2022
DOI: 10.1039/d2fd00072e
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The impact of AlphaFold2 on experimental structure solution

Abstract: AlphaFold2 predicts protein folds from sequence, which can be used for experimental structural biology, in construction and de novo protein design, prediction of complexes and perhaps even effects of mutations and conformational space exploration.

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Cited by 15 publications
(12 citation statements)
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“…Another example where AlphaFold2 is currently reaching its limits are enzymes with co-factors. In these enzymes, a conformational change is initiated, which in turn enables the enzyme to bind its specific substrate to trigger further reactions [28]. AlphaFold2 seems to be unable to predict loop changes caused by the binding of before mentioned cofactors.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Another example where AlphaFold2 is currently reaching its limits are enzymes with co-factors. In these enzymes, a conformational change is initiated, which in turn enables the enzyme to bind its specific substrate to trigger further reactions [28]. AlphaFold2 seems to be unable to predict loop changes caused by the binding of before mentioned cofactors.…”
Section: Discussionmentioning
confidence: 99%
“…As a result, time-consuming methods for molecular prediction, such as nuclear magnetic resonance (NMR), can be replaced by such machine learning-based approaches. Nevertheless, for complex interactions in and between large macromolecules, such as the interaction between RNA and proteins and in-between proteins, experimental techniques, including but not limited to NMR and X-Ray crystallography, can be applied [28,29]. These laboratory techniques are also the basis for a commonly used approach for structural prediction based on homology modeling.…”
Section: Introductionmentioning
confidence: 99%
“…Such interactions are especially crucial for drug discovery. It is to be expected, therefore, that much of AlphaFold’s criticism is related to the fact that it omits protein-ligand interactions in its predictions [ 18 , 73 ].…”
Section: Critique Of Alphafoldmentioning
confidence: 99%
“…Recently a machine learning based method AlphaFold2 has successfully demonstrated the ability in predicting protein folds (25) and multimeric interfaces (26) given a query sequence. However, this approach is limited in studying the actomyosin cycle, due to its inability to handle ligand and mutation effects, as well as multiple conformational states and transitions (27,28). Developed over decades, all-atom molecular dynamics (MD) simulations have become powerful in studying the mechanisms of biomolecular machines (29)(30)(31).…”
Section: Significance Statementmentioning
confidence: 99%
“…How-ever, this approach is limited in studying the actomyosin cycle, due to its inability to handle ligand and mutation effects, as well as multiple conformational states and transitions. 29,30 Developed over decades, all-atom molecular dynamics (MD) simulations have become powerful in studying the mechanisms of biomolecular machines. [31][32][33][34][35] By combining comparative modeling and Gaussian accelerated molecular dynamics (GaMD), 36 we develop a computational approach that characterizes the conformational ensembles of actomyosin at different ligand states.…”
Section: Introductionmentioning
confidence: 99%