使用蛋白质块从蛋白质结构构建自定义片段库。

IF 3
Surbhi Dhingra, Stéphane Téletchéa, Ramanathan Sowdhamini, Yves-Henri Sanejouand, Alexandre G de Brevern, Frédéric Cadet, Bernard Offmann
{"title":"使用蛋白质块从蛋白质结构构建自定义片段库。","authors":"Surbhi Dhingra, Stéphane Téletchéa, Ramanathan Sowdhamini, Yves-Henri Sanejouand, Alexandre G de Brevern, Frédéric Cadet, Bernard Offmann","doi":"10.1016/j.biochi.2025.08.011","DOIUrl":null,"url":null,"abstract":"<p><p>The remarkable structural diversity of modern proteins reflects millions of years of evolution, during which sequence space has expanded while many structural features remain conserved. This conservation is evident not only among homologous proteins but also in the recurrence of supersecondary motifs across unrelated proteins, underscoring the abundance and robustness of these structural units. Here, we present a novel pipeline for generating customized protein fragment libraries using protein blocks (PBs)-a structural alphabet that encodes local backbone conformations. Our method efficiently extracts structurally similar fragments from a curated, non-redundant protein structure database by converting three-dimensional structures into one-dimensional PB sequences. By integrating predicted PB sequences with the PB-ALIGN and PB-kPRED tools, our approach identifies relevant fragments independently of sequence homology. Fragment quality is further assessed using a new scoring function that combines secondary structure similarity and PB alignment metrics. The resulting libraries contain fragments of at least seven PBs (11 amino acid residues), covering over 70 % of the local backbone structure. Our results demonstrate that PBs enable efficient mining of high-quality structural fragments from diverse protein spaces, including proteins with disordered regions. The pipeline is accessible as an online tool (PB-Frag, http://pbpred-us2b.univ-nantes.fr/pbfrag).</p>","PeriodicalId":93898,"journal":{"name":"Biochimie","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using protein blocks to build custom fragment libraries from protein structures.\",\"authors\":\"Surbhi Dhingra, Stéphane Téletchéa, Ramanathan Sowdhamini, Yves-Henri Sanejouand, Alexandre G de Brevern, Frédéric Cadet, Bernard Offmann\",\"doi\":\"10.1016/j.biochi.2025.08.011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The remarkable structural diversity of modern proteins reflects millions of years of evolution, during which sequence space has expanded while many structural features remain conserved. This conservation is evident not only among homologous proteins but also in the recurrence of supersecondary motifs across unrelated proteins, underscoring the abundance and robustness of these structural units. Here, we present a novel pipeline for generating customized protein fragment libraries using protein blocks (PBs)-a structural alphabet that encodes local backbone conformations. Our method efficiently extracts structurally similar fragments from a curated, non-redundant protein structure database by converting three-dimensional structures into one-dimensional PB sequences. By integrating predicted PB sequences with the PB-ALIGN and PB-kPRED tools, our approach identifies relevant fragments independently of sequence homology. Fragment quality is further assessed using a new scoring function that combines secondary structure similarity and PB alignment metrics. The resulting libraries contain fragments of at least seven PBs (11 amino acid residues), covering over 70 % of the local backbone structure. Our results demonstrate that PBs enable efficient mining of high-quality structural fragments from diverse protein spaces, including proteins with disordered regions. The pipeline is accessible as an online tool (PB-Frag, http://pbpred-us2b.univ-nantes.fr/pbfrag).</p>\",\"PeriodicalId\":93898,\"journal\":{\"name\":\"Biochimie\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biochimie\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.biochi.2025.08.011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biochimie","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.biochi.2025.08.011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

现代蛋白质显著的结构多样性反映了数百万年的进化过程,在此过程中,序列空间扩大了,而许多结构特征仍然保持保守。这种保守性不仅在同源蛋白中很明显,而且在不相关蛋白的超二级基序的复发中也很明显,强调了这些结构单元的丰度和稳健性。在这里,我们提出了一种新的管道,用于使用蛋白质块(PBs)生成定制的蛋白质片段文库——一种编码局部主干构象的结构字母表。我们的方法通过将三维结构转换为一维PB序列,有效地从精心设计的非冗余蛋白质结构数据库中提取结构相似的片段。通过将预测的PB序列与PB- align和PB- kpred工具相结合,我们的方法可以独立于序列同源性识别相关片段。使用结合二级结构相似性和PB对齐度量的新评分函数进一步评估片段质量。得到的文库包含至少7个PBs(11个氨基酸残基)的片段,覆盖了70%以上的局部主链结构。我们的研究结果表明,PBs能够有效地从不同的蛋白质空间中挖掘高质量的结构片段,包括具有无序区域的蛋白质。该管道可以作为在线工具访问(PB-Frag, http://pbpred-us2b.univ-nantes.fr/pbfrag)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using protein blocks to build custom fragment libraries from protein structures.

The remarkable structural diversity of modern proteins reflects millions of years of evolution, during which sequence space has expanded while many structural features remain conserved. This conservation is evident not only among homologous proteins but also in the recurrence of supersecondary motifs across unrelated proteins, underscoring the abundance and robustness of these structural units. Here, we present a novel pipeline for generating customized protein fragment libraries using protein blocks (PBs)-a structural alphabet that encodes local backbone conformations. Our method efficiently extracts structurally similar fragments from a curated, non-redundant protein structure database by converting three-dimensional structures into one-dimensional PB sequences. By integrating predicted PB sequences with the PB-ALIGN and PB-kPRED tools, our approach identifies relevant fragments independently of sequence homology. Fragment quality is further assessed using a new scoring function that combines secondary structure similarity and PB alignment metrics. The resulting libraries contain fragments of at least seven PBs (11 amino acid residues), covering over 70 % of the local backbone structure. Our results demonstrate that PBs enable efficient mining of high-quality structural fragments from diverse protein spaces, including proteins with disordered regions. The pipeline is accessible as an online tool (PB-Frag, http://pbpred-us2b.univ-nantes.fr/pbfrag).

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
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:604180095
Book学术官方微信