Announcing the worldwide Protein Data Bank

The Protein Data Bank

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The protein data bank: A computer-based archival file for macromolecular structures

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[30] Macromolecular crystallographic information file

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Trends of antimalarial marine natural products: progresses, challenges and opportunities

Hai, Y., Cai, Z., Li, P.-J., Wei, M., Wang, C., Gu, Y., & Shao, C. (2022) · Natural Product Reports · doi:10.1039/d1np00075f · Cited by 37

Immunoglobulin M perception by FcμR

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Synthesis, dielectric properties, molecular docking and ADME studies of pyrrole-3-ones

Çapan, İ., Gümüş, M., Gökçe, H., Çetin, H., Sert, Y., & Koca, İ. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1914174 · Cited by 36

New palladium (II) complexes from halogen substituted Schiff base ligands: Synthesis, spectroscopic, biological activity, density functional theory, and molecular docking investigations

Waziri, I., Yusuf, T. L., Zarma, H. A., Oselusi, S. O., Coetzee, L.-C. C., & Adeyinka, A. S. (2023) · Inorganica Chimica Acta · doi:10.1016/j.ica.2023.121505 · Cited by 36

Community recommendations on cryoEM data archiving and validation

Kleywegt, G. J., Adams, P. D., Butcher, S. J., Lawson, C. L., Rohou, A. L., Rosenthal, P. B., Subramaniam, S., Topf, M., Abbott, S., Baldwin, P. R., et al. (2024) · IUCrJ · doi:10.1107/s2052252524001246 · Cited by 39

Molecular Docking for Predictive Toxicology

Trisciuzzi, D., Alberga, D., Leonetti, F., Novellino, E., Nicolotti, O., & Mangiatordi, G. F. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7899-1_8 · Cited by 36

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Rodriguez-Rivas, J., Marsili, S., Juan, D., & Valencia, A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611861114 · Cited by 36

Potential mechanism prediction of Cold-Damp Plague Formula against COVID-19 via network pharmacology analysis and molecular docking

Han, L., Wei, X.-X., Zheng, Y.-J., Zhang, L.-L., Wang, X.-M., Yang, H.-Y., Ma, X., Zhao, L.-H., & Tong, X. (2020) · Chinese Medicine · doi:10.1186/s13020-020-00360-8 · Cited by 36

Opportunities and challenges for assigning cofactors in cryo-EM density maps of chlorophyll-containing proteins

Gisriel, C. J., Wang, J., Brudvig, G. W., & Bryant, D. A. (2020) · Communications Biology · doi:10.1038/s42003-020-01139-1 · Cited by 36

A conformation-selective monoclonal antibody against a small molecule-stabilised signalling-deficient form of TNF

Lightwood, D. J., Munro, R., Porter, J. R., McMillan, D., Carrington, B., Turner, A. V., Scott-Tucker, A., Hickford, E. S., Schmidt, A., Fox, D. N. A., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20825-6 · Cited by 36

From molecules to patients: the clinical applications of biological databases and electronic health records

Ramlal, A., Ahmad, S., Kumar, L. P., Khan, F. N., & Chongtham, R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-89824-9.00009-4 · Cited by 36

A method for intuitively extracting macromolecular dynamics from structural disorder

Pearce, N. M., & Gros, P. (2021) · Nature Communications · doi:10.1038/s41467-021-25814-x · Cited by 36

Overcoming Acquired Drug Resistance to Cancer Therapies through Targeted STAT3 Inhibition

Singh, S., Gomez, H. J., Thakkar, S., Singh, S. P., & Parihar, A. S. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24054722 · Cited by 38

Improving the representation of peptide‐like inhibitor and antibiotic molecules in the Protein Data Bank

Dutta, S., Dimitropoulos, D., Feng, Z., Persikova, I., Sen, S., Shao, C., Westbrook, J., Young, J. Y., Zhuravleva, M. A., Kleywegt, G. J., et al. (2013) · Biopolymers · doi:10.1002/bip.22434 · Cited by 36

Molecular recognition in the sphingosine 1-phosphate receptor family

Pham, T. T., Fells, J. I., Osborne, D. A., North, E. J., Naor, M. M., & Parrill, A. L. (2007) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2007.11.001 · Cited by 36

