Vladimir O. Porokhin

The paper introduces BGCat, a machine learning tool for fine-grained classification of biosynthetic gene cluster-encoded products, improving upon existing methods and labeling over 100k BGCs.

The paper introduces a co-design paradigm to enhance binding affinity between Ndh2 and quinones, demonstrating its application in improving biosensing capabilities.

The paper develops a computational method to identify enzyme promiscuity-induced metabolic network disruptions in engineered microbial hosts and evaluates its application in two case studies.

The paper predicts new metabolites and reactions in E. coli using enzyme promiscuity and metabolomics data, enhancing the iML1515 metabolic model.