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
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The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.
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Experimentally determined structures of ~100,000 proteins represent a very small fraction of the size and diversity of the universe of proteins.
“the experimentally determined structures of ~100,000 proteins1 represent a very small fraction of the size and diversity of the universe of proteins.”
From A structural biology community assessment of AlphaFold2 applications (2022) · cites this paper as [1]
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Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669 · see citing works
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · see citing works
Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C.-L., Abiona, O., Graham, B. S., & McLellan, J. S. (2020) · Science · doi:10.1126/science.abb2507 · Cited by 8,897 · see citing works
Structure, function and antigenicity of the SARS-CoV-2 spike glycoprotein
Walls, A. C., Park, Y.-J., Tortorici, M. A., Wall, A., McGuire, A. T., & Veesler, D. (2020) · doi:10.1101/2020.02.19.956581 · Cited by 8,586 · see citing works
DrugBank 5.0: a major update to the DrugBank database for 2018
Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1037 · Cited by 8,404 · see citing works
UniProt: a worldwide hub of protein knowledge
The UniProt Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1049 · Cited by 7,698 · see citing works
O'Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1189 · Cited by 6,357 · see citing works
The Pfam protein families database: towards a more sustainable future
Finn, R. D., Coggill, P., Eberhardt, R. Y., Eddy, S. R., Mistry, J., Mitchell, A. L., Potter, S. C., Punta, M., Qureshi, M., Sangrador-Vegas, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1344 · Cited by 5,975 · see citing works
The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works
Self-citationStructural basis for the recognition of SARS-CoV-2 by full-length human ACE2
Yan, R., Zhang, Y., Li, Y., Xia, L., Guo, Y., & Zhou, Q. (2020) · Science · doi:10.1126/science.abb2762 · Cited by 4,950 · see citing works
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M., & Söding, J. (2017) · Nature Biotechnology · doi:10.1038/nbt.3988 · Cited by 4,840 · see citing works
The Gene Ontology Resource: 20 years and still GOing strong
The Gene Ontology Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1055 · Cited by 4,747 · see citing works
Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors
Jin, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang, L., Peng, C., et al. (2020) · Nature · doi:10.1038/s41586-020-2223-y · Cited by 4,432 · see citing works
A SARS-CoV-2 protein interaction map reveals targets for drug repurposing
Gordon, D. E., Jang, G. M., Bouhaddou, M., Xu, J., Obernier, K., White, K. M., O’Meara, M. J., Rezelj, V. V., Guo, J. Z., Swaney, D. L., et al. (2020) · Nature · doi:10.1038/s41586-020-2286-9 · Cited by 4,405 · see citing works
Self-citationAnnouncing the worldwide Protein Data Bank
Berman, H., Henrick, K., & Nakamura, H. (2003) · Nature Structural & Molecular Biology · doi:10.1038/nsb1203-980 · Cited by 3,093 · see citing works
A conditional knockout resource for the genome-wide study of mouse gene function
Skarnes, W. C., Rosen, B., West, A. P., Koutsourakis, M., Bushell, W., Iyer, V., Mujica, A. O., Thomas, M., Harrow, J., Cox, T., et al. (2011) · Nature · doi:10.1038/nature10163 · Cited by 1,708 · see citing works
Structure of the RNA-dependent RNA polymerase from COVID-19 virus
Gao, Y., Yan, L., Huang, Y., Liu, F., Zhao, Y., Cao, L., Wang, T., Sun, Q., Ming, Z., Zhang, L., et al. (2020) · Science · doi:10.1126/science.abb7498 · Cited by 1,614 · see citing works
A highly conserved cryptic epitope in the receptor binding domains of SARS-CoV-2 and SARS-CoV
Yuan, M., Wu, N. C., Zhu, X., Lee, C.-C. D., So, R. T. Y., Lv, H., Mok, C. K. P., & Wilson, I. A. (2020) · Science · doi:10.1126/science.abb7269 · Cited by 1,405 · see citing works
Structure of the 70S Ribosome Complexed with mRNA and tRNA
Selmer, M., Dunham, C., Murphy, F., Weixlbaumer, A., Petry, S., Kelley, A. C., Weir, J., & Ramakrishnan, V. (2006) · Science · doi:10.1126/science.1131127 · Cited by 1,352 · see citing works
Burley, S. K., Berman, H. M., Bhikadiya, C., Bi, C., Chen, L., Costanzo, L. D., Christie, C. H., Dalenberg, K., Duarte, J. M., Dutta, S., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1004 · Cited by 1,249 · see citing works
Self-citationProtein 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
Self-citationCold Spring Harbor Symposia on Quantitative Biology.
