Making antibody generation rapid, scalable, and democratic through machine learning and continuous evolution
Making antibody generation rapid, scalable, and democratic through machine learning and continuous evolution
批准号:
10687279
负责人:
Andrew Kruse
金额:
$166.79万
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:
未结题
起止时间:
2020-09-10 至 2025-08-31
关键词:
AcetylcholineAffinityAnimalsAntibodiesAntibody AffinityAntibody FormationAntigen PresentationAntigen TargetingAntigen-Presenting CellsAntigensArchitectureAreaBackBiogenic Amine ReceptorsBiological SciencesBiomedical ResearchCell Surface ReceptorsCellsCentral Nervous SystemChemistryClinicCollectionCommunitiesCustomCytometryDataData SetDemocracyDetergentsDiagnosticDirected Molecular EvolutionDockingDopamineElementsEngineeringEpinephrineEvolutionExplosionG-Protein-Coupled ReceptorsGenerationsGenesGeneticHistologyHumanHybridomasImageImmune checkpoint inhibitorImmune systemImmunizationImmunizeImmunoglobulin FragmentsImmunoprecipitationLibrariesMachine LearningMedical ResearchMedicineMethodsModelingMolecularMolecular BiologyMolecular ConformationMonoclonal AntibodiesNeurobiologyNeurosciencesNeurotransmittersNobel PrizeOutcomePathogen detectionPhage DisplayPharmaceutical PreparationsPheromonePlayProcessProductionProductivityProliferatingProtein EngineeringProteinsProteomePublic HealthReagentResearchResearch PersonnelRoleSignal TransductionSpecificitySpeedSurfaceSystemTechniquesTestingTherapeuticTrainingTubeUpdateV(D)J RecombinationWestern BlottingYeastsaddictionantibody engineeringantibody librariesantigen bindingbiomarker discoverycancer therapycostcrowdsourcingdecision researchdesignempowermentepidemic responseexperimental studyfollow-upimprovedin vivoinnovationinsightinterestmachine learning algorithmmachine learning modelnanobodiesnew technologynovelreceptorresponsescaffoldstructural biologytool
中文摘要
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英文摘要
Project Summary/Abstract
It is hard to overstate the importance of monoclonal antibodies in the life sciences. Antibodies are critical tools in biomedical
research and diagnostics (e.g. western blotting, immunoprecipitation, cytometry, biomarker discovery, and histology), are
one of the most rapidly growing class of therapeutics, and are the basis for myriad new strategies in cancer therapy, such as
checkpoint inhibitors that are revolutionizing treatment. Unfortunately, current methods for the generation of custom
antibodies, including animal immunization and phage display, are slow, costly, inaccessible to most researchers, and often
unsuccessful. We propose Autonomously EvolvinG Yeast-displayed antibodieS (AEGYS), a system for the continuous and
rapid evolution of high-quality antibodies against custom antigens that requires only the simple culturing of yeast cells. We
believe this can be achieved by combining cutting-edge generative machine learning algorithms for antibody library design
with a new technology for in vivo continuous evolution and a yeast antigen-presenting cell that we will engineer. If
successful, AEGYS should have a transformative impact across the whole of biomedicine by turning monoclonal antibody
generation into a rapid, scalable, and accessible process where any lab with standard molecular biology capabilities can
generate custom antibodies on demand simply by “immunizing” a test tube of yeast cells with an antigen. We anticipate
that this democratization of antibody generation will also result in an explosion of crowdsourced antibody sequence data
that will train our machine learning algorithms to design better antibody libraries for AEGYS, starting a virtuous cycle. We
ourselves will use AEGYS to generate a panel of subtype- and conformation-specific nanobodies against biogenic amine
receptors including those that respond to acetylcholine, adrenaline, dopamine, and other neurotransmitters, so that we can
understand their role in neurobiology and addiction.!
期刊论文(5)
专著(0)
科研奖励(0)
会议论文
Protein design using structure-based residue preferences.
使用基于结构的残基偏好进行蛋白质设计。
DOI:
10.1038/s41467-024-45621-4
发表时间:
2024
期刊:
Nature communications
影响因子:
16.6
作者:
[Ding,David, Shaw,AdaY, Sinai,Sam, Rollins,Nathan, Prywes,Noam, Savage,DavidF, Laub,MichaelT, Marks,DeboraS]
通讯作者:
Marks,DeboraS
Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders.
人类蛋白质组的深度生成模型揭示了一百多个与罕见遗传疾病有关的新基因。
DOI:
10.1101/2023.11.27.23299062
发表时间:
2023
期刊:
medRxiv : the preprint server for health sciences
影响因子:
--
作者:
[Orenbuch,Rose, Kollasch,AaronW, Spinner,HansenD, Shearer,CourtneyA, Hopf,ThomasA, Franceschi,Dinko, Dias,Mafalda, Frazer,Jonathan, Marks,DeboraS]
通讯作者:
Marks,DeboraS
Project 1: Structure, function, and inhibition of SEDS-family peptidoglycan polymerases
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批准号:10699954
-
项目类别:
-
资助金额:$77.55万
-
财政年份:2022
-
负责人:Andrew Kruse
-
依托单位:
Molecular basis of spore germination
-
批准号:10659224
-
项目类别:
-
资助金额:$84.72万
-
财政年份:2022
-
负责人:Andrew Kruse
-
依托单位:
Making antibody generation rapid, scalable, and democratic through machine learning and continuous evolution
-
批准号:10474638
-
项目类别:
-
资助金额:$168.27万
-
财政年份:2020
-
负责人:Andrew Kruse
-
依托单位:
Making antibody generation rapid, scalable, and democratic through machine learning and continuous evolution
-
批准号:10021311
-
项目类别:
-
资助金额:$169.06万
-
财政年份:2020
-
负责人:Andrew Kruse
-
依托单位:
Making antibody generation rapid, scalable, and democratic through machine learning and continuous evolution
-
批准号:10260452
-
项目类别:
-
资助金额:$166.52万
-
财政年份:2020
-
负责人:Andrew Kruse
-
依托单位:
Molecular mechanisms of sigma receptor signaling
-
批准号:9236106
-
项目类别:
-
资助金额:$32.29万
-
财政年份:2017
-
负责人:Andrew Kruse
-
依托单位:
Molecular mechanisms of sigma receptor signaling
-
批准号:9906922
-
项目类别:
-
资助金额:$32.47万
-
财政年份:2017
-
负责人:Andrew Kruse
-
依托单位:
Molecular mechanisms of adiponectin signaling and PAQR function
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批准号:9349368
-
项目类别:
-
资助金额:$42.38万
-
财政年份:2015
-
负责人:Andrew Kruse
-
依托单位:
Molecular mechanisms of adiponectin signaling and PAQR function
-
批准号:9144473
-
项目类别:
-
资助金额:$42.38万
-
财政年份:2015
-
负责人:Andrew Kruse
-
依托单位:
海外基金