Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12
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Every second year, the community experiment “Critical Assessment of Techniques for Structure Prediction” (CASP) is conducting an independent blind assessment of structure prediction methods, providing a framework for comparing the performance of different approaches and discussing the latest developments in the field. Yet, developers of automated computational modeling methods clearly benefit from more frequent evaluations based on larger sets of data. The “Continuous Automated Model EvaluatiOn (CAMEO)” platform complements the CASP experiment by conducting fully automated blind prediction assessments based on the weekly pre‐release of sequences of those structures, which are going to be published in the next release of the PDB Protein Data Bank. CAMEO publishes weekly benchmarking results based on models collected during a 4‐day prediction window, on average assessing ca. 100 targets during a time frame of 5 weeks. CAMEO benchmarking data is generated consistently for all participating methods at the same point in time, enabling developers to benchmark and cross‐validate their method's performance, and directly refer to the benchmarking results in publications. In order to facilitate server development and promote shorter release cycles, CAMEO sends weekly email with submission statistics and low performance warnings. Many participants of CASP have successfully employed CAMEO when preparing their methods for upcoming community experiments. CAMEO offers a variety of scores to allow benchmarking diverse aspects of structure prediction methods. By introducing new scoring schemes, CAMEO facilitates new development in areas of active research, for example, modeling quaternary structure, complexes, or ligand binding sites.
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During method development, a temporally held out validation set from CAMEO (August 2021 to January 2022) was used.
“During method development (e.g. hyperparameter selection), we used a temporally held out validation set obtained from the Continuous Automated Model EvaluatiOn (CAMEO) server (36) by filtering from August 2021 to January 2022.”
From Evolutionary-scale prediction of atomic level protein structure with a language model (2022) · cites this paper as [36]
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Cites
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 · see citing works
Comparative Protein Modelling by Satisfaction of Spatial Restraints
Šali, A., & Blundell, T. L. (1993) · Journal of Molecular Biology · doi:10.1006/jmbi.1993.1626 · Cited by 12,977 · see citing works
Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences
Li, W.-Z., & Godzik, A. (2006) · Bioinform. · doi:10.1093/bioinformatics/btl158 · Cited by 10,994 · see citing works
The Phyre2 web portal for protein modeling, prediction and analysis
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N., & Sternberg, M. J. E. (2015) · Nature Protocols · doi:10.1038/nprot.2015.053 · Cited by 9,652 · see citing works
SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information
Biasini, M., Bienert, S., Waterhouse, A. M., Arnold, K., Studer, G., Schmidt, T., Kiefer, F., Cassarino, T. G., Bertoni, M., Bordoli, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku340 · Cited by 4,621 · see citing works
Self-citationThe HHpred interactive server for protein homology detection and structure prediction
Söding, J., Biegert, A., & Lupas, A. N. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki408 · Cited by 3,703 · see citing works
ROCR: visualizing classifier performance in R
Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti623 · Cited by 3,205 · see citing works
Recognition of errors in three‐dimensional structures of proteins
Sippl, M. J. (1993) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340170404 · Cited by 2,156 · see citing works
VERIFY3D: assessment of protein models with three-dimensional profiles.
Eisenberg, D., Lüthy, R., & Bowie, J. (1997) · Methods in Enzymology · doi:10.1016/s0076-6879(97)77022-8 · Cited by 2,149 · see citing works
Protein structure prediction and analysis using the Robetta server
Kim, D. E., Chivian, D. C., & Baker, D. A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh468 · Cited by 2,099 · see citing works
PISCES: a protein sequence culling server
Wang, G., & Dunbrack, R. L. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg224 · Cited by 1,664 · see citing works
Template-based protein structure modeling using the RaptorX web server
Källberg, M., Wang, H., Wang, S., Peng, J., Wang, Z., Lu, H. J., & Xu, J. (2012) · Nature Protocols · doi:10.1038/nprot.2012.085 · Cited by 1,576 · see citing works
How significant is a protein structure similarity with TM-score = 0.5?
