BFVD-a large repository of predicted viral protein structures.

IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Nucleic Acids Research Pub Date : 2024-11-22 DOI:10.1093/nar/gkae1119
Rachel Seongeun Kim, Eli Levy Karin, Milot Mirdita, Rayan Chikhi, Martin Steinegger
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Abstract

The AlphaFold Protein Structure Database (AFDB) is the largest repository of accurately predicted structures with taxonomic labels. Despite providing predictions for over 214 million UniProt entries, the AFDB does not cover viral sequences, severely limiting their study. To address this, we created the Big Fantastic Virus Database (BFVD), a repository of 351 242 protein structures predicted by applying ColabFold to the viral sequence representatives of the UniRef30 clusters. By utilizing homology searches across two petabases of assembled sequencing data, we improved 36% of these structure predictions beyond ColabFold's initial results. BFVD holds a unique repertoire of protein structures as over 62% of its entries show no or low structural similarity to existing repositories. We demonstrate how a substantial fraction of bacteriophage proteins, which remained unannotated based on their sequences, can be matched with similar structures from BFVD. In that, BFVD is on par with the AFDB, while holding nearly three orders of magnitude fewer structures. BFVD is an important virus-specific expansion to protein structure repositories, offering new opportunities to advance viral research. BFVD can be freely downloaded at bfvd.steineggerlab.workers.dev and queried using Foldseek and UniProt labels at bfvd.foldseek.com.

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BFVD--预测病毒蛋白质结构的大型资源库。
AlphaFold 蛋白结构数据库(AFDB)是最大的带有分类标签的精确预测结构库。尽管 AFDB 为超过 2.14 亿个 UniProt 条目提供了预测,但它并不涵盖病毒序列,这严重限制了对病毒序列的研究。为了解决这个问题,我们创建了大型神奇病毒数据库(BFVD),这是一个包含 351 242 种蛋白质结构的资源库,通过将 ColabFold 应用于 UniRef30 聚类中的病毒序列代表进行预测。通过在两个测序数据集合数据库中进行同源性搜索,我们改进了 36% 的结构预测结果,超过了 ColabFold 的初始结果。BFVD 拥有独特的蛋白质结构库,因为其 62% 以上的条目与现有数据库没有结构相似性或结构相似性很低。我们展示了大量噬菌体蛋白质是如何与 BFVD 中的相似结构相匹配的。在这一点上,BFVD 与 AFDB 不相上下,但其结构数量却少了近三个数量级。BFVD 是对蛋白质结构库的重要扩展,为推进病毒研究提供了新的机遇。BFVD可在bfvd.steineggerlab.workers.dev免费下载,也可在bfvd.foldseek.com使用Foldseek和UniProt标签查询。
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来源期刊
Nucleic Acids Research
Nucleic Acids Research 生物-生化与分子生物学
CiteScore
27.10
自引率
4.70%
发文量
1057
审稿时长
2 months
期刊介绍: Nucleic Acids Research (NAR) is a scientific journal that publishes research on various aspects of nucleic acids and proteins involved in nucleic acid metabolism and interactions. It covers areas such as chemistry and synthetic biology, computational biology, gene regulation, chromatin and epigenetics, genome integrity, repair and replication, genomics, molecular biology, nucleic acid enzymes, RNA, and structural biology. The journal also includes a Survey and Summary section for brief reviews. Additionally, each year, the first issue is dedicated to biological databases, and an issue in July focuses on web-based software resources for the biological community. Nucleic Acids Research is indexed by several services including Abstracts on Hygiene and Communicable Diseases, Animal Breeding Abstracts, Agricultural Engineering Abstracts, Agbiotech News and Information, BIOSIS Previews, CAB Abstracts, and EMBASE.
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