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: 10613959
• 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/10613959
• 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; 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.

项目摘要 尽管过去二十年见证了细胞组分（例如外显子组）的大规模分析，但其结果仍然是不稳定的。 对药物发现和精准医疗的影响不大。例如，6/7的候选药物不合格 在最近的FDA临床试验中，安全性和3/4的疗效失败。这些尚未解决但相关的问题，安全性和有效性， 反映出在理解疾病、分子功能 和药物治疗。当代药物发现的一个关键概念限制是， 单一药物用于单一蛋白质靶向疾病模型。事实上，大多数疾病都是由多种原因引起的。 故障分子无论是疾病治疗还是精准医学诊断， 不能鉴定疾病相关的作用模式（MOA）蛋白。为了开始解决这些问题， 目前MIRA的建议，我们开发了一个有前途的蛋白质结构和基于网络的人工智能（AI） 方法，MEDICASCY，预测疾病相关MOA蛋白，药物适应症，副作用和疗效; 然而，还需要做更多的工作。在此，我们建议在成功的基础上， 基于AI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的结构， 他们的情结。虽然我们预测了97%的人类蛋白质中至少一个结构域的合适模型， 通过深度学习，我们将预测缺失结构域的结构，结构域-结构域方向和蛋白质- 蛋白质复合物我们将扩展小分子虚拟配体筛选（VLS）来预测结合亲和力 基于这样的见解，即相互作用的环蛋白亚口袋几何形状和化学性质是保守的， 蛋白质家族通常是特殊的化学结构，并且可能是低自由能复合物。隐藏蛋白 口袋，最近被认为是重要的药物靶点，将被预测，并包括在我们的VLS方法。 基于抗体的免疫疗法是强大的，但具有与小分子类似的安全性和有效性问题; 因此，MEDICASCY-X将预测其安全性和有效性。虽然医疗工作的“平均 人类”，MEDICASCY-X将考虑个人的遗传和表观遗传概况，使其真正的精度 医疗工具我们将预测哪些MOA蛋白应该被靶向，以及蛋白质的MOA是否是由于缺失或缺失引起的。 功能的增益。相同的框架将预测协同药物-药物相互作用。另一种区分轻重缓急的方法 MOA蛋白是由疾病共病：蛋白质发生在多种疾病可能是重要的。如果疾病 我们将构建疾病的“系统发生”树，以促进疾病的预测。 更深入地了解疾病的相互关系。作为算法有效性的原理证明 正在开发中，将开发用于各种难治性疾病的新的临床前治疗。因此，这 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。