Xu, J., & Zhang, Y. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq066 · Cited by 1,142 · see citing works
Mariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt473 · Cited by 1,138 · see citing works
Self-citationThe worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data
Berman, H. M., Henrick, K., Nakamura, H., & Markley, J. L. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl971 · Cited by 1,124 · see citing works
LGA: a method for finding 3D similarities in protein structures
Zemla, A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg571 · Cited by 1,069 · see citing works
Bertoni, M., Kiefer, F., Biasini, M., Bordoli, L., & Schwede, T. (2017) · Scientific Reports · doi:10.1038/s41598-017-09654-8 · Cited by 791 · see citing works
Self-citationA large‐scale experiment to assess protein structure prediction methods
Moult, J., Pedersen, J. T., Judson, R., & Fidelis, K. (1995) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340230303 · Cited by 555 · see citing works
Structural basis of potent Zika–dengue virus antibody cross-neutralization
Spaeth, G. B., Dejnirattisai, W., Rouvinski, A., Vaney, M. C., Medits, I., Sharma, A., Simon‐Lorière, E., Sakuntabhai, A., Cao‐Lormeau, V., Haouz, A., et al. (2016) · Nature · doi:10.1038/nature18938 · Cited by 534 · see citing works
The RCSB Protein Data Bank: new resources for research and education
Rose, P. W., Bi, C., Bluhm, W. F., Christie, C. H., Dimitropoulos, D., Dutta, S., Green, R. K., Goodsell, D. S., Prlić, A., Quesada, M., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1200 · Cited by 523 · see citing works
Global analysis of protein folding using massively parallel design, synthesis, and testing
Rocklin, G. J., Chidyausiku, T. M., Goreshnik, I., Ford, A., Houliston, S., Lemak, A., Carter, L., Ravichandran, R., Mulligan, V. K., Chevalier, A., et al. (2017) · Science · doi:10.1126/science.aan0693 · Cited by 501 · see citing works
ECOD: an evolutionary classification of protein domains
Cheng, H., Schaeffer, R. D., Liao, Y., Kinch, L. N., Pei, J., Shi, S., Kim, B.-H., & Grishin, N. V. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003926 · Cited by 426 · cited 1× · see citing works
“In CASP this problem is addressed by splitting the target structures into actual assessment units (AUs) [21] by human intervention with the support of computational approaches[22, 23].”
BackgroundMaxSub: an automated measure for the assessment of protein structure prediction quality
Siew, N., Elofsson, A., Rychlewski, L., & Fischer, D. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.9.776 · Cited by 401 · see citing works
Raptorx: Exploiting structure information for protein alignment by statistical inference
Peng, J., & Xu, J. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23175 · Cited by 362 · see citing works
Critical assessment of methods of protein structure prediction (CASP)—Round XII
Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25415 · Cited by 327 · see citing works
Self-citationYang, Y., Faraggi, E., Zhao, H., & Zhou, Y. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr350 · Cited by 325 · see citing works
A Knowledge-Based Energy Function for Protein−Ligand, Protein−Protein, and Protein−DNA Complexes
Zhang, C., Liu, S., Zhu, Q., & Zhou, Y. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049314d · Cited by 287 · see citing works
The Protein Model Portal—a comprehensive resource for protein structure and model information
Haas, J., Roth, S., Arnold, K., Kiefer, F., Schmidt, T., Bordoli, L., & Schwede, T. F. (2013) · Database · doi:10.1093/database/bat031 · Cited by 268 · see citing works
Self-citationMoult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25064 · Cited by 226 · see citing works
Self-citationVoroMQA: Assessment of protein structure quality using interatomic contact areas
Olechnovič, K., & Venclovas, Č. (2017) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25278 · Cited by 181 · see citing works
Mukherjee, S., & Zhang, Y. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp318 · Cited by 178 · see citing works
OpenStructure: an integrated software framework for computational structural biology
Biasini, M., Schmidt, T., Bienert, S., Mariani, V., Studer, G., Haas, J., Johner, N., Schenk, A., Philippsen, A., & Schwede, T. (2013) · Acta Crystallographica Section D: Biological Crystallography · doi:10.1107/s0907444913007051 · Cited by 178 · see citing works
Self-citationAssessment of CASP7 predictions for template‐based modeling targets
Kopp, J., Bordoli, L., Battey, J., Kiefer, F., & Schwede, T. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21753 · Cited by 170 · see citing works
Self-citationStructure and Function of the Nuclear Pore Complex Cytoplasmic mRNA Export Platform
