A series of PDB-related databanks for everyday needs

Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features

Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320

Overview of the CCP 4 suite and current developments

Winn, M. D., Ballard, C. C., Cowtan, K. D., Dodson, E. J., Emsley, P., Evans, P. R., Keegan, R. M., Krissinel, E. B., Leslie, A. G. W., McCoy, A., et al. (2011) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444910045749 · Cited by 12,353

Biopython: freely available python tools for computational molecular biology and bioinformatics

Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080

Announcing 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

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

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

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

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

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

Multiplex assessment of protein variant abundance by massively parallel sequencing

Matreyek, K. A., Starita, L. M., Stephany, J. J., Martin, B., Chiasson, M. A., Gray, V. E., Kircher, M., Khechaduri, A., Dines, J. N., Hause, R. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0122-z · Cited by 436

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

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

Predicting functional effects of missense variants in voltage-gated sodium and calcium channels

Heyne, H. O., Baez-Nieto, D., Iqbal, S., Palmer, D. S., Brunklaus, A., May, P., Collaborative, E., Johannesen, K. M., Lauxmann, S., Lemke, J. R., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay6848 · Cited by 140

Martinize2 and Vermouth provide a unified framework for molecular topology generation

Kroon, P. C., Grünewald, F., Barnoud, J., van Tilburg, M., Brasnett, C., Souza, P. C., Wassenaar, T. A., & Marrink, S. J. (2025) · eLife · doi:10.7554/elife.90627 · Cited by 124

The accuracy of protein structures in solution determined by AlphaFold and NMR

Fowler, N. J., & Williamson, M. P. (2022) · Structure · doi:10.1016/j.str.2022.04.005 · Cited by 104

DSSP 4: FAIR annotation of protein secondary structure

Hekkelman, M. L., Salmoral, D. Á., Perrakis, A., & Joosten, R. P. (2025) · Protein Science · doi:10.1002/pro.70208 · Cited by 97

Mining the CRBN target space redefines rules for molecular glue-induced neosubstrate recognition

Petzold, G., Gainza, P., Annunziato, S., Lamberto, I., Trenh, P., McAllister, L. A., DeMarco, B., Schwander, L., Bunker, R. D., Zlotosch, M., et al. (2025) · Science · doi:10.1126/science.adt6736 · Cited by 117

Interpreting the molecular mechanisms of disease variants in human transmembrane proteins

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

New Biological Insights from Better Structure Models

Touw, W. G., Joosten, R. P., & Vriend, G. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.02.002 · Cited by 31

A model for hydrophobic protrusions on peripheral membrane proteins

Fuglebakk, E., & Reuter, N. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006325 · Cited by 29

Structure-informed microbial population genetics elucidate selective pressures that shape protein evolution

Kiefl, E., Esen, O. C., Miller, S. E., Kroll, K. L., Willis, A. D., Rappé, M. S., Pan, T., & Eren, A. M. (2023) · Science Advances · doi:10.1126/sciadv.abq4632 · Cited by 26

DeepSS2GO: protein function prediction from secondary structure

Song, F. V., Su, J., Huang, S., Zhang, N., Li, K., Ni, M., & Liao, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae196 · Cited by 26

Critical assessment of coiled-coil predictions based on protein structure data

Simm, D., Hatje, K., Waack, S., & Kollmar, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-91886-w · Cited by 24

‘Asynchronous reciprocal coupling of Martini 2.2 coarse-grained and CHARMM36 all-atom simulations in an automated multiscale framework’

López, C. A., Zhang, X., Aydin, F., Shrestha, R., Van, Q. N., Stanley, C. B., Carpenter, T. S., Nguyen, K., Patel, L. A., Chen, D., et al. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00168 · Cited by 18

Hidden information on protein function in censuses of proteome foldedness

Cox, D., Ang, C.-S., Nillegoda, N. B., Reid, G. E., & Hatters, D. M. (2022) · Nature Communications · doi:10.1038/s41467-022-29661-2 · Cited by 16

Vibrio cholerae’s ToxRS bile sensing system

Gubensäk, N., Sagmeister, T., Buhlheller, C., Geronimo, B. D., Wagner, G. E., Petrowitsch, L., Gräwert, M. A., Rotzinger, M., Berger, T. M. I., Schäfer, J., et al. (2023) · eLife · doi:10.7554/elife.88721 · Cited by 14

Functional and structural analysis of a cyclization domain in a cyclic β-1,2-glucan synthase

Tanaka, N., Saito, R., Kobayashi, K., Nakai, H., Kamo, S., Kuramochi, K., Taguchi, H., Nakajima, M., & Masaike, T. (2024) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-024-13013-9 · Cited by 13

FDXR variants cause adrenal insufficiency and atypical sexual development

Pignatti, E., Slone, J., Gómez Cano, M. Á., Campbell, T. M., Vu, J., Sauter, K.-S., Pandey, A. V., Martínez-Azorín, F., Alonso-Riaño, M., Neilson, D. E., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.179071 · Cited by 12

Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost

(2024) · doi:10.1002/pro.5239 · Cited by 12

Using deep-learning predictions of inter-residue distances for model validation

Sánchez Rodríguez, F., Chojnowski, G., Keegan, R. M., & Rigden, D. J. (2022) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798322010415 · Cited by 11

Plant-associated fungi co-opt ancient antimicrobials for host manipulation

Mesny, F., Wolf, V., López-Moral, A., Kraege, A., Punt, W., Liu, S., Zhu, J., Park, J., Sato, Y., & Thomma, B. P. H. J. (2026) · Science Advances · doi:10.1126/sciadv.aec1406 · Cited by 11

Outer membrane β‐barrel structure prediction through the lens of AlphaFold2

Topitsch, A., Schwede, T., & Pereira, J. (2024) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26552 · Cited by 9

A scalable equivariant graph network framework for precise protein function prediction

Ran, Z., Guo, X., Pan, T., Bi, Y., Hao, Y., Sun, H., Song, J., & Li, F. (2025) · Genome Biology · doi:10.1186/s13059-025-03886-y · Cited by 8

Constructing and interpreting a large-scale variant effect map for an ultrarare disease gene: Comprehensive prediction of the functional impact of PSAT1 genotypes

(2023) · doi:10.1371/journal.pgen.1010972 · Cited by 6

Utilizing the scale-invariant feature transform algorithm to align distance matrices facilitates systematic protein structure comparison

Guo, Z., Wang, Y., & Ou, G. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae064 · Cited by 6

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

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

Anillin-related Mid1 as an adaptive and multimodal contractile ring anchoring protein: A simulation study

Hall, A. R., Choi, Y. K., Im, W., & Vavylonis, D. (2024) · Structure · doi:10.1016/j.str.2023.11.010 · Cited by 3

Assessing the helical stability of polyXYs at the boundaries of intrinsically disordered regions with MD simulations

Gonçalves-Kulik, M., Baptista, L. A., Schmid, F., & Andrade-Navarro, M. A. (2025) · Computational and Structural Biotechnology Reports · doi:10.1016/j.csbr.2025.100054