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.

项目摘要 尽管过去二十年来见证了对细胞成分的大规模分析，例如exomes，他们的 对药物发现和精确药物的影响很小。例如，6/7候选毒品失败 在最近的FDA临床试验中，安全性和3/4的有效性失败。这些未解决但相关的问题，安全性和有效性， 反映了理解疾病之间三角相互关系的显着差距，分子功能， 和药物治疗。当代药物发现的关键概念限制是经常被隐式假定的 单蛋白靶病模型的单一药物。实际上，大多数疾病是由多种疾病引起的 分子故障。无论是疾病治疗还是精确医学诊断，通常都有 无法鉴定与疾病相关的作用模式（MOA）蛋白。为了开始解决这些问题 当前的Mira提案，我们开发了一种有希望的蛋白质结构和基于网络的人工智能（AI） 方法，医学，以预测与疾病相关的MOA蛋白，药物适应症，副作用和有效性； 但是，需要做更多的事情。在这里，我们建议以我们的成功为基础，并建立一个综合的 基于AI的方法，Medicascy-X，涉及以下内容：确定药物MOA的第一步 脱靶相互作用是识别其蛋白质靶标。这需要所有人类蛋白质的结构， 他们的综合体。虽然我们预测了97％的人类蛋白质中至少一个域的合适模型，但使用 深度学习，我们将预测缺失域，域域取向和蛋白质的结构 蛋白质复合物。我们将延长小分子虚拟配体筛选（VLS）以预测结合亲和力 基于以下洞察力，即相互作用的环蛋白子口几何形状和化学是在跨越 蛋白质家族通常是特权化学结构，可能是低自由能配合物。隐秘蛋白质 口袋最近被认为是重要的药物靶标，将被预测并包括在我们的VLS方法中。 基于抗体的免疫疗法功能强大，但具有与小分子相似的安全性和效率问题。 这就是他们的安全性和效率将由Medicascy-X预测。而Medicascy在“平均 人类”，Medicascy-X将考虑个体的遗传和表观遗传学特征，以使其成为真正的精确度 药品工具。我们将预测应靶向哪种MOA蛋白，以及蛋白质的MOA是否是由于损失或 功能增益。同一框架将预测协同的药物相互作用。优先级的另一种方法 MOA蛋白质是通过疾病合并症：多种疾病中发生的蛋白质可能很重要。如果疾病 可以预测合并症，我们将构造疾病的“系统发育”树，以促进 对疾病相互关系的更深入了解。作为算法有效性原理的证明 随着开发，将开发针对各种棘手疾病的新型临床前治疗。那，这个 项目可以提高药物发现和精确药物的成功率，同时减少时间和成本。