Fernández-Martı́nez, J., Kim, S. J., Shi, Y., Upla, P., Pellarin, R., Gagnon, M., Chemmama, I. E., Wang, J., Nudelman, I., Zhang, W., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.028 · Cited by 159 · see citing works
CAD‐score: A new contact area difference‐based function for evaluation of protein structural models
Olechnovič, K., Kulberkytė, E., & Venclovas, Č. (2013) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.24172 · Cited by 153 · see citing works
Outcome of a Workshop on Applications of Protein Models in Biomedical Research
Schwede, T. F., Šali, A., Honig, B. H., Levitt, M., Berman, H. M., Jones, D. T., Brenner, S. E., Burley, S. K., Das, R., Dokholyan, N. V., et al. (2009) · Structure · doi:10.1016/j.str.2008.12.014 · Cited by 148 · see citing works
Self-citationEVA: Large-scale analysis of secondary structure prediction
Rost, B., & Eyrich, V. A. (2001) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10051 · Cited by 134 · see citing works
LiveBench‐1: Continuous benchmarking of protein structure prediction servers
Bujnicki, J. M., Elofsson, A., Fischer, D., & Rychlewski, L. (2001) · Protein Science · doi:10.1110/ps.40501 · Cited by 130 · see citing works
The ModFOLD4 server for the quality assessment of 3D protein models
McGuffin, L. J., Buenavista, M. T., & Roche, D. B. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt294 · Cited by 116 · see citing works
ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models
Maghrabi, A. H. A., & McGuffin, L. J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx332 · Cited by 111 · see citing works
McGuffin, L. J., Atkins, J. D., Salehe, B. R., Shuid, A. N., & Roche, D. B. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv236 · Cited by 106 · see citing works
Assessment of protein assembly prediction in CASP12
Lafita, A., Bliven, S., Kryshtafovych, A., Bertoni, M., Monastyrskyy, B., Duarte, J. M., Schwede, T., & Capitani, G. (2017) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25408 · Cited by 72 · see citing works
Self-citationIhssen, J., Haas, J., Kowarik, M., Wiesli, L., Wacker, M., Schwede, T. F., & Thöny‐Meyer, L. (2015) · Open Biology · doi:10.1098/rsob.140227 · Cited by 71 · see citing works
Self-citationProQ2: estimation of model accuracy implemented in Rosetta
Uziela, K., & Wallner, B. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv767 · Cited by 62 · see citing works
Accurate template‐based modeling in CASP12 using the IntFOLD4‐TS, ModFOLD6, and ReFOLD methods
McGuffin, L. J., Shuid, A. N., Kempster, R., Maghrabi, A. H. A., Nealon, J. O., Salehe, B. R., Atkins, J. D., & Roche, D. B. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25360 · Cited by 62 · see citing works
Evaluation of the template-based modeling in CASP12
Kryshtafovych, A., Monastyrskyy, B., Fidelis, K., Moult, J., Schwede, T., & Tramontano, A. (2017) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25425 · Cited by 55 · see citing works
Self-citationPRIMO: An Interactive Homology Modeling Pipeline
Hatherley, R., Brown, D. K., Glenister, M., & Bishop, Ö. T. (2016) · PLoS ONE · doi:10.1371/journal.pone.0166698 · Cited by 54 · see citing works
Evaluation of template-based modeling in CASP13
Croll, T., Sammito, M., Kryshtafovych, A., & Read, R. J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25800 · Cited by 48 · see citing works
Kinch, L. N., Li, W., Schaeffer, R. D., Dunbrack, R. L., Monastyrskyy, B., Kryshtafovych, A., & Grishin, N. V. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24982 · Cited by 36 · see citing works
Methods for estimation of model accuracy in CASP12
Elofsson, A., Joo, K., Keasar, C., Lee, J., Maghrabi, A. H. A., Manavalan, B., McGuffin, L. J., Hurtado, D. M., Mirabello, C., Pilstål, R., et al. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25395 · Cited by 34 · see citing works
Cover Image, Volume 84, Issue S1
Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25151 · Cited by 28 · see citing works
Self-citationRBO Aleph: leveraging novel information sources for protein structure prediction
Mabrouk, M. A., Putz, I., Werner, T., Schneider, M., Neeb, M., Bartels, P., & Brock, O. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv357 · Cited by 17 · see citing works
Khoury, G. A., Smadbeck, J. B., Kieslich, C. A., Koskosidis, A. J., Guzman, Y. A., Tamamis, P., & Floudas, C. A. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25274 · Cited by 14 · see citing works
Abriata, L. A., Kinch, L. N., Tamò, G. E., Monastyrskyy, B., Kryshtafovych, A., & Peraro, M. D. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25403 · Cited by 14 · see citing works
Xu, D., Zhou, J., Lou, X., He, J., Ran, T., & Wang, W. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.m116.758110 · Cited by 13 · see citing works
