Open Access
| Issue |
BIO Web Conf.
Volume 182, 2025
The 3rd International Conference on Food Science and Bio-medicine (ICFSB 2025)
|
|
|---|---|---|
| Article Number | 02013 | |
| Number of page(s) | 4 | |
| Section | Biomedical Research and Applications | |
| DOI | https://doi.org/10.1051/bioconf/202518202013 | |
| Published online | 02 July 2025 | |
- J. Jumper et al. Highly accurate protein structure prediction with alphafold. Nature, 577(7792):583–589, 2021. [CrossRef] [PubMed] [Google Scholar]
- G. Ahdritz et al. Openproteinset: Training data for structural biology at scale. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks, 2023. [Google Scholar]
- M. Steinegger and J. Soding. Clustering huge protein sequence sets in linear time.¨ Nature Communications, 9(1):2542, 2018. [CrossRef] [PubMed] [Google Scholar]
- Z. Lin et al. Evolutionary-scale prediction of atomiclevel protein structure with a language model. Science, 379(6637):1123–1130, 2023. [CrossRef] [PubMed] [Google Scholar]
- A. Elofsson. Progress at protein structure prediction, as seen in casp15. Current Opinion in Structural Biology, 80:102594, 2023. [CrossRef] [PubMed] [Google Scholar]
- M. Remmert, A. Biegert, A. Hauser, and J. Soding. Hhblits: lightning-fast iterative protein sequence searching by hmm-hmm alignment.¨ Nature Methods, 9(2):173–175, 2012. [CrossRef] [Google Scholar]
- L. S. Johnson, S. R. Eddy, and E. Portugaly. Hidden markov model speed heuristic and iterative hmm search procedure. BMC Bioinformatics, 11(1):431, 2010. [CrossRef] [PubMed] [Google Scholar]
- G. Ahdritz et al. Openfold: Retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization. bioRxiv, 2022. [Google Scholar]
- M. Mirdita, K. Schutze, Y. Moriwaki, L. Heo, S. Ovchinnikov, and M. Steinegger. Colabfold: making protein folding accessible to all.¨ Nature Methods, 19(6):679–682, 2022. [CrossRef] [PubMed] [Google Scholar]
- A. L. Mitchell et al. Mgnify: the microbiome analysis resource in 2020. Nucleic Acids Research, 48(D1):D570–D578, 2020. [PubMed] [Google Scholar]
- R. Chowdhury et al. Single-sequence protein structure prediction using a language model and deep learning. Nature Biotechnology, 2022. [Google Scholar]
- A. Rives et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences, 118(15):e2016239118, 2021. [CrossRef] [PubMed] [Google Scholar]
- M. Heinzinger, A. Elnaggar, Y. Wang, C. Dallago, D. Nechaev, F. Matthes, and B. Rost. Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinformatics, 20(723), 2019. [CrossRef] [Google Scholar]
- D. T. Jones, D. W. A. Buchan, D. Cozzetto, and M. Pontil. Psicov: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics, 28(2):184–190, 2011. [Google Scholar]
- wwPDB Consortium. Protein data bank: the single global archive for 3d macromolecular structure data. Nucleic Acids Research, 47(D1):D520–D528, 2018. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.