Donghyo Kim

De novo design of cysteine proteases

This study designs de novo cysteine proteases with high activity and structural novelty, achieving 19% success rate and up to 3 x 10^7 rate enhancement over background reactions.

Computational design of metalloproteases

This paper designs de novo metalloproteases using RoseTTAFold Diffusion 2, achieving high activity and specificity for cleaving various substrates.

Atom level enzyme active site scaffolding using RFdiffusion2

RFdiffusion2 scaffolds unindexed atom-level catalytic active sites via flow matching without inverse rotamer enumeration, solving all 41 in silico benchmark cases and yielding active de novo enzymes across four reactions.

Computational Design of Metallohydrolases

This paper designs highly active metallohydrolases using a new generative AI method, achieving kcat/KM values up to 53,000 M-1 s-1 without experimental optimization.

A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer Drugs

The paper constructs a co-essentiality network from CRISPR screens to prioritize anticancer targets and predict drug responses, outperforming conventional networks in drug repurposing.

Link clustering explains non-central essential genes in protein interaction networks

The paper identifies link clustering as a principle explaining non-central essential genes in PPI networks, showing they have distinct functional and evolutionary characteristics compared to central essential genes.