Berg, H. (1989) · Journal of Electroanalytical Chemistry · doi:10.1016/0022-0728(89)87283-3 · Cited by 1,154 · see citing works
PROSITE: a documented database using patterns and profiles as motif descriptors
Sigrist, C. J. A. (2002) · Briefings in Bioinformatics · doi:10.1093/bib/3.3.265 · Cited by 913 · cited 3× · see citing works
“• protein or nucleic acid polymer Sequence Motif searching, using three different types of input format [Simple (e.g. CXCXXL), PROSITE (e.g. C-X-C-X(2)-[LIVMYFWC]) and RegEx (e.g. CXCX{2}[LIVMYFWC])]; • Structure Similarity to an existing Chain or Assembly of a given Entry (identified by PDB ID), in either Strict or R…”
BackgroundInfluentialMature HIV-1 capsid structure by cryo-electron microscopy and all-atom molecular dynamics
Zhao, G., Perilla, J. R., Yufenyuy, E. L., Meng, X., Chen, B., Ning, J., Ahn, J., Gronenborn, A. M., Schulten, K., Aiken, C., et al. (2013) · Nature · doi:10.1038/nature12162 · Cited by 801 · see citing works
Rose, P. W., Prlić, A., Altunkaya, A., Bi, C., Bradley, A. R., Christie, C. H., Costanzo, L. D., Duarte, J. M., Dutta, S., Feng, Z., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1000 · Cited by 800 · see citing works
Self-citationFox, N. K., Brenner, S. E., & Chandonia, J.-M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1240 · Cited by 762 · see citing works
The RCSB Protein Data Bank: redesigned web site and web services
Rose, P. W., Beran, B., Bi, C., Bluhm, W. F., Dimitropoulos, D., Goodsell, D. S., Prlić, A., Quesada, M., Quinn, G. B., Westbrook, J., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1021 · Cited by 659 · see citing works
Self-citationCell type–specific genetic regulation of gene expression across human tissues
Kim-Hellmuth, S., Aguet, F., Oliva, M., Muñoz-Aguirre, M., Kasela, S., Wucher, V., Castel, S. E., Hamel, A. R., Viñuela, A., Roberts, A. L., et al. (2020) · Science · doi:10.1126/science.aaz8528 · Cited by 530 · see citing works
Self-citationThe RCSB PDB information portal for structural genomics
Kouranov, A. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj120 · Cited by 523 · 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
Self-citationThe RCSB Protein Data Bank: views of structural biology for basic and applied research and education
Rose, P. W., Prlić, A., Bi, C., Bluhm, W. F., Christie, C. H., Dutta, S., Green, R. K., Goodsell, D. S., Westbrook, J., Woo, J., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1214 · Cited by 519 · see citing works
Self-citationCold Spring Harbor Symposia on Quantitative Biology
H., R. G. (1935) · Science · doi:10.1126/science.81.2100.311 · Cited by 418 · see citing works
The Protein Data Bank and structural genomics
Westbrook, J. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg068 · Cited by 407 · see citing works
Self-citationCrystal structure of Nsp15 endoribonuclease NendoU from SARS‐CoV ‐2
Kim, Y., Jedrzejczak, R. P., Maltseva, N., Wilamowski, M., Endres, M. J., Godzik, A., Michalska, K., & Joachimiak, A. (2020) · Protein Science · doi:10.1002/pro.3873 · Cited by 386 · see citing works
CATH: an expanded resource to predict protein function through structure and sequence
Dawson, N. L., Lewis, T. E., Das, S., Lees, J. G., Lee, D., Ashford, P., Orengo, C. A., & Sillitoe, I. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1098 · Cited by 380 · see citing works
RCSB Protein Data Bank: Enabling biomedical research and drug discovery
Goodsell, D. S., Zardecki, C., Costanzo, L. D., Duarte, J. M., Hudson, B. P., Persikova, I., Segura, J., Shao, C., Voigt, M., Westbrook, J., et al. (2019) · Protein Science · doi:10.1002/pro.3730 · Cited by 357 · see citing works
Self-citationSCOP2 prototype: a new approach to protein structure mining
Andreeva, A. E., Howorth, D., Chothia, C., Kulesha, E., & Murzin, A. G. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1242 · Cited by 336 · see citing works
Jia, Z., Yan, L., Ren, Z.-L., Wu, L., Wang, J., Guo, J., Zheng, L., Ming, Z., Zhang, L., Lou, Z., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz409 · Cited by 334 · see citing works
The Protein Data Bank: unifying the archive
Westbrook, J. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.245 · Cited by 309 · see citing works
Self-citationRathnayake, A. D., Zheng, J. H., Kim, Y., Perera, K. D., Mackin, S., Meyerholz, D. K., Kashipathy, M. M., Battaile, K. P., Lovell, S., Perlman, S. M., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.abc5332 · Cited by 283 · see citing works
Pharos: collating protein information to shed light on the druggable genome
Nguyen, D.-T., Mathias, S., Bologa, C., Brunak, S., Fernandez, N., Gaulton, A., Hersey, A., Holmes, J., Jensen, L. J., Karlsson, A., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1072 · Cited by 279 · see citing works
Validation of Structures in the Protein Data Bank
