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.

项目总结