AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

By T. C. Terwilliger, D. Liebschner, T. Croll, Christopher J. Williams, A. McCoy, Billy K. Poon, Pavel V. Afonine, R. Oeffner, Jane S. Richardson, Randy J. Read and 1 more. bioRxiv, 2023

298 citations recorded by the source corpus.

An analysis of AlphaFold protein structure predictions shows that while in many cases the predictions are highly accurate, there are also many instances where the predicted structures or parts of predicted structures do not agree with experimentally resolved data. Therefore, care must be taken when using these predictions for informing structural hypotheses. Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5d7a68cc2346e688913a3f303690cc05799a1af9

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Citations in the corpus

UCSF ChimeraX: Tools for structure building and analysis (2023, background): “AlphaFold structure prediction (Jumper et al., 2021) can produce starting protein models, albeit not as accurate as from experiment (Terwilliger et al., 2023), but suitable for subsequent refinement in a cryo-EM map.”