Department of Computer Science, Virginia Tech, Blacksburg, VA 24061.
Proc Natl Acad Sci U S A. 2023 Aug 8;120(32):e2303499120. doi: 10.1073/pnas.2303499120. Epub 2023 Jul 31.
Transformer neural networks have revolutionized structural biology with the ability to predict protein structures at unprecedented high accuracy. Here, we report the predictive modeling performance of the state-of-the-art protein structure prediction methods built on transformers for 69 protein targets from the recently concluded 15th Critical Assessment of Structure Prediction (CASP15) challenge. Our study shows the power of transformers in protein structure modeling and highlights future areas of improvement.
Transformer 神经网络以能够以前所未有的高精度预测蛋白质结构而在结构生物学领域掀起了一场革命。在这里,我们报告了最近结束的第 15 届蛋白质结构预测关键评估 (CASP15) 挑战赛中基于转换器的最先进蛋白质结构预测方法在 69 个蛋白质靶标上的预测建模性能。我们的研究展示了转换器在蛋白质结构建模中的强大功能,并强调了未来的改进领域。