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A Perspective on the Prospective Use of AI in Protein Structure Prediction

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Binding mechanisms of intrinsically disordered proteins: Insights from experimental studies and structural predictions

Orand, T., & Jensen, M. R. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102958 · Cited by 33 · see citing works

Analysing protein complexes in plant science: insights and limitation with AlphaFold 3

Lin, P.-Y., Huang, S.-C., Chen, K.-L., Huang, Y., Liao, C.-Y., Lin, G.-J., Lee, H., & Chen, P. (2025) · Botanical studies · doi:10.1186/s40529-025-00462-2 · Cited by 34 · see citing works

Peptide design to control protein–protein interactions

Wier, S. P. V., & Beekman, A. M. (2025) · Chemical Society Reviews · doi:10.1039/d4cs00243a · Cited by 32 · see citing works

Structural Biology of RNA and Protein‐RNA Complexes after AlphaFold3

Hennig, J. (2025) · ChemBioChem · doi:10.1002/cbic.202401047 · Cited by 32 · see citing works

Emerging frontiers in protein structure prediction following the AlphaFold revolution

Rennie, M. L., & Oliver, M. R. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2024.0886 · Cited by 27 · see citing works

Bias in, bias out – AlphaFold-Multimer and the structural complexity of protein interfaces

Strom, J. M., & Luck, K. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103002 · Cited by 22 · see citing works

Direct interaction between TDP-43 and Tau promotes their co-condensation, while suppressing Tau fibril formation and seeding

Simonetti, F., Zhong, W., Hutten, S., Uliana, F., Schifferer, M., Rezaei, A., Ramirez, L. M., Hochmair, J., Sankar, R., Gopalan, A. B., et al. (2025) · The EMBO Journal · doi:10.1038/s44318-025-00590-2 · Cited by 22 · see citing works

Prospects for synthetic biology in 21st century agriculture

Ye, X., Qin, K., Fernie, A. R., & Zhang, Y. (2024) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2024.12.016 · Cited by 22 · see citing works

Decoding the functional impact of the cancer genome through protein–protein interactions

Fu, H., Mo, X., & Ivanov, A. A. (2025) · Nature reviews. Cancer · doi:10.1038/s41568-024-00784-6 · Cited by 21 · see citing works

Multimeric protein interaction and complex prediction: Structure, dynamics and function

Lu, D., Yu, S., Huang, Y., & Gong, X. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.009 · Cited by 20 · see citing works

Structural study of UFL1‐UFC1 interaction uncovers the role of UFL1 N‐terminal helix in ufmylation

Banerjee, S., Varga, J. K., Kumar, M., Zoltsman, G., Rotem‐Bamberger, S., Cohen‐Kfir, E., Isupov, M. N., Rosenzweig, R., Schueler‐Furman, O., & Wiener, R. (2023) · EMBO Reports · doi:10.15252/embr.202356920 · Cited by 20 · see citing works

High-throughput discovery of inhibitory protein fragments with AlphaFold

Savinov, A., Swanson, S., Keating, A. E., & Li, G.-W. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2322412122 · Cited by 18 · see citing works

Training bias and sequence alignments shape protein–peptide docking by AlphaFold and related methods

Guan, L., & Keating, A. E. (2025) · Protein Science · doi:10.1002/pro.70331 · Cited by 17 · see citing works

Structural host-virus interactome profiling of intact infected cells

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Integrating Large-Scale Protein Structure Prediction into Human Genetics Research

Marrero, M. C., Jänes, J., Baptista, D., & Beltrão, P. (2024) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120622-020615 · Cited by 13 · see citing works

A CDK1 phospho-switch reprograms TRAIP to unload replisomes in mitosis

Can, G., Shyian, M., Krishnamoorthy, A., Ahmed, S., Lim, Y., Wu, A., Pavani, R., Zaher, M. S., Nussenzweig, A., Räschle, M., et al. (2026) · Science · doi:10.1126/science.aeh1834 · Cited by 13 · see citing works