Gore, S., Sanz-García, E., Hendrickx, P., Gutmanas, A., Westbrook, J., Yang, H.-W., Feng, Z.-K., Baskaran, K., Berrisford, J., Hudson, B., et al. (2017) · Structure · doi:10.1016/j.str.2017.10.009 · Cited by 245 · see citing works
Self-citationCrystallography: Protein Data Bank
(1971) · Nature New Biology · doi:10.1038/newbio233223b0 · Cited by 221 · see citing works
Westbrook, J., Shao, C., Feng, Z.-K., Zhuravleva, M., Velankar, S., & Young, J. Y. (2015) · Bioinform. · doi:10.1093/bioinformatics/btu789 · Cited by 177 · see citing works
Self-citationYoung, J. Y., Westbrook, J., Feng, Z., Sala, R., Peisach, E., Oldfield, T. J., Sen, S., Gutmanas, A., Armstrong, D. R., Berrisford, J. M., et al. (2017) · Structure · doi:10.1016/j.str.2017.01.004 · Cited by 159 · see citing works
Self-citationMol*: Towards a Common Library and Tools for Web Molecular Graphics
Sehnal, D., Rose, A., Koča, J., Burley, S. K., & Velankar, S. S. (2018) · Eurographics · doi:10.2312/molva.20181103 · Cited by 158 · see citing works
Self-citationMarkley, J. L., Ulrich, E. L., Berman, H. M., Henrick, K., Nakamura, H., & Akutsu, H. (2008) · Journal of Biomolecular NMR · doi:10.1007/s10858-008-9221-y · Cited by 134 · see citing works
Crystal structures of SARS-CoV-2 ADP-ribose phosphatase: from the apo form to ligand complexes
Michalska, K., Kim, Y., Jedrzejczak, R. P., Maltseva, N. I., Stols, L. M., Endres, M. J., & Joachimiak, A. (2020) · IUCrJ · doi:10.1107/s2052252520009653 · Cited by 133 · see citing works
The distribution and query systems of the RCSB Protein Data Bank
Bourne, P. E. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh096 · Cited by 117 · see citing works
The PDB data uniformity project
Bhat, T. N. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.1.214 · Cited by 114 · see citing works
Structure of faustovirus, a large dsDNA virus
Klose, T., Reteno, D. G. I., Benamar, S., Hollerbach, A., Colson, P., Scola, B. L., & Rossmann, M. G. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1523999113 · Cited by 106 · see citing works
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Goodsell, D. S., Dutta, S., Zardecki, C., Voigt, M., Berman, H. M., & Burley, S. K. (2015) · PLoS Biology · doi:10.1371/journal.pbio.1002140 · Cited by 104 · see citing works
Self-citationNew tools and functions in data‐out activities at Protein Data Bank Japan (PDBj)
Kinjo, A. R., Bekker, G., Wako, H., Endo, S., Tsuchiya, Y., Sato, H., Nishi, H., Kinoshita, K., Suzuki, H., Kawabata, T., et al. (2017) · Protein Science · doi:10.1002/pro.3273 · Cited by 101 · see citing works
How Structural Biologists and the Protein Data Bank Contributed to Recent FDA New Drug Approvals
Westbrook, J., & Burley, S. K. (2018) · Structure · doi:10.1016/j.str.2018.11.007 · Cited by 88 · see citing works
Self-citationPDBe: towards reusable data delivery infrastructure at protein data bank in Europe
Mir, S., Alhroub, Y., Anyango, S., Armstrong, D. R., Berrisford, J. M., Clark, A. R., Conroy, M. J., Dana, J. M., Deshpande, M., Gupta, D. S., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1070 · Cited by 83 · see citing works
Real time structural search of the Protein Data Bank
Guzenko, D., Burley, S. K., & Duarte, J. M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007970 · Cited by 62 · see citing works
Self-citationYoung, J. Y., Westbrook, J., Feng, Z., Peisach, E., Persikova, I., Sala, R., Sen, S., Berrisford, J. M., Swaminathan, G. J., Oldfield, T. J., et al. (2018) · Database · doi:10.1093/database/bay002 · Cited by 59 · see citing works
Self-citationImplementation of GlycanBuilder to draw a wide variety of ambiguous glycans
Tsuchiya, S., Aoki, N. P., Shinmachi, D., Matsubara, M., Yamada, I., Aoki-Kinoshita, K. F., & Narimatsu, H. (2017) · Carbohydrate Research · doi:10.1016/j.carres.2017.04.015 · Cited by 41 · see citing works
A global coalition to sustain core data
Anderson, W. P. (2017) · Nature · doi:10.1038/543179a · Cited by 40 · see citing works
CellPAINT: Interactive Illustration of Dynamic Mesoscale Cellular Environments
Gardner, A. V., Autin, L., Barbaro, B. A., Olson, A. J., & Goodsell, D. S. (2018) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2018.2877076 · Cited by 39 · see citing works
Self-citationReal-time structural motif searching in proteins using an inverted index strategy
Bittrich, S., Burley, S. K., & Rose, A. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008502 · Cited by 37 · see citing works
Self-citationAnalysis of impact metrics for the Protein Data Bank
Markosian, C., Costanzo, L. D., Sekharan, M. R., Shao, C., Burley, S. K., & Zardecki, C. (2018) · Scientific Data · doi:10.1038/sdata.2018.212 · Cited by 36 · see citing works
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Self-citationBinaryCIF and CIFTools—Lightweight, efficient and extensible macromolecular data management
