CASP is a community experiment to advance methods of computing three-dimensional protein structure from amino acid sequence. Core components are rigorous blind testing of methods and evaluation of the results by independent assessors. In the most recent experiment (CASP14) deep learning methods from one research group consistently delivered computed structures rivalling the corresponding experimental ones in accuracy. In this sense, the results represent a solution to the classical protein folding problem, at least for single proteins. The models have already been shown to be capable of providing solutions for problematic crystal structures, and there are broad implications for the rest of structural biology. Other research groups also substantially improved performance. Here we describe these results and outline some of the many implications. Other related areas of CASP, including modeling of protein complexes, structure refinement, estimation of model accuracy, and prediction of inter-residue contacts and distances, are also described.

中文翻译：

---

蛋白质结构预测 (CASP) 方法的严格评估 – 第 XIV 轮


CASP 是一项社区实验，旨在推进根据氨基酸序列计算三维蛋白质结构的方法。核心部分是对方法进行严格的盲测并由独立评估人员对结果进行评估。在最近的实验 (CASP14) 中，一个研究小组的深度学习方法始终提供与相应实验结构的准确性相媲美的计算结构。从这个意义上说，这些结果代表了经典蛋白质折叠问题的解决方案，至少对于单个蛋白质而言。这些模型已被证明能够为有问题的晶体结构提供解决方案，并且对结构生物学的其余部分具有广泛的影响。其他研究小组也大幅提高了性能。在这里，我们描述了这些结果并概述了其中的一些含义。还描述了 CASP 的其他相关领域，包括蛋白质复合物建模、结构细化、模型精度估计以及残基间接触和距离的预测。