Challenges in bridging the gap between protein structure prediction and functional interpretation

Highly accurate protein structure prediction with AlphaFold

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SWISS-MODEL: homology modelling of protein structures and complexes

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Explaining and Harnessing Adversarial Examples

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Intriguing properties of neural networks

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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

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Evolutionary-scale prediction of atomic level protein structure with a language model

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Protein complex prediction with AlphaFold-Multimer

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Highly accurate protein structure prediction for the human proteome

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Scoring function for automated assessment of protein structure template quality

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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models

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De novo design of protein structure and function with RFdiffusion

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Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

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Protein misfolding, aggregation, and conformational strains in neurodegenerative diseases

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lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests

Mariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt473 · Cited by 1,138 · 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

Improved prediction of protein-protein interactions using AlphaFold2

Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · see citing works

Structure-based prediction of protein–protein interactions on a genome-wide scale

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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

AlphaFold and Implications for Intrinsically Disordered Proteins

Ruff, K. M., & Pappu, R. V. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167208 · Cited by 635 · see citing works

FoldX 5.0: working with RNA, small molecules and a new graphical interface

Delgado, J., Radusky, L., Cianferoni, D., & Serrano, L. G. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz184 · Cited by 523 · see citing works

Protein Misfolding Diseases

Hartl, F. U. (2017) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061516-044518 · Cited by 519 · see citing works

AlphaFill: enriching AlphaFold models with ligands and cofactors

Hekkelman, M. L., de Vries, I., Joosten, R. P., & Perrakis, A. (2023) · Nature Methods · doi:10.1038/s41592-022-01685-y · Cited by 511 · see citing works

Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated?

Ittisoponpisan, S., Islam, S. A., Khanna, T., Alhuzimi, E. M., David, A., & Sternberg, M. J. E. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.04.009 · Cited by 484 · see citing works

Interactome3D: adding structural details to protein networks

Mosca, R., Céol, A., & Aloy, P. (2013) · Nature Methods · doi:10.1038/nmeth.2289 · Cited by 479 · see citing works

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

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Ensembl Genomes 2022: an expanding genome resource for non-vertebrates

Yates, A. D., Allen, J., Amode, R., Azov, A. G., Barba, M., Becerra, A., Bhai, J. N. N. T., Campbell, L. I., Martinez, M. C., Chakiachvili, M., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1007 · Cited by 422 · see citing works

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

Terwilliger, T. C., Liebschner, D., Croll, T. I., Williams, C. J., McCoy, A. J., Poon, B. K., Afonine, P. V., Oeffner, R. D., Richardson, J. S., Read, R. J., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02087-4 · Cited by 411 · see citing works

High-resolution <i>de novo</i> structure prediction from primary sequence

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Three-dimensional reconstruction of protein networks provides insight into human genetic disease

Wang, X., Wei, X., Thijssen, B., Das, J., Lipkin, S. M., & Yu, H. (2012) · Nature Biotechnology · doi:10.1038/nbt.2106 · Cited by 385 · see citing works

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

Ren, F., Ding, X., Zheng, M., Korzinkin, M., Cai, X., Zhu, W., Mantsyzov, A., Aliper, A., Aladinskiy, V., Cao, Z., et al. (2023) · Chemical Science · doi:10.1039/d2sc05709c · Cited by 365 · see citing works

Clustering predicted structures at the scale of the known protein universe

Barrio-Hernandez, I., Yeo, J., Jänes, J., Mirdita, M., Gilchrist, C. L. M., Wein, T., Varadi, M., Velankar, S., Beltrao, P., & Steinegger, M. (2023) · Nature · doi:10.1038/s41586-023-06510-w · Cited by 362 · see citing works

AI-based structure prediction empowers integrative structural analysis of human nuclear pores

Mosalaganti, S., Obarska-Kosinska, A., Siggel, M., Taniguchi, R., Turoňová, B., Zimmerli, C. E., Buczak, K., Schmidt, F. H., Margiotta, E., Mackmull, M.-T., et al. (2022) · Science · doi:10.1126/science.abm9506 · Cited by 355 · see citing works

Critical assessment of protein intrinsic disorder prediction

Necci, M., Piovesan, D., Predictors, C., Hoque, M. T., Walsh, I., Iqbal, S., Vendruscolo, M., Sormanni, P., Wang, C., Raimondi, D., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01117-3 · Cited by 343 · see citing works

Can AlphaFold2 predict the impact of missense mutations on structure?

