Public/Private Collaboration for High-Quality Protein-Ligand Data

描述（由申请人提供）：开发可靠对接和评分（d/s）的一个关键需求是包含高质量实验蛋白质-配体复合物结构以及准确结合亲和力数据的大型数据集。将利用PI在这一领域的广泛专业知识并通过社区参与创建在线d/s资源（d/sResource）。SA 1将建立最大的，免费访问的蛋白质-配体复合物的数据库与实验确定的结合亲和力从文献。这个新资源将建立在现有的两个最大的蛋白质配体数据集之上：Wang的PDBbind和Carlson的Binding MOAD。SA 2将生成新的实验数据。从不同研究小组和使用不同实验技术/条件产生的结合亲和力数据之间缺乏一致性是另一个主要障碍。为了解决这一缺陷，选定的蛋白质-配体复合物的解离常数（Kds）将使用两种互补技术：等温量热法和表面等离子体共振。此外，将确定配体的重要物理化学性质（logP/logD，pKa和溶解度），并解决其他晶体结构。SA 3将从社区收集数据。将要求制药公司、NIH和学术界沉积大型数据集。该目标包括解决沉积到d/sResource中的部分完成的晶体结构，并分析沉积数据的配体和蛋白质之间的多样性/相似性，以优先考虑SA 2中的进一步实验研究。SA 4概述了拟议的社区外展。d/sResource将不是一个仅限于Android的奋进。数据将存放在许多存储库中：结构到PDB，ITC数据到BindingDB，化学信息到Pubchem和NIST数据库。将寻求与其他团体合作。将举行D/S竞赛和会议，并将建立访问学者计划，以鼓励新的发展和促进资源共享。 该项目将提供一个独特的资源，需要在基于结构的药物设计领域的改进。更好的技术将节省开发新疗法的时间和金钱，最终以更低的成本更快地为更多的人提供新药。

DESCRIPTION (provided by applicant): One critical need for the development of reliable docking and scoring (d/s) is a large dataset containing high quality experimental protein-ligand complex structures, together with accurate binding affinity data. An online d/s resource (d/sResource) will be created by taking advantage of the PIs extensive expertise in this area and through community participation. SA1 will build the largest, freely accessible database of protein-ligand complexes with experimentally determined binding affinities from literature. This new resource will build off of the two largest protein-ligand datasets in existence: Wang's PDBbind and Carlson's Binding MOAD. SA2 will generate new experimental data. The lack of consistency between binding affinity data generated from different research groups and using different experimental techniques/conditions is another major hurdle. To address this deficiency, dissociation constants (Kds) for selected protein-ligand complexes will be determined using two complementary techniques: isothermal calorimetry and surface plasmon resonance. Furthermore, important physicochemical properties for the ligands will be determined (logP/logD, pKa, and solubility), and additional crystal structures will be solved. SA3 will curate data from the community. Deposition of large datasets will be requested from pharma, the NIH, and academia. This Aim includes solving partially completed crystal structures deposited into the d/sResource and analysis of deposited data for diversity/similarity across the ligands and proteins to prioritize for further experimental investigation in SA2. SA4 outlines the proposed community outreach. The d/sResource will not be a Michigan-only endeavor. Data will be deposited in many repositories: structures into the PDB, ITC data into BindingDB, and chemical information into Pubchem and NIST databases. Collaborations will be sought with other groups. Contests and meetings for d/s will be held, and a Visiting Scholars Program will be established to encourage new developments and facilitate the sharing of resources. The project will provide a unique resource that is needed to improve in the field of structure-based drug design. Better techniques will save time and money in the development of new treatments, ultimately providing new drugs more quickly and at less expense to the greater population.