Sehnal, D., Bittrich, S., Velankar, S. S., Koča, J., Vařeková, R. S., Burley, S. K., & Rose, A. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008247 · Cited by 30 · see citing works
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Goodsell, D. S., Zardecki, C., Berman, H. M., & Burley, S. K. (2020) · Biochemistry and Molecular Biology Education · doi:10.1002/bmb.21360 · Cited by 25 · see citing works
Self-citationBeierlein, J. M., McNamee, L. M., Walsh, M. J., Kaitin, K. I., DiMasi, J. A., & Ledley, F. D. (2017) · Clinical Therapeutics · doi:10.1016/j.clinthera.2017.06.001 · Cited by 24 · see citing works
Integrative illustration for coronavirus outreach
Goodsell, D. S., Voigt, M., Zardecki, C., & Burley, S. K. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000815 · Cited by 22 · see citing works
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Self-citationTowards Coordinated International Support of Core Data Resources for the Life SciencesPreprint
Anderson, W., Apweiler, R., Bateman, A., Bauer, G. A., Berman, H. M., Blake, J. A., Blomberg, N., Burley, S. K., Cochrane, G., Francesco, V. D., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/110825 · Cited by 18 · see citing works
Self-citationCrystal structure of RNA binding domain of nucleocapsid phosphoprotein from SARS coronavirus 2
Chang, C., Michalska, K., Jedrzejczak, R. P., Maltseva, N., Endres, M. J., Godzik, A., Kim, Y., & Joachimiak, A. (2020) · Worldwide Protein Data Bank · doi:10.2210/pdb6vyo/pdb · Cited by 18 · see citing works
X-Ray Crystal Structure of the SARS Coronavirus Main Protease
Bonanno, J. B., Fowler, R., Gupta, S., Hendle, J., Lorimer, D. D., Romero, R., Sauder, J. M., Wei, C., Liu, E. T., Burley, S. K., et al. (2003) · Worldwide Protein Data Bank · doi:10.2210/pdb1q2w/pdb · Cited by 16 · see citing works
Self-citationMinasov, G., Shuvalova, L., Wiersum, G., & Satchell, K. J. F. (2020) · Worldwide Protein Data Bank · doi:10.2210/pdb6wji/pdb · Cited by 10 · see citing works
Minasov, G., Shuvalova, L., Rosas‐Lemus, M., Kiryukhina, O., Brunzelle, J. S., & Satchell, K. J. F. (2020) · Worldwide Protein Data Bank · doi:10.2210/pdb6wvn/pdb · Cited by 2 · see citing works
Real-time structural motif searching in proteins using an inverted index strategyPreprint
Bittrich, S., Burley, S. K., & Rose, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.11.293977 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
5 of the 80 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- [8]Westbrook J., Henrick K., Ulrich E.L., Berman H.M. Hall S.R., McMahon B. 3.6.2 The Protein Data Bank exchange data dictionary in International Tables for Crystallography, Vol. G. Definition and Exchange of Crystallographic Data. 2005; Dordrecht Springer 195– 198.
- [9]Fitzgerald P.M.D., Westbrook J.D., Bourne P.E., McMahon B., Watenpaugh K.D., Berman H.M. Hall S.R., McMahon B. 4.5 Macromolecular dictionary (mmCIF) in International Tables for Crystallography, Vol. G. Definition and Exchange of Crystallographic Data. 2005; Dordrecht, The Netherlands Springer 295– 443.
- [16]Michalska K., Kim Y., Jedrzejczak R., Maltseva N. I., Stols L., Endres M., Joachimiak A. The crystal structure of papain-like protease of SARS CoV-2. 2020; doi:10.2210/pdb6W9C/pdb. PMC7467174 32939273 · DOI
- [71]Rose P.W., Prlic A., Altunkaya A., Bi C., Bradley A.R., Christie C.H., Costanzo L.D., Duarte J.M., Dutta S., Feng Z. et al. The RCSB Protein Data Bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res. 2017; 45: D271– D281. 27794042 PMC5210513
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Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · see citing works
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van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01773-0 · Cited by 2,596 · see citing works
The carbohydrate-active enzyme database: functions and literature
Drula, E., Garron, M.-L., Dogan, S., Lombard, V., Henrissat, B., & Terrapon, N. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1045 · Cited by 2,439 · 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
Self-citationMODOMICS: a database of RNA modification pathways. 2021 update
Boccaletto, P., Stefaniak, F., Ray, A., Cappannini, A., Mukherjee, S., Purta, E., Kurkowska, M., Shirvanizadeh, N., Destefanis, E., Groza, P., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1083 · Cited by 811 · see citing works
A structural biology community assessment of AlphaFold2 applications
Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675 · see citing works
Automated model building and protein identification in cryo-EM maps
Jamali, K., Käll, L., Zhang, R., Brown, A., Kimanius, D., & Scheres, S. H. W. (2024) · Nature · doi:10.1038/s41586-024-07215-4 · Cited by 686 · see citing works
Automated model building and protein identification in cryo-EM maps
Jamali, K., Käll, L., Zhang, R., Brown, A., Kimanius, D., & Scheres, S. H. W. (2023) · doi:10.1101/2023.05.16.541002 · Cited by 686 · see citing works
