Deep learning based antibody design using high-throughput affinity testing of synthetic sequences
使用合成序列的高通量亲和力测试进行基于深度学习的抗体设计
基本信息
- 批准号:10362725
- 负责人:
- 金额:$ 13.97万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-03-09 至 2025-02-28
- 项目状态:未结题
- 来源:
- 关键词:AffinityAnimalsAntibodiesAntibody AffinityAntigensArchitectureBindingBiological AssayBudgetsClassificationCloud ComputingCommunicable DiseasesComputing MethodologiesDNA SequenceDataData SetDiseaseGoalsHumanImmunizeImmunotherapeutic agentLearningMachine LearningMalignant NeoplasmsMethodologyMethodsModelingMolecular MachinesOligonucleotidesOutputPerformancePhage DisplayPropertyRandomizedResearchServicesSpecific qualifier valueSpecificityStatistical ModelsTechnologyTest ResultTestingTherapeuticThinnessTimeTrainingTreatment EfficacyUpdateVirus DiseasesWorkcloud basedcommercializationcomputing resourcescostdeep learningdesignexperimental studyhuman diseaseimprovediterative designlearning strategymachine learning methodmachine learning modelmanufacturemathematical methodsmolecular dynamicsnovelnovel strategiesoutcome predictionreceptor
项目摘要
Project Summary
We will develop and apply a new high-throughput methodology for rapidly
designing and testing antibodies for a myriad of purposes, including cancer and
infectious disease immunotherapeutics. We will improve upon current
approaches for antibody design by providing time, cost, and humane benefits
over immunized animal methods and greatly improving the power of present
synthetic methods that use randomized designs. To accomplish this, we will
display millions of computationally designed antibody sequences using recently
available technology, test the displayed antibodies in a high-throughput format at
low cost, and use the resulting test data to train molecular dynamics and
machine learning methods to generate new sequences for testing. Based on our
test data our computational method will identify sequences that have ideal
properties for target binding and therapeutic efficacy. We will accomplish these
goals with three specific aims. We will develop a new approach to integrated
molecular dynamics and machine learning using control targets and known
receptor sequences to refine our methods for receptor generalization and model
updating from observed data (Aim 1). We will design an iterative framework
intended to enable identification of highly effective antibodies within a minimal
number of experiments, in which our methods automatically propose promising
antibody sequences to profile in subsequent assays (Aim 2). We will employ
rounds of automated synthetic design, affinity test, and model improvement to
produce highly target-specific antibodies. (Aim 3).
!
项目摘要
我们将开发和应用一种新的高通量方法,
设计和测试抗体用于各种目的,包括癌症和
传染病免疫治疗我们将改进现有的
通过提供时间、成本和人性化的好处来设计抗体的方法
过度免疫的动物方法,大大提高了目前的能力,
使用随机设计的合成方法。为实现这一目标,我们将
展示了数百万个计算设计的抗体序列,
可用的技术,以高通量的形式测试展示的抗体,
低成本,并使用得到的测试数据来训练分子动力学,
机器学习方法来生成用于测试的新序列。基于我们
我们计算方法将识别具有理想
用于靶结合和治疗功效的性质。我们将实现这些目标
有三个具体目标。我们将开发一种新的方法,
分子动力学和机器学习使用控制目标和已知的
受体序列,以完善我们的方法,受体的泛化和模型
根据观测数据进行更新(目标1)。我们将设计一个迭代框架
旨在能够在最小范围内鉴定高效抗体,
实验的数量,其中我们的方法自动提出有前途的
抗体序列,以在随后的测定中进行分析(目的2)。我们会委聘
一轮轮的自动合成设计、亲和力测试和模型改进,
产生高度针对性的抗体。(Aim 3)。
!
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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David K Gifford其他文献
Computational discovery of gene modules and regulatory networks
基因模块和调控网络的计算发现
- DOI:
10.1038/nbt890 - 发表时间:
2003-10-12 - 期刊:
- 影响因子:41.700
- 作者:
Ziv Bar-Joseph;Georg K Gerber;Tong Ihn Lee;Nicola J Rinaldi;Jane Y Yoo;François Robert;D Benjamin Gordon;Ernest Fraenkel;Tommi S Jaakkola;Richard A Young;David K Gifford - 通讯作者:
David K Gifford
David K Gifford的其他文献
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{{ truncateString('David K Gifford', 18)}}的其他基金
Machine learning optimized autoimmune therapeutics with a focus on Type 1 Diabetes
机器学习优化自身免疫疗法,重点关注 1 型糖尿病
- 批准号:
10697204 - 财政年份:2023
- 资助金额:
$ 13.97万 - 项目类别:
Deep learning based antibody design using high-throughput affinity testing of synthetic sequences
使用合成序列的高通量亲和力测试进行基于深度学习的抗体设计
- 批准号:
10116306 - 财政年份:2018
- 资助金额:
$ 13.97万 - 项目类别:
High-Throughput Native Context Mapping and Modeling of Regulatory DNA
监管 DNA 的高通量本地上下文映射和建模
- 批准号:
9350382 - 财政年份:2016
- 资助金额:
$ 13.97万 - 项目类别:
High-throughput methods for elucidating the control of chromatin accessibility
阐明染色质可及性控制的高通量方法
- 批准号:
9066734 - 财政年份:2015
- 资助金额:
$ 13.97万 - 项目类别:
High-throughput methods for elucidating the control of chromatin accessibility
阐明染色质可及性控制的高通量方法
- 批准号:
8861021 - 财政年份:2015
- 资助金额:
$ 13.97万 - 项目类别:
High-throughput methods for elucidating the control of chromatin accessibility
阐明染色质可及性控制的高通量方法
- 批准号:
9267524 - 财政年份:2015
- 资助金额:
$ 13.97万 - 项目类别:
Integrated Genome Discovery at Single Base Pair Resolution
单碱基对分辨率的综合基因组发现
- 批准号:
9041894 - 财政年份:2012
- 资助金额:
$ 13.97万 - 项目类别:
Integrated Genome Discovery at Single Base Pair Resolution
单碱基对分辨率的综合基因组发现
- 批准号:
8546274 - 财政年份:2012
- 资助金额:
$ 13.97万 - 项目类别:
Integrated Genome Discovery at Single Base Pair Resolution
单碱基对分辨率的综合基因组发现
- 批准号:
8402454 - 财政年份:2012
- 资助金额:
$ 13.97万 - 项目类别:
Integrated Genome Discovery at Single Base Pair Resolution
单碱基对分辨率的综合基因组发现
- 批准号:
8701330 - 财政年份:2012
- 资助金额:
$ 13.97万 - 项目类别:
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