Accurate prediction of protein structures and interactions using a 3-track network

Cited by (141)

Items 101–141 of 141

1. Engineering brain-derived neurotrophic factor mRNA delivery for the treatment of Alzheimer’s disease
   Li, Haoyuan, Cao, Yan, Ye, Jingjing, Yang, Zhipeng, Chen, Qimingxing, Liu, Xiaoqin, Zhang, Bohan, Qiao, Jingwen, Tang, Qisheng, Yang, Huiying et al. · 2023 · Chemical Engineering Journal
2. 3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources
   Varadi, Mihaly, Nair, Sreenath, Sillitoe, Ian, Tauriello, Gerardo, Anyango, Stephen, Bienert, Stefan, Borges, Clemente, Deshpande, Mandar, Green, Tim, Hassabis, Demis et al. · 2022 · GigaScience
3. Computed cancer interactome explains the effects of somatic mutations in cancers
   Zhang, Jing, Pei, Jimin, Durham, Jesse, Bos, Tasia, Cong, Qian · 2022 · Protein Science
4. Language models for materials discovery and sustainability: Progress, challenges, and opportunities
   Pei, Zongrui, Yin, Junqi, Zhang, Jiaxin · 2025 · Progress in Materials Science
5. The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors
   Varadi, Mihaly, Bordin, Nicola, Orengo, Christine, Velankar, Sameer · 2023 · Current Opinion in Structural Biology
6. Protein multiple conformations prediction using multi-objective evolution algorithm
   Hou, Ming-Hua, Jin, Si-Rong, Cui, Xin-Yue, Peng, Chun-Xiang, Zhao, Kai-Long, Song, Le, Zhang, Gui-Jun · 2023
7. Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1
   Nicoli, Alessandro, Haag, Franziska, Marcinek, Patrick, He, Ruiming, Kreißl, Johanna, Stein, Jörg, Marchetto, Alessandro, Dunkel, Andreas, Hofmann, Thomas, Krautwurst, Dietmar et al. · 2023 · Journal of Chemical Information and Modeling
8. Deep learning methods for 3D structural proteome and interactome modeling
   Lee, Dongjin, Xiong, Dapeng, Wierbowski, Shayne, Li, Le, Liang, Siqi, Yu, Haiyuan · 2022 · Current Opinion in Structural Biology
9. Toward the Complete Functional Characterization of a Minimal Bacterial Proteome
   Bianchi, David M., Pelletier, James F., Hutchison, Clyde A., Glass, John I., Luthey-Schulten, Zaida · 2022 · The Journal of Physical Chemistry B
10. Limits and potential of combined folding and docking
    Pozzati, Gabriele, Zhu, Wensi, Bassot, Claudio, Lamb, John, Kundrotas, Petras, Elofsson, Arne · 2022 · Bioinformatics
11. AlphaFold-predicted protein structures and small-angle X-ray scattering: insights from an extended examination of selected data in the Small-Angle Scattering Biological Data Bank
    Brookes, Emre, Rocco, Mattia, Vachette, Patrice, Trewhella, Jill · 2023 · Journal of Applied Crystallography
12. AlphaFold Prediction of Structural Ensembles of Disordered Proteins
    Brotzakis, Z. Faidon, Zhang, Shengyu, Vendruscolo, Michele · 2023
13. Predicting the conformational flexibility of antibody and T-cell receptor CDRs
    Spoendlin, Fabian C., Fernández-Quintero, Monica L., Raghavan, Sai S. R., Turner, Hannah L., Gharpure, Anant, Loeffler, Johannes R., Wong, Wing K., Bujotzek, Alexander, Georges, Guy, Ward, Andrew B. et al. · 2025
14. A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction
    Wu, Ming-Hsiu, Xie, Ziqian, Zhi, Degui · 2025 · Communications Chemistry
15. Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints
    Zhang, Yuanyuan, Zhang, Zicong, Kagaya, Yuki, Terashi, Genki, Zhao, Bowen, Xiong, Yi, Kihara, Daisuke · 2025 · Communications Biology
16. Application of artificial scaffold systems in microbial metabolic engineering
    Liu, Nana, Dong, Wei, Yang, Huanming, Li, Jing-Hua, Chiu, Tsan-Yu · 2023 · Frontiers in Bioengineering and Biotechnology
17. Artificial intelligence in the experimental determination and prediction of macromolecular structures
    Thorn, Andrea · 2022 · Current Opinion in Structural Biology
18. Generative artificial intelligence performs rudimentary structural biology modeling
    Ille, Alexander M., Markosian, Christopher, Burley, Stephen K., Mathews, Michael B., Pasqualini, Renata, Arap, Wadih · 2024 · Scientific Reports
19. Structural assembly of the bacterial essential interactome
    Gómez Borrego, Jordi, Torrent Burgas, Marc · 2024 · eLife
20. Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots
    Nordquist, Erik B., Zhao, Mingtian, Kumar, Anmol, MacKerell, Alexander D. · 2024 · Journal of Chemical Information and Modeling
21. Probing the modulation of enzyme kinetics by multi-temperature, time-resolved serial crystallography