Cutting-edge deep-learning based tools for metagenomic research

Karin, E. L., & Steinegger, M. (2025) · National Science Review · doi:10.1093/nsr/nwaf056 · Cited by 10 · see citing works

A Computational Pipeline for Accurate Prioritization of Protein‐Protein Binding Candidates in High‐Throughput Protein Libraries

Mondal, A., Singh, B., Felkner, R. H., Falco, A. D., Swapna, G., Montelione, G. T., Roth, M. J., & Pérez, A. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202405767 · Cited by 10 · see citing works

Toxoplasma effector TgROP1 establishes membrane contact sites with the endoplasmic reticulum during infection

Mehra, C., Alvarado-Valverde, J., Matias, A. M. N., Torelli, F., Medeiros, T., Straub, J., Asaki, J. D., Bradley, P. J., Luck, K., Lawo, S., et al. (2025) · Nature Microbiology · doi:10.1038/s41564-025-02193-3 · Cited by 9 · see citing works

The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology

Chakraborty, C., Bhattacharya, M., & Lee, S.-S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1739303 · Cited by 10 · see citing works

AlphaFold2 SLiM screen for LC3-LIR interactions in autophagy

Stuke, J. F. M., & Hummer, G. (2025) · Autophagy · doi:10.1080/15548627.2025.2493999 · Cited by 8 · see citing works

Disobind: A sequence-based, partner-dependent contact map and interface residue predictor for intrinsically disordered regions

Majila, K., Ullanat, V., & Viswanath, S. (2026) · Cell Systems · doi:10.1016/j.cels.2025.101486 · Cited by 8 · see citing works

Peptidomimetics design and characterization: Bridging experimental and computer-based approaches

Romagnoli, A., Rexha, J., Perta, N., Cristofano, S. D., Borgognoni, N., Venturini, G., Pignotti, F., Raimondo, D., Borsello, T., & Marino, D. D. (2024) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2024.07.002 · Cited by 8 · see citing works

Histone variant macroH2A1 regulates synchronous firing of replication origins in the inactive X chromosome

Arroyo, M. D. C. G., Casas-Delucchi, C. S., Pabba, M. K., Prorok, P., Pradhan, S. K., Rausch, C., Lehmkuhl, A., Maiser, A., Buschbeck, M., Pasque, V., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae734 · Cited by 7 · see citing works

MFIB 2.0: a major update of the database of protein complexes formed by mutual folding of the constituting protein chains

Fichó, E., Pancsa, R., Magyar, C., Kálmán, Z. E., Schád, É., Németh, B. Z., Simon, I., Dobson, L., & Tusnády, G. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae976 · Cited by 7 · see citing works

Structures of dynamic interactors at native proteasomes by PhIX-MS and cryo-electron microscopy

Lee, K., Negi, H., Chen, X., Atallah-Yunes, K., Truslow, S., Castelino, R. E., Guest, M. R., Ciancone, A. M., Lu, X., Tarasov, S. G., et al. (2026) · Molecular Cell · doi:10.1016/j.molcel.2026.06.032 · Cited by 6 · see citing works

Regularly updated benchmark sets for statistically correct evaluations of AlphaFold applications

Dobson, L., Tusnády, G. E., & Tompa, P. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf104 · Cited by 6 · see citing works

The “DDVF” motif used by viral and bacterial proteins to hijack RSK kinases mimics a short linear motif (SLiM) found in proteins related to the RAS-ERK MAP kinase pathway

Veinstein, M., Stroobant, V., Wavreil, F., Michiels, T., & Sorgeloos, F. (2025) · PLoS Pathogens · doi:10.1371/journal.ppat.1013016 · Cited by 6 · see citing works

Considering the ethics of large machine learning models in the chemical sciences

Spotte-Smith, E. W. C. (2025) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/adec3c · Cited by 6 · see citing works

Integrative modeling meets deep learning: Recent advances in modeling protein assemblies

Shor, B., & Schneidman‐Duhovny, D. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102841 · Cited by 6 · see citing works

A predicted structural interactome reveals binding interference from intrinsically disordered regions

