Covalent protein-ligand binding affinities with VM2
Covalent protein-ligand binding affinities with VM2
批准号:
10311541
负责人:
Simon Webb
金额:
$78.59万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-06-01 至 2023-11-30
关键词:
Active SitesAddressAffinityAlgorithmsAreaAspirinBindingBinding ProteinsBinding SitesBiological AssayCategoriesClinicalComplexComputer softwareCoupledDataDevelopmentDockingDoseDrug DesignDrug IndustryEnvironmentEpidermal Growth Factor ReceptorFree EnergyImmune responseLeadLigand BindingLigandsMethodologyMethodsMiningMolecularMolecular ConformationNatureOutputPenicillinsPharmaceutical PreparationsPharmacologic SubstancePhasePhosphotransferasesPotential EnergyProcessProteinsQuantum MechanicsReactionReproductionResidenciesRiskSchemeScientistScoring MethodSeriesSmall Business Innovation Research GrantSoftware ToolsSpeedStructureThermodynamicsTimeToxic effectX-Ray Crystallographybasecomputerized toolsconformercovalent bonddesigndrug discoverydrug marketfallshuman diseaseinhibitorinhibitor therapyinsightmolecular modelingnovelparallel processingproduct developmentprogramsprospectiveresidencesmall moleculetool
中文摘要
项目摘要
直到最近,制药工业中基于小分子结构的药物设计工作一直集中在
关于配体与靶蛋白的可逆非共价结合。另一种机制是
配体与靶蛋白的共价结合,目前市场上约30%的药物属于
这一类.然而,对于这些药物中的大多数,共价结合机制是偶然的,而不是偶然的。
设计策略,其作用机制在其临床用途确立后很久才被发现。
青霉素和阿司匹林就是这方面的突出例子。尽管具有固有的优势,
共价结合机制,如增加的效力和增加的停留时间,它是有目的的
避免作为设计策略,因为担心不可逆的脱靶相互作用会导致增加
毒性和免疫反应的风险。然而,随着减轻这些风险的战略的出现,
从而允许开发所谓的靶向共价抑制剂(TCI),
许多制药行业的共价抑制剂药物发现计划。基本设计策略
TCI发现计划所采用的方法旨在实现两步过程,首先从可逆的非-
与靶蛋白共价结合,然后在亲电试剂与靶蛋白之间形成共价键。
配体(通常称为弹头)和蛋白质中的亲核中心。特克斯和凯科斯群岛的多面性
选择性和效力的机制对于基于结构的发现努力是具有挑战性的。而且
目前缺乏可以提供准确定量洞察的计算工具,这意味着优化
的TCI必须完全通过昂贵且重复的合成、测定和X射线来完成
结晶学这项快速SBIR提案的目的是开发一种新的分子建模方法,
可以提供热力学结合以及反应可逆性信息软件工具,
对预期的共价抑制剂分子进行排序的目的。该工具将计算共价键的自由能
候选分子与靶蛋白残基的结合,以及前-
反应性非共价结合步骤。它还将提供一个计算的自由能为基础的估计反应
可逆性该软件将基于采矿最小自由能计算方法,
作为VeraChem的VM2免费能源软件平台的扩展而开发。
英文摘要
Project Summary
Small molecule structure based drug design efforts in the pharmaceutical industry have, until recently, focused
on the reversible non-covalent binding of ligands with target proteins. An alternative mechanism is the
covalent binding of the ligand to the target protein, with around 30% of currently marketed drugs falling into
this category. However, for most of these drugs, the covalent binding mechanism was serendipitous, not a
design strategy, with their mechanism of action discovered long after their clinical utility had been established.
Penicillin and aspirin are prominent examples of this. Despite the inherent advantages provided by the
covalent binding mechanism, such as increased potency and increased residence times, it was purposefully
avoided as a design strategy due to concerns that irreversible off- target interactions would lead to increased
risk of toxicity and immunological response. However, the emergence of strategies to mitigate these risks,
thereby allowing development of so-called targeted covalent inhibitors (TCIs), has prompted the establishment
of numerous pharmaceutical industry covalent inhibitor drug discovery programs. The basic design strategy
employed by TCI discovery programs aims at achieving a two-step process that first starts with reversible non-
covalent binding with the target protein, followed by covalent bond formation between an electrophile on the
ligand (often called a warhead) and a nucleophilic center in the protein. This multifaceted nature of the TCI
mechanism for selectivity and potency is challenging for structure based discovery efforts. Furthermore, the
current absence of computational tools that can provide accurate quantitative insight means that optimization
of TCIs must be done exclusively by expensive and repeated rounds of synthesis, assay, and X-ray
crystallography. The aim of this fast-track SBIR proposal, then, is to develop a novel molecular modeling
software tool that can provide thermodynamic binding as well as reaction reversibility information for
purposes of ranking prospective covalent inhibitor molecules. This tool will calculate free energies of covalent
binding of candidate molecules to targeted protein residues, as well as free energies of binding for the pre-
reactive non-covalent binding step. It will also provide a calculated free energy-based estimate of reaction
reversibility. This software will be based on mining minima free energy calculation methodology and will be
developed as an extension of VeraChem’s VM2 free energy software platform.
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科研奖励(0)
会议论文
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资助金额:$31.15万
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财政年份:2023
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负责人:Simon Webb
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依托单位:
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财政年份:2014
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批准号:9040209
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资助金额:$73.47万
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财政年份:2014
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依托单位:
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项目类别:
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资助金额:$69.4万
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负责人:Simon Webb
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依托单位:
Multilevel Parallelization of Software for Accurate Protein-Ligand Affinities
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批准号:7906160
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项目类别:
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资助金额:$14.08万
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财政年份:2010
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负责人:Simon Webb
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依托单位:
Multilevel Parallelization of Software for Accurate Protein-Ligand Affinities
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资助金额:$72.99万
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财政年份:2010
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负责人:Simon Webb
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依托单位:
Multilevel Parallelization of Software for Accurate Protein-Ligand Affinities
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资助金额:$70.85万
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财政年份:2010
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负责人:Simon Webb
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依托单位:
海外基金