Zhou, Y., Zhang, Y., Lian, X., Li, F., Wang, C., Zhu, F., Qiu, Y., & Chen, Y. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab953 · Cited by 667 · see citing works
Erdős, G., Pajkos, M., & Dosztányi, Z. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab408 · Cited by 588 · see citing works
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 · see citing works
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 · see citing works
Li, X., Wei, S., Niu, S., Ma, X., Li, H., Jing, M., & Zhao, Y. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105389 · Cited by 422 · see citing works
Drug Design by Pharmacophore and Virtual Screening Approach
Giordano, D., Biancaniello, C., Argenio, M. A., & Facchiano, A. M. (2022) · Pharmaceuticals · doi:10.3390/ph15050646 · Cited by 410 · see citing works
High-resolution <i>de novo</i> structure prediction from primary sequence
Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al. (2022) · doi:10.1101/2022.07.21.500999 · Cited by 392 · see citing works
Burley, S. K., Bhatt, R., Bhikadiya, C., Bi, C., Biester, A., Biswas, P., Bittrich, S., Blaumann, S., Brown, R., Chao, H., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1091 · Cited by 342 · see citing works
Self-citationAnnotation of biologically relevant ligands in UniProtKB using ChEBI
Coudert, E., Gehant, S., de Castro, E., Pozzato, M., Baratin, D., Neto, T., Sigrist, C. J. A., Redaschi, N., Bridge, A., The UniProt Consortium, et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac793 · Cited by 350 · see citing works
DRAMP 3.0: an enhanced comprehensive data repository of antimicrobial peptides
Shi, G., Kang, X., Dong, F., Liu, Y., Zhu, N., Hu, Y., Xu, H., Lao, X., & Zheng, H. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab651 · Cited by 319 · see citing works
Piersimoni, L., Kastritis, P. L., Arlt, C., & Sinz, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00786 · Cited by 308 · see citing works
CAMPR4: a database of natural and synthetic antimicrobial peptides
Gawde, U., Chakraborty, S., Waghu, F. H., Barai, R. S., Khanderkar, A., Indraguru, R., Shirsat, T., & Idicula‐Thomas, S. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac933 · Cited by 282 · see citing works
Towards a structurally resolved human protein interaction network
Burke, D. F., Bryant, P., Barrio-Hernandez, I., Memon, D., Pozzati, G., Shenoy, A., Zhu, W., Dunham, A. S., Albanese, P., Keller, A., et al. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00910-8 · Cited by 269 · see citing works
Rose, Y., Duarte, J. M., Lowe, R., Segura, J., Bi, C., Bhikadiya, C., Chen, L., Rose, A., Bittrich, S., Burley, S. K., et al. (2020) · Journal of Molecular Biology · doi:10.1016/j.jmb.2020.11.003 · Cited by 241 · see citing works
Self-citationDeep Learning in Virtual Screening: Recent Applications and Developments
Kimber, T. B., Chen, Y., & Volkamer, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094435 · Cited by 213 · see citing works
GPCRdb in 2023: state-specific structure models using AlphaFold2 and new ligand resources
Pándy‐Szekeres, G., Caroli, J., Mamyrbekov, A., Kermani, A. A., Keserű, G. M., Kooistra, A. J., & Gloriam, D. E. I. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1013 · Cited by 205 · see citing works
Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Duarte, J. M., Dutta, S., Fayazi, M., Feng, Z., et al. (2021) · Protein Science · doi:10.1002/pro.4213 · Cited by 203 · see citing works
Self-citationHIT 2.0: an enhanced platform for Herbal Ingredients' Targets
Yan, D., Zheng, G., Wang, C., Chen, Z., Mao, T., Gao, J., Yan, Y., Chen, X., Ji, X., Yu, J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1011 · Cited by 198 · see citing works
Application of molecular dynamics simulation in biomedicine
Wu, X., Xu, L., Li, E., & Dong, G. (2022) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14038 · Cited by 201 · see citing works
TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction
Lu, W., Wu, Q., Zhang, J., Rao, J., Li, C., & Zheng, S. (2022) · neural information processing systems · doi:10.52202/068431-0525 · Cited by 174 · see citing works
Chandonia, J., Guan, L., Lin, S., Yu, C., Fox, N. K., & Brenner, S. E. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1054 · Cited by 173 · see citing works
A brief history of de novo protein design: minimal, rational, and computational.
Woolfson, D. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167160 · Cited by 139 · see citing works
Deng, F., Li, Y., Yang, B., Sang, R., Deng, W., Kansara, M., Lin, F. P. Y., Thavaneswaran, S., Thomas, D. M., & Goldys, E. M. (2024) · Nature Communications · doi:10.1038/s41467-024-46001-8 · Cited by 160 · see citing works
ConsensusPathDB 2022: molecular interactions update as a resource for network biology
Kamburov, A., & Herwig, R. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1128 · Cited by 146 · see citing works