Peng, J., & Zhao, L. (2026) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013899 · Cited by 5 · see citing works

Bacterial Systematic Genetics and Integrated Multi-Omics: Beyond Static Genomics Toward Predictive Models

Sakaguchi, T., Irifune, Y., Kamada, R., & Sakaguchi, K. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26199326 · Cited by 3 · see citing works

From concept to application: Exploring the evolution and potential of DUBTAC technology

Wang, D., Min, W., Jiang, B., Sun, H., Sun, C., & Yang, P. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.11.022 · Cited by 5 · see citing works

Cysteine-enabled cleavability to advance cross-linking mass spectrometry for global analysis of endogenous protein-protein interactions

Jiao, F., Braitbard, M., Yu, C., Shor, B., Wulff, B., Schneidman‐Duhovny, D., & Huang, L. (2025) · Nature Communications · doi:10.1038/s41467-025-66023-0 · Cited by 5 · see citing works

Unifying Constraints Linking Protein Folding and Native Dynamics Decoded from AlphaFold

Zhang, Z., Ren, W., Xie, L., Zheng, Y., Guan, X., Wang, J., Li, W., & Tang, Q.-Y. (2026) · Physical Review Letters · doi:10.1103/7j2j-f8f7 · Cited by 5 · see citing works

TET dioxygenases localize at splicing speckles and promote RNA splicing

Hastert, F. D., Weber, J., Bauer, C., Zhadan, A., Singh, D. N. D., Dix, T. C., Arnold, R., Bessonov, S., Soller, M., Leonhardt, H., et al. (2025) · Nucleus · doi:10.1080/19491034.2025.2536902 · Cited by 5 · see citing works

Evaluation of Physics‐Based Protein Design Methods for Predicting Single Residue Effects on Peptide Binding Specificities

Ayyildiz, M., Noske, J., Gisdon, F. J., Kynast, J. P., & Höcker, B. (2025) · Journal of Computational Chemistry · doi:10.1002/jcc.70160 · Cited by 4 · see citing works

The rise of AlphaFold in drug design

Stecuła, A., Paul, R., Litchfield, K., Dalton, S. E., Low, C. M. R., Reis, C. R., & Congreve, M. (2025) · Progress in medicinal chemistry · doi:10.1016/bs.pmch.2025.09.002 · Cited by 4 · see citing works

Vancomycin resistance in gram-positive infections: evolutionary strategies of survival

Hu, T., & Wang, L. (2026) · Archives of Microbiology · doi:10.1007/s00203-025-04698-1 · Cited by 4 · see citing works

Generation of a high confidence set of domain–domain interface types to guide protein complex structure predictions by AlphaFold

Geist, J. L., Lee, C. Y., Strom, J. M., Naveja, J. J., & Luck, K. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae482 · Cited by 4 · see citing works

AlphaCRV: a pipeline for identifying accurate binder topologies in mass-modeling with AlphaFold

Guzmán-Vega, F. J., & Arold, S. T. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae131 · Cited by 4 · see citing works

A simple workflow to identify novel small linear motif (SLiM)-mediated interactions with AlphaFold

Veinstein, M., Janssens, V., Iorga, B. I., Helaers, R., Michiels, T., & Sorgeloos, F. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf501 · Cited by 3 · see citing works

Artificial Intelligence in Chemical Engineering: Protein Design from First Principles to Structural Prediction

Bailey, J., Spina, S. C., Hu, A., Phan, N., Getman, R. B., & Kimmel, B. R. (2026) · ACS Engineering Au · doi:10.1021/acsengineeringau.5c00099 · Cited by 3 · see citing works

AlphaFold-based peptide structure prediction: Opportunities, limitations, and future directions

Zhang, B., Zhu, J., Chen, H.-F., & Liu, L. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108977 · Cited by 3 · see citing works

Avl9 defines a family of DENN domain GTPase-activating proteins

Vignogna, R. C., & Fromme, J. C. (2026) · The Journal of Cell Biology · doi:10.1083/jcb.202601003 · Cited by 3 · see citing works