Buel, G. R., & Walters, K. J. (2022) · Nature Structural &amp; Molecular Biology · doi:10.1038/s41594-021-00714-2 · Cited by 332 · see citing works

ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins

Abanades, B., Wong, W. K., Boyles, F., Georges, G. J., Bujotzek, A., & Deane, C. M. (2023) · Communications Biology · doi:10.1038/s42003-023-04927-7 · Cited by 316 · see citing works

High‐accuracy protein structure prediction in <scp>CASP14</scp>

Pereira, J., Simpkin, A. J., Hartmann, M. D., Rigden, D. J., Keegan, R. M., & Lupas, A. N. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26171 · Cited by 314 · see citing works

3did: a catalog of domain-based interactions of known three-dimensional structure

Mosca, R., Ceol, A. G. M., Stein, A., Olivella, R., & Aloy, P. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt887 · Cited by 307 · see citing works

State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold

Roney, J. P., & Ovchinnikov, S. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.238101 · Cited by 281 · 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 &amp; Molecular Biology · doi:10.1038/s41594-022-00910-8 · Cited by 269 · see citing works

SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

Stein, R. A., & Mchaourab, H. S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010483 · Cited by 258 · see citing works

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Gao, M., Nakajima An, D., Parks, J. M., & Skolnick, J. (2022) · Nature Communications · doi:10.1038/s41467-022-29394-2 · Cited by 252 · see citing works

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M., & Forman-Kay, J. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304302120 · Cited by 240 · see citing works

Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search

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Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins

Kucukkal, T. G., Petukh, M. G., Li, L., & Alexov, E. G. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.01.003 · Cited by 220 · see citing works

DPP9 sequesters the C terminus of NLRP1 to repress inflammasome activation

Hollingsworth, L. R., Sharif, H., Griswold, A. R., Fontana, P., Mintseris, J., Dagbay, K. B., Paulo, J. A., Gygi, S. P., Bachovchin, D. A., & Wu, H. (2021) · Nature · doi:10.1038/s41586-021-03350-4 · Cited by 197 · see citing works

PAE viewer: a webserver for the interactive visualization of the predicted aligned error for multimer structure predictions and crosslinks

Elfmann, C., & Stülke, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad350 · Cited by 191 · see citing works

Predicting equilibrium distributions for molecular systems with deep learning

Zheng, S., He, J., Liu, C., Shi, Y., Lu, Z., Feng, W., Ju, F., Wang, J., Zhu, J., Min, Y., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00837-3 · Cited by 178 · see citing works

On Human Disease-Causing Amino Acid Variants: Statistical Study of Sequence and Structural Patterns

Petukh, M. G., Kucukkal, T. G., & Alexov, E. G. (2015) · Human Mutation · doi:10.1002/humu.22770 · Cited by 166 · see citing works

AlphaPulldown—a python package for protein–protein interaction screens using AlphaFold-Multimer

Yu, D., Chojnowski, G., Rosenthal, M., & Kosinski, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac749 · Cited by 162 · see citing works

Intrinsic protein disorder and conditional folding in <scp>AlphaFoldDB</scp>

Piovesan, D., Monzon, A. M., & Tosatto, S. C. E. (2022) · Protein Science · doi:10.1002/pro.4466 · Cited by 160 · 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

Efficient and accurate prediction of protein structure using RoseTTAFold2Preprint

Baek, M., Anishchenko, I. V., Humphreys, I. R., Cong, Q., Baker, D. A., & DiMaio, F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.24.542179 · Cited by 152 · 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

Protein complexes in cells by AI‐assisted structural proteomics

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What's next for AlphaFold and the AI protein-folding revolution

Callaway, E. (2022) · Nature · doi:10.1038/d41586-022-00997-5 · Cited by 146 · see citing works

An atlas of protein homo-oligomerization across domains of life

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

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AlphaFold2 protein structure prediction: Implications for drug discovery

Borkakoti, N., & Thornton, J. M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102526 · Cited by 137 · see citing works

Assessment of the assessment: Evaluation of the model quality estimates in CASP10

Kryshtafovych, A., Barbato, A., Fidelis, K. A., Monastyrskyy, B., Schwede, T. F., & Tramontano, A. L. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24347 · Cited by 131 · see citing works