Protein–protein docking with binding site patch prediction and network‐based terms enhanced combinatorial scoring

Gong, X., Wang, P., Yang, F., Chang, S., Liu, B., He, H., Cao, L., Xu, X., Li, C., Chen, W., et al. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22831 · Cited by 36

Analysis 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

AI and the democratization of knowledge

Dessimoz, C., & Thomas, P. D. (2024) · Scientific Data · doi:10.1038/s41597-024-03099-1 · Cited by 45

Accurate and efficient protein sequence design through learning concise local environment of residues

Huang, B., Fan, T., Wang, K., Zhang, H., Yu, C., Nie, S., Qi, Y., Zheng, W.-M., Han, J., Fan, Z., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad122 · Cited by 34

Data Deposition and Annotation at the Worldwide Protein Data Bank

Dutta, S., Burkhardt, K., Swaminathan, G. J., Kosada, T., Henrick, K., Nakamura, H., & Berman, H. M. (2008) · Methods in molecular biology · doi:10.1007/978-1-60327-058-8_5 · Cited by 35

Spatiotemporal persistence of multiple, diverse clades and toxins of Corynebacterium diphtheriae

Will, R. C., Ramamurthy, T., Sharma, N. C., Veeraraghavan, B., Sangal, L., Haldar, P., Pragasam, A. K., Vasudevan, K., Kumar, D., Das, B., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21870-5 · Cited by 37

In-silico Investigations of quinine and quinidine as potential Inhibitors of AKR1B1 and AKR1B10: Functional and structural characterization

Ejaz, S. A., Saeed, A., Birmani, P. R., Katubi, K. M., Elqahtani, Z. M., Al-Buriahi, M. S., Ujan, R., Siddique, F., Ahmed, S. B., & Alrowaili, Z. A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271602 · Cited by 36

Heavy Metal-Associated Isoprenylated Plant Proteins (HIPPs) at Plasmodesmata: Exploring the Link between Localization and Function

Barr, Z. K., Werner, T., & Tilsner, J. (2023) · Plants · doi:10.3390/plants12163015 · Cited by 37

Current Trends and Challenges in Drug-Likeness Prediction: Are They Generalizable and Interpretable?

Zhu, W., Wang, Y., Niu, Y., Zhang, L., & Liu, Z. (2023) · Health Data Science · doi:10.34133/hds.0098 · Cited by 41

Photocyclic behavior of rhodopsin induced by an atypical isomerization mechanism

Gulati, S., Jastrzębska, B., Banerjee, S., Placeres, Á. L., Miszta, P., Gao, S., Gunderson, K. G., Tochtrop, G. P., Filipek, S., Katayama, K., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617446114 · Cited by 34

Statistical Analysis and Prediction of Covalent Ligand Targeted Cysteine Residues

Zhang, W., Pei, J., & Lai, L. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00163 · Cited by 34

ChannelsDB: database of biomacromolecular tunnels and pores

Pravda, L., Sehnal, D., Vařeková, R. S., Navrátilová, V., Toušěk, D., Berka, K., Otyepka, M., & Koča, J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx868 · Cited by 37

Molecular dynamics simulations of viral neuraminidase inhibitors with the human neuraminidase enzymes: Insights into isoenzyme selectivity

Richards, M. R., Guo, T., Hunter, C. D., & Cairo, C. W. (2018) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2018.05.035 · Cited by 35

Insight into novel RNA-binding activities via large-scale analysis of lncRNA-bound proteome and IDH1-bound transcriptome

Liu, L., Li, T., Song, G., He, Q., Yin, Y., Lu, J. Y., Bi, X., Wang, K., Luo, S., Chen, Y., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz032 · Cited by 34

Structure of mammalian plasma fetuin-B and its mechanism of selective metallopeptidase inhibition

Cuppari, A., Körschgen, H., Fahrenkamp, D., Schmitz, C., Guevara, T., Karmilin, K., Kuske, M., Olf, M., Dietzel, E., Yiallouros, I., et al. (2019) · IUCrJ · doi:10.1107/s2052252519001568 · Cited by 36

