释放计算机方法在设计SARS-CoV-2抗体方面的潜力。

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in bioinformatics Pub Date : 2025-02-13 eCollection Date: 2025-01-01 DOI:10.3389/fbinf.2025.1533983
Tasshitra Subramaniam, Siti Aisyah Mualif, Weng Howe Chan, Khairul Bariyyah Abd Halim
{"title":"释放计算机方法在设计SARS-CoV-2抗体方面的潜力。","authors":"Tasshitra Subramaniam, Siti Aisyah Mualif, Weng Howe Chan, Khairul Bariyyah Abd Halim","doi":"10.3389/fbinf.2025.1533983","DOIUrl":null,"url":null,"abstract":"<p><p>Antibodies are naturally produced safeguarding proteins that the immune system generates to fight against invasive invaders. For centuries, they have been produced artificially and utilized to eradicate various infectious diseases. Given the ongoing threat posed by COVID-19 pandemics worldwide, antibodies have become one of the most promising treatments to prevent infection and save millions of lives. Currently, <i>in silico</i> techniques provide an innovative approach for developing antibodies, which significantly impacts the formulation of antibodies. These techniques develop antibodies with great specificity and potency against diseases such as SARS-CoV-2 by using computational tools and algorithms. Conventional methods for designing and developing antibodies are frequently costly and time-consuming. However, <i>in silico</i> approach offers a contemporary, effective, and economical paradigm for creating next-generation antibodies, especially in accordance with recent developments in bioinformatics. By utilizing multiple antibody databases and high-throughput approaches, a unique antibody construct can be designed <i>in silico</i>, facilitating accurate, reliable, and secure antibody development for human use. Compared to their traditionally developed equivalents, a large number of <i>in silico</i>-designed antibodies have advanced swiftly to clinical trials and became accessible sooner. This article helps researchers develop SARS-CoV-2 antibodies more quickly and affordably by giving them access to current information on computational approaches for antibody creation.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1533983"},"PeriodicalIF":2.8000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865036/pdf/","citationCount":"0","resultStr":"{\"title\":\"Unlocking the potential of <i>in silico</i> approach in designing antibodies against SARS-CoV-2.\",\"authors\":\"Tasshitra Subramaniam, Siti Aisyah Mualif, Weng Howe Chan, Khairul Bariyyah Abd Halim\",\"doi\":\"10.3389/fbinf.2025.1533983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Antibodies are naturally produced safeguarding proteins that the immune system generates to fight against invasive invaders. For centuries, they have been produced artificially and utilized to eradicate various infectious diseases. Given the ongoing threat posed by COVID-19 pandemics worldwide, antibodies have become one of the most promising treatments to prevent infection and save millions of lives. Currently, <i>in silico</i> techniques provide an innovative approach for developing antibodies, which significantly impacts the formulation of antibodies. These techniques develop antibodies with great specificity and potency against diseases such as SARS-CoV-2 by using computational tools and algorithms. Conventional methods for designing and developing antibodies are frequently costly and time-consuming. However, <i>in silico</i> approach offers a contemporary, effective, and economical paradigm for creating next-generation antibodies, especially in accordance with recent developments in bioinformatics. By utilizing multiple antibody databases and high-throughput approaches, a unique antibody construct can be designed <i>in silico</i>, facilitating accurate, reliable, and secure antibody development for human use. Compared to their traditionally developed equivalents, a large number of <i>in silico</i>-designed antibodies have advanced swiftly to clinical trials and became accessible sooner. This article helps researchers develop SARS-CoV-2 antibodies more quickly and affordably by giving them access to current information on computational approaches for antibody creation.</p>\",\"PeriodicalId\":73066,\"journal\":{\"name\":\"Frontiers in bioinformatics\",\"volume\":\"5 \",\"pages\":\"1533983\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-02-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865036/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fbinf.2025.1533983\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fbinf.2025.1533983","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

抗体是免疫系统自然产生的保护蛋白,用来对抗入侵的入侵者。几个世纪以来,它们一直是人工生产的,用于根除各种传染病。鉴于COVID-19大流行在全球范围内构成的持续威胁,抗体已成为预防感染和挽救数百万人生命的最有希望的治疗方法之一。目前,硅技术为开发抗体提供了一种创新的方法,这对抗体的形成产生了重大影响。这些技术通过使用计算工具和算法,开发出针对SARS-CoV-2等疾病具有很强特异性和效力的抗体。设计和开发抗体的传统方法通常既昂贵又耗时。然而,在硅片方法提供了一个当代的,有效的,和经济的范式来创建下一代抗体,特别是根据生物信息学的最新发展。通过利用多个抗体数据库和高通量方法,可以在计算机上设计独特的抗体结构,促进准确,可靠和安全的抗体开发供人类使用。与传统研发的同类产品相比,大量由芯片设计的抗体已经迅速进入临床试验阶段,并且可以更快地获得。这篇文章帮助研究人员更快、更经济地开发出SARS-CoV-2抗体,为他们提供了有关抗体创建计算方法的当前信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unlocking the potential of in silico approach in designing antibodies against SARS-CoV-2.

Antibodies are naturally produced safeguarding proteins that the immune system generates to fight against invasive invaders. For centuries, they have been produced artificially and utilized to eradicate various infectious diseases. Given the ongoing threat posed by COVID-19 pandemics worldwide, antibodies have become one of the most promising treatments to prevent infection and save millions of lives. Currently, in silico techniques provide an innovative approach for developing antibodies, which significantly impacts the formulation of antibodies. These techniques develop antibodies with great specificity and potency against diseases such as SARS-CoV-2 by using computational tools and algorithms. Conventional methods for designing and developing antibodies are frequently costly and time-consuming. However, in silico approach offers a contemporary, effective, and economical paradigm for creating next-generation antibodies, especially in accordance with recent developments in bioinformatics. By utilizing multiple antibody databases and high-throughput approaches, a unique antibody construct can be designed in silico, facilitating accurate, reliable, and secure antibody development for human use. Compared to their traditionally developed equivalents, a large number of in silico-designed antibodies have advanced swiftly to clinical trials and became accessible sooner. This article helps researchers develop SARS-CoV-2 antibodies more quickly and affordably by giving them access to current information on computational approaches for antibody creation.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.60
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信