J., Han, Q., Huang, M., Zou, X., Yang, Q., Yuan, Y., Bie, Y., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.10.008 · Cited by 337

An Activity-Guided Map of Electrophile-Cysteine Interactions in Primary Human T Cells

Vinogradova, E. V., Zhang, X., Remillard, D., Lazar, D. C., Suciu, R. M., Wang, Y., Bianco, G., Yamashita, Y., Crowley, V. M., Schafroth, M. A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.07.001 · Cited by 351

GPCRdb in 2021: integrating GPCR sequence, structure and function

Kooistra, A. J., Mordalski, S., Pándy-Szekeres, G., Esguerra, M., Mamyrbekov, A., Munk, C., Keserű, G. M., & Gloriam, D. E. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1080 · Cited by 351

Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders

Yang, C., Farias, F. H. G., Ibanez, L., Suhy, A., Sadler, B., Fernandez, M. V., Wang, F., Bradley, J. L., Eiffert, B., Bahena, J. A., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00886-6 · Cited by 290

The PROTACtable genome

Schneider, M., Radoux, C. J., Hercules, A., Ochoa, D., Dunham, I., Zalmas, L.-P., Hessler, G., Ruf, S., Shanmugasundaram, V., Hann, M. M., et al. (2021) · Nature Reviews Drug Discovery · doi:10.1038/s41573-021-00245-x · Cited by 273

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

Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank

Steinegger, M., & Salzberg, S. L. (2020) · Genome Biology · doi:10.1186/s13059-020-02023-1 · Cited by 246

Toward better drug discovery with knowledge graph

Zeng, X., Tu, X., Liu, Y., Fu, X., & Su, Y. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.09.003 · Cited by 229

Accelerating antibiotic discovery through artificial intelligence

Melo, M. C. R., Maasch, J. R. M. A., & de la Fuente-Nunez, C. (2021) · Communications Biology · doi:10.1038/s42003-021-02586-0 · Cited by 234

Deep Learning in Protein Structural Modeling and Design

Gao, W., Mahajan, S. P., Sulam, J., & Gray, J. J. (2020) · Patterns · doi:10.1016/j.patter.2020.100142 · Cited by 227

MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models

Moretti, S., Tran, V. D. T., Mehl, F., Ibberson, M., & Pagni, M. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa992 · Cited by 177

Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning

Kroll, A., Rousset, Y., Hu, X.-P., Liebrand, N. A., & Lercher, M. J. (2023) · Nature Communications · doi:10.1038/s41467-023-39840-4 · Cited by 186

DrugCentral 2021 supports drug discovery and repositioning

Avram, S., Bologa, C. G., Holmes, J., Bocci, G., Wilson, T. B., Nguyen, D.-T., Curpan, R., Halip, L., Bora, A., Yang, J. J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa997 · Cited by 189

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

Machine learning: a powerful tool for gene function prediction in plants

Mahood, E. H., Kruse, L. H., & Moghe, G. D. (2020) · Applications in Plant Sciences · doi:10.1002/aps3.11376 · Cited by 153

Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age

Johnson, A. A., Shokhirev, M. N., Wyss-Coray, T., & Lehallier, B. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101070 · Cited by 166

TCRD and pharos 2021: mining the human proteome for disease biology

Sheils, T. K., Mathias, S. L., Kelleher, K. J., Siramshetty, V. B., Nguyen, D.-T., Bologa, C. G., Jensen, L. J., Vidović, D., Koleti, A., Schürer, S. C., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa993 · Cited by 160

Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design

Hie, B., Bryson, B. D., & Berger, B. (2020) · Cell Systems · doi:10.1016/j.cels.2020.09.007 · Cited by 154

The IUPHAR/BPS guide to PHARMACOLOGY in 2022: curating pharmacology for COVID-19, malaria and antibacterials

Harding, S. D., Armstrong, J. F., Faccenda, E., Southan, C., Alexander, S. P. H., Davenport, A. P., Pawson, A. J., Spedding, M., Davies, J. A., & NC-IUPHAR (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1010 · Cited by 149

GeneCaRNA: A Comprehensive Gene-centric Database of Human Non-coding RNAs in the GeneCards Suite.

