Interplay of inherent promiscuity and specificity in protein biochemical function with applications to drug discovery and exome analysis

蛋白质生化功能固有的混杂性和特异性与药物发现和外显子组分析应用的相互作用

• 批准号: 10399478
• 负责人: JEFFREY SKOLNICK
• 金额: $ 49.1万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-06 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10399478
• 关键词: Address; Affinity; Algorithms; Antibodies; Artificial Intelligence; Binding; Biochemical; Biological; Chemical Structure; Chemistry; Clinical Trials; Complex; Diagnostic; Disease; Disease model; Drug Interactions; Drug Targeting; Effectiveness; Epigenetic Process; Explosion; Free Energy; Genetic; Geometry; Human; Immunotherapy; Individual; Knowledge; Ligands; Modeling; Molecular; Network-based; Pharmaceutical Preparations; Pharmacotherapy; Phylogenetic Analysis; Protein Family; Proteins; Risk; Safety; Specificity; Structure; Therapeutic; Time; Trees; Work; base; comorbidity; cost; cryptic protein; deep learning; drug candidate; drug discovery; exome; gain of function; improved; insight; novel; pre-clinical; precision medicine; protein complex; protein structure; screening; side effect; small molecule; success; tool; virtual

PROJECT SUMMARY Although the past two decades witnessed the large-scale analyses of cellular components, e.g. exomes, their impact on drug discovery and precision medicine has been modest. For example, 6/7 drug candidates failed safety and 3/4 failed efficacy in recent FDA clinical trials. These unsolved, but related issues, safety and efficacy, reflect significant gaps in understanding of the triangular interrelationship between diseases, molecular function, and drug treatments. A key conceptual limitation of contemporary drug discovery is the often implicitly assumed single drug for a single protein target disease model. In reality, most diseases are caused by multiple malfunctioning molecules. Whether it be disease treatment or precision medicine diagnostics, there is often an inability to identify disease-associated mode of action (MOA) proteins. To begin to address these issues, in the current MIRA proposal, we developed a promising protein structure and network-based Artificial Intelligence (AI) approach, MEDICASCY, to predict disease-associated MOA proteins, drug indications, side effects and efficacy; however, much more needs to be done. Here, we propose to build on our successes and develop an integrated AI-based approach, MEDICASCY-X, that addresses the following: The first step in determining a drug’s MOA and off-target interactions is to identity its protein targets. This requires the structures of all human proteins and their complexes. While we predicted suitable models for at least one domain in 97% of human proteins, using deep learning, we will predict the structures of the missing domains, domain-domain orientations and protein- protein complexes. We will extend small molecule virtual ligand screening (VLS) to predict binding affinities based on the insight that interacting ring-protein subpocket geometries and chemistry are conserved across protein families, are often privileged chemical structures and are likely low free energy complexes. Cryptic protein pockets, recently recognized as important drug targets, will be predicted and included in our VLS approach. Antibody-based immunotherapies are powerful but have similar safety and efficacy issues as small-molecules; thus, their safety and efficacy will be predicted by MEDICASCY-X. While MEDICASCY works on an “averaged human”, MEDICASCY-X will consider individual genetic and epigenetic profiles to make it a true precision medicine tool. We will predict which MOA proteins should be targeted and if a protein’s MOA is due to a loss or gain of function. The same framework will predict synergistic drug-drug interactions. Another way to prioritize MOA proteins is by disease comorbidity: proteins occurring in multiple diseases are likely important. If disease comorbidity can be predicted, we will construct the “Phylogenetic” Tree(s) of Diseases that would facilitate a deeper understanding of disease interrelationships. As proof of principle of the effectiveness of the algorithms being developed, novel preclinical treatments for a variety of intractable diseases will be developed. Thus, this project could enhance the success rates of drug discovery and precision medicine while reducing time and cost.

项目概要 尽管在过去二十年前，对细胞成分（例如外显子组）的大规模分析 对药物发现和精准医学的影响不大，例如，6/7 的候选药物失败了。 在最近的 FDA 临床试验中，安全性和 3/4 的功效失败了，这些尚未解决但相关的问题，安全性和功效， 反映了对疾病、分子功能、 当代药物发现的一个关键概念限制是经常隐含的假设。 单一药物针对单一蛋白质靶点的疾病模型 事实上，大多数疾病都是由多种疾病引起的。 无论是疾病治疗还是精准医学诊断，通常都会出现故障。 无法识别疾病相关的作用模式（MOA）蛋白为了开始解决这些问题，在 基于当前的 MIRA 提案，我们开发了一种有前途的蛋白质结构和基于网络的人工智能 (AI) MEDICASCY 方法，预测与疾病相关的 MOA 蛋白、药物适应症、副作用和功效； 然而，我们还有很多工作要做，我们建议在我们的成功基础上发展一个综合性的系统。 基于人工智能的方法 MEDICASCY-X 解决了以下问题： 确定药物 MOA 的第一步 和脱靶相互作用是为了识别其蛋白质靶点，这需要所有人类蛋白质和结构。 虽然我们使用 97% 的人类蛋白质中的至少一个结构域预测了合适的模型。 深度学习，我们将预测缺失域的结构、域-域方向和蛋白质- 我们将扩展小分子虚拟配体筛选（VLS）来预测结合亲和力。 基于相互作用的环蛋白亚袋几何形状和化学在整个过程中保守的见解 蛋白质家族通常具有特殊的化学结构，并且可能是低自由能复合物。 最近被认为是重要药物靶点的口袋将被预测并包含在我们的 VLS 方法中。 基于抗体的免疫疗法很强大，但也存在与小分子类似的安全性和有效性问题； 因此，MEDICASCY-X 将预测它们的安全性和有效性，而 MEDICASCY 的工作原理是“平均”。 人类”，MEDICASCY-X 将考虑个体遗传和表观遗传特征，使其成为真正的精确度 我们将预测哪些 MOA 蛋白应该作为目标，以及蛋白质的 MOA 是否是由于缺失或缺失造成的。 相同的框架将预测药物之间的协同相互作用。 MOA 蛋白与疾病合并症有关：多种疾病中出现的蛋白质可能很重要。 如果合并症是可以预测的，我们将构建疾病的“系统发育”树，以促进 对疾病相互关系的更深入理解作为算法有效性的原理证明。 正在开发中，将开发针对各种疑难杂症的新型临床前治疗方法。 该项目可以提高药物发现和精准医疗的成功率，同时减少时间和成本。