Exploring Protein–Protein Docking Tools: Comprehensive Insights into Traditional and Deep-Learning Approaches

Barhoon, M., & Mahdiuni, H. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01029 · Cited by 3 · see citing works

AlphaFold‐driven discovery of oxysterol‐binding protein ‐ related protein‐phosphoinositide 3‐, 4‐, and 5‐phosphatase interactions using new generation confidence scores

Dall’Armellina, F., Urbé, S., & Rigden, D. J. (2026) · Protein Science · doi:10.1002/pro.70572 · Cited by 3 · see citing works

A Computational Pipeline for Accurate Prioritization of Protein‐Protein Binding Candidates in High‐Throughput Protein Libraries

Mondal, A., Singh, B., Felkner, R. H., Falco, A. D., Swapna, G., Montelione, G. T., Roth, M. J., & Pérez, A. (2024) · Angewandte Chemie · doi:10.1002/ange.202405767 · Cited by 3 · see citing works

PPI-ID: Streamlining protein-protein interaction prediction through domain and SLiM mapping

Goodwin, H. V., & Atkinson, N. S. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013062 · Cited by 3 · see citing works

Heterodimerization of staphylococcal phage φ2638A endolysin isoforms and their functional role in bacterial lysis

Zinsli, L. V., Sobieraj, A. M., Du, J., Ernst, P. J., Meile, S., Kilcher, S., Iseli, C., Keller, A. P., Dreier, B., Mittl, P. R. E., et al. (2025) · microLife · doi:10.1093/femsml/uqaf011 · Cited by 2 · see citing works

Engineering Peptide Modulators for T-Cell Migration by Structural Scaffold Matching

Gattringer, J., Hasinger, S., Weidmann, A., Walczewska-Szewc, K., Jadhav, K. B., Zrzavy, T., Steinmaurer, A., Baeten, P., Perisic, M., Cochrane, W., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c00677 · Cited by 2 · see citing works

AlphaFold-Based Protein-Protein Interaction Prediction Methods Classification

Faisal, T., Alofairi, A. A., & Mohsen‬‏, ‪. A. (2025) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta66764.2025.11132127 · Cited by 2 · see citing works

The proteome-scale study of iron-binding proteins of bread wheat

Bharti, A. K., & Verma, S. K. (2025) · Metallomics · doi:10.1093/mtomcs/mfaf036 · Cited by 2 · see citing works

Need for speed: advances in the era of high throughput interaction proteomics

Frommelt, F., Uliana, F., Aebersold, R. H., & Fossati, A. (2026) · Molecular Omics · doi:10.1093/molecular-omics/aaiag012 · Cited by 2 · see citing works

AlphaFold opens the doors to deorphanizing secreted proteins

Viswanath, S. (2024) · Cell Systems · doi:10.1016/j.cels.2024.10.010 · Cited by 2 · see citing works

Non-covalent SUMO interactions with ligases and effectors: SUMO-interacting motifs and beyond

Mishra, A., Cisse, E. H., & Suskiewicz, M. J. (2025) · Essays in Biochemistry · doi:10.1042/ebc20253039 · Cited by 2 · see citing works

Structural Analysis of a Metamorphic Protein using AlphaFold2Preprint

Tyree, S., & Kim, Y.-I. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.04.621945 · Cited by 2 · see citing works

Integrating AlphaFold2 models and clinical data to improve the assessment of Short Linear Motifs (SLiMs) and their variants’ pathogenicity

Brunello, F. G., Erra, L., Nicola, J., & Martí, M. A. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012829 · Cited by 1 · see citing works

Competition between binding partners of yeast Pex3 affects peroxisome biology

de Lange, E. M. F., Chojnowski, G., Bürgi, J., Vlijm, R., Wilmanns, M., & van der Klei, I. J. (2025) · FEBS Journal · doi:10.1111/febs.70229 · Cited by 1 · see citing works

A Survey of Predicted Protein–Protein Interactions Involving Disordered Regions in Humans

Pei, J., Zhang, J. J., & Cong, Q. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169636 · Cited by 1 · see citing works