Assessment of predictions in the model quality assessment category

Cozzetto, D., Kryshtafovych, A., Ceriani, M., & Tramontano, A. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21669 · Cited by 130 · see citing works

The Contribution of Missense Mutations in Core and Rim Residues of Protein–Protein Interfaces to Human Disease

David, A., & Sternberg, M. J. E. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.004 · Cited by 125 · see citing works

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

Bordin, N., Sillitoe, I., Nallapareddy, V., Rauer, C., Lam, S. D., Waman, V. P., Sen, N., Heinzinger, M., Littmann, M., Kim, S., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04488-9 · Cited by 103 · see citing works

Adversarial attack and defense technologies in natural language processing: A survey

Qiu, S., Liu, Q., Zhou, S., & Huang, W. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.020 · Cited by 94 · see citing works

Protein arginine methyltransferase 3 promotes glycolysis and hepatocellular carcinoma growth by enhancing arginine methylation of lactate dehydrogenase A

Yu, L., Han, P., Chen, Y., Wang, H., Wang, S., Wang, M.-R., Liu, J., Yan, W., Tian, D., & Liu, M. (2022) · Clinical and Translational Medicine · doi:10.1002/ctm2.686 · Cited by 93 · see citing works

Evaluation of model quality predictions in CASP9

Kryshtafovych, A., Fidelis, K., & Tramontano, A. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.23180 · Cited by 90 · see citing works

TmAlphaFold database: membrane localization and evaluation of AlphaFold2 predicted alpha-helical transmembrane protein structures

Dobson, L., Szekeres, L. I., Gerdán, C., Langó, T., Zeke, A., & Tusnády, G. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac928 · Cited by 87 · see citing works

Structural analysis and architectural principles of the bacterial amyloid curli

Sleutel, M., Pradhan, B., Volkov, A. N., & Remaut, H. K. (2023) · Nature Communications · doi:10.1038/s41467-023-38204-2 · Cited by 85 · see citing works

PDBe-KB: collaboratively defining the biological context of structural data

PDBe-KB consortium, Varadi, M., Anyango, S., Armstrong, D., Berrisford, J., Choudhary, P., Deshpande, M., Nadzirin, N., Nair, S. S., Pravda, L., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab988 · Cited by 82 · see citing works

Predicting the pathogenicity of missense variants using features derived from AlphaFold2

Schmidt, A., Röner, S., Mai, K., Klinkhammer, H., Kircher, M., & Ludwig, K. U. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad280 · Cited by 79 · see citing works

Methods of model accuracy estimation can help selecting the best models from decoy sets: assessment of model accuracy estimations in CASP11

Kryshtafovych, A., Barbato, A., Monastyrskyy, B., Fidelis, K., Schwede, T., & Tramontano, A. (2015) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.24919 · Cited by 77 · see citing works

Evaluation of CASP8 model quality predictions

Cozzetto, D., Kryshtafovych, A., & Tramontano, A. L. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22534 · Cited by 76 · see citing works

The case for post-predictional modifications in the AlphaFold Protein Structure Database

Bagdonas, H., Fogarty, C. A., Fadda, E., & Agirre, J. (2021) · Nature Structural & Molecular Biology · doi:10.1038/s41594-021-00680-9 · Cited by 75 · see citing works

Novel machine learning approaches revolutionize protein knowledge

Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72 · see citing works

Missense3D-DB web catalogue: an atom-based analysis and repository of 4M human protein-coding genetic variants

Khanna, T., Hanna, G., Sternberg, M. J. E., & David, A. (2021) · Human Genetics · doi:10.1007/s00439-020-02246-z · Cited by 67 · see citing works

AlphaDesign: A <i>de novo</i> protein design framework based on AlphaFold

Jendrusch, M., Korbel, J. O., & Sadiq, S. K. (2021) · doi:10.1101/2021.10.11.463937 · Cited by 65 · see citing works

Assessment of model accuracy estimations in CASP12

Kryshtafovych, A., Monastyrskyy, B., Fidelis, K., Schwede, T., & Tramontano, A. (2017) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25371 · Cited by 63 · see citing works

Calpain as a therapeutic target in cancer

Шаповалов, І., Harper, D., & Greer, P. A. (2022) · Expert Opinion on Therapeutic Targets · doi:10.1080/14728222.2022.2047178 · Cited by 61 · see citing works

Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFoldPreprint

Li, Z., Liu, X., Chen, W., Shen, F., Bi, H., Ke, G., & Zhang, L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.04.502811 · Cited by 59 · see citing works

