Rachel Seongeun Kim

Bioinformatics, SNU

[Centre of Bioinformatics Research & Technology @CbirtDirector]

🚀@MSFTResearch introduces #BioEmu1—an AI breakthrough generating thousands of protein structures per hour on a single GPU!🧬

With unmatched speed vs. traditional MD simulations, BioEmu-1 is unlocking new frontiers in protein science & drug discovery.

🔗 https://t.co/ezZH3fUqqf

[ISME - International Society for Microbial Ecology @ISME_microbes]

ISME supports graduate students and postdoctoral fellows to further develop their academic knowledge and skills in a host institution outside of their country of study. Make sure to submit your application by 31 March 2025.
🔗https://t.co/qQG3aHqDdR
#MicrobialEcology #microbes

[ISME - International Society for Microbial Ecology @ISME_microbes]

The ISME ECSC is glad to spotlight Rachel Seongeun Kim, and her paper "BFVD—a large repository of predicted viral protein structures"

https://t.co/oMP0xL5py7

(1/2)

[Biology+AI Daily @BiologyAIDaily]

BFVD: A Large Repository of Predicted Viral Protein Structures @NAR_Open

• The Big Fantastic Virus Database (BFVD) is the largest repository of predicted viral protein structures, containing 351,242 models derived from viral sequence representatives in UniRef30 clusters using ColabFold and AlphaFold2.

• BFVD enhances the quality of predictions for 36% of its structures by integrating homologs mined from over two petabases of sequence data in Logan, resulting in deeper alignments and improved structure confidence.

• The database reveals unprecedented structural diversity, with 62% of entries showing low or no similarity to existing repositories like AFDB or PDB, highlighting its importance for studying viral protein functions and evolution.

• BFVD supports bacteriophage research by enabling structural annotation of proteins previously classified as “hypothetical,” showcasing its utility in decoding unannotated metagenomic data.

• The web server (https://t.co/RHsRyYqCbZ) provides tools for structural searches, visualization, and taxonomic exploration, integrating UniProt labels and clustering data for ease of access and enhanced usability.

• By focusing on viral proteins excluded from major databases like AFDB, BFVD offers a compact but comprehensive resource tailored for advancing viral research and supporting structural virology.

• Future expansions aim to include viral multimer structures and enhance search capabilities with tools like Foldseek-Multimer, ensuring continued contributions to virology and computational biology.

@thesteinegger @RayanChikhi @milot_mirdita @eunbelivable
💻Code: https://t.co/fbY3xJzG8R
📜Paper: https://t.co/BQ5UJFo1hL

#ViralProteins #StructuralBiology #Bioinformatics #AlphaFold2 #Virology #OpenScience