Barshir, R., Fishilevich, S., Iny-Stein, T., Zelig, O., Mazor, Y., Guan-Golan, Y., Safran, M., & Lancet, D. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.166913 · Cited by 134

PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins

Lazar, T., Martínez-Pérez, E., Quaglia, F., Hatos, A., Chemes, L. B., Iserte, J. A., Méndez, N. A., Garrone, N. A., Saldaño, T. E., Marchetti, J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1021 · Cited by 142

Global analysis of shared T cell specificities in human non-small cell lung cancer enables HLA inference and antigen discovery

Chiou, S.-H., Tseng, D., Reuben, A., Mallajosyula, V., Molina, I. S., Conley, S., Wilhelmy, J., McSween, A. M., Yang, X., Nishimiya, D., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.02.014 · Cited by 133

Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis

Kim, H., Park, S., Jeong, I. G., Song, S. H., Jeong, Y., Kim, C.-S., & Lee, K. H. (2021) · ACS Nano · doi:10.1021/acsnano.0c06946 · Cited by 130

Reproducibility in systems biology modelling

Tiwari, K., Kananathan, S., Roberts, M. G., Meyer, J. P., Sharif Shohan, M. U., Xavier, A., Maire, M., Zyoud, A., Men, J., Ng, S., et al. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209982 · Cited by 131

Automatic construction of metabolic models with enzyme constraints

Bekiaris, P. S., & Klamt, S. (2020) · BMC Bioinformatics · doi:10.1186/s12859-019-3329-9 · Cited by 110

CREB stimulates GPX4 transcription to inhibit ferroptosis in lung adenocarcinoma

Wang, Z., Zhang, X., Tian, X., Yang, Y., Ma, L., Wang, J., & Yu, Y. (2021) · Oncology Reports · doi:10.3892/or.2021.8039 · Cited by 122

Identification of antigens presented by MHC for vaccines against tuberculosis

Bettencourt, P., Müller, J., Nicastri, A., Cantillon, D., Madhavan, M., Charles, P. D., Fotso, C. B., Wittenberg, R., Bull, N., Pinpathomrat, N., et al. (2020) · npj Vaccines · doi:10.1038/s41541-019-0148-y · Cited by 110

Advances and Applications in the Quest for Orthologs

Glover, N., Dessimoz, C., Ebersberger, I., Forslund, S. K., Gabaldón, T., Huerta-Cepas, J., Martin, M.-J., Muffato, M., Patricio, M., Pereira, C., et al. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz150 · Cited by 94

Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging

Lehallier, B., Shokhirev, M. N., Wyss‐Coray, T., & Johnson, A. A. (2020) · Aging Cell · doi:10.1111/acel.13256 · Cited by 106

ATP synthase evolution on a cross-braced dated tree of life

Mahendrarajah, T. A., Moody, E. R. R., Schrempf, D., Szánthó, L. L., Dombrowski, N., Davín, A. A., Pisani, D., Donoghue, P. C. J., Szöllősi, G. J., Williams, T. A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42924-w · Cited by 86

Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties

Sala, D., Hildebrand, P. W., & Meiler, J. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1121962 · Cited by 80

Systematic detection of functional proteoform groups from bottom-up proteomic datasets

Bludau, I., Frank, M., Dörig, C., Cai, Y., Heusel, M., Rosenberger, G., Picotti, P., Collins, B. C., Röst, H., & Aebersold, R. (2021) · Nature Communications · doi:10.1038/s41467-021-24030-x · Cited by 88

Whole-genome analysis of probiotic product isolates reveals the presence of genes related to antimicrobial resistance, virulence factors, and toxic metabolites, posing potential health risks

Wang, Y., Liang, Q., Lu, B., Shen, H., Liu, S., Shi, Y., Leptihn, S., Li, H., Wei, J., Liu, C., et al. (2021) · BMC Genomics · doi:10.1186/s12864-021-07539-9 · Cited by 88

A multi-scale map of cell structure fusing protein images and interactions

Qin, Y., Huttlin, E. L., Winsnes, C. F., Gosztyla, M. L., Wacheul, L., Kelly, M. R., Blue, S. M., Zheng, F., Chen, M., Schaffer, L. V., et al. (2021) · Nature · doi:10.1038/s41586-021-04115-9 · Cited by 82

The Transcriptional Landscape of Microglial Genes in Aging and Neurodegenerative Disease

Bonham, L. W., Sirkis, D. W., & Yokoyama, J. S. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01170 · Cited by 83

Discovery of high-confidence human protein-coding genes and exons by whole-genome PhyloCSF helps elucidate 118 GWAS loci

Mudge, J. M., Jungreis, I., Hunt, T., Gonzalez, J. M., Wright, J. C., Kay, M., Davidson, C., Fitzgerald, S., Seal, R., Tweedie, S., et al. (2019) · Genome Research · doi:10.1101/gr.246462.118 · Cited by 80

Building structural models of a whole mycoplasma cell

Maritan, M., Autin, L., Karr, J., Covert, M. W., Olson, A. J., & Goodsell, D. S. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167351 · Cited by 75

Paralog dependency indirectly affects the robustness of human cells

Dandage, R., & Landry, C. R. (2019) · Molecular Systems Biology · doi:10.15252/msb.20198871 · Cited by 65

