Accelerating Cryptic Pocket Discovery Using AlphaFold.
Accelerating Cryptic Pocket Discovery Using AlphaFold.
复制标题
使用AlphaFold加速隐藏口袋的发现。
DOI：
10.1021/acs.jctc.2c01189
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发表时间：
2023-07-25
影响因子：
5.5
通讯作者：
Bowman, Gregory R.
中科院分区：
文献类型：
--
作者：
Meller, Artur;Bhakat, Soumendranath;Solieva, Shahlo;Bowman, Gregory R.
隐藏的口袋，或口袋中没有配体，实验确定的结构，具有很大的潜力作为药物靶点。然而，隐藏的口袋开口往往超出了传统的生物分子模拟的范围，因为某些隐藏的口袋开口涉及慢动作。在这里，我们研究AlphaFold是否可以通过直接生成具有开放口袋的结构或生成具有部分开放口袋的结构来加速隐藏口袋的发现，这些结构可以用作模拟的起点。我们使用AlphaFold为10个已知的神秘口袋示例生成集合，其中包括从PDB提取AlphaFold训练数据后沉积的5个。我们发现，在10个案例中，有6个AlphaFold样本处于开放状态。对于plasmepsin II，一种来自疟疾病原体的天冬氨酸蛋白酶，AlphaFold仅捕获部分口袋开口。因此，我们从AlphaFold-generated结构的集合中进行了模拟，并表明这种策略对隐藏的口袋打开进行了采样，即使从无配体实验结构中发射的等量模拟未能做到这一点。从AlphaFold种子模拟构建的马尔可夫状态模型（MSM）快速产生了一个神秘口袋打开的自由能景观，这与使用良好回火的元自洽模型生成的相同景观非常一致。总之，我们的结果表明，AlphaFold在隐藏口袋发现中发挥了有用的作用，但许多隐藏口袋可能仍然难以单独使用AlphaFold进行采样。
Cryptic pockets, or pockets absent in ligand-free, experimentally determined structures, hold great potential as drug targets. However, cryptic pocket openings are often beyond the reach of conventional biomolecular simulations because certain cryptic pocket openings involve slow motions. Here, we investigate whether AlphaFold can be used to accelerate cryptic pocket discovery either by generating structures with open pockets directly or generating structures with partially open pockets that can be used as starting points for simulations. We use AlphaFold to generate ensembles for 10 known cryptic pocket examples, including five that were deposited after AlphaFold’s training data were extracted from the PDB. We find that in 6 out of 10 cases AlphaFold samples the open state. For plasmepsin II, an aspartic protease from the causative agent of malaria, AlphaFold only captures a partial pocket opening. As a result, we ran simulations from an ensemble of AlphaFold-generated structures and show that this strategy samples cryptic pocket opening, even though an equivalent amount of simulations launched from a ligand-free experimental structure fails to do so. Markov state models (MSMs) constructed from the AlphaFold-seeded simulations quickly yield a free energy landscape of cryptic pocket opening that is in good agreement with the same landscape generated with well-tempered metadynamics. Taken together, our results demonstrate that AlphaFold has a useful role to play in cryptic pocket discovery but that many cryptic pockets may remain difficult to sample using AlphaFold alone.
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