Molecular docking in organic, inorganic, and hybrid systems: a tutorial review
Mohanty, M., & Mohanty, P. S. (2023) · Monatshefte für Chemie - Chemical Monthly · doi:10.1007/s00706-023-03076-1 · Cited by 147 · see citing works
A structural biology community assessment of AlphaFold 2 applications
Akdel, M., Pires, D. E. V., Porta Pardo, E., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2021) · doi:10.1101/2021.09.26.461876 · Cited by 122 · see citing works
Chalkley, M. J., Mann, S. I., & DeGrado, W. F. (2021) · Nature Reviews Chemistry · doi:10.1038/s41570-021-00339-5 · Cited by 140 · 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
Self-citationArtificial Intelligence in Pharmaceutical Sciences
Lu, M., Yin, J., Zhu, Q., Lin, G., Mou, M., Liu, F., Pan, Z., You, N., Lian, X., Li, F., et al. (2023) · Engineering · doi:10.1016/j.eng.2023.01.014 · Cited by 139 · see citing works
PDB ‐101: Educational resources supporting molecular explorations through biology and medicine
Zardecki, C., Dutta, S., Goodsell, D. S., Lowe, R., Voigt, M., & Burley, S. K. (2021) · Protein Science · doi:10.1002/pro.4200 · Cited by 127 · see citing works
Self-citationConte, A. D., Camagni, G. F., Clementel, D., Minervini, G., Monzón, A. M., Ferrari, C., Piovesan, D., & Tosatto, S. C. E. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae337 · Cited by 125 · see citing works
The structural context of posttranslational modifications at a proteome-wide scale
Bludau, I., Willems, S., Zeng, W., Strauss, M. T., Hansen, F. M., Tanzer, M. C., Karayel, Ö., Schulman, B. A., & Mann, M. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001636 · Cited by 119 · see citing works
Integrative Modeling of PROTAC-Mediated Ternary Complexes
Weng, G., Li, D., Kang, Y., & Hou, T. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01576 · Cited by 130 · 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
Self-citationFang, 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 · see citing works
Deep generative modeling for protein design
Strokach, A. V., & Kim, P. M. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.11.008 · Cited by 114 · see citing works
Bisht, A., Tewari, D., Kumar, S., & Chandra, S. (2023) · Molecular Diversity · doi:10.1007/s11030-023-10684-w · Cited by 115 · see citing works
Review of machine learning methods for RNA secondary structure prediction
Zhao, Q., Zhao, Z., Fan, X., Yuan, Z., Mao, Q., & Yao, Y. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009291 · Cited by 106 · see citing works
Toripalimab: the First Domestic Anti-Tumor PD-1 Antibody in China
Zhang, L., Hao, B., Geng, Z., & Geng, Q. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2021.730666 · Cited by 103 · see citing works
The Impact of Software Used and the Type of Target Protein on Molecular Docking Accuracy
Ivanova, L. B., & Karelson, M. M. (2022) · Molecules · doi:10.3390/molecules27249041 · Cited by 100 · see citing works
PLMSearch: Protein language model powers accurate and fast sequence search for remote homology
Liu, W., Wang, Z., You, R., Xie, C., Wei, H., Xiong, Y., Yang, J., & Zhu, S. (2024) · Nature Communications · doi:10.1038/s41467-024-46808-5 · Cited by 96 · see citing works
AlphaFold illuminates half of the dark human proteins
Binder, J. L., Berendzen, J., Stevens, A. O., He, Y., Wang, J., Dokholyan, N. V., & Oprea, T. I. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102372 · Cited by 81 · see citing works
GraphscoreDTA: optimized graph neural network for protein–ligand binding affinity prediction
Wang, K., Zhou, R., Tang, J. P., & Li, M. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad340 · Cited by 92 · see citing works
The MHC Motif Atlas: a database of MHC binding specificities and ligands
Tadros, D. M., Eggenschwiler, S., Racle, J., & Gfeller, D. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac965 · Cited by 87 · see citing works
Sala, D., Hildebrand, P. W., & Meiler, J. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1121962 · Cited by 80 · see citing works
PRODORIC: state-of-the-art database of prokaryotic gene regulation
Dudek, C.-A., & Jahn, D. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1110 · Cited by 90 · see citing works
Tislelizumab: A Modified Anti-tumor Programmed Death Receptor 1 Antibody
Zhang, L., Geng, Z., Hao, B., & Geng, Q. (2022) · Cancer Control · doi:10.1177/10732748221111296 · Cited by 90 · see citing works
Unlocking the potential of enzyme engineering via rational computational design strategies
Zhou, L., Tao, C., Shen, X., Sun, X., Wang, J., & Yuan, Q. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108376 · Cited by 93 · see citing works
Bittrich, S., Bhikadiya, C., Bi, C., Chao, H., Duarte, J. M., Dutta, S., Fayazi, M., Henry, J., Khokhriakov, I., Lowe, R., et al. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.167994 · Cited by 83 · see citing works