Colocalization of different neurotransmitter transporters on synaptic vesicles is sparse except for VGLUT1 and ZnT3

Upmanyu, N., Jin, J., Emde, H. V. D., Ganzella, M., Bösche, L., Malviya, V. N., Zhuleku, E., Politi, A. Z., Ninov, M., Silbern, I., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.02.008 · Cited by 69

Plasma proteins, cognitive decline, and 20-year risk of dementia in the Whitehall II and Atherosclerosis Risk in Communities studies

Lindbohm, J. V., Mars, N., Walker, K. A., Singh‐Manoux, A., Livingston, G., Brunner, E. J., Sipilä, P. N., Saksela, K., Ferrie, J. E., Lovering, R. C., et al. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12419 · Cited by 70

Synergistic and antagonistic drug interactions in the treatment of systemic fungal infections

Wambaugh, M. A., Denham, S. T., Ayala, M., Brammer, B., Stonhill, M. A., & Brown, J. C. (2020) · eLife · doi:10.7554/elife.54160 · Cited by 65

Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque

Fernández-Torras, A., Duran-Frigola, M., Bertoni, M., Locatelli, M., & Aloy, P. (2022) · Nature Communications · doi:10.1038/s41467-022-33026-0 · Cited by 59

A multi-omic characterization of temperature stress in a halotolerant Scenedesmus strain for algal biotechnology

Calhoun, S., Bell, T. A. S., Dahlin, L. R., Kunde, Y., LaButti, K., Louie, K. B., Kuftin, A., Treen, D., Dilworth, D., Mihaltcheva, S., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-01859-y · Cited by 60

Development of compact transcriptional effectors using high-throughput measurements in diverse contexts

Tycko, J., Van, M. V., Aradhana, DelRosso, N., Ye, H., Yao, D., Valbuena, R., Vaughan-Jackson, A., Xu, X., Ludwig, C., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02442-6 · Cited by 57

Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models

Li, F.-Z., Amini, A. P., Yue, Y., Yang, K. K., & Lu, A. X. (2024) · doi:10.1101/2024.02.05.578959 · Cited by 51

De novo variants in SNAP25 cause an early-onset developmental and epileptic encephalopathy

Klöckner, C., Sticht, H., Zacher, P., Popp, B., Babcock, H. E., Bakker, D. P., Barwick, K., Bonfert, M. V., Bönnemann, C. G., Brilstra, E. H., et al. (2021) · Genetics in Medicine · doi:10.1038/s41436-020-01020-w · Cited by 47

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

Unique Cell Surface Mannan of Yeast Pathogen Candida auris with Selective Binding to IgG

Yan, L., Xia, K., Yu, Y., Miliakos, A., Chaturvedi, S., Zhang, F., Chen, S., Chaturvedi, V., & Linhardt, R. J. (2020) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.9b00450 · Cited by 47

Systematic analysis and prediction of genes associated with monogenic disorders on human chromosome X

Leitão, E., Schröder, C., Parenti, I., Dalle, C., Rastetter, A., Kühnel, T., Kuechler, A., Kaya, S., Gérard, B., Schaefer, E., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34264-y · Cited by 47

Oncology and Pharmacogenomics Insights in Polycystic Ovary Syndrome: An Integrative Analysis

Yumiceba, V., López-Cortés, A., Pérez-Villa, A., Yumiseba, I., Guerrero, S., García-Cárdenas, J. M., Armendáriz-Castillo, I., Guevara-Ramírez, P., Leone, P. E., Zambrano, A. K., et al. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.585130 · Cited by 40

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

Scaling laws in enzyme function reveal a new kind of biochemical universality

Gagler, D. C., Karas, B., Kempes, C. P., Malloy, J., Mierzejewski, V., Goldman, A. D., Kim, H., & Walker, S. I. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106655119 · Cited by 34

Next Generation Protein Structure Predictions and Genetic Variant Interpretation

Diwan, G. D., Gonzalez-Sanchez, J. C., Apic, G., & Russell, R. B. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167180 · Cited by 30

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

Race to the Bottom: Competition and Quality in Science

Hill, R., & Stein, C. (2025) · The Quarterly Journal of Economics · doi:10.1093/qje/qjaf010 · Cited by 40

AlloMAPS 2: allosteric fingerprints of the AlphaFold and Pfam-trRosetta predicted structures for engineering and design

Tan, Z. W., Tee, W.-V., Guarnera, E., & Berezovsky, I. N. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac828 · Cited by 28

Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer

Plattner, C., Lamberti, G., Blattmann, P., Kirchmair, A., Rieder, D., Loncova, Z., Sturm, G., Scheidl, S., Ijsselsteijn, M., Fotakis, G., et al. (2023) · iScience · doi:10.1016/j.isci.2023.108399 · Cited by 27