Olechnovič, K., & Venclovas, Č. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku191 · Cited by 9 · see citing works
Uçarlı, C., McGuffin, L. J., Çaputlu, S., Aravena, A., & Gürel, F. (2016) · Scientific Reports · doi:10.1038/srep20966 · Cited by 8 · see citing works
Flagged first, then by citations
3 of the 60 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [53]Bittrich S F.H.a.D.L. eQuant - A Server for Fast Protein Model Quality Assessment by Integrating High-Dimensional Data and Machine Learning. Beyond Databases, Architectures and Structures Advanced Technologies for Data Mining and Knowledge Discovery 2016 Springer 14
Cited by
Sorted by citations
SWISS-MODEL: homology modelling of protein structures and complexes
Waterhouse, A. M., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T., Rempfer, C., Bordoli, L., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky427 · Cited by 13,005 · see citing works
Self-citationAccurate 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 5,542 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · see citing works
Improved protein structure prediction using predicted interresidue orientations
Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914677117 · Cited by 1,421 · see citing works
Protein Data Bank: the single global archive for 3D macromolecular structure data
wwPDB consortium, Burley, S. K., Berman, H. M., Bhikadiya, C., Bi, C., Chen, L., Costanzo, L. D., Christie, C., Duarte, J. M., Dutta, S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky949 · Cited by 1,163 · see citing works
QMEANDisCo—distance constraints applied on model quality estimation
Studer, G., Rempfer, C., Waterhouse, A. M., Gumienny, R., Haas, J., & Schwede, T. F. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz828 · Cited by 1,043 · see citing works
Self-citationGeneralized biomolecular modeling and design with RoseTTAFold All-Atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2024) · Science · doi:10.1126/science.adl2528 · Cited by 978 · see citing works
Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chao, H., Chen, L., Craig, P., Crichlow, G. V., Dalenberg, K., Duarte, J. M., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1077 · Cited by 903 · see citing works
Simulating 500 million years of evolution with a language model
Hayes, T., Rao, R., Akin, H., Sofroniew, N. J., Oktay, D., Lin, Z., Verkuil, R., Tran, V. Q., Deaton, J., Wiggert, M., et al. (2025) · Science · doi:10.1126/science.ads0018 · Cited by 937 · see citing works
Artificial intelligence in cancer target identification and drug discovery
You, Y., Lai, X., Pan, Y. F., Zheng, H., Vera, J., Liu, S., Deng, S., & Zhang, L. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00994-0 · Cited by 476 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Critical assessment of methods of protein structure prediction (CASP)—Round XII
Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25415 · Cited by 327 · see citing works
Self-citationImproved protein structure refinement guided by deep learning based accuracy estimation
Hiranuma, N., Park, H., Baek, M., Anishchenko, I., Dauparas, J., & Baker, D. (2021) · Nature Communications · doi:10.1038/s41467-021-21511-x · Cited by 243 · see citing works
Computational approaches to therapeutic antibody design: established methods and emerging trends
Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219 · see citing works
“Holistically, benchmarking of bioinformatic antibody methods on a par with existing protein-generic initiatives such as CASP, CAPRI or CAMEO [237] will benefit the entire computational antibody field.”
MethodAssessment of three‐dimensional RNA structure prediction in CASP15
Das, R., Kretsch, R. C., Simpkin, A. J., Mulvaney, T., Pham, P., Rangan, R., Bu, F., Keegan, R. M., Topf, M., Rigden, D. J., et al. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26602 · Cited by 139 · see citing works
Accurate prediction of protein structures and interactions using a three-track neural network
Baek, M., DiMaio, F., Anishchenko, I. V., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2022) · Yearbook of pediatric endocrinology · doi:10.1530/ey.19.15.15 · Cited by 199 · see citing works
ProteinNet: a standardized data set for machine learning of protein structure
AlQuraishi, M. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2932-0 · Cited by 141 · see citing works
Burley, S. K., Berman, H. M., Duarte, J. M., Feng, Z., Flatt, J. W., Hudson, B. P., Lowe, R., Peisach, E., Piehl, D. W., Rose, Y., et al. (2022) · Biomolecules · doi:10.3390/biom12101425 · Cited by 144 · see citing works
AI-Driven Deep Learning Techniques in Protein Structure Prediction
Chen, L., Li, Q., Nasif, K. F. A., Xie, Y., Deng, B., Niu, S., Pouriyeh, S., Dai, Z., Chen, J., & Xie, Y. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25158426 · Cited by 147 · see citing works