Self-citationVARIDT 2.0: structural variability of drug transporter
Fu, T., Li, F., Zhang, Y., Yin, J., Qiu, W., Li, X., Liu, X., Xin, W., Wang, C., Yu, L., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1013 · Cited by 88 · see citing works
PDBx/mmCIF Ecosystem: Foundational Semantic Tools for Structural Biology
Westbrook, J., Young, J. Y., Shao, C., Feng, Z.-K., Guranovic, V., Lawson, C., Vallat, B. K., Adams, P., Berrisford, J., Bricogne, G., et al. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167599 · Cited by 73 · see citing works
Self-citationQuah, Y., Tong, S.-R., Bojarska, J., Giller, K., Tan, S.-A. S. W., Ziora, Z. M., Esatbeyoglu, T., & Chai, T. (2023) · Molecules · doi:10.3390/molecules28031233 · Cited by 86 · see citing works
Germline-encoded amino acid-binding motifs drive immunodominant public antibody responses
Shrock, E. L., Timms, R., Kula, T., Mena, E. L., West, A., Guo, R., Lee, I.-H., Cohen, A. A., McKay, L. G. A., Bi, C.-H., et al. (2023) · Science · doi:10.1126/science.adc9498 · Cited by 81 · see citing works
Self-citationProteomicsDB: toward a FAIR open-source resource for life-science research
Lautenbacher, L., Samaras, P., Müller, J., Grafberger, A., Shraideh, M., Rank, J. D., Fuchs, S., Schmidt, T., The, M., Dallago, C., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1026 · Cited by 80 · see citing works
Zhu, H., Zhang, Y., Li, W., & Huang, N. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232415961 · Cited by 80 · see citing works
SulfAtlas, the sulfatase database: state of the art and new developments
Stam, M. R., Lelièvre, P., Hoebeke, M., Corre, E., Barbeyron, T., & Michel, G. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac977 · Cited by 79 · see citing works
Deng, X., Li, Y., Chen, Y., Hu, Q., Zhang, W., Chen, L., Lu, X., Zeng, J., Ma, X., & Efferth, T. (2024) · Cellular & Molecular Biology Letters · doi:10.1186/s11658-024-00631-4 · Cited by 79 · see citing works
Self-citationAPPRIS: selecting functionally important isoforms
Rodrı́guez, J. M., del Pozo, F. C., Cerdán-Vélez, D., Domenico, T. D., Vázquez, J., & Tress, M. L. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1058 · Cited by 76 · see citing works
Dockey: a modern integrated tool for large-scale molecular docking and virtual screening
Du, L., Geng, C., Zeng, Q., Huang, T. L., Tang, J., Chu, Y., & Zhao, K. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad047 · Cited by 79 · see citing works
Sun, T., Di, K., Hu, J., Shi, Q., & Irfan, M. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.104216 · Cited by 77 · see citing works
Role of genomics in combating COVID-19 pandemic
Saravanan, K. A., Panigrahi, M., Kumar, H., Rajawat, D., Nayak, S. S., Bhushan, B., & Dutt, T. (2022) · Gene · doi:10.1016/j.gene.2022.146387 · Cited by 75 · see citing works
Bittrich, S., Rose, Y., Segura, J., Lowe, R., Westbrook, J. D., Duarte, J. M., & Burley, S. K. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab813 · Cited by 79 · see citing works
Self-citationProtein–Ligand Blind Docking Using CB-Dock2
Liu, Y., & Cao, Y. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3441-7_6 · Cited by 78 · see citing works
Giri, N., Roy, R. S., & Cheng, J. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102536 · Cited by 59 · see citing works
SYNBIP: synthetic binding proteins for research, diagnosis and therapy
Wang, X., Li, F., Qiu, W., Xu, B., Li, Y., Lian, X., Yu, H., Zhang, Z., Wang, J., Li, Z., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab926 · Cited by 68 · see citing works
Kumar, P., & Woolfson, D. N. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab631 · Cited by 51 · see citing works
De novo design of discrete, stable 310-helix peptide assemblies
Kumar, P., Paterson, N. G., Clayden, J., & Woolfson, D. N. (2022) · Nature · doi:10.1038/s41586-022-04868-x · Cited by 67 · see citing works
Calibrated geometric deep learning improves kinase–drug binding predictions
Luo, Y., Liu, Y., & Peng, J. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00751-0 · Cited by 67 · see citing works
DICER: structure, function, and regulation
Vergani-Junior, C. A., Tonon-da-Silva, G., Inan, M. D., & Mori, M. A. (2021) · Biophysical Reviews · doi:10.1007/s12551-021-00902-w · Cited by 72 · see citing works
The Nucleic Acid Knowledgebase: a new portal for 3D structural information about nucleic acids
Lawson, C. L., Berman, H., Chen, L., Vallat, B. K., & Zirbel, C. L. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad957 · Cited by 69 · see citing works
Self-citationFerdausi, N., Islam, S., Rimti, F. H., Quayum, S. T., Arshad, E. M., Ibnat, A., Islam, T., Arefin, A., Ema, T. I., Biswas, P., et al. (2022) · Journal of Advanced Veterinary and Animal Research · doi:10.5455/javar.2022.i588 · Cited by 65 · see citing works
Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.