Assessment of Protein Model Structure Accuracy Estimation in CASP13: Challenges in the Era of Deep Learning

Won, J., Baek, M., Monastyrskyy, B., Kryshtafovych, A., & Seok, C. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25804 · Cited by 57 · see citing works

Assessment of protein model structure accuracy estimation in CASP14 : Old and new challenges

Kwon, S., Won, J., Kryshtafovych, A., & Seok, C. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26192 · Cited by 56 · see citing works

Folding the unfoldable: using AlphaFold to explore spurious proteins

Monzon, V., Haft, D. H., & Bateman, A. G. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab043 · Cited by 54 · see citing works

Fuzzy Drug Targets: Disordered Proteins in the Drug-Discovery Realm

Saurabh, S., Nadendla, K., Purohit, S. S., Sivakumar, P. M., & Çetinel, S. (2023) · ACS Omega · doi:10.1021/acsomega.2c07708 · Cited by 50 · see citing works

Deep learning for protein complex structure prediction

Bryant, P. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102529 · Cited by 49 · see citing works

PTX3 structure determination using a hybrid cryoelectron microscopy and AlphaFold approach offers insights into ligand binding and complement activation

Noone, D. P., Dijkstra, D. J., van der Klugt, T. T., van Veelen, P. A., de Ru, A. H., Hensbergen, P. J., Trouw, L. A., & Sharp, T. H. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2208144119 · Cited by 45 · see citing works

Zero‐shot mutation effect prediction on protein stability and function using RoseTTAFold

Mansoor, S., Baek, M., Juergens, D., Watson, J. L., & Baker, D. A. (2023) · Protein Science · doi:10.1002/pro.4780 · Cited by 40 · see citing works

AlphaFold2 enables accurate deorphanization of ligands to single-pass receptors

Banhos Danneskiold-Samsøe, N., Kavi, D., Jude, K. M., Nissen, S. B., Wat, L. W., Coassolo, L., Zhao, M., Santana-Oikawa, G. A., Broido, B. B., Garcia, K. C., et al. (2024) · Cell Systems · doi:10.1016/j.cels.2024.10.004 · Cited by 35 · see citing works

3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

Varadi, M., Nair, S., Sillitoe, I., Tauriello, G., Anyango, S., Bienert, S., Borges, C., Deshpande, M., Green, T., Hassabis, D., et al. (2022) · GigaScience · doi:10.1093/gigascience/giac118 · Cited by 32 · see citing works

Ancient proteins resolve controversy over the identity of Genyornis eggshell

Demarchi, B., Stiller, J., Grealy, A. C., Mackie, M., Deng, Y., Gilbert, M. T. P., Clarke, J. A., Legendre, L. J., Boano, R., Sicheritz‐Pontén, T., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109326119 · Cited by 29 · see citing works

Illuminating Intrinsically Disordered Proteins with Integrative Structural Biology

Evans, R., Ramisetty, S. K., Kulkarni, P., & Weninger, K. (2023) · Biomolecules · doi:10.3390/biom13010124 · Cited by 27 · see citing works

SphereGrinder - reference structure-based tool for quality assessment of protein structural models

Antczak, P. L. M., Ratajczak, T., Łukasiak, P., & Błażewicz, J. (2015) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2015.7359765 · Cited by 27 · see citing works

Improved the heterodimer protein complex prediction with protein language models

Chen, B., Xie, Z., Qiu, J., Ye, Z., Xu, J., & Tang, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad221 · Cited by 27 · see citing works

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Sen, N., Anishchenko, I., Bordin, N., Sillitoe, I., Velankar, S., Baker, D., & Orengo, C. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac187 · Cited by 25 · see citing works

Evolutionary conservation of the structure and function of meiotic Rec114−Mei4 and Mer2 complexesPreprint

Daccache, D., Jonge, E. D., Liloku, P., Mechleb, K., Haddad, M., Corthaut, S., Sterckx, Y. G. ‐., Volkov, A. N., & Bouuaert, C. C. (2023) · Genes & Development · doi:10.1101/gad.350462.123 · Cited by 24 · see citing works

Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology

Tordai, H., Suhajda, E., Sillitoe, I., Nair, S., Varadi, M., & Hegedus, T. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23168877 · Cited by 22 · see citing works

E6AP AZUL interaction with UBQLN1/2 in cells, condensates, and an AlphaFold-NMR integrated structure