Biomolecular Solvation Structure Revealed by Molecular Dynamics Simulations

Wall, M. E., Calabrò, G., Bayly, C. I., Mobley, D. L., & Warren, G. L. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.8b13613 · Cited by 35

Applications of X-ray Powder Diffraction in Protein Crystallography and Drug Screening

Spiliopoulou, M., Valmas, A., Triandafillidis, D.-P., Kosinas, C., Fitch, A. N., Karavassili, F., & Margiolaki, I. (2020) · Crystals · doi:10.3390/cryst10020054 · Cited by 37

Biochemical and structural insights into how amino acids regulate pyruvate kinase muscle isoform 2

Nandi, S., & Dey, M. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra120.013030 · Cited by 35

Representation of viruses in the remediated PDB archive

Lawson, C. L., Dutta, S., Westbrook, J., Henrick, K., & Berman, H. M. (2008) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444908017393 · Cited by 35

The Sam domain of the lipid phosphatase Ship2 adopts a common model to interact with Arap3-Sam and EphA2-Sam

Leone, M., Cellitti, J. F., & Pellecchia, M. (2009) · BMC Structural Biology · doi:10.1186/1472-6807-9-59 · Cited by 35

Characterizing the complexity of enzymes on the basis of their mechanisms and structures with a bio-computational analysis

Holliday, G., Fischer, J. D., Mitchell, J. B. O., & Thornton, J. (2011) · The FEBS Journal · doi:10.1111/j.1742-4658.2011.08190.x · Cited by 39

Web-based visualisation and analysis of 3D electron-microscopy data from EMDB and PDB

Lagerstedt, I. C., Moore, W. J. M., Patwardhan, A., Sanz‐García, E., Best, C., Swedlow, J. R., & Kleywegt, G. J. (2013) · Journal of Structural Biology · doi:10.1016/j.jsb.2013.09.021 · Cited by 35

Structural and Thermodynamic Properties of Nanoparticle–Protein Complexes: A Combined SAXS and SANS Study

Spinozzi, F., Ceccone, G., Moretti, P., Campanella, G. S. V., Ferrero, C., Combet, S., Ojea‐Jiménez, I., & Ghigna, P. (2017) · Langmuir · doi:10.1021/acs.langmuir.6b04072 · Cited by 35

MIReAD, a minimum information standard for reporting arthropod abundance data

Rund, S. S. C., Braak, K., Cator, L. J., Copas, K., Emrich, S., Giraldo-Calderón, G. I., Johansson, M. A., Heydari, N., Hobern, D., Kelly, S. A., et al. (2019) · Scientific Data · doi:10.1038/s41597-019-0042-5 · Cited by 37

Development and Use of CUREs in Biochemistry

Provost, J. J., Bell, J. K., & Bell, J. E. (2019) · ACS symposium series · doi:10.1021/bk-2019-1337.ch007 · Cited by 35

On the estimation of the molecular inaccessible volume and the molecular accessible surface of a ligand in protein–ligand systems

Konstantinidis, K., Karakasiliοtis, I., Anagnostopoulos, K., & Boulougouris, G. C. (2021) · Molecular Systems Design & Engineering · doi:10.1039/d1me00053e · Cited by 34

Prediction of drug-likeness using graph convolutional attention network

Sun, J., Wen, M., Wang, H., Ruan, Y., Yang, Q., Kang, X., Zhang, H., Zhang, Z., & Lü, H. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac676 · Cited by 34

The computational-based structure of Dwarf14 provides evidence for its role as potential strigolactone receptor in plants

Gaiji, N., Cardinale, F., Prandi, C., Bonfante, P., & Ranghino, G. (2012) · BMC Research Notes · doi:10.1186/1756-0500-5-307 · Cited by 35

Structural and Biochemical Insights into Dimethylsulfoniopropionate Cleavage by Cofactor-Bound DddK from the Prolific Marine Bacterium Pelagibacter

Schnicker, N., Silva, S. M. D., Todd, J. D., & Dey, M. (2017) · Biochemistry · doi:10.1021/acs.biochem.7b00099 · Cited by 34