Limits and potential of combined folding and docking

Pozzati, G., Zhu, W., Bassot, C., Lamb, J., Kundrotas, P., & Elofsson, A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab760 · Cited by 25

Improved enzyme functional annotation prediction using contrastive learning with structural inference

Yang, Y., Jerger, A., Feng, S., Wang, Z., Brasfield, C., Cheung, M. S., Zucker, J., & Guan, Q. (2024) · Communications Biology · doi:10.1038/s42003-024-07359-z · Cited by 24

Data integration across conditions improves turnover number estimates and metabolic predictions

Wendering, P., Arend, M., Razaghi-Moghadam, Z., & Nikoloski, Z. (2023) · Nature Communications · doi:10.1038/s41467-023-37151-2 · Cited by 23

Identifying carbohydrate-active enzymes of Cutaneotrichosporon oleaginosus using systems biology

Fuchs, T., Melcher, F., Rerop, Z. S., Lorenzen, J., Shaigani, P., Awad, D., Haack, M., Prem, S. A., Masri, M., Mehlmer, N., et al. (2021) · Microbial Cell Factories · doi:10.1186/s12934-021-01692-2 · Cited by 22

The protein inputs of an ultra-predictive aging clock represent viable anti-aging drug targets

Johnson, A. A., Shokhirev, M. N., & Lehallier, B. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101404 · Cited by 22

FAIR adoption, assessment and challenges at UniProt

Garcia, L., Bolleman, J., Gehant, S., Redaschi, N., Martin, M., UniProt Consortium, Bateman, A., Magrane, M., Martin, M., Orchard, S., et al. (2019) · Scientific Data · doi:10.1038/s41597-019-0180-9 · Cited by 20

A comprehensive WGS-based pipeline for the identification of new candidate genes in inherited retinal dystrophies

González-del Pozo, M., Fernández-Suárez, E., Bravo-Gil, N., Méndez-Vidal, C., Martín-Sánchez, M., Rodríguez-de la Rúa, E., Ramos-Jiménez, M., Morillo-Sánchez, M. J., Borrego, S., & Antiñolo, G. (2022) · npj Genomic Medicine · doi:10.1038/s41525-022-00286-0 · Cited by 21

Single-cell damagenome profiling unveils vulnerable genes and functional pathways in human genome toward DNA damage

Zhu, Q., Niu, Y., Gundry, M., & Zong, C. (2021) · Science Advances · doi:10.1126/sciadv.abf3329 · Cited by 18

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

Broad functional profiling of fission yeast proteins using phenomics and machine learning

Rodríguez-López, M., Bordin, N., Lees, J., Scholes, H., Hassan, S., Saintain, Q., Kamrad, S., Orengo, C., & Bähler, J. (2023) · eLife · doi:10.7554/elife.88229.3 · Cited by 18

Evolutionary crossroads of cell signaling: PP1 and PP2A substrate sites in intrinsically disordered regions

Hoermann, B., & Köhn, M. (2021) · Biochemical Society Transactions · doi:10.1042/bst20200175 · Cited by 14

Clinical and Genetic Characterization of Craniosynostosis in Saudi Arabia

Alghamdi, M., Alhumsi, T. R., Altweijri, I., Alkhamis, W. H., Barasain, O., Cardona-Londoño, K. J., Ramakrishnan, R., Guzmán-Vega, F. J., Arold, S. T., Ali, G., et al. (2021) · Frontiers in Pediatrics · doi:10.3389/fped.2021.582816 · Cited by 15

Integrating Multimeric Threading With High-throughput Experiments for Structural Interactome of Escherichia coli

Gong, W., Guerler, A., Zhang, C., Warner, E., Li, C., & Zhang, Y. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.166944 · Cited by 13

FunRes: resolving tissue-specific functional cell states based on a cell–cell communication network model

Jung, S., Singh, K., & del Sol, A. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa283 · Cited by 9

Licence Profile: Apache License, Version 2.0

Sinclair, A. H. (2010) · International Free and Open Source Software Law Review · doi:10.5033/ifosslr.v2i2.42 · Cited by 6

Computational clustering for viral reference proteomes

Chen, C., Huang, H., Mazumder, R., et al. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw110 · Cited by 4

On the Unknown Proteins of Eukaryotic Proteomes

Sanejouand, Y. (2023) · Journal of Molecular Evolution · doi:10.1007/s00239-023-10116-1 · Cited by 2

On the unknown proteins of eukaryotic proteomesPreprint

Sanejouand, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.11001

InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic

Sonnhammer, E. L. L., & Östlund, G. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1203 · Cited by 482