Abraham Osinuga

OpenKinetics Predictor unifies thirteen enzyme kinetic predictors behind one interface and API; on E. coli iML1515, eight kcat predictors correlate pairwise at r 0.08 to 0.67.

RealKcat predicts enzyme kinetics from sequence using a curated dataset and embeddings, showing sensitivity to catalytic mutations and robustness across evaluations.

The paper uses machine learning on proteomics data to predict growth rates of Rhodopseudomonas palustris on lignin-derived substrates and identifies proteomic features that encode growth potential across environments.

The paper uses dynamic modeling to analyze sphingolipid biosynthesis in Arabidopsis, identifying key enzymes critical for homeostasis and cell viability.