Bisardi, M., Rodriguez-Rivas, J., Zamponi, F., & Weigt, M. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msab321
A structure and evolutionary-based classification of solute carriers
Ferrada, E., & Superti-Furga, G. (2022) · iScience · doi:10.1016/j.isci.2022.105096 · Cited by 65 · see citing works
Visual pH Sensors: From a Chemical Perspective to New Bioengineered Materials
Costanzo, L. D., & Panunzi, B. (2021) · Molecules · doi:10.3390/molecules26102952 · Cited by 62 · see citing works
Computational drug development for membrane protein targets
Li, H., Sun, X., Cui, W., Xu, M., Dong, J., Ekundayo, B., Ni, D., Rao, Z., Guo, L., Stahlberg, H., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01987-2 · Cited by 62 · see citing works
Liu, T., Wang, J., Tong, Y., Wu, L., Xie, Y., He, P., Lin, S., & Hu, X. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-023-04840-x · Cited by 63 · see citing works
The 2021 Nucleic Acids Research database issue and the online molecular biology database collection
Rigden, D. J., & Fernández, X. M. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1216 · Cited by 59 · see citing works
Dynamic Protein Corona of Gold Nanoparticles with an Evolving Morphology
Nandakumar, A., Wei, W., Siddiqui, G., Tang, H., Li, Y., Käkinen, A., Wan, X., Koppel, K., Lin, S., Davis, T. P., et al. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c19824 · Cited by 60 · see citing works
Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design
Schoeder, C. T., Schmitz, S., Adolf‐Bryfogle, J., Sevy, A. M., Finn, J. A., Sauer, M. F., Bozhanova, N. G., Mueller, B. K., Sangha, A. K., Bonet, J., et al. (2021) · Biochemistry · doi:10.1021/acs.biochem.0c00912 · Cited by 57 · see citing works
Ragab, M. A., Eldehna, W. M., Nocentini, A., Bonardi, A., Okda, H. E., Elgendy, B., Ibrahim, T. S., Abd-Alhaseeb, M. M., Gratteri, P., Supuran, C. T., et al. (2023) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2023.115180 · Cited by 58 · see citing works
RLBind: a deep learning method to predict RNA–ligand binding sites
Wang, K., Zhou, R., Wu, Y., & Li, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac486 · Cited by 57 · see citing works
Advances and perspectives in discovery and functional analysis of small secreted proteins in plants
Hu, X., Lu, H., Hassan, M. M., Zhang, J., Yuan, G., Abraham, P. E., Shrestha, H. K., Solis, M. I. V., Chen, J., Tschaplinski, T. J., et al. (2021) · Horticulture Research · doi:10.1038/s41438-021-00570-7 · Cited by 56 · see citing works
Pandiyan, S., & Wang, L. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106140 · Cited by 60 · see citing works
Zhang, W., Roy Burman, S. S., Chen, J., Donovan, K. A., Cao, Y., Shu, C., Zhang, B., Zeng, Z., Gu, S., Zhang, Y., et al. (2022) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.008 · Cited by 51 · see citing works
Alam, R., Imon, R. R., Talukder, M. E. K., Akhter, S., Hossain, M. A., Ahammad, F., & Rahman, M. M. (2021) · RSC Advances · doi:10.1039/d1ra06842c · Cited by 53 · see citing works
Icariin: A Potential Molecule for Treatment of Knee Osteoarthritis
Zhang, J., Fan, F., Liu, A., Zhang, C., Li, Q., Zhang, C., He, F., & Shang, M. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.811808 · Cited by 53 · see citing works
De novo molecular design with deep molecular generative models for PPI inhibitors
Wang, J., Chu, Y., Mao, J., Jeon, H.-N., Jin, H., Zeb, A., Jang, Y., Cho, K., Song, T., & No, K. T. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac285 · Cited by 52 · see citing works
Güngör, T., Ozleyen, A., Yılmaz, Y. B., Si̇yah, P., Ay, M., Durdağı, S., & Tumer, T. B. (2021) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2021.113566 · Cited by 52 · see citing works
An analysis of protein language model embeddings for fold prediction
Villegas-Morcillo, A., Gomez, A. M., & Sanchez, V. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac142 · Cited by 46 · see citing works
- Authors
- 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., Dutta, S., Feng, Z., Ganesan, S., Goodsell, D. S., Ghosh, S., Green, R. K., Guranović, V., Guzenko, D., Hudson, B. P., Lawson, C. L., Liang, Y., Lowe, R., Namkoong, H., Peisach, E., Persikova, I., Randle, C., Rose, A., Rose, Y., Sali, A., Segura, J., Sekharan, M., Shao, C., Tao, Y.-P., Voigt, M., Westbrook, J. D., Young, J. Y., Zardecki, C., & Zhuravleva, M.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- San Diego Supercomputer Center (US)
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
- University of California, San Francisco (US)
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Biotherapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Corresponding author
- Burley, Stephen K
- ORCID
- Burley, Stephen K
- Bhikadiya, Charmi
- Bittrich, Sebastian
- Chen, Li
- Crichlow, Gregg V
- Duarte, Jose M
- Dutta, Shuchismita
- Feng, Zukang
- Ganesan, Sai
- Goodsell, David S
- Ghosh, Sutapa
- Guranović, Vladimir
- Guzenko, Dmytro
- Hudson, Brian P
- Lawson, Catherine L
- Liang, Yuhe
- Peisach, Ezra
- Persikova, Irina
- Rose, Alexander
- Rose, Yana
- Segura, Joan
- Sekharan, Monica
- Shao, Chenghua
- Voigt, Maria
- Westbrook, John D
- Young, Jasmine Y
- Zardecki, Christine
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-11-19
- Volume
- 49
- Issue
- D1
- Pages
- D437-D451
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC7779003
- OpenAlex
- W3104537585
- Cited by
- 1,581
- References
- 80
- Citations by year
- 2026: 188 · 2025: 196 · 2024: 272 · 2023: 352 · 2022: 374 · 2021: 194 · 2020: 3
- FWCI
- 91.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Science Foundation (DBI-1832184)
- U.S. Department of Energy (DE-SC0019749)
- National Cancer Institute
- National Institute of Allergy and Infectious Diseases
- National Institute of General Medical Sciences (R01GM133198)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- structural bioinformatics, Boolean search, SARS-CoV-2 proteins, COVID-19 pandemic, host cell proteins, macromolecular crystallography, NMR spectroscopy, 3D electron microscopy, micro-electron diffraction, structural biology education
- MeSH terms
- COVID-19, SARS-CoV-2, Biotechnology, Humans, Protein Conformation, Proteins, Software, Viral Proteins, Computational Biology, Databases, Protein, Biomedical Research, Macromolecular Substances, Bioengineering, Pandemics
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