Computational analysis of kinase inhibitor selectivity using structural knowledge

Lo, Y., Liu, T., Morrissey, K. M., Kakiuchi-Kiyota, S., Johnson, A. R., Broccatelli, F., Zhong, Y., Joshi, A., & Altman, R. B. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty582 · Cited by 34

Synthesis and evaluation of aryliden- and hetarylidenfuranone derivatives of usnic acid as highly potent Tdp1 inhibitors

Zakharova, O. D., Luzina, O. A., Zakharenko, A. L., Sokolov, D., Filimonov, A. S., Dyrkheeva, N. S., Chepanova, A. A., Ilina, E. S., Ilyina, A., Klabenkova, K., et al. (2018) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2018.07.039 · Cited by 34

Identifying Extrinsic versus Intrinsic Drivers of Variation in Cell Behavior in Human iPSC Lines from Healthy Donors

Vigilante, A., Laddach, A., Moens, N., Meleckyte, R., Leha, A., Ghahramani, A., Culley, O. J., Kathuria, A., Hurling, C., Vickers, A., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.01.094 · Cited by 34

Structural determinants of inhibition of Porphyromonas gingivalis gingipain K by KYT-36, a potent, selective, and bioavailable peptidase inhibitor

Guevara, T., Rodríguez-Banqueri, A., Łasica, A. M., Książęk, M., Potempa, B. A., Potempa, J., & Gomis‐Rüth, F. X. (2019) · Scientific Reports · doi:10.1038/s41598-019-41354-3 · Cited by 34

Multiple Optimal Phenotypes Overcome Redox and Glycolytic Intermediate Metabolite Imbalances in Escherichia coli pgi Knockout Evolutions

McCloskey, D., Xu, S., Sandberg, T. E., Brunk, E., Hefner, Y., Szubin, R., Feist, A. M., & Palsson, B. Ø. (2018) · Applied and Environmental Microbiology · doi:10.1128/aem.00823-18 · Cited by 34

PDB‐tools web: A user‐friendly interface for the manipulation of PDB files

Jiménez‐García, B., Teixeira, J. M. C., Trellet, M. E., Rodrigues, J., & Bonvin, A. M. J. J. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26018 · Cited by 34

Pan-3C Protease Inhibitor Rupintrivir Binds SARS-CoV-2 Main Protease in a Unique Binding Mode

Lockbaum, G. J., Henes, M., Lee, J. M., Timm, J., Nalivaika, E. A., Thompson, P. R., Yılmaz, N. K., & Schiffer, C. A. (2021) · Biochemistry · doi:10.1021/acs.biochem.1c00414 · Cited by 33

Synthesis, single crystal X-ray, DFT, Hirshfeld surface and molecular docking studies of 9-(2,4-dichlorophenyl)-4a-hydroxy-tetramethyl-octahydro-1H-xanthene-1,8(2H)-dione

Fatima, A., Khanum, G., Sharma, A., Siddiqui, N., Muthu, S., Butcher, R. J., Srivastava, S. K., & Javed, S. (2022) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2022.133613 · Cited by 34

cell2mol: encoding chemistry to interpret crystallographic data

Vela, S., Laplaza, R., Cho, Y., & Corminbœuf, C. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00874-9 · Cited by 37

Pharmacological hallmarks of allostery at the M4 muscarinic receptor elucidated through structure and dynamics

Vuckovic, Z., Wang, J., Pham, V., Mobbs, J. I., Belousoff, M. J., Bhattarai, A., Burger, W. A., Thompson, G., Yeasmin, M., Nawaratne, V., et al. (2023) · eLife · doi:10.7554/elife.83477 · Cited by 34

Computational Methods for RNA Structure Validation and Improvement

Jain, S., Richardson, D. C., & Richardson, J. S. (2015) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2015.01.007 · Cited by 34

Generation of MHC class II:peptide ligands for CD4 T cell allorecognition of MHC Class II molecules

Leddon, S., & Sant, A. (2010) · Current Opinion in Organ Transplantation · doi:10.1097/mot.0b013e32833bfc5c · Cited by 35

