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Rational Design of High-Affinity Peptide Drug Candidates

高亲和力肽候选药物的合理设计

基本信息

批准号：
7671034
负责人：
Sriram Shankar
金额：
$ 27.75万
依托单位：
LYNNTECH, INC.
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-01 至 2011-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7671034
关键词：
ANXA5 gene Adhesions Affect Affinity Affinity Chromatography Amino Acid Sequence Amino Acids Annexin A1 Automation Binding Biocompatible Bioinformatics Biological Assay Biological Factors Biological Markers Biological Process Biotechnology Blast Cell Cell Death Cell physiology Chemicals Chemistry Classification Complement Complex Computational Biology Computers Custom Data Databases Detection Development Diagnosis Disease Fluorescence Generations Homologous Protein Hour Human In Situ Integrins Investigation Journals Knowledge Label Lead Libraries Ligand Binding Ligands Literature Malignant Neoplasms Manuals Maps Marketing Mediating Methodology Methods Microarray Analysis Octreotide Parent-Child Relations Pathology Pathway interactions Pattern Peptide Library Peptide Sequence Determination Peptides Phage Display Pharmaceutical Preparations Pharmacologic Substance Phase I Clinical Trials Play Process Property Protein Binding Protein Databases Protein Deregulation Protein-Protein Interaction Map Proteins Proteomics Publishing RGD (sequence)Reading Reagent Regulation Robotics Role SSTR2 gene Sampling Screening procedure Sequence Homology Series Signal Transduction Source Specificity Spectrin Structure Techniques Technology Testing Therapeutic Agents Therapeutic Uses Time Validation Variant abstracting annexin A5 base cell growth combinatorial computer program cost data mining density design drug candidate drug development drug discovery experience heuristics high throughput screening immunogenic improved in vitro Assay innovation interest knowledge base lead series migration novel strategies novel therapeutics numb protein optimism peptidomimetics pre-clinical prospective protein aminoacid sequence protein function protein phosphatase inhibitor-2 protein protein interaction protein purification research and development small molecule stereochemistry success text searching tool

项目摘要

DESCRIPTION (provided by applicant): The R&D of new drugs is a protracted and expensive process. The typical drug discovery process evolves from target identification to preclinical development and marketing. Several studies estimate the average cost of development of a single new drug in excess of $800 million. A large portion of this expense is attributed to abandoned lead candidates initially obtained from screening vast random combinatorial libraries of compounds for activity against the target but which fail for various reasons. Recent vast strides in the unraveling of the human interactive have allowed the mapping of a large numbers of protein-protein interactions, several of which are key to the pathology of disease. The identification of modulators of protein function and the process of transforming these into high-content lead series are key activities in modern drug discovery. A rapid methodology to identify high-affinity peptide drug candidates able to modulate key protein interactions, and having high potential for success as drug leads, will have huge benefits. Short high-affinity peptide ligands are highly desirable drug candidates because they are robust, easily synthesized in large quantities, and readily purified. Several short peptides have demonstrated significant ability to modulate protein-protein interactions - e.g. octreotide (SSTR2) and RGD peptides (1v23 integrins), and several others have led to the development of highly potent peptidomimetics. In preliminary studies, Lynntech has demonstrated that it is feasible to obtain high-affinity peptide ligands to target proteins based on their known amino acid sequence alone using a modular approach that combined computational biology and bioinformatics tools with a unique, advanced, high-density peptide microarray for high throughput screening of candidate ligands. Several high-affinity peptide ligands (20-30 nM affinity) were obtained after a single array screening, with the potential to refine the affinity further using iterative arrays. The methodology used conveys itself readily for automation. This study proposes the creation of a new bioinformatics engine for High-Affinity Ligand Optimization (HALO) that will enable the rapid, automated, and customized generation, screening, and identification of high affinity peptide ligands to any protein-protein interaction integral to disease pathology. HALO is a highly potent tool that will be useful for rapid hit-to-lead peptide drug candidate generation and is expected to revolutionize and expedite the process of drug discovery.

描述（申请人提供）：新药的研发是一个漫长而昂贵的过程。典型的药物发现过程从目标识别到临床前开发和营销。几项研究估计，开发一种新药的平均成本超过8亿美元。这一费用的很大一部分归因于最初通过筛选大量随机化合物组合文库以获得针对靶标的活性而放弃的先导候选物，但由于各种原因而失败。最近在揭示人类相互作用方面取得了巨大进展，这使得绘制大量蛋白质-蛋白质相互作用的图谱成为可能，其中一些是疾病病理学的关键。蛋白质功能调节剂的鉴定和将其转化为高含量先导系列的过程是现代药物发现的关键活动。一种快速识别高亲和力肽候选药物的方法，能够调节关键蛋白质的相互作用，并且作为药物先导具有很高的成功潜力，将带来巨大的好处。短的高亲和肽配体是非常理想的候选药物，因为它们健壮，易于大量合成，易于纯化。一些短肽已经证明了调节蛋白质-蛋白质相互作用的显著能力-例如奥曲肽（SSTR2）和RGD肽（1v23整合素），以及其他一些已经导致了高效肽模拟物的发展。在初步研究中，Lynntech已经证明，使用模块化方法，将计算生物学和生物信息学工具与独特、先进、高密度肽微阵列相结合，用于高通量筛选候选配体，获得基于已知氨基酸序列的高亲和力肽配体是可行的。通过单阵列筛选获得了几个高亲和力的肽配体（亲和度为20-30 nM），并有可能通过迭代阵列进一步优化亲和度。所使用的方法很容易实现自动化。本研究提出创建一个新的生物信息学引擎，用于高亲和力配体优化（HALO），该引擎将能够快速、自动化和定制地生成、筛选和鉴定与疾病病理相关的任何蛋白质-蛋白质相互作用的高亲和力肽配体。HALO是一种非常有效的工具，可用于快速生成候选肽药物，并有望彻底改变和加快药物发现过程。