School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK.
Nucleic Acids Res. 2021 Jul 2;49(W1):W589-W596. doi: 10.1093/nar/gkab300.
ReFOLD3 is unique in its application of gradual restraints, calculated from local model quality estimates and contact predictions, which are used to guide the refinement of theoretical 3D protein models towards the native structures. ReFOLD3 achieves improved performance by using an iterative refinement protocol to fix incorrect residue contacts and local errors, including unusual bonds and angles, which are identified in the submitted models by our leading ModFOLD8 model quality assessment method. Following refinement, the likely resulting improvements to the submitted models are recognized by ModFOLD8, which produces both global and local quality estimates. During the CASP14 prediction season (May-Aug 2020), we used the ReFOLD3 protocol to refine hundreds of 3D models, for both the refinement and the main tertiary structure prediction categories. Our group improved the global and local quality scores for numerous starting models in the refinement category, where we ranked in the top 10 according to the official assessment. The ReFOLD3 protocol was also used for the refinement of the SARS-CoV-2 targets as a part of the CASP Commons COVID-19 initiative, and we provided a significant number of the top 10 models. The ReFOLD3 web server is freely available at https://www.reading.ac.uk/bioinf/ReFOLD/.
ReFOLD3 的独特之处在于其逐渐施加约束的应用,这些约束是根据局部模型质量估计和接触预测计算得出的,用于指导理论 3D 蛋白质模型向天然结构的细化。ReFOLD3 通过使用迭代细化协议来修复不正确的残基接触和局部错误来提高性能,包括由我们领先的 ModFOLD8 模型质量评估方法在提交模型中识别出的异常键和角度。细化后,ModFOLD8 会识别提交模型中可能的改进,并生成全局和局部质量估计。在 CASP14 预测季节(2020 年 5 月至 8 月)期间,我们使用 ReFOLD3 协议细化了数百个 3D 模型,包括细化和主要三级结构预测类别。我们组在细化类别中提高了许多起始模型的全局和局部质量评分,在官方评估中排名前 10。ReFOLD3 协议还被用于 SARS-CoV-2 靶标的细化,作为 CASP Commons COVID-19 计划的一部分,我们提供了大量排名前 10 的模型。ReFOLD3 网络服务器可在 https://www.reading.ac.uk/bioinf/ReFOLD/ 免费使用。