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AlphaFold predictions are valuable hypotheses, and accelerate but do not replace experimental structure determination
bioRxiv - Biochemistry Pub Date : 2023-05-19 , DOI: 10.1101/2022.11.21.517405
Thomas C. Terwilliger, Dorothee Liebschner, Tristan I. Croll, Christopher J. Williams, Airlie J. McCoy, Billy K. Poon, Pavel V. Afonine, Robert D. Oeffner, Jane S. Richardson, Randy J. Read, Paul D. Adams

AI-based methods such as AlphaFold have revolutionized structural biology, often making it possible to predict protein structures with high accuracy. The accuracies of these predictions vary, however, and they do not include ligands, covalent modifications or other environmental factors. Here we focus on very-high-confidence parts of AlphaFold predictions, evaluating how well they can be expected to describe the structure of a protein in a particular environment. We compare predictions with experimental crystallographic maps of the same proteins for 102 crystal structures. In many cases, those parts of AlphaFold predictions that were predicted with very high confidence matched experimental maps remarkably closely. In other cases, these 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. Overall, Cα atoms in very-high-confidence parts of AlphaFold predictions differed from corresponding crystal structures by a median of 0.6 Å, and about 10% of these differed by more than 2 Å, each about twice the values found for pairs of crystal structures containing the same components but determined in different space groups. 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.

中文翻译:

AlphaFold 预测是有价值的假设,可以加速但不能取代实验结构确定

基于 AI 的方法(例如 AlphaFold)彻底改变了结构生物学,通常可以高精度地预测蛋白质结构。然而,这些预测的准确性各不相同,它们不包括配体、共价修饰或其他环境因素。在这里,我们专注于 AlphaFold 预测的非常高置信度的部分,评估它们在特定环境中描述蛋白质结构的预期程度。我们将预测与 102 种晶体结构的相同蛋白质的实验晶体图进行比较。在许多情况下,AlphaFold 预测的那些以非常高的置信度预测的部分与实验地图非常接近。在其他情况下,这些预测通过失真和域方向与全球范围内的实验地图不同,并在局部范围内主链和侧链构象。总的来说,CAlphaFold 预测的极高置信度部分中的 α 原子与相应的晶体结构的中值差异为 0.6 Å,其中约 10% 的差异超过 2 Å,每个约为包含相同的组件,但在不同的空间群中确定。我们建议将 AlphaFold 预测视为非常有用的假设。我们进一步建议,重要的是在解释 AlphaFold 预测时考虑预测的可信度,并进行实验结构确定以验证结构细节,特别是那些涉及未包含在预测中的相互作用的细节。
更新日期:2023-05-22
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