Toward Quantitative Domain–Motif Interactomes: State of the Art, Challenges, and Perspectives

Alvarado-Valverde, J., & Luck, K. (2026) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092624-110614 · Cited by 1 · see citing works

Caveat emptor: predicting and modeling protein–DNA recognition and binding via machine-learning computational approaches

Esler, M. A., Werther, R. A., Doyle, L., Ubilla‐Rodriguez, N. C., Schwensen, J. S., Hallinan, J. P., Lambert, A. R., Young, J. C., Silverstein, M., & Stoddard, B. L. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag608 · Cited by 1 · see citing works

Reconstituted ferredoxin–MEP pathway of Apicomplexa in Escherichiacoli as an in situ screening platform for inhibitors and essential enzyme mutations

Akuh, O.-A., Maus, D., Blume, M., Saliba, K., & Seeber, F. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110726 · Cited by 1 · see citing works

Biological networks in Barley (Hordeum vulgare L.): From molecular interactions to systems-level understanding

Panahi, B., Holasou, H. A., Hamid, R., Shahriari, A. G., & Ghorbanzadeh, Z. (2026) · Current Plant Biology · doi:10.1016/j.cpb.2026.100624 · Cited by 1 · see citing works

Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families

Singh, S., Singh, R., & Sharma, S. (2026) · BioData Mining · doi:10.1186/s13040-026-00577-7 · Cited by 1 · see citing works

Assessment of machine‐learning predictions for the Mediator complex subunit MED25 ACID domain interactions with transactivation domains

(2024) · doi:10.1002/1873-3468.14837 · Cited by 1 · see citing works

Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box

Walker, D. R., Fujimura, G., Vanegas, J. M., & Barbar, E. J. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1531793 · Cited by 1 · see citing works

Fast and scalable querying of eukaryotic linear motifs with gget elm

Luebbert, L., Hoang, C., Kumar, M., & Pachter, L. S. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae095 · Cited by 1 · see citing works

Mass Spectrometry‐Based Methods to Investigate Protein–Protein Interactions in Cells and Their Application in Drug Discovery

Branon, T. C. (2025) · Wiley eBooks · doi:10.1002/9781394236145.ch16 · Cited by 1 · see citing works

Protist biochemistry: Functional SUF system facilitated mitochondrial loss

Lawton, M., & Gawryluk, R. M. R. (2024) · Current Biology · doi:10.1016/j.cub.2024.07.054

Metabolic control of enteroendocrine cell fate through a redox state sensor CtBPPreprint

Rovenko, B. M., Moisio, K., Biehler, C., Girych, M., Hallasaari, A., Deniz, O., Bluhm, S., Viitanen, A., Kokki, K., Fabris, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.30.662346

CtGH76, a Glycoside Hydrolase 76 from Chaetomium thermophilum, with Elongated Glycan-Binding Canyon

Ruppenthal, S. R., Wang, P., Watad, M., Rosner, C. J., Vogt, M. S., Friedrich, M., Voigt, A.-L., Petz, A., Gnau, P., & Essen, L. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146589

Definition and Discovery of Tandem SH3-Binding Motifs Interacting with Members of the p47phox-Related Protein Family

Kálmán, Z. E., Lázár, T., Dobson, L., & Pancsa, R. (2025) · Biomolecules · doi:10.3390/biom15121641

Future of protein structure modeling

Wang, Y.-H. A. (2025) · Journal of High School Science · doi:10.64336/001c.140648

In silico characterisation of C11orf42 as a potential therapeutic target in triple-negative breast cancer

Alfaifi, M., & Kamli, H. M. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.108940

The alfalfa U-box E3 ligase MsPUB210 interacts with MsICE1 and positively regulates cold tolerance in transgenic Arabidopsis

Wang, M., Li, S., Zhu, X., Guo, M., Guo, C., & Shu, Y. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1772992

Abscisic acid-responsive element binding factor 1 (OoAREB1) transcription factor mediates drought stress response in Oxytropis ochrocephala via interaction with sucrose nonfermenting 1-related protein kinase 2.1 (OoSnRK2.1) and OoSnRK2.6