    Schulz, Eike C., Prester, Andreas, von Stetten, David, Gore, Gargi, Hatton, Caitlin E., Bartels, Kim, Leimkohl, Jan-Philipp, Schikora, Hendrik, Ginn, Helen M., Tellkamp, Friedjof et al. · 2025 · Nature Communications
22. The translation initiation factor homolog <i>eif4e1c</i> regulates cardiomyocyte metabolism and proliferation during heart regeneration
    Rao, Anupama, Lyu, Baken, Jahan, Ishrat, Lubertozzi, Anna, Zhou, Gao, Tedeschi, Frank, Jankowsky, Eckhard, Kang, Junsu, Carstens, Bryan, Poss, Kenneth D. et al. · 2023 · Development
23. Deep learning opens up protein science’s next frontiers
    Miller, Johanna L. · 2021 · Physics Today
24. OPUS-Mut: Studying the Effect of Protein Mutation through Side-Chain Modeling
    Xu, Gang, Wang, Qinghua, Ma, Jianpeng · 2023 · Journal of Chemical Theory and Computation
25. Identification and characterisation of the tegument-expressed aldehyde dehydrogenase SmALDH_312 of Schistosoma mansoni, a target of disulfiram
    Beutler, Mandy, Harnischfeger, Julie, Weber, Michael H.W., Hahnel, Steffen R., Quack, Thomas, Blohm, Ariane, Ueberall, Monique E., Timm, Thomas, Lochnit, Günter, Rennar, Georg A. et al. · 2023 · European Journal of Medicinal Chemistry
26. Efficient protein structure generation with sparse denoising models
    Jendrusch, Michael, Korbel, Jan O. · 2025 · Nature Machine Intelligence
27. A fully automated benchmarking suite to compare macromolecular complexes
    Studer, Gabriel, Robin, Xavier, Bienert, Stefan, Durairaj, Janani, Škrinjar, Peter, Tauriello, Gerardo, Waterhouse, Andrew Mark, Schwede, Torsten · 2026 · Nature Methods
28. Resolving the conformational ensemble of a membrane protein by integrating small-angle scattering with AlphaFold
    Lidbrink, Samuel Eriksson, Howard, Rebecca J., Haloi, Nandan, Lindahl, Erik · 2025 · PLOS Computational Biology
29. Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins
    Kandathil, Shaun M, Greener, Joe G, Lau, Andy M, Jones, David T · 2020
30. Computational modeling multiple conformational states of proteins with residual dipolar coupling data
    Abdollahi, Hamed, Prestegard, James H., Valafar, Homayoun · 2023 · Current Opinion in Structural Biology
31. Nuclear Receptor Interdomain Communication is Mediated by the Hinge with Ligand Specificity
    Hazarika, Saurov, Yu, Tracy, Biswas, Arumay, Dube, Namita, Villalona, Priscilla, Okafor, C. Denise · 2024 · Journal of Molecular Biology
32. PDBminer to Find and Annotate Protein Structures for Computational Analysis
    Degn, Kristine, Beltrame, Ludovica, Tiberti, Matteo, Papaleo, Elena · 2023
33. Unifying Constraints Linking Protein Folding and Native Dynamics Decoded from AlphaFold
    Zhang, Zecheng, Ren, Weitong, Xie, Liangxu, Zheng, Yuxiang, Guan, Xingyue, Wang, Jun, Li, Wenfei, Tang, Qian-Yuan · 2026 · Physical Review Letters
34. Exploring voltage-gated sodium channel conformations and protein-protein interactions using AlphaFold2
    Lopez-Mateos, Diego, Narang, Kush, Yarov-Yarovoy, Vladimir · 2024
35. Influence of Stereochemistry in a Local Approach for Calculating Protein Conformations
    da Rocha, Wagner, Liberti, Leo, Mucherino, Antonio, Malliavin, Thérèse E. · 2024 · Journal of Chemical Information and Modeling
36. DeepAssembly2: A Web Server for Protein Complex Structure Assembly Based on Domain-Domain Interactions
    Xia, Yuhao, Pu, Yilin, Wang, Suhui, Zhuang, Jianan, Liu, Dong, Hou, Minghua, Zhang, Guijun · 2025 · Journal of Molecular Biology
37. AlphaMut: A Deep Reinforcement Learning Model to Suggest Helix-Disrupting Mutations
    Bhargav, Prathith, Mukherjee, Arnab · 2025 · Journal of Chemical Theory and Computation
38. ECOD: Classification of domains in AFDB Swiss-Prot structure predictions
    Schaeffer, R. Dustin, Zhang, Jing, Cong, Qian, Grishin, Nick V. · 2026 · PLOS Computational Biology
39. HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses
    Kravchenko, Anna, de Vries, Sjoerd Jacob, Smaïl-Tabbone, Malika, Chauvot de Beauchene, Isaure · 2024 · BMC Bioinformatics
40. Reshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1
    Liu, Lihang, Liu, Yang, Ye, Xianbin, Zhang, Shanzhuo, Li, Yuxin, Zhu, Kunrui, Xue, Yang, Zhou, Jingbo, Zhang, Xiaonan, Fang, Xiaomin · 2026 · Nature Machine Intelligence
41. Scalable emulation of protein equilibrium ensembles with generative deep learning
    Lewis, Sarah, Hempel, Tim, Jiménez Luna, José, Gastegger, Michael, Xie, Yu, Foong, Andrew Y. K., García Satorras, Victor, Abdin, Osama, Veeling, Bastiaan S., Zaporozhets, Iryna et al. · 2024 · bioRxiv (Cold Spring Harbor Laboratory)