IntFOLD: an integrated web resource for high performance protein structure and function prediction
McGuffin, L. J., Adiyaman, R., Maghrabi, A. H. A., Shuid, A. N., Brackenridge, D. A., Nealon, J. O., & Philomina, L. S. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz322 · Cited by 131 · see citing works
Analysis of distance‐based protein structure prediction by deep learning in CASP13
Xu, J., & Wang, S. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25810 · Cited by 131 · see citing works
RCSB Protein Data bank: Tools for visualizing and understanding biological macromolecules in 3D
Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chao, H., Chen, L., Craig, P., Crichlow, G. V., Dalenberg, K., Duarte, J. M., et al. (2022) · Protein Science · doi:10.1002/pro.4482 · Cited by 119 · see citing works
A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation
Tang, X., Dai, H., Knight, E., Wu, F., Li, Y., Li, T., & Gerstein, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae338 · Cited by 126 · see citing works
Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2023) · doi:10.1101/2023.10.09.561603 · Cited by 106 · see citing works
Convolutions are competitive with transformers for protein sequence pretraining
Yang, K. K., Fusi, N., & Lu, A. X. (2024) · Cell Systems · doi:10.1016/j.cels.2024.01.008 · Cited by 137 · see citing works
I-TASSER gateway: A protein structure and function prediction server powered by XSEDE
Zheng, W. H., Zhang, C., Bell, E. W., & Zhang, Y. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.04.011 · Cited by 108 · see citing works
Torrisi, M., Kaleel, M., & Pollastri, G. (2019) · Scientific Reports · doi:10.1038/s41598-019-48786-x · Cited by 91 · see citing works
Estimation of model accuracy in CASP13
Cheng, J. J., Choe, M., Elofsson, A., Han, K., Hou, J., Maghrabi, A. H. A., McGuffin, L. J., Hurtado, D. M., Olechnovič, K., Schwede, T. F., et al. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25767 · Cited by 83 · see citing works
Self-citationdos Santos-Silva, C. A., Zupin, L., Oliveira-Lima, M., Vilela, L. M. B., Bezerra-Neto, J. P., Ferreira‐Neto, J. R. C., Ferreira, J. D. C., Silva, R. L. D. O., Pires, C. D. J., Aburjaile, F. F., et al. (2020) · Bioinformatics and Biology Insights · doi:10.1177/1177932220952739 · Cited by 84 · see citing works
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 · see citing works
ModFOLD8: accurate global and local quality estimates for 3D protein models
McGuffin, L. J., Aldowsari, F. M. F., Alharbi, S. M. A., & Adiyaman, R. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab321 · Cited by 75 · see citing works
CAMEO - Perspectives on the future of fully automated evaluation of structure prediction methods
Robin, X., Haas, J., Gumienny, R., Smolinski, A., Tauriello, G., & Schwede, T. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26213 · Cited by 75 · see citing works
Self-citationA Review of Deep Learning Methods for Antibodies
Graves, J., Byerly, J., Priego, E., Makkapati, N., Parish, S., Medellin, B. P., & Berrondo, M. (2020) · Antibodies · doi:10.3390/antib9020012 · Cited by 73 · see citing works
VoroMQA web server for assessing three-dimensional structures of proteins and protein complexes
Olechnovič, K., & Venclovas, Č. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz367 · Cited by 69 · see citing works
Evaluation of template-based modeling in CASP13
Croll, T., Sammito, M., Kryshtafovych, A., & Read, R. J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25800 · Cited by 48 · see citing works
ELIXIR: providing a sustainable infrastructure for life science data at European scale
Harrow, J., Drysdale, R. A., Smith, A., Repo, S., Lanfear, J., & Blomberg, N. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab481 · Cited by 64 · see citing works
PointSite: A Point Cloud Segmentation Tool for Identification of Protein Ligand Binding Atoms
Yan, X., Lu, Y., Li, Z., Wei, Q., Gao, X., Wang, S., Wu, S., & Cui, S. R. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01512 · Cited by 62 · see citing works
Toward Increased Reliability, Transparency, and Accessibility in Cross-linking Mass Spectrometry
Leitner, A., Bonvin, A. M. J. J., Borchers, C. H., Chalkley, R. J., Chamot‐Rooke, J., Combe, C., Cox, J., Dong, M., Fischer, L., Götze, M., et al. (2020) · Structure · doi:10.1016/j.str.2020.09.011 · Cited by 58 · see citing works
Comparative analysis of methods for evaluation of protein models against native structures
Olechnovič, K., Monastyrskyy, B., Kryshtafovych, A., & Venclovas, Č. (2018) · Bioinform. · doi:10.1093/bioinformatics/bty760 · Cited by 50 · see citing works
Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking
Wagner, J., Churas, C., Liu, S., Swift, R. V., Chiu, M. W., Shao, C., Feher, V. A., Burley, S. K., Gilson, M. K., & Amaro, R. E. (2018) · Structure · doi:10.1016/j.str.2019.05.012 · Cited by 55 · see citing works
Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14
Anishchenko, I. V., Baek, M., Park, H., Hiranuma, N., Kim, D. E., Dauparas, J., Mansoor, S., Humphreys, I. R., & Baker, D. A. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26194 · Cited by 50 · see citing works
Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures
Berman, H. M., Adams, P. D., Bonvin, A. M. J. J., Burley, S. K., Carragher, B. O., Chiu, W., DiMaio, F., Ferrin, T. E., Gabanyi, M. J., Goddard, T. D., et al. (2019) · Structure · doi:10.1016/j.str.2019.11.002 · Cited by 49 · see citing works
Self-citationMoura, W. S., de Souza, S. R., Campos, F. S., Cangussu, A. S. R., Santos, E. M. S., Andrade, B. S., Gomes, C. H. B., Viana, K. F., Haddi, K., de Oliveira, E. E., et al. (2020) · Industrial Crops and Products · doi:10.1016/j.indcrop.2020.112142 · Cited by 48 · see citing works
Mulnaes, D., Porta, N., Clemens, R., Apanasenko, I., Reiners, J., Gremer, L., Neudecker, P., Smits, S. H. J., & Gohlke, H. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00825 · Cited by 45 · see citing works
VoroCNN: deep convolutional neural network built on 3D Voronoi tessellation of protein structures
Igashov, I., Olechnovič, K., Kadukova, M., Venclovas, Č., & Grudinin, S. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab118 · Cited by 45 · see citing works
An end-to-end framework for the prediction of protein structure and fitness from single sequence
Chen, Y., Xu, Y., Liu, D., Xing, Y., & Gong, H. (2024) · Nature Communications · doi:10.1038/s41467-024-51776-x · Cited by 45 · see citing works
Haas, J., Gumienny, R., Barbato, A., Ackermann, F., Tauriello, G., Bertoni, M., Studer, G., Smolinski, A., & Schwede, T. F. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25815 · Cited by 45 · see citing works
Self-citationAssessment of protein assembly prediction in CASP13
Guzenko, D., Lafita, A., Monastyrskyy, B., Kryshtafovych, A., & Duarte, J. M. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25795 · Cited by 39 · see citing works
Burley, S. K., & Berman, H. M. (2021) · Structure · doi:10.1016/j.str.2021.04.010 · Cited by 40 · see citing works
Shen, T., Wu, J., Lan, H., Zheng, L., Pei, J., Wang, S., Liu, W., & Huang, J. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26232 · Cited by 32 · see citing works
Nanoemulsion of Ocotea indecora (Shott) Mez essential oil: Larvicidal effects against Aedes aegypti
Machado, F. P., Folly, D., Enriquez, J. J. S., Neto, C. B. D. M., Esteves, R., Araújo, R. S., Toledo, P. F. S., Mantilla‐Afanador, J. G., Santos, M. G., de Oliveira, E. E., et al. (2022) · Industrial Crops and Products · doi:10.1016/j.indcrop.2022.116031 · Cited by 32 · see citing works
GRaSP: a graph-based residue neighborhood strategy to predict binding sites
Santana, C. A., Silveira, S. D. A., Moraes, J. P., Izidoro, S. C., de Melo-Minardi, R. C., Ribeiro, A. J. M., Tyzack, J. D., Borkakoti, N., & Thornton, J. M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa805 · Cited by 30 · see citing works
Assessment of Nucleic Acid Structure Prediction in <scp>CASP16</scp>
Kretsch, R. C., Hummer, A. M., He, S., Yuan, R., Zhang, J., Karagianes, T., Cong, Q., Kryshtafovych, A., & Das, R. (2026) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.70072 · Cited by 51 · see citing works
Britto, I. O., Araujo, S. H. D. C., Toledo, P. F. S., Lima, G. D. D. A., Salustiano, I. V., Alves, J. R., Mantilla‐Afanador, J. G., Kohlhoff, M., de Oliveira, E. E., & Leite, J. P. V. (2021) · Pest Management Science · doi:10.1002/ps.6504 · Cited by 28 · see citing works
Jumbo, L. O. V., Corrêa, M. J. M., Gomes, J. M., Armijos, M. J. G., Valarezo, E., Mantilla‐Afanador, J. G., Machado, F. P., Rocha, L., Aguiar, R. W. D. S., & de Oliveira, E. E. (2022) · Industrial Crops and Products · doi:10.1016/j.indcrop.2022.114611 · Cited by 27 · see citing works
Leman, J. K., Lyskov, S., Lewis, S. M., Adolf‐Bryfogle, J., Alford, R. F., Barlow, K. A., Ben-Aharon, Z., Farrell, D. P., Fell, J. S., Hansen, W. A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27222-7 · Cited by 24 · see citing works
Role of solvent accessibility for aggregation-prone patches in protein folding
Mishra, A., Ranganathan, S., Jayaram, B., & Sattar, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-31289-6 · Cited by 25 · see citing works
Arming Yourself for The In Silico Protein Design Revolution
Walker, S. P., Yallapragada, V. V. B., & Tangney, M. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.10.003 · Cited by 25 · see citing works
Adiyaman, R., & McGuffin, L. J. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab300 · Cited by 24 · see citing works
