A new mRNA structure prediction based approach to identifying improved signal peptides for bone morphogenetic protein 2.

IF 3.5 3区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY BMC Biotechnology Pub Date : 2024-05-23 DOI:10.1186/s12896-024-00858-1
Piers Wilkinson, Brian Jackson, Hazel Fermor, Robert Davies
{"title":"A new mRNA structure prediction based approach to identifying improved signal peptides for bone morphogenetic protein 2.","authors":"Piers Wilkinson, Brian Jackson, Hazel Fermor, Robert Davies","doi":"10.1186/s12896-024-00858-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Signal peptide (SP) engineering has proven able to improve production of many proteins yet is a laborious process that still relies on trial and error. mRNA structure around the translational start site is important in translation initiation and has rarely been considered in this context, with recent improvements in in silico mRNA structure potentially rendering it a useful predictive tool for SP selection. Here we attempt to create a method to systematically screen candidate signal peptide sequences in silico based on both their nucleotide and amino acid sequences. Several recently released computational tools were used to predict signal peptide activity (SignalP), localization target (DeepLoc) and predicted mRNA structure (MXFold2). The method was tested with Bone Morphogenetic Protein 2 (BMP2), an osteogenic growth factor used clinically for bone regeneration. It was hoped more effective BMP2 SPs could improve BMP2-based gene therapies and reduce the cost of recombinant BMP2 production.</p><p><strong>Results: </strong>Amino acid sequence analysis indicated 2,611 SPs from the TGF-β superfamily were predicted to function when attached to BMP2. mRNA structure prediction indicated structures at the translational start site were likely highly variable. The five sequences with the most accessible translational start sites, a codon optimized BMP2 SP variant and the well-established hIL2 SP sequence were taken forward to in vitro testing. The top five candidates showed non-significant improvements in BMP2 secretion in HEK293T cells. All showed reductions in secretion versus the native sequence in C2C12 cells, with several showing large and significant decreases. None of the tested sequences were able to increase alkaline phosphatase activity above background in C2C12s. The codon optimized control sequence and hIL2 SP showed reasonable activity in HEK293T but very poor activity in C2C12.</p><p><strong>Conclusions: </strong>These results support the use of peptide sequence based in silico tools for basic predictions around signal peptide activity in a synthetic biology context. However, mRNA structure prediction requires improvement before it can produce reliable predictions for this application. The poor activity of the codon optimized BMP2 SP variant in C2C12 emphasizes the importance of codon choice, mRNA structure, and cellular context for SP activity.</p>","PeriodicalId":8905,"journal":{"name":"BMC Biotechnology","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11112908/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Biotechnology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12896-024-00858-1","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Signal peptide (SP) engineering has proven able to improve production of many proteins yet is a laborious process that still relies on trial and error. mRNA structure around the translational start site is important in translation initiation and has rarely been considered in this context, with recent improvements in in silico mRNA structure potentially rendering it a useful predictive tool for SP selection. Here we attempt to create a method to systematically screen candidate signal peptide sequences in silico based on both their nucleotide and amino acid sequences. Several recently released computational tools were used to predict signal peptide activity (SignalP), localization target (DeepLoc) and predicted mRNA structure (MXFold2). The method was tested with Bone Morphogenetic Protein 2 (BMP2), an osteogenic growth factor used clinically for bone regeneration. It was hoped more effective BMP2 SPs could improve BMP2-based gene therapies and reduce the cost of recombinant BMP2 production.

Results: Amino acid sequence analysis indicated 2,611 SPs from the TGF-β superfamily were predicted to function when attached to BMP2. mRNA structure prediction indicated structures at the translational start site were likely highly variable. The five sequences with the most accessible translational start sites, a codon optimized BMP2 SP variant and the well-established hIL2 SP sequence were taken forward to in vitro testing. The top five candidates showed non-significant improvements in BMP2 secretion in HEK293T cells. All showed reductions in secretion versus the native sequence in C2C12 cells, with several showing large and significant decreases. None of the tested sequences were able to increase alkaline phosphatase activity above background in C2C12s. The codon optimized control sequence and hIL2 SP showed reasonable activity in HEK293T but very poor activity in C2C12.

Conclusions: These results support the use of peptide sequence based in silico tools for basic predictions around signal peptide activity in a synthetic biology context. However, mRNA structure prediction requires improvement before it can produce reliable predictions for this application. The poor activity of the codon optimized BMP2 SP variant in C2C12 emphasizes the importance of codon choice, mRNA structure, and cellular context for SP activity.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种基于 mRNA 结构预测的新方法,用于识别骨形态发生蛋白 2 的改良信号肽。
背景:翻译起始位点周围的 mRNA 结构对翻译起始非常重要,但在这种情况下却很少被考虑,而最近对 mRNA 结构的硅学改进有可能使其成为选择信号肽的有用预测工具。在此,我们尝试创建一种方法,根据候选信号肽的核苷酸序列和氨基酸序列对其进行系统的硅学筛选。我们使用了几种最近发布的计算工具来预测信号肽活性(SignalP)、定位目标(DeepLoc)和预测的 mRNA 结构(MXFold2)。该方法用骨形态发生蛋白 2(BMP2)进行了测试,BMP2 是一种临床上用于骨再生的成骨生长因子。希望更有效的 BMP2 SPs 能改善基于 BMP2 的基因疗法,并降低重组 BMP2 的生产成本:氨基酸序列分析表明,有 2,611 个 TGF-β 超家族的 SP 与 BMP2 连接后可发挥作用。体外测试采用了五个最容易进入翻译起始位点的序列、一个经过密码子优化的 BMP2 SP 变体和一个成熟的 hIL2 SP 序列。前五名候选序列在 HEK293T 细胞中的 BMP2 分泌方面均无明显改善。与原生序列相比,所有候选序列在 C2C12 细胞中的分泌量都有所下降,其中几个序列的分泌量下降幅度大且显著。没有一个测试序列能使 C2C12 细胞中的碱性磷酸酶活性高于背景值。经过密码子优化的对照序列和 hIL2 SP 在 HEK293T 中显示出合理的活性,但在 C2C12 中活性很低:这些结果支持在合成生物学背景下使用基于肽序列的硅学工具对信号肽活性进行基本预测。然而,mRNA 结构预测需要改进,才能为这一应用提供可靠的预测结果。经过密码子优化的 BMP2 SP 变体在 C2C12 中的活性很差,这强调了密码子选择、mRNA 结构和细胞环境对 SP 活性的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
BMC Biotechnology
BMC Biotechnology 工程技术-生物工程与应用微生物
CiteScore
6.60
自引率
0.00%
发文量
34
审稿时长
2 months
期刊介绍: BMC Biotechnology is an open access, peer-reviewed journal that considers articles on the manipulation of biological macromolecules or organisms for use in experimental procedures, cellular and tissue engineering or in the pharmaceutical, agricultural biotechnology and allied industries.
期刊最新文献
Fabrication of apigenin and adenosine-loaded nanoparticles against doxorubicin-induced myocardial infarction by reducing inflammation and oxidative stress. Limonene encapsulated alginate/collagen as antibiofilm drug against Acinetobacter baumannii. Fusarium verticillioides pigment: production, response surface optimization, gamma irradiation and encapsulation studies. Kinetic and thermodynamic analysis of alizarin Red S biosorption by Alhagi maurorum: a sustainable approach for water treatment. Biological activities of Hypericum spectabile extract optimized using artificial neural network combined with genetic algorithm application.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1