Buel, G. R., Chen, X., Myint, W., Kayode, O., Folimonova, V., Cruz, A. M., Skorupka, K. A., Matsuo, H., & Walters, K. J. (2023) · Structure · doi:10.1016/j.str.2023.01.012 · Cited by 21 · see citing works

Structure of the human Ccr4‐Not nuclease module using X‐ray crystallography and electron paramagnetic resonance spectroscopy distance measurements

Zhang, Q., Pavanello, L., Potapov, A., Bartlam, M. G., & Winkler, G. S. (2021) · Protein Science · doi:10.1002/pro.4262 · Cited by 20 · see citing works

Protein Folding Neural Networks Are Not RobustPreprint

Jha, S. K., Ramanathan, A., Ewetz, R., Velasquez, A., & Jha, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.04460 · Cited by 14 · see citing works

Enabling cryo‐EM density interpretation from yeast native cell extracts by proteomics data and AlphaFold structures

Tüting, C., Schmidt, L., Skalidis, I., Sinz, A., & Kastritis, P. L. (2023) · PROTEOMICS · doi:10.1002/pmic.202200096 · Cited by 14 · see citing works

Structural biology at the scale of proteomes

Bouatta, N., & AlQuraishi, M. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-00924-w · Cited by 12 · see citing works

Accurate Mutation Effect Prediction using RoseTTAFoldPreprint

Mansoor, S., Baek, M., Juergens, D., Watson, J. L., & Baker, D. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.04.515218 · Cited by 10 · see citing works

What is hidden in the darkness? Deep-learning assisted large-scale protein family curation uncovers novel protein families and foldsPreprint

Durairaj, J., Waterhouse, A. M., Mets, T., Brodiazhenko, T., Abdullah, M., Studer, G., Akdel, M., Andreeva, A. E., Bateman, A. G., Tenson, T., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.14.532539 · Cited by 7 · see citing works

Cracking the code of cellular protein–protein interactions: Alphafold and whole‐cell crosslinking to the rescue

Träger, T. K., & Kastritis, P. L. (2023) · Molecular Systems Biology · doi:10.15252/msb.202311587 · Cited by 5 · see citing works

On the Robustness of AlphaFold: A COVID-19 Case StudyPreprint

Alkhouri, I., Jha, S. K., Beckus, A., Atia, G., Velasquez, A., Ewetz, R., Ramanathan, A., & Jha, S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.04093 · Cited by 3 · see citing works

Faculty Opinions recommendation of Discovery of deaminase functions by structure-based protein clustering.

Huang, S., & Ye, K. (2023) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.742698923.793599148 · Cited by 1 · see citing works

Sodium Dodecylbenzenesulfonate-induced oxidative stress and tissue damage in Tubifex tubifex: A comprehensive toxicological study

Chakraborty, D., Ghosh, S., Mandal, A. H., Roy, T., Sadhu, A., Paria, D. S., Saha, N. C., Sopjani, M., Saha, S., & Faggio, C. (2025) · Environmental Toxicology and Pharmacology · doi:10.1016/j.etap.2025.104778 · Cited by 18 · see citing works

How did we get there? AI applications to biological networks and sequences

Anteghini, M., Gualdi, F., & Oliva, B. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110064 · Cited by 9 · see citing works

Comparative evaluation of the prediction accuracy of AlphaFold and ESMFold for monomeric and dimeric proteins

Mahtha, S. K., Sureshkumar, V., & Mohanty, D. (2026) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqag002 · Cited by 8 · see citing works

Molecular versatility of polyproline II helices: from natural proteins to biomimetic materials

Seco, F. C. S. S., Amador, S., Conzuelo, F., Baptista, A. C., Morgado, L., & Pina, A. S. (2026) · Journal of Materials Chemistry B · doi:10.1039/d5tb01841b · Cited by 7 · see citing works

Comprehensive Studies on the Regulation of Type 2 Diabetes by Cucurbitane-Type Triterpenoids in Momordica charantia L.: Insights from Network Pharmacology and Molecular Docking and Dynamics

Niu, Y., Li, P., & Pang, Z. (2025) · Pharmaceuticals · doi:10.3390/ph18040474 · Cited by 7 · see citing works

Geometric deep learning assists protein engineering. Opportunities and Challenges