The role of molecular modelling in biomedical research

Tramontano, A. L. (2006) · FEBS Letters · doi:10.1016/j.febslet.2006.04.011 · Cited by 34

Ser/Thr Motifs in Transmembrane Proteins: Conservation Patterns and Effects on Local Protein Structure and Dynamics

del Val, C., White, S. H., & Bondar, A. (2012) · The Journal of Membrane Biology · doi:10.1007/s00232-012-9452-4 · Cited by 36

Structure-Function Analysis of Vaccinia Virus H7 Protein Reveals a Novel Phosphoinositide Binding Fold Essential for Poxvirus Replication

Kolli, S., Meng, X., Wu, X. Y., Shengjuler, D., Cameron, C. E., Xiang, Y., & Deng, J. (2014) · Journal of Virology · doi:10.1128/jvi.03073-14 · Cited by 34

Computational methods for the design of potent aromatase inhibitors

Favia, A. D., Nicolotti, O., Stefanachi, A., Leonetti, F., & Carotti, A. (2013) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2013.768983 · Cited by 33

Redox and Non-Redox Mechanism ofIn VitroCyclooxygenase Inhibition by Natural Quinones

Landa, P., Kutil, Z., Temml, V., Vuorinen, A., Jan, M., Dvořáková, M., Maršík, P., Kokoška, L., Přibylová, M., Schuster, D., et al. (2011) · Planta Medica · doi:10.1055/s-0031-1280430 · Cited by 34

Partial-occupancy binders identified by the Pan-Dataset Density Analysis method offer new chemical opportunities and reveal cryptic binding sites

Pearce, N. M., Bradley, A. R., Krojer, T. J., Marsden, B. D., Deane, C. M., & von Delft, F. (2017) · Structural Dynamics · doi:10.1063/1.4974176 · Cited by 34

Serine Hydrolase KIAA1363: Toxicological and Structural Features with Emphasis on Organophosphate Interactions

Nomura, D. K., Durkin, K. A., Chiang, K. P., Quistad, G. B., Cravatt, B. F., & Casida, J. E. (2006) · Chemical Research in Toxicology · doi:10.1021/tx060117m · Cited by 34

Exploring innovative strategies for identifying anti-breast cancer compounds by integrating 2D/3D-QSAR, molecular docking analyses, ADMET predictions, molecular dynamics simulations, and MM-PBSA approaches

Rhabori, S. E., Alaqarbeh, M., Allouche, Y. E., Naanaai, L., Aissouq, A. E., Bouachrıne, M., Chtita, S., & Khalil, F. (2024) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2024.139500 · Cited by 34

A Data-Driven Evolutionary Algorithm for Mapping Multibasin Protein Energy Landscapes

Clausen, R., & Shehu, A. (2015) · Journal of Computational Biology · doi:10.1089/cmb.2015.0107 · Cited by 34

Paramagnetic Relaxation Assisted Docking of a Small Indole Compound in the HIV-1 gp41 Hydrophobic Pocket

Gochin, M., Zhou, G., & Phillips, A. H. (2010) · ACS Chemical Biology · doi:10.1021/cb100368d · Cited by 33

Identification of Clinically Approved Drugs Indacaterol and Canagliflozin for Repurposing to Treat Epidermal Growth Factor Tyrosine Kinase Inhibitor-Resistant Lung Cancer

Li, H., Tong, C. W. S., Leung, Y., Wong, M., To, K. K. W., & Leung, K. (2017) · Frontiers in Oncology · doi:10.3389/fonc.2017.00288 · Cited by 34

The NAMI A – human ferritin system: a biophysical characterization

Ciambellotti, S., Pratesi, A., Severi, M., Ferraro, G., Alessio, E., Merlino, A., & Luigi Messori, A. (2018) · Dalton Transactions · doi:10.1039/c8dt00860d · Cited by 33

Crystal structure and functional properties of the human CCR4-CAF1 deadenylase complex

Chen, Y., Khazina, E., Izaurralde, E., & Weichenrieder, O. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab414 · Cited by 34

UnityMol prototype for FAIR sharing of molecular-visualization experiences: from pictures in the cloud to collaborative virtual reality exploration in immersive 3D environments

