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Improved protein structure prediction using potentials from deep learning

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Cited by:

  1. Zachary C. Drake & Justin T. Seffernick & Steffen Lindert, 2022. "Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
  2. Tian Lan & Huan Wang & Qi An, 2024. "Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  3. Tao Ni & Qiuyao Jiang & Pei Cing Ng & Juan Shen & Hao Dou & Yanan Zhu & Julika Radecke & Gregory F. Dykes & Fang Huang & Lu-Ning Liu & Peijun Zhang, 2023. "Intrinsically disordered CsoS2 acts as a general molecular thread for α-carboxysome shell assembly," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
  4. Zhiye Guo & Jian Liu & Jeffrey Skolnick & Jianlin Cheng, 2022. "Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  5. Naughtin, Claire & Hajkowicz, Stefan & Schleiger, Emma & Bratanova, Alexandra & Cameron, Alicia & Zamin, T & Dutta, A, 2022. "Our Future World: Global megatrends impacting the way we live over coming decades," MPRA Paper 113900, University Library of Munich, Germany.
  6. Lei Wang & Jiangguo Zhang & Dali Wang & Chen Song, 2022. "Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins," PLOS Computational Biology, Public Library of Science, vol. 18(3), pages 1-27, March.
  7. Qiufen Chen & Yuanzhao Guo & Jiuhong Jiang & Jing Qu & Li Zhang & Han Wang, 2023. "The Relative Distance Prediction of Transmembrane Protein Surface Residue Based on Improved Residual Networks," Mathematics, MDPI, vol. 11(3), pages 1-16, January.
  8. Nicolas Renaud & Cunliang Geng & Sonja Georgievska & Francesco Ambrosetti & Lars Ridder & Dario F. Marzella & Manon F. Réau & Alexandre M. J. J. Bonvin & Li C. Xue, 2021. "DeepRank: a deep learning framework for data mining 3D protein-protein interfaces," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
  9. Noelia Ferruz & Steffen Schmidt & Birte Höcker, 2022. "ProtGPT2 is a deep unsupervised language model for protein design," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  10. Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  11. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2022. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Papers 2201.07168, arXiv.org.
  12. Willow Coyote-Maestas & David Nedrud & Antonio Suma & Yungui He & Kenneth A. Matreyek & Douglas M. Fowler & Vincenzo Carnevale & Chad L. Myers & Daniel Schmidt, 2021. "Probing ion channel functional architecture and domain recombination compatibility by massively parallel domain insertion profiling," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
  13. Benoit Stijlemans & Patrick Baetselier & Inge Molle & Laurence Lecordier & Erika Hendrickx & Ema Romão & Cécile Vincke & Wendy Baetens & Steve Schoonooghe & Gholamreza Hassanzadeh-Ghassabeh & Hannelie, 2024. "Q586B2 is a crucial virulence factor during the early stages of Trypanosoma brucei infection that is conserved amongst trypanosomatids," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
  14. Lauren L. Porter & Allen K. Kim & Swechha Rimal & Loren L. Looger & Ananya Majumdar & Brett D. Mensh & Mary R. Starich & Marie-Paule Strub, 2022. "Many dissimilar NusG protein domains switch between α-helix and β-sheet folds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  15. Luis S. Piloto & Ari Weinstein & Peter Battaglia & Matthew Botvinick, 2022. "Intuitive physics learning in a deep-learning model inspired by developmental psychology," Nature Human Behaviour, Nature, vol. 6(9), pages 1257-1267, September.
  16. Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  17. Januschowski, Tim & Wang, Yuyang & Torkkola, Kari & Erkkilä, Timo & Hasson, Hilaf & Gasthaus, Jan, 2022. "Forecasting with trees," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1473-1481.