Vinuesa, J. A. G. , Rojas, J. A., Soto-García, N., Martínez, N., Álvarez-Saravia, D., Uribe-Paredes, R., Davari, M. D., Conca, C., Asenjo, J. A., & Medina-Ortiz, D. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108790 · Cited by 5 · see citing works

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φPreprint

Li, W. (2024) · Preprints.org · doi:10.20944/preprints202404.0576.v1 · Cited by 5 · see citing works

Functional Versatility of Vibrio cholerae Outer Membrane Proteins

Mathieu‐Denoncourt, A., & Duperthuy, M. (2025) · Applied Microbiology · doi:10.3390/applmicrobiol5030064 · Cited by 4 · see citing works

Gaps between medical biology and AI drug discovery

Yin, Y., Fu, A., & Wang, L. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104512 · Cited by 4 · see citing works

Can AI-Predicted Complexes Teach Machine Learning to Compute Drug Binding Affinity?

Hsu, W.-T., Grevtsev, S., Herz, A. M., Douglas, T., Magarkar, A., & Biggin, P. C. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01848 · Cited by 4 · see citing works

Deciphering the proteome of Escherichia coli K-12: Integrating transcriptomics and machine learning to annotate hypothetical proteins

Chakraborty, S., Ardern, Z., Aliyu, H., & Kaster, A. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.07.036 · Cited by 3 · see citing works

Integrative Proteomic and Machine Learning Analysis Identifies Novel Predictors and Risk Model for Diabetic Macrovascular Complications

Ma, C., Dai, Z., Xiao, B., Chen, Y., Fu, L., & Tai, S. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70807 · Cited by 3 · see citing works

In silico drug discovery pipelines targeting antibiotic resistance: from genomes to leads

Gupta, R., Singh, P., Ravi, K., Abomughaid, M. M., Lakhanpal, S., Sharma, R. V., Kumar, N., & Jha, N. K. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1839606 · Cited by 3 · see citing works

Modern Approaches to Protein Constructions: A Comprehensive Review of Computational Tools and Databases for De Novo Protein Design and Engineering

Mia, M. M., Sultana, H., Amin, M. A., Hossain, M. S., Imam, H., Mohiuddin, A. K. M., & Mahmud, S. (2025) · Engineering Reports · doi:10.1002/eng2.13112 · Cited by 3 · see citing works

CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation

Gou, W., Ge, W., Tan, Y., Li, M., Fan, G., & Yu, H. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00732-4 · Cited by 3 · see citing works

Two different and robustly modeled DNA binding modes of Competence Protein ComP - systematic modeling with AlphaFold 3, RoseTTAFold2NA, Chai-1 and re-docking in HADDOCK

Helsem, S. A., Alfsnes, K., Frye, S. A., Hesselberg Løvestad, A., & Ambur, O. H. (2025) · PLOS One · doi:10.1371/journal.pone.0315160 · Cited by 3 · see citing works

Artificial intelligence-driven insights into the modulation of enzyme activity toward accelerated industrial applications

Han, H., Zhang, Q., Chen, Y., Wang, Z.-X., Su, X.-Y., Zhu, Y., Wang, X., & Feng, Z.-X. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.152018 · Cited by 2 · see citing works

Applications and limitations of AI tools in enzyme design

Teijeiro-Juiz, R., Egeler, N., Jamróg, G., Ringel, M., Brück, T., Geronimo, B. D., & Loll, B. (2026) · Protein Science · doi:10.1002/pro.70698 · Cited by 2 · see citing works

Integrative Structural Modeling of Intrinsically Disordered Regions in a Human HDAC2 Chromatin Remodeling ComplexPreprint

Nde, J., Kempf, C. G., Zimmermann, R. C., Cesare, J., Zhang, Y., Workman, J. L., Florens, L. A., & Washburn, M. P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.08.669391 · Cited by 2 · see citing works

MODELLER-Based Homology Modeling: A Critical Review of Protocols, Advances, and Computational Challenges

Suguna, P., & Sundari, T. M. (2026) · Journal of Medico Informatics · doi:10.64659/jomi/211255 · Cited by 1 · see citing works

Enhancing Interpretability and Explainability of Protein Function Prediction ML Model Using Explainable AI

Katiyar, A., & Yadav, R. (2026) · Engineering Reports · doi:10.1002/eng2.70851 · Cited by 1 · see citing works

