Mapping Protein Surfaces in Computational Drug Design

摘要：基于结构的药物发现（SBDD）的最大限制是需要忽略水 和蛋白质的灵活性。在这里，我们概述了克服这些限制的模拟方法。 该提案的重点是开发MixMD，我们的混合溶剂分子动力学（MD）的方法。MixMD 识别蛋白质表面上的关键结合子位点（热点）。蛋白质是在一个盒子里模拟的， 水与5%小的有机探针共溶剂。沃茨和探测器沿着沿着对当地环境进行采样 蛋白质表面和具有高探针占有率的位点被鉴定为热点。MixMD具有上级 性能优于其他助溶剂MD方法，如Mackerell的SILCS和Barril的MDmix。其他方法 被许多虚假的、误导性的、“额外的”位点所困扰，这些位点与真实的结合位点无法区分， 阻碍了预期的应用。 我们的长期目标是通过开发更准确地模拟蛋白质配体的方法来改善SBDD 约束力我们的基本假设是：1）MixMD对结合产额的物理学描述更完整 更好的热点预测比传统的SBDD方法和2）定性和定量数据， MixMD可用于SBDD。 该提案概述了发展MixMD和增加其对SBDD影响的两个领域。具体目标1 开发了计算热点探针的自由能、熵和熵的方法。 将比较基于占用、基于能量和基于动力学的方法， 计算这些键绑定属性。具体目标2将解决SBDD中的一系列关键挑战。第一、 MixMD将用于鉴定结合位点中的桥接水分子。显然，热点位置确定了 可置换的水，但同样重要的是精确定位所需的结合位点中的桥接沃茨。二是 将审查难以进入的神秘地点的情况。在绘制网站地图时，我们将确定 口袋打开和探针结合是连续的事件，其中探针“捕获”打开状态或协同 探针通过推压隐蔽口袋的可延展扭转来“诱导”开放状态的事件。 最后，MixMD数据将用于预测结合位点的药物敏感性。Druggable的非冗余集 和更少的可药物化结合位点（NRDLD）将用于基于 热点，它们的亲和力，它们的接近度，以及它们在蛋白质中的埋藏程度。

ABSTRACT: The greatest limitation for structure-based drug discovery (SBDD) is the need to neglect water and protein flexibility in most modeling. Here, we outline simulation methods that overcome these limitations. This proposal focuses on developing MixMD, our method for mixed-solvent molecular dynamics (MD). MixMD identifies critical binding sub-sites on protein surfaces (hotspots). Proteins are simulated in a box of explicit water with 5% small, organic probe cosolvents. The waters and probes sample the local environments along the protein surface, and sites with high occupancy of probes are identified as hotspots. MixMD has superior performance over other cosolvent MD methods like MacKerell’s SILCS and Barril’s MDmix. Other methods are plagued by many spurious, misleading, “extra” sites that indistinguishable from real binding sites, which greatly hinders prospective applications. Our long-term goal is to improve SBDD by developing methods that more accurately model protein-ligand binding. Our underlying hypotheses are 1) MixMD’s more complete description of the physics of binding yields better hotspot predictions than traditional SBDD methods and 2) both qualitative and quantitative data from MixMD can be used in SBDD. This proposal outlines two areas for developing MixMD and increasing its impact on SBDD. Specific Aim 1 develops methods for calculating the free energies, entropies, and enthalpies of the hotspot probes. Comparisons will be made between occupancy-based, energy-based, and kinetics-based methods for calculating those key binding properties. Specific Aim 2 will address a series of key challenges in SBDD. First, MixMD will be used to identify bridging water molecules in binding sites. Clearly, hotspot locations ascertain displaceable water, but it is just as important to pinpoint required, bridging waters in binding sites. Second, the accessibility of difficult, cryptic sites will be examined. While mapping the sites, we will determine whether pocket opening and probe binding are sequential events where probes “capture” open states or concerted events where probes “induce” open states by pushing against the malleable torsions of the cryptic pocket. Lastly, MixMD data will be used to predict druggabilities of binding sites. The Non-Redundant set of Druggable and Less Druggable binding sites (NRDLD) will be used to derive a druggability index based on number of hotspots, their affinities, their proximities, and their degree of burial in the protein.