Olanders, G., Testa, G., Tibo, A., Nittinger, E., & Tyrchan, C. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01475 · Cited by 26 · see citing works
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 · see citing works
Xu, G., Wang, Q., & Ma, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab529 · Cited by 24 · see citing works
Structure of the divergent human astrovirus MLB capsid spike
Delgado-Cunningham, K., López, T., Khatib, F., Arias, C. F., & DuBois, R. M. (2022) · Structure · doi:10.1016/j.str.2022.10.010 · Cited by 21 · see citing works
Automated benchmarking of combined protein structure and ligand conformation prediction
Leemann, M., Sagasta, A., Eberhardt, J., Schwede, T. F., Robin, X., & Durairaj, J. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26605 · Cited by 23 · see citing works
Self-citationAssessing the role of evolutionary information for enhancing protein language model embeddings
Erckert, K., & Rost, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-71783-8 · Cited by 20 · see citing works
GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs
Santana, C. A., Izidoro, S. C., de Melo-Minardi, R. C., Tyzack, J. D., Ribeiro, A. J. M., Pires, D. E. V., Thornton, J. M., & Silveira, S. D. A. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac323 · Cited by 20 · see citing works
Ma, C., Xue, T., Peng, Q., Zhang, J., Guan, J., Ding, W., Li, Y., Xia, P., Zhou, L., Zhao, T., et al. (2023) · Cell Research · doi:10.1038/s41422-023-00826-y · Cited by 19 · see citing works
Machine learning applications in macromolecular X-ray crystallography
Vollmar, M., & Evans, G. (2021) · Crystallography Reviews · doi:10.1080/0889311x.2021.1982914 · Cited by 19 · see citing works
Estimating the Quality of 3D Protein Models Using the ModFOLD7 Server
Maghrabi, A. H. A., & McGuffin, L. J. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0708-4_4 · Cited by 18 · see citing works
OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods
Xu, G., Wang, Q., & Ma, J. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00951 · Cited by 18 · see citing works
Orengo, C. A., Velankar, S. S., Wodak, S. J., Zoete, V., Bonvin, A. M. J. J., Elofsson, A., Feenstra, K. A., Gerloff, D. L., Hamelryck, T., Hancock, J. M., et al. (2020) · F1000Research · doi:10.12688/f1000research.20559.1 · Cited by 16 · see citing works
Osório, P. R. A., Dias, F. R., Mourão, D. D. S. C., Araujo, S. H. D. C., Toledo, P. F. S., Silva, A. C. F., Vieira, W. A. D. S., Câmara, M. P. S., Moura, W. S., Aguiar, R. W. A., et al. (2021) · Industrial Crops and Products · doi:10.1016/j.indcrop.2021.113728 · Cited by 18 · see citing works
The SWISS‐MODEL Repository of 3D Protein Structures and Models
Robin, X., Waterhouse, A. M., Bienert, S., Studer, G., Alexander, L. T., Tauriello, G., Schwede, T. F., & Pereira, J. (2023) · Methods and principles in medicinal chemistry · doi:10.1002/9783527830497.ch6 · Cited by 16 · see citing works
Self-citationDalcin, M. S., Dias, B. L., Jumbo, L. O. V., Oliveira, A. C. S. S., Araujo, S. H. D. C., Moura, W. S., Mourão, D. D. S. C., Ferreira, T. P. D. S., Campos, F. S., Cangussu, A. S. R., et al. (2022) · Molecules · doi:10.3390/molecules27165173 · Cited by 15 · see citing works
Bioinformatic Approaches for Characterizing Molecular Structure and Function of Food Proteins
Helmick, H. D. B., Jain, A., Terashi, G., Liceaga, A. M., Bhunia, A. K., Kihara, D., & Kokini, J. L. (2023) · Annual Review of Food Science and Technology · doi:10.1146/annurev-food-060721-022222 · Cited by 15 · see citing works
Improved protein model quality assessments by changing the target function
Uziela, K., Hurtado, D. M., Shu, N., Wallner, B., & Elofsson, A. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25492 · Cited by 13 · see citing works
Toledo, P. F. S., Araujo, S. H. D. C., Mantilla‐Afanador, J. G., Silva, A. C. F., Machado, F. P., Rocha, L., & de Oliveira, E. E. (2024) · Neotropical Entomology · doi:10.1007/s13744-023-01109-2 · Cited by 12 · see citing works
Recent advances and challenges in protein complex model accuracy estimation
Liang, F., Sun, M., Xie, L., Zhao, X., Liu, D., Zhao, K., & Zhang, G. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.049 · Cited by 15 · see citing works
Dias, B. L., Sarmento, R. A., Venzon, M., Jumbo, L. O. V., dos Santos, L. S. S., Moura, W. S., Mourão, D. D. S. C., Fernandes, P. R. D. S., Neitzke, T. R., Oliveira, J. V. D. A., et al. (2024) · Biology · doi:10.3390/biology13070479 · Cited by 12 · see citing works
The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks
Krämer, C., Chodera, J. D., Damm‐Ganamet, K. L., Gilson, M. K., Günther, J., Lessel, U. F., Lewis, R. A., Mobley, D. L., Nittinger, E., Pecina, A., et al. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02296 · Cited by 13 · see citing works