  18. Chengwei Zeng & Yiren Jian & Soroush Vosoughi & Chen Zeng & Yunjie Zhao, 2023. "Evaluating native-like structures of RNA-protein complexes through the deep learning method," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
  19. Lisa Van den Broeck & Dinesh Kiran Bhosale & Kuncheng Song & Cássio Flavio Fonseca de Lima & Michael Ashley & Tingting Zhu & Shanshuo Zhu & Brigitte Van De Cotte & Pia Neyt & Anna C. Ortiz & Tiffany R, 2023. "Functional annotation of proteins for signaling network inference in non-model species," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  20. Jong Woo Bae & Sangtae Kim & V. Narry Kim & Jong-Seo Kim, 2021. "Photoactivatable ribonucleosides mark base-specific RNA-binding sites," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
  21. Erika Erickson & Japheth E. Gado & Luisana Avilán & Felicia Bratti & Richard K. Brizendine & Paul A. Cox & Raj Gill & Rosie Graham & Dong-Jin Kim & Gerhard König & William E. Michener & Saroj Poudel &, 2022. "Sourcing thermotolerant poly(ethylene terephthalate) hydrolase scaffolds from natural diversity," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  22. Chao Li & Qiming Yang & Bowen Pang & Tiance Chen & Qian Cheng & Jiaomin Liu, 2021. "A Mixed Strategy of Higher-Order Structure for Link Prediction Problem on Bipartite Graphs," Mathematics, MDPI, vol. 9(24), pages 1-13, December.
  23. Agnese I. Curatolo & Ofer Kimchi & Carl P. Goodrich & Ryan K. Krueger & Michael P. Brenner, 2023. "A computational toolbox for the assembly yield of complex and heterogeneous structures," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  24. Emilie Montembault & Irène Deduyer & Marie-Charlotte Claverie & Lou Bouit & Nicolas J. Tourasse & Denis Dupuy & Derek McCusker & Anne Royou, 2023. "Two RhoGEF isoforms with distinct localisation control furrow position during asymmetric cell division," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  25. Simone Vannuccini & Ekaterina Prytkova, 2021. "Artificial Intelligence’s New Clothes? From General Purpose Technology to Large Technical System," SPRU Working Paper Series 2021-02, SPRU - Science Policy Research Unit, University of Sussex Business School.
  26. Liu, Bokai & Wang, Yizheng & Rabczuk, Timon & Olofsson, Thomas & Lu, Weizhuo, 2024. "Multi-scale modeling in thermal conductivity of Polyurethane incorporated with Phase Change Materials using Physics-Informed Neural Networks," Renewable Energy, Elsevier, vol. 220(C).
  27. Aaron Gupta & Kevin S. Kao & Rachel Yamin & Deena A. Oren & Yehuda Goldgur & Jonathan Du & Pete Lollar & Eric J. Sundberg & Jeffrey V. Ravetch, 2023. "Mechanism of glycoform specificity and in vivo protection by an anti-afucosylated IgG nanobody," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  28. Md Tauhidul Islam & Zixia Zhou & Hongyi Ren & Masoud Badiei Khuzani & Daniel Kapp & James Zou & Lu Tian & Joseph C. Liao & Lei Xing, 2023. "Revealing hidden patterns in deep neural network feature space continuum via manifold learning," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
  29. Cong Zhang & Di-Fei Zhou & Meng-Ying Wang & Ya-Zhen Song & Chong Zhang & Ming-Ming Zhang & Jing Sun & Lu Yao & Xu-Hua Mo & Zeng-Xin Ma & Xiao-Jie Yuan & Yi Shao & Hao-Ran Wang & Si-Han Dong & Kai Bao , 2024. "Phosphoribosylpyrophosphate synthetase as a metabolic valve advances Methylobacterium/Methylorubrum phyllosphere colonization and plant growth," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  30. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
  31. Hajkowicz, Stefan & Naughtin, Claire & Sanderson, Conrad & Schleiger, Emma & Karimi, Sarvnaz & Bratanova, Alexandra & Bednarz, Tomasz, 2022. "Artificial intelligence for science – adoption trends and future development pathways," MPRA Paper 115464, University Library of Munich, Germany.
  32. Charlotte Loh & Thomas Christensen & Rumen Dangovski & Samuel Kim & Marin Soljačić, 2022. "Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  33. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2023. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3313-3335, June.
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