2023年3月14日 · AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with...

alphafold2安装方式有两种：docker安装和非docker安装。 Deepmind 开源代码中提供的是构建docker镜像来运行af2。 由于网络环境差异，在国内完全按照代码中说明一步步来安装是基本不可能的。 这里我们使用非docker方式安装和运行alphafold2，可以顺畅完成整个安装流程，主要参考 github.com/kalininalab/。 1. 硬件需求. 仅仅使用alphafold进行蛋白结构预测而不进行模型训练的话，af2其实对GPU完全没有要求，全部预测都可以通过CPU进行（当然速度会慢，但确实能 …

2024年5月16日 · AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and serotonin 2A (...

2022年11月7日 · Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions;...

AlphaFold 2 (AF2) was the star of CASP14, the last biannual structure prediction experiment. Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or near experimental resolution. Here, we present our ...

在此，使用alphafold2预测出三维结构模型，基于得到的蛋白质pdb文件，利用软件预测蛋白质与铜离子之间具有氢键作用的氨基酸来确定预测其活性位点，之后通过定点突变技术衡量突变体蛋白的功能来确认活性中心。 （预测软件很多，可后续在为小伙伴推荐） （a）C-score表示预测结果的可信度，范围是 [0-1]，得分越高，预测结果越可信。 （d）Consensus Binding Residues表示潜在配位体氨基酸。 可以看到，当蛋白以铜离子为活性中时，其得分最高且远高于第二、三个， …

af2（以及它之前的第一个 af，虽然以一种看似奇怪的方式进行训练：不是在整个蛋白质上，而是在蛋白质片段上，或者 af2 团队所说的「多肽作物」。 他们不短， 通常有几百个残基。

2022年6月23日 · We show that PAE maps from AF2 are correlated with the distance variation (DV) matrices from molecular dynamics (MD) simulations, which reveals that the PAE maps can predict the dynamical nature...

2024年3月19日 · Abstract: AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users.

2022年1月15日 · The improved version, AlphaFold2 (AF2) achieved an accuracy level much higher than other predictors at CASP14 [3, 4]. Importantly, DeepMind released their code with deep learning models and deposited AF2-predicted structures for the human and 20 other proteomes in collaboration with EBI (https://alphafold.ebi.ac.uk).