Mark Hutchinson, Jeffrey A Ruffolo, Nantaporn Haskins, Michael Iannotti, Giuliana Vozza, Tony Pham, Nurjahan Mehzabeen, Harini Shandilya, Keith Rickert, Rebecca Croasdale-Wood, Melissa Damschroder, Ying Fu, Andrew Dippel, Jeffrey J Gray, Gilad Kaplan
{"title":"通过计算设计提高抗体在无抗原情况下的热稳定性和亲和力。","authors":"Mark Hutchinson, Jeffrey A Ruffolo, Nantaporn Haskins, Michael Iannotti, Giuliana Vozza, Tony Pham, Nurjahan Mehzabeen, Harini Shandilya, Keith Rickert, Rebecca Croasdale-Wood, Melissa Damschroder, Ying Fu, Andrew Dippel, Jeffrey J Gray, Gilad Kaplan","doi":"10.1080/19420862.2024.2362775","DOIUrl":null,"url":null,"abstract":"<p><p>Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. <i>In silico</i> tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and <i>in silico</i> antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (T<sub>onset</sub>, T<sub>m</sub>, T<sub>agg</sub>), affinity (K<sub>D</sub>) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in T<sub>m1</sub>), with most clones retaining the favorable developability profile of the parental antibody. Additional <i>in silico</i> tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of <i>in silico</i> antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"16 1","pages":"2362775"},"PeriodicalIF":5.6000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11195458/pdf/","citationCount":"0","resultStr":"{\"title\":\"Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen.\",\"authors\":\"Mark Hutchinson, Jeffrey A Ruffolo, Nantaporn Haskins, Michael Iannotti, Giuliana Vozza, Tony Pham, Nurjahan Mehzabeen, Harini Shandilya, Keith Rickert, Rebecca Croasdale-Wood, Melissa Damschroder, Ying Fu, Andrew Dippel, Jeffrey J Gray, Gilad Kaplan\",\"doi\":\"10.1080/19420862.2024.2362775\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. <i>In silico</i> tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and <i>in silico</i> antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (T<sub>onset</sub>, T<sub>m</sub>, T<sub>agg</sub>), affinity (K<sub>D</sub>) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in T<sub>m1</sub>), with most clones retaining the favorable developability profile of the parental antibody. Additional <i>in silico</i> tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of <i>in silico</i> antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.</p>\",\"PeriodicalId\":18206,\"journal\":{\"name\":\"mAbs\",\"volume\":\"16 1\",\"pages\":\"2362775\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11195458/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"mAbs\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/19420862.2024.2362775\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"mAbs","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/19420862.2024.2362775","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/20 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen.
Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.
期刊介绍:
mAbs is a multi-disciplinary journal dedicated to the art and science of antibody research and development. The journal has a strong scientific and medical focus, but also strives to serve a broader readership. The articles are thus of interest to scientists, clinical researchers, and physicians, as well as the wider mAb community, including our readers involved in technology transfer, legal issues, investment, strategic planning and the regulation of therapeutics.