Pre-computed free energy maps for rapid structure-based ligand design

预先计算的自由能图，用于快速基于结构的配体设计

• 批准号: 8832859
• 负责人: ALEXANDER D MACKERELL
• 金额: $ 15.72万
• 依托单位: SILCSBIO, LLC
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8832859
• 关键词: Address; Affinity; Binding; Binding Sites; Chemicals; Computer Assisted; Computer software; Computing Methodologies; Data; Data Set; Databases; Development; Disease; Drug Design; Elements; Emerging Technologies; Environment; Evaluation; Foundations; Free Energy; Generations; Goals; Hot Spot; Housing; Imagery; Lead; Licensing; Ligand Binding; Ligands; Maps; Marketing; Membrane Proteins; Methodology; Methods; Modeling; Modification; Outcome; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Probability; Process; Production; Proteins; Quantitative Evaluations; RNA; Reading; Relative (related person); Reporting; Reproduction; Research Contracts; Research Infrastructure; Research Proposals; Sales; Site; Small Business Innovation Research Grant; Solutions; Structure; Surface; Technology; Time; Toxicology; Validation; Vendor; Work; aqueous; base; commercial application; commercialization; computational chemistry; computer cluster; cost; design; drug development; drug discovery; flexibility; functional group; graphical user interface; improved; interest; macromolecule; molecular dynamics; novel; novel strategies; novel therapeutics; open source; pharmacophore; preconditioning; protein complex; protein structure; public health relevance; scaffold; screening; simulation; solute; success

DESCRIPTION (provided by applicant): In the proposed study, a novel approach which has already generated multiple commercial sales for SilcsBio, LLC, Site-Identification by Ligand Competitive Saturation (SILCS), will be further developed to address customer requests and concerns around SILCS utility. Successful commercial application of computational methods for ligand design require a platform that provides both qualitative data to direct the design process and rapid production of quantitative data to allow for rigorous evaluation of specific ligand possibilities Both of those attributes form the core of SILCS technology. However, it is necessary to extend the SILCS technology to be accessible to a wide range of computational software packages and to be readily used in database screenings. Most importantly, while customers report large improvements in predictive capacity using SILCS technology (reduced time to lead from 12 months to 6 months, identification of unique scaffolds for an 'undruggable" target, better matches of scoring to actual performance), modest improvements in accuracy are requested by customers in order to have a large impact on the development of new therapeutics. For a given target, SILCS involves a one-time up-front preconditioning step where the target protein, RNA or any macromolecule target of interest, in the absence of any drug-like ligands, is immersed in an aqueous solution of small organic solutes and subjected to exhaustive molecular dynamics (MD) simulations. From these simulations 3D probability distributions of different chemical functional classes on the entire surface of the target are generated based on rigorous free energy criteria, including protein flexibility. These probability distributions are then converted to free energies based on a Boltzmann distribution yielding "Grid Free Energies (GFE)." The probability distributions are normalized with respect to the organic solutes in pure aqueous solution, such that the GFEs include energetic contributions from desolvation of both the solutes and the protein surface, as well as interactions of the solutes with the protein. The GFE distributions, termed FragMaps, may then be used in a qualitative fashion to identify regions on the protein surface that interact favorably with differet classes of functional groups ("hot spots"), thereby directing ligand design. The GFEs may also be used to estimate ligand relative free energies of binding thereby facilitating quantitative evaluation of specific design outcomes. As the SILCS method is based on one-time preconditioning simulations of the target molecule, a process that can be completed in several days on commodity computer clusters, the quantitative free energy binding estimates based on the FragMaps can be performed on large numbers of compounds in a matter seconds allowing for wide ranges of ligand modifications to be evaluated. Specific goals for the proposed SBIR include 1) extending SILCS technology and GFE FragMaps to formats accessible to computational chemistry software packages, 2) improving the accuracy of SILCS GFE FragMaps and 3) extending SILCS GFEs for use in database screening. Success of the Phase I aspect of the proposal will be based on the following milestones: i) SILCS computational platform: Ability to generate, read and visualize SILCS GFE FragMaps using publically and commercially available computational chemistry software, 2) SILCS accuracy: Improved prediction of relative binding energies of known ligand-protein complexes, and 3) SILCS database screening: Ability to identify known ligands from database screening using pharmacophore models based on SILCS GFEs. Commercialization of these substantial improvements to the SILCS technology will occur through SilcsBio's existing marketing channels which include partnering with computational chemistry software vendors to make the technology available for licensing for in-house use by pharmaceutical companies and SilcsBio acting as a contract research organization (CRO) performing structure-based ligand design. CRO work includes supplying and interpreting GFE FragMaps to customers, a product that can, importantly, be supplied without customers revealing their IP as the Fragmaps are generated in the absence of ligands. Successful completion of this Phase I proposal is anticipated to lay the foundation for a Phase II SBIR to improve and expand the SILCS platform by 1) extending the technology to occluded target ligand binding sites not accessible to the surrounding aqueous environment, such as those commonly found in many GPCRs, 2) extending SILCS into a product that will be marketable for in-house use by pharmaceutical companies and 3) developing the necessary infrastructure required for SilcsBio to be a successful CRO.