Jumbo, L. O. V., Armijos, M. J. G., da Silva, P. B., Gomes, J. M., Svacina, T., Costa, L. T. M., Valarezo, E., Mantilla‐Afanador, J. G., Armijos, O. M. H., Aguiar, R. W. D. S., et al. (2025) · Journal of Xenobiotics · doi:10.3390/jox15030091 · Cited by 12 · see citing works
Practical Considerations for Atomistic Structure Modeling with Cryo-EM Maps
Kim, D. N., Gront, D., & Sanbonmatsu, K. Y. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00090 · Cited by 12 · see citing works
Improved estimation of model quality using predicted inter-residue distance
Ye, L., Wu, P., Peng, Z., Gao, J., Liu, J., & Yang, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab632 · Cited by 12 · see citing works
OPUS-Mut: Studying the Effect of Protein Mutation through Side-Chain Modeling
Xu, G., Wang, Q., & Ma, J. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00847 · Cited by 12 · see citing works
Current methods and applications in computational protein design for food industry
Chen, Q., Xiao, Y., Zhang, W., & Mu, W. (2019) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2019.1682513 · Cited by 12 · see citing works
Nagarajan, S. K., Babu, S., Sohn, H., & Madhavan, T. (2020) · ACS Omega · doi:10.1021/acsomega.0c02847 · Cited by 12 · see citing works
Mimetic Neural Networks: A Unified Framework for Protein Design and Folding
Eliasof, M., Boesen, T., Haber, E., Keasar, C., & Treister, E. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.715006 · Cited by 11 · see citing works
Assisting and accelerating NMR assignment with restrained structure prediction
Liu, S., Chu, H., Xie, Y., Wu, F., Mu, F., Wei, J., Ni, N., Wang, C., Zhang, J., Chen, M., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08466-1 · Cited by 10 · see citing works
Luo, Y., & Cai, J. (2025) · Current Proteomics · doi:10.2174/0115701646374018250227075247 · Cited by 13 · see citing works
A fully automated benchmarking suite to compare macromolecular complexes
Studer, G., Robin, X., Bienert, S., Durairaj, J., Škrinjar, P., Tauriello, G., Waterhouse, A. M., & Schwede, T. (2026) · Nature Methods · doi:10.1038/s41592-025-02973-z · Cited by 10 · see citing works
“To directly compare reference and model binding sites, RMSD-LP and LDDT-LP have been introduced, where LP stands for ligand pocket, and are similar to the previously described LDDT-BS score 72 .”
Self-citationDeep learning applications in protein crystallography
Matinyan, S., Filipčík, P., & Abrahams, J. P. (2024) · Acta Crystallographica Section A Foundations and Advances · doi:10.1107/s2053273323009300 · Cited by 10 · see citing works
REALDIST: Real-valued protein distance predictionPreprint
Adhikari, B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.28.402214 · Cited by 9 · see citing works
Kaushik, R., & Zhang, K. Y. J. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.032 · Cited by 9 · see citing works
A novel deleterious oxytocin variant is associated with the lower twinning ratio in Awassi ewes
Khazaal, N. M., Alghetaa, H., Al‐Shuhaib, M. B. S., AL-Thuwaini, T. M., & Alkhammas, A. H. (2022) · Animal Biotechnology · doi:10.1080/10495398.2022.2152038 · Cited by 9 · see citing works
OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer
Xu, G., Luo, Z., Yan, Y., Wang, Q., & Ma, J. (2024) · Structure · doi:10.1016/j.str.2024.03.015 · Cited by 8 · see citing works
Toward the assessment of predicted inter-residue distance
Du, Z., Peng, Z., & Yang, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab781 · Cited by 9 · see citing works
- Authors
- Haas, J., Barbato, A., Behringer, D., Studer, G., Roth, S., Bertoni, M., Mostaguir, K., Gumienny, R., & Schwede, T.
- Affiliations
- Biozentrum University of Basel Switzerland
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Schwede, Torsten
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2017-12-17
- Volume
- 86
- Issue
- S1
- Pages
- 387-398
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC5820194
- OpenAlex
- W2770647690
- Cited by
- 168
- References
- 60
- Citations by year
- 2026: 4 · 2025: 20 · 2024: 23 · 2023: 24 · 2022: 20 · 2021: 26 · 2020: 21 · 2019: 21 · 2018: 8 · 2017: 1
- FWCI
- 5.69
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- edoc (University of Basel) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (U01 GM093324‐01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- caspase-12, protein structure prediction, automated benchmarking, protein structure modeling, scoring system, ligand binding site prediction, method performance evaluation
- MeSH terms
- Binding Sites, Humans, Ligands, Models, Molecular, Protein Binding, Protein Conformation, Proteins, Computational Biology, Sequence Analysis, Protein, Databases, Protein
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.
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