Type IV minor pilin ComN predicted the USS-receptor in Pasteurellaceae

Helsem, S. A., Alfsnes, K., Frye, S. A., Løvestad, A. H., & Ambur, O. H. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1647523 · Cited by 1 · see citing works

A transposon-like strategy for proteins

Ventura, B. D. (2025) · Nature Chemical Biology · doi:10.1038/s41589-025-01947-8

Ensemble Model for Prediction of Protein Backbone Torsion Angles

S, S. C., Prathibhavani, P. M., & Venugopal, K. R. (2025) · IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE) · doi:10.1109/icwite64848.2025.11306931

Data-Driven Toxicity Prediction: Advances in Machine Learning, Deep Learning, and Predictive Tools - A Systematic Review

Al-Hussaniy, H. A., Ali, K. A., Jasim, S. T., & Al-Samydai, A. (2026) · Current Clinical Pharmacology · doi:10.2174/0127724328441651260211220550

A Disorder-Aware Computational Framework to Identify Structurally Tractable Targets in Proliferative Vitreoretinopathy

Djulbegovic, M. B., Hadzijahic, N., Gonzalez, D. J. T., Antonietti, M., Zafar, S., & Kuriyan, A. E. (2026) · Ophthalmology Science · doi:10.1016/j.xops.2026.101249

Mapping the structural coverage of Arabidopsis thaliana plant developmental proteins: Insights from Experimental and AlphaFold ApproachesPreprint

Rode, S., Sudarsanam, K., Bhalla, H., Srivastava, A., & Sankaranarayanan, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.31.729038

Emerging computational tools and advanced technologies in chemical and medical research: applications in bioinformatics, drug discovery, and precision healthcare: a review

Avula, B., Bhimireddi, R., Kumar, A., Pandey, S., Tungala, K., Eraganaboyina, S., Ravi, G., & Singh, A. K. (2026) · Journal of the Iranian Chemical Society · doi:10.1007/s13738-026-03481-3

Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering

Teijeiro-Juiz, R., Brück, T., & Loll, B. (2026) · Molecules · doi:10.3390/molecules31132359

Machine Learning in Protein Structure Prediction

Thai, K., Nguyen, D.-N., Vo, T.-P., & Le, M.-T. (2026) · Royal Society of Chemistry eBooks · doi:10.1039/9781837070206-00064

Silica-Embedded Proteins Enable Higher Yield and Atomic Ion Detection in Deep-UV Laser-Assisted Atom Probe Tomography

Eriksson, G., Mayweg, D., Inverardi, G. N., Carnovale, F., Hulander, M., Thuvander, M., Petrolli, L., Lattanzi, G., Taioli, S., & Andersson, M. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.6c02770

Integrative Structural Modeling of Intrinsically Disordered Regions in a Human HDAC2 Chromatin Remodeling Complex

Nde, J., Kempf, C. G., Zimmermann, R. C., Cesare, J., Zhang, Y., Workman, J. L., Florens, L. A., & Washburn, M. P. (2026) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6c00256

From Sequence to Structure: AlphaFold and the AI Revolution in Aquaculture Drug Discovery and Vaccine Design

Üstüner, E., & Öz, M. (2026) · Biotechnology and Bioengineering · doi:10.1002/bit.70398

AI-Assisted WES Variant Interpretation in Neurodevelopmental Disorders: A Narrative Review of Existing Tools

Treccarichi, S., Vinci, M., Musumeci, A., Rando, R. G., Fallea, A., Brancato, D., Bruno, F., Federico, C., Saccone, S., Elia, M., et al. (2026) · Genes · doi:10.3390/genes17101276

Risk prediction models for macrovascular complications in type 2 diabetes mellitus: from traditional risk scores to multimodal intelligent prediction

An, X., Cai, J., Cao, X., & Li, J. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1980631

Protein Structure Validation Using PDBMine and Data Analytics Approaches

Pandala, N., Brown, K., & Valafar, H. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94950-0_27 · Cited by 2 · see citing works

Protein structure and function prediction using machine learning algorithms

Shagaya, G. (2023) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.11130521

Computational Tools for Structural Analysis of Proteins

Brezovský, J., Sethi, A., & Surpeta, B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00089-0 · Cited by 1 · see citing works

Novel Protein Structure Validation using PDBMine and Data Analytics ApproachesPreprint

Pandala, N., Brown, K., & Valafar, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.07.637116

FORECASTING PROTEIN STRUCTURES AND FUNCTIONAL ROLES

Researcher (2023) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.15576157