Martinez, X., & Baaden, M. (2021) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798321002941 · Cited by 33

Network Pharmacology Reveals That Resveratrol Can Alleviate COVID-19-Related Hyperinflammation

Xiao, Z., Ye, Q., Duan, X., & Xiang, T. (2021) · Disease Markers · doi:10.1155/2021/4129993 · Cited by 34

Worldwide Protein Data Bank (wwPDB): A virtual treasure for research in biotechnology

Behzadi, P., & Gajdács, M. (2021) · European Journal of Microbiology and Immunology · doi:10.1556/1886.2021.00020 · Cited by 34

Conserved recombination patterns across coronavirus subgenera

de Klerk, A., Swanepoel, P., Lourens, R., Zondo, M., Abodunran, I., Lytras, S., MacLean, O. A., Robertson, D. L., Pond, S. L. K., Zehr, J. D., et al. (2022) · Virus Evolution · doi:10.1093/ve/veac054 · Cited by 33

Multitarget Molecular Hybrids of Cinnamic Acids

Peperidou, A., Καπουκρανίδου, Δ., Kontogiorgis, C. A., & Hadjipavlou‐Litina, D. (2014) · Molecules · doi:10.3390/molecules191220197 · Cited by 33

Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure PredictionPreprint

Busia, A., & Jaitly, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.03865 · Cited by 33

Catalpol Protects Against Pulmonary Fibrosis Through Inhibiting TGF-β1/Smad3 and Wnt/β-Catenin Signaling Pathways

Yang, F., Hou, Z.-F., Zhu, H.-Y., Chen, X.-X., Li, W.-Y., Cao, R.-S., Li, Y., Chen, R., & Zhang, W. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.594139 · Cited by 32

Computational Modeling of Pharmaceuticals with an Emphasis on Crossing the Blood–Brain Barrier

Alves, P. A., Camargo, L. C., de Souza, G. M., Mortari, M. R., & Homem-de-Mello, M. (2025) · Pharmaceuticals · doi:10.3390/ph18020217 · Cited by 35

STAAR: Statistical analysis of aromatic rings

Jenkins, D. D., Harris, J., Howell, E. E., Hinde, R. J., & Baudry, J. Y. (2012) · Journal of Computational Chemistry · doi:10.1002/jcc.23164 · Cited by 32

The archiving and dissemination of biological structure data

Berman, H. M., Burley, S. K., Kleywegt, G. J., Markley, J. L., Nakamura, H., & Velankar, S. S. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.06.018 · Cited by 32

Unifying Microscopic and Continuum Treatments of van der Waals and Casimir Interactions

Venkataram, P. S., Hermann, J., Tkatchenko, A., & Rodríguez, A. W. (2017) · Physical Review Letters · doi:10.1103/physrevlett.118.266802 · Cited by 32

Innovative Data Management in advanced characterization: Implications for materials design

Romanos, N. A., Kalogerini, M., Koumoulos, E. P., Morozinis, A. K., Sebastiani, M., & Charitidis, C. A. (2019) · Materials Today Communications · doi:10.1016/j.mtcomm.2019.100541 · Cited by 32

Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models

Nguyen, N., Strnad, O., Klein, T. R., Luo, D., Alharbi, R., Wonka, P., Maritan, M., Mindek, P., Autin, L., Goodsell, D. S., et al. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030415 · Cited by 34

Making the invisible enemy visible

Croll, T. I., Diederichs, K., Fischer, F., Fyfe, C. D., Gao, Y., Horrell, S., Joseph, A. P., Kandler, L., Kippes, O., Kirsten, F., et al. (2021) · Nature Structural & Molecular Biology · doi:10.1038/s41594-021-00593-7 · Cited by 31

Modernized uniform representation of carbohydrate molecules in the Protein Data Bank

Shao, C., Feng, Z., Westbrook, J., Peisach, E., Berrisford, J. M., Ikegawa, Y., Kurisu, G., Velankar, S. S., Burley, S. K., & Young, J. Y. (2021) · Glycobiology · doi:10.1093/glycob/cwab039 · Cited by 32

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