‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures

By E. Callaway. Nature, 2020

373 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fbfc91615651f9b547fe2a8d2f9fa6e35a898996

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Citations in the corpus

Estimating conformational heterogeneity of tryptophan synthase with a template‐based Alphafold2 approach (2022, methodology): “AF2 is recognized as one of the milestones in protein structure prediction, and has boosted the application of deeplearning methods for many other applications.(26) Despite the impressive performance of AF2 algorithms in predicting the native lowest in energy structure of proteins, application of AF”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “Moreover, the most recent developments of deep learning for protein structure prediction (41 – 43) foreshadow new methods in the design, taking advantage of learned principles of the protein structure (44, 45).”
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB (2022, methodology): “• High quality: The median predictive score of Al-phaFold2 reaches 92.4%, comparable to experimental techniques (Callaway, 2020).”
Using AlphaFold to predict the impact of single mutations on protein stability and function (2021, methodology): “Crucially, AlphaFold models showed an accuracy of their predicted structures that was comparable to structures solved by experimental methods, like X-ray crystallography, NMR, and Cryo-EM [3].”
Identification of Potential WSB1 Inhibitors by AlphaFold Modeling, Virtual Screening, and Molecular Dynamics Simulation Studies (2022, background): “AlphaFold2 is artificial intelligence for protein structure prediction, which is a new neural network-based model that can predict protein structures with atomic-level accuracy [29, 30].”
Artificial intelligence and machine learning for medical imaging: A technology review. (2021, neutral): “A very recent example is the breakthrough of AI in the CASP competition [244].”
Rethinking Protein Drug Design with Highly Accurate Structure Prediction of Anti-CRISPR Proteins (2021, background): “The recent release of AlphaFold2, the winner of CASP14, which achieves highly accurate protein structure predictions [25,26], is revolutionary for the ﬁeld of life sciences and medicine, and is expected to accelerate critical research in a large number of ﬁelds ranging from structural biology to…”
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021, background): “With the development of structural biology and protein structure prediction, especially the recent Alphafold II model [4], there are growing three-dimensional (3D) structure protein data, which enables a new paradigm for structure-based drug discovery [3, 13, 27].”
Multi-Scale Representation Learning on Proteins (2022, methodology): “, 2021, RoseTTAFold] and protein structure determination methods such as cryo-electron microscopy [Callaway, 2020], and (ii.”
Deep learning techniques have significantly impacted protein structure prediction and protein design. (2021, background): “5 for all domains (except for one whose structure was solved by NMR) in the CASP14 experiment, marking the solution of the single-domain protein structure prediction problem at a fold level [68].”
The trRosetta server for fast and accurate protein structure prediction (2021, background): “The work of AlphaFold2 is regarded as one of the milestones in protein structure prediction and is anticipated to ‘ change everything ’ 10 .”