Han, X.-M., Feng, J., Guan, H., Niu, F., Wang, J., Li, X., Guo, B., He, W., Wei, Y.-H., & Fu, Y. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.150973

Proteome-wide prediction of interactions between structured domains and peptide motifs reveals functionally coherent subnetworks

Saha, A., Tang, C. L., Murray, D., & Honig, B. H. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2527957123

In Silico Structure-Based Interactomic Analysis of the Scaffolding Protein DCAF7Preprint

Mezghrani, A., Reys, V., & Labesse, G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.13.724911

SPPIDER-seq: sequence-based partner-aware predictor of protein-protein interaction sites

Porollo, A., Jadhav, O., Alvarez, A., & Chen, J.-C. (2026) · Bioinform. · doi:10.1093/bioinformatics/btag388

From Thanatos to transcription: the emerging biology of THAP proteins

Rampal, B., Brisson, A., Kuijpers, L. B. H., Cappadocia, L., & Samarut, É. (2026) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-026-06318-7

Protease suppression by native I9 inhibitor improves recombinant protein production in Trichoderma reesei

Valkonen, M. J., Girych, M., Havukainen, S., Sveholm, E., Luoto, J. C., Schmidt, G., Adlung, N., Aalto, A., Ollila, O. H. S., Mattila, H., et al. (2026) · Bioresource Technology · doi:10.1016/j.biortech.2026.135405

The topology of transmembrane protein-protein interaction interfaces is encoded in their physicochemical featuresPreprint

Allmesberger-Riegler, L., Frommelt, F., Santini, B. L., Superti‐Furga, G., Ferrada, E., & Sykacek, P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.16.738731

Structural insights into the activation of phospholipase D1 by the small GTPase ARL11

Marr, D. J., Gao, S., Frohman, M. A., & Airola, M. V. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.113375

Functional characterization of CiCIPK8 and CiCIPK11 reveals their roles in osmotic stress via CBL-CIPK signaling in pecan (Carya illinoinensis)

Wang, M., Shao, H., Wei, J., Wu, Y., Niu, M., Tan, P., Liu, H., Wu, J., Zhang, R., Hu, X., et al. (2026) · Plant Physiology and Biochemistry · doi:10.1016/j.plaphy.2026.111704

Predicting single mutation effects on binding affinity at protein-protein interfaces based on MMPBSA calculationsPreprint

Noske, J., Lepoivre, T., Janzen, M., & Höcker, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.22.753561

Protein interactions with small molecules, peptides and proteins share residue cluster classes

Castillo, J. M. H., Macias, E. H. V., & del Rio, G. (2026) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2026.109614

GeoSeq-EGNN: A geometry-sequence fused equivariant graph neural network for predicting short linear motifs within intrinsically disordered regions

Zhang, J., Zhang, J., Zheng, L., Ruan, H., Li, Y., Li, Y., & Luo, G. (2026) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2026.109610

Structural compatibility is not enough: Current challenges in protein-protein interaction prediction

Elofsson, A. (2026) · Molecular Cell · doi:10.1016/j.molcel.2026.08.030

AlphaFold-driven structure-based drug discovery: Capabilities, benchmarks and limitations for medicinal chemistry

Pathak, M. (2026) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2026.119442

The lysis mechanism of the haloarchaeal virus HFTV1

Kuiper, B. P., van Tilburg, M., Bäcker, L. E., Maity, S., Ariyanto, Y. S., Petters, J. G., Aguirre-Sourrouille, Z., Nijland, J. G., Karsens, H. A., Berrelkamp, G., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.118117

Bias in, bias out – AlphaFold-Multimer and the structural complexity of protein interfaces

Strom, J. M., & Luck, K. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103002 · Cited by 22 · see citing works

PPI-ID: Streamlining protein-protein interaction prediction through domain and SLiM mapping

Goodwin, H. V., & Atkinson, N. S. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013062 · Cited by 3 · see citing works

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