Adam Godzik

This study investigates the evolutionary history of the TLR2 cavity in vertebrates and invertebrates using AI protein structure predictions, showing its ancestral origin and convergent evolution in different species.

This study uses spatial transcriptomics on FFPE kidney biopsy tissues to identify distinct transcriptomic signatures for different types of kidney transplant rejections.

The paper provides a global atlas of predicted functional domains in Legionella pneumophila effectors using 3D structure prediction, identifying novel domains and demonstrating their functional impact.

This study shows how small variations in LRR repeats of human TLRs lead to structural and functional diversity, enabling recognition of different ligands and immune responses.

The paper uses single-cell transcriptomics and machine learning to analyze platelet subpopulations in sepsis, COVID-19, and SLE, identifying disease-associated transcriptional signatures and potential therapeutic targets.

The paper introduces a drug resistance tracker to monitor and predict the emergence of mutations in SARS-CoV-2 that confer resistance to antiviral drugs targeting Mpro and PLpro.

The paper reports the high-resolution crystal structure of SARS-CoV-2 Nsp15/NendoU and compares it with structures from SARS and MERS coronaviruses.

The paper introduces difference contact maps (DCMs) to analyze conformational flexibility in proteins by identifying critical interactions and functional residues across alternative conformations.

The paper identifies molecular properties that determine oxidative stress resistance in bacteria by analyzing protein susceptibility to oxidation in Escherichia coli and Deinococcus radiodurans.

This study uses a crowdsourcing approach to stratify ALS patients, identifying four distinct clusters with unique predictors and improving outcome prediction.

Cancer3D 2.0 provides an interactive platform to analyze 3D mutation patterns in cancer subsets, enabling identification of novel driver regions in specific patient subgroups.

The paper identifies 128 PPI interfaces enriched with somatic mutations across 5,989 tumors, showing how mutations in cancer driver genes affect protein interactions and tumor behavior.