Integrating cheminformatics and molecular simulations for virtual drug screening

 描述(由申请人提供)：基于结构的虚拟筛选(VS)的高效和准确方法的发展在计算药物发现领域仍然是一个巨大的挑战。该领域的突出和公认的研究问题包括大多数方法的相对计算效率低下，这限制了用于虚拟筛选的分子文库的大小；命中率低；以及对配体结合亲和力和姿势的不准确预测。建议的研究通过使用创新的和计算效率高的方法来解决这些挑战，这些方法充分整合了化学信息学和分子模拟的互补领域的概念，以设计出集成的两步VS方法。基于我们在化学信息学和QSAR建模方面的经验，我们的目标是开发新的、计算高效的化学信息学方法，以预处理可用于生物筛选的非常大的(约107种化合物)化学库，并消除高达99%的不可能的配体。只有剩下的1%的可能配体将通过依赖于分子模拟技术的较慢但准确的系综灵活对接方法进行评估。化学信息学步骤还将产生关于特权蛋白质-配体相互作用的重要信息，这些信息将用于现场处理步骤，以指导基于结构的虚拟筛选，并避免配体姿势的过度采样。此外，还将采用后处理化学信息学方法，从对接计算中过滤掉诱饵姿势。我们混合方法的最终目标是在受体结合的构象中获得一小部分高亲和力的计算命中，这些构象可以通过实验进行验证。我们将追求以下三个具体目标：1)开发基于化学信息学的新的虚拟筛选方法，以消除不可能的配体和不可能的姿势，并生成关于首选蛋白质-配体相互作用的信息；2)开发新的、高效的、灵活的集成对接方法，以首选的蛋白质-配体相互作用为指导，选择最可能的配体并预测其结合姿势；3)将所开发的分层虚拟筛选工作流程应用于几个治疗靶点，并在实验分析中测试高置信度计算命中。这个项目产生的所有计算工具都将公开提供。这一提议是创新的，因为拟议的VS平台将是VS不同方法的独特结合，结合了它们相应的优势。这项建议意义重大，因为该项目的实施将使基于结构的药物发现工具的效率、准确性和实验证实的影响得到实质性改进。

 DESCRIPTION (provided by applicant): The development of highly efficient and accurate approaches to structure-based virtual screening (VS) continues to represent a formidable challenge in the field of computational drug discovery. Outstanding and widely recognized research problems in the field include the relative computational inefficiency of most approaches, which limits the size of molecular libraries used for virtual screening; the low hit rate; and the inaccurate prediction of ligand binding affinity and pose. The proposed studies address these challenges by using innovative and computationally efficient approaches to VS that fully integrate concepts from the complementary fields of cheminformatics and molecular simulation to devise an integrated two-step VS methodology. Building upon our experience in cheminformatics and QSAR modeling, we aim to develop novel, computationally efficient cheminformatics approaches to pre-process very large (on the order of 107 compounds) chemical libraries available for biological screening, and eliminate up to 99% of improbable ligands. Only the remaining 1% of probable ligands will be evaluated by slower but accurate ensemble flexible docking approaches relying on molecular simulation techniques. The cheminformatics step will also produce important information on privileged protein-ligand interactions that will be used in a live-processing step to guide the structure-based virtual screening and avoid oversampling of ligand poses. Moreover, post- processing cheminformatics methods will be implemented to filter out decoy poses from docking calculations. The ultimate goal of our hybrid methodology is to arrive at a small set of high-affinity computational hits in receptor-bound conformations that can be validated experimentally. We will pursue this goal following three specific aims: 1) Develop novel cheminformatics-based virtual screening approaches to eliminate both improbable ligands and improbable poses, as well as generate information on preferred protein-ligand interactions; 2) Develop new, efficient flexible ensemble docking methods guided by the preferred protein- ligand interactions to select the most probable ligands and predict their binding poses; 3) Apply the developed hierarchical virtual screening workflow to several therapeutic targets and test high-confidence computational hits in experimental assays. All computational tools resulting from this project will be made publicly available. This proposal is innovative because the proposed VS platform will result from a unique marriage of disparate approaches for VS, combining their corresponding strengths. This proposal is significant because the implementation of this project will enable substantial improvement in the efficiency, accuracy, and experimentally-confirmed impact of structure-based drug discovery tools.