描述（由申请人提供）：在拟定研究中，将进一步开发一种新方法，该方法已为SilcsBio，LLC，通过配体竞争饱和度（SILCS）进行位点识别产生了多项商业销售，以解决客户对SILCS效用的要求和担忧。配体设计计算方法的成功商业应用需要一个平台，该平台既能提供定性数据来指导设计过程，又能快速产生定量数据，以严格评估特定配体的可能性。这两个属性构成了SILCS技术的核心。然而，有必要扩展SILCS技术，使其可用于各种计算软件包，并可随时用于数据库筛选。最重要的是，虽然客户报告使用SILCS技术在预测能力方面有很大的改进（将领先时间从12个月减少到6个月，为“不可药物化”的目标识别独特的支架，评分与实际性能的更好匹配），但客户要求在准确性方面有适度的改进，以便对新疗法的开发产生重大影响。 对于给定的靶标，SILCS涉及一次性预先预处理步骤，其中在不存在任何药物样配体的情况下，将靶蛋白、RNA或任何感兴趣的大分子靶标浸入小有机溶质的水溶液中并进行详尽的分子动力学（MD）模拟。从这些模拟中，基于严格的自由能标准（包括蛋白质柔性）生成目标整个表面上不同化学功能类的3D概率分布。这些概率分布然后被转换成基于玻尔兹曼分布的自由能，产生“网格自由能（GFE）”。概率分布相对于纯水溶液中的有机溶质进行归一化，使得GFE包括溶质和蛋白质表面的去溶剂化以及溶质与蛋白质的相互作用的能量贡献。GFE分布，称为FragMaps，然后可以以定性的方式用于鉴定蛋白质表面上的区域，这些区域有利地与功能基团（“热点”）的非限制性类别相互作用，从而指导配体设计。GFE也可用于估计配体结合的相对自由能，从而促进特定设计结果的定量评价。由于SILCS方法是基于目标分子的一次性预处理模拟，这是一个可以在商品计算机集群上在几天内完成的过程，因此可以在几秒钟内对大量化合物进行基于FragMaps的定量自由能结合估计，从而可以评估广泛的配体修饰。拟议SBIR的具体目标包括：1）将SILCS技术和GFE FragMap扩展为计算化学软件包可访问的格式，2）提高SILCS GFE FragMap的准确性，3）扩展SILCS GFE用于数据库筛选。 该提案的第一阶段方面的成功将基于以下里程碑：i）SILCS计算平台：能够使用市场上和市售的计算化学软件生成、读取和可视化SILCS GFE FragMap，2）SILCS准确性：改进已知配体-蛋白质复合物的相对结合能的预测，以及3）SILCS数据库筛选：能够使用基于SILCS GFE的药效团模型从数据库筛选中识别已知配体。 这些对SILCS技术的实质性改进的商业化将通过SilcsBio现有的营销渠道进行，其中包括与计算化学软件供应商合作，使该技术可用于制药公司内部使用的许可，SilcsBio作为合同研究组织（CRO）进行基于结构的配体设计。CRO的工作包括向客户提供和解释GFE FragMaps，重要的是，该产品可以在客户不透露其IP的情况下提供，因为Fragmaps是在不存在配体的情况下生成的。 该第一阶段提案的成功完成预计将为第二阶段SBIR奠定基础，以通过以下方式改进和扩展SILCS平台：1）将该技术扩展到周围水性环境无法接近的封闭靶配体结合位点，例如在许多GPCR中常见的那些，2）将SILCS扩展为可供制药公司内部使用的产品，3）开发SilcsBio成为成功的CRO所需的必要基础设施。