School of Biological Sciences, University of Reading, Whiteknights, ReadingRG6 6EX, UK.
Nucleic Acids Res. 2023 Jul 5;51(W1):W274-W280. doi: 10.1093/nar/gkad297.
The IntFOLD server based at the University of Reading has been a leading method over the past decade in providing free access to accurate prediction of protein structures and functions. In a post-AlphaFold2 world, accurate models of tertiary structures are widely available for even more protein targets, so there has been a refocus in the prediction community towards the accurate modelling of protein-ligand interactions as well as modelling quaternary structure assemblies. In this paper, we describe the latest improvements to IntFOLD, which maintains its competitive structure prediction performance by including the latest deep learning methods while also integrating accurate model quality estimates and 3D models of protein-ligand interactions. Furthermore, we also introduce our two new server methods: MultiFOLD for accurately modelling both tertiary and quaternary structures, with performance which has been independently verified to outperform the standard AlphaFold2 methods, and ModFOLDdock, which provides world-leading quality estimates for quaternary structure models. The IntFOLD7, MultiFOLD and ModFOLDdock servers are available at: https://www.reading.ac.uk/bioinf/.
基于雷丁大学的 IntFOLD 服务器在过去十年中一直是提供免费获取蛋白质结构和功能准确预测的领先方法。在 AlphaFold2 之后的世界中，甚至更多蛋白质靶标也有了广泛可用的准确三级结构模型，因此预测社区的重点已经重新转向准确建模蛋白质-配体相互作用以及建模四级结构组装。在本文中，我们描述了 IntFOLD 的最新改进，它通过包含最新的深度学习方法，同时还集成了准确的模型质量估计和蛋白质-配体相互作用的 3D 模型，从而保持其有竞争力的结构预测性能。此外，我们还介绍了我们的两个新的服务器方法：MultiFOLD 用于准确建模三级和四级结构，其性能已被独立验证优于标准的 AlphaFold2 方法，以及 ModFOLDdock，它提供了领先世界的四级结构模型的质量估计。IntFOLD7、MultiFOLD 和 ModFOLDdock 服务器可在以下网址获得：https://www.reading.ac.uk/bioinf/。
Zotero 插件
