AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility.
AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility.
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DOI：
10.1371/journal.pcbi.1004586
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发表时间：
2015-12
影响因子：
4.3
通讯作者：
Sanner MF
中科院分区：
文献类型：
--
作者：
Ravindranath PA;Forli S;Goodsell DS;Olson AJ;Sanner MF
药物类分子与受体的自动对接是基于结构的药物设计的重要工具。虽然模拟受体的灵活性对正确预测配体结合很重要，但它仍然具有挑战性。这项工作的重点是通过明确指定一组先验的受体侧链来模拟受体灵活性的方法。该方法面临的挑战包括：1)搜索空间呈指数级增长，需要更高效的搜索方法；2)假阳性增加，需要为灵活的受体对接量身定制评分功能。我们提出了AutoDockFR-AutoDock for Flexible Receptors (ADFR)，这是一种基于AutoDock4评分功能的新型对接引擎，它通过新的遗传算法（GA）和自定义评分功能解决了上述挑战。我们使用Astex多样化集验证ADFR，证明其遗传算法的效率和可靠性比AutoDock4中实现的遗传算法有所提高。我们证明，当配体与载脂蛋白受体交叉对接时，配体结合的成功率大大提高，这需要侧链构象改变。这些交叉对接实验基于两个数据集：1)SEQ17 -一个包含17对载子全息结构的受体多样性集；2) CDK2 -由一种CDK2载子结构和52种已知结合抑制剂组成的配体多样性集。我们发现，当交叉对接配体进入具有多达14个柔性侧链的受体载子构象时，ADFR比AutoDock Vina在两个数据集上报告的交叉对接配体更正确，SEQ17的解决方案为70.6%比35.3%，CDK2的解决方案为76.9%比61.5%。在这两个数据集上，ADFR在许多排名靠前的解决方案上也优于AutoDock Vina。此外，我们发现正确对接的CDK2配合物平均能重建全息配合物中配体和移动受体原子之间79.8%的成对原子相互作用。最后，我们证明了降低受体内部能量的权重可以提高正确停靠姿势的排名，并且当增加侧链灵活性时，AutoDockFR的运行时间呈线性增长。对接程序被广泛用于识别与给定受体相互作用以抑制其功能的药物样分子。虽然已知受体在配体结合时改变构象，但大多数对接程序将小分子建模为柔性，而将受体建模为刚性，从而限制了对接可以应用的治疗靶点的范围。在这里，我们介绍了一个新的对接程序AutoDockFR，它通过允许大量明确指定的受体侧链探索其构象空间来模拟部分受体的灵活性，同时为给定配体寻找能量上有利的结合姿势。我们表明，通过在实验确定的没有配体存在的受体构象（即载脂蛋白构象）的结合位点包括受体灵活性，我们实现了更高的对接成功率。先前的方法基于先验和明确指定的受体被认为是柔性的部分，迄今为止仅限于少数柔性蛋白质侧链（2-5），因此需要事先了解受体侧链在与给定配体结合时发生构象变化的情况。AutoDockFR在识别多达14个柔性受体侧链问题的正确解决方案方面的能力降低了这一要求。
Automated docking of drug-like molecules into receptors is an essential tool in structure-based drug design. While modeling receptor flexibility is important for correctly predicting ligand binding, it still remains challenging. This work focuses on an approach in which receptor flexibility is modeled by explicitly specifying a set of receptor side-chains a-priori. The challenges of this approach include the: 1) exponential growth of the search space, demanding more efficient search methods; and 2) increased number of false positives, calling for scoring functions tailored for flexible receptor docking. We present AutoDockFR–AutoDock for Flexible Receptors (ADFR), a new docking engine based on the AutoDock4 scoring function, which addresses the aforementioned challenges with a new Genetic Algorithm (GA) and customized scoring function. We validate ADFR using the Astex Diverse Set, demonstrating an increase in efficiency and reliability of its GA over the one implemented in AutoDock4. We demonstrate greatly increased success rates when cross-docking ligands into apo receptors that require side-chain conformational changes for ligand binding. These cross-docking experiments are based on two datasets: 1) SEQ17 –a receptor diversity set containing 17 pairs of apo-holo structures; and 2) CDK2 –a ligand diversity set composed of one CDK2 apo structure and 52 known bound inhibitors. We show that, when cross-docking ligands into the apo conformation of the receptors with up to 14 flexible side-chains, ADFR reports more correctly cross-docked ligands than AutoDock Vina on both datasets with solutions found for 70.6% vs. 35.3% systems on SEQ17, and 76.9% vs. 61.5% on CDK2. ADFR also outperforms AutoDock Vina in number of top ranking solutions on both datasets. Furthermore, we show that correctly docked CDK2 complexes re-create on average 79.8% of all pairwise atomic interactions between the ligand and moving receptor atoms in the holo complexes. Finally, we show that down-weighting the receptor internal energy improves the ranking of correctly docked poses and that runtime for AutoDockFR scales linearly when side-chain flexibility is added. Docking programs are widely used to identify drug-like molecules interacting with a given receptor to inhibit its function. Although receptors are known to change conformation upon ligand binding, most docking programs model small molecules as flexible while modeling receptors as rigid, thus limiting the range of therapeutic targets for which docking can be applied. Here we introduce a new docking program, AutoDockFR, which simulates partial receptor flexibility by allowing a large number of explicitly specified receptor side-chains to explore their conformational space, while searching for energetically favorable binding poses for a given ligand. We show that we achieve higher docking success rates by including receptor flexibility in the binding site of receptor conformations that are experimentally determined without the ligand present (i.e. apo conformations). Previous approaches based on the a-priori and explicit specification of the part of the receptor to be considered flexible, have so far been limited to a small number of flexible protein side-chains (2–5), thus requiring prior knowledge of receptor side-chains undergoing conformational change upon binding of a given ligand. The demonstrated ability of AutoDockFR in identifying correct solutions for problems with up to 14 flexible receptor side-chains lessens this requirement.