A. Jinich

Annotation-free prediction of microbial dioxygen utilization

This paper predicts microbial dioxygen utilization from genome sequences without gene annotation, achieving >70% accuracy and demonstrating environmental correlations.

Predicting enzyme substrate chemical structure with protein language models

This paper predicts chemical substrate structures for orphan enzymes using protein language models, achieving high accuracy for Mycobacterium tuberculosis SDRs and SAM-methyltransferases.

Genome-wide co-essentiality analysis in Mycobacterium tuberculosis reveals an itaconate defense enzyme module

The paper uses co-essentiality analysis of TnSeq data to identify gene modules in Mtb, focusing on an itaconate defense enzyme cluster and proposing novel functional associations.

Multiple acyl-CoA dehydrogenase deficiency kills Mycobacterium tuberculosis in vitro and during infection

This study identifies EtfDMtb as an essential dehydrogenase for fatty acid β-oxidation in Mtb, showing that disrupting it kills the bacterium in vitro and in mice.

The Mycobacterium tuberculosis transposon sequencing database (MtbTnDB): a large-scale guide to genetic conditional essentiality

The paper builds MtbTnDB, a database of Mtb TnSeq screens, enabling comparison of genetic essentiality across conditions and analysis of gene function.

A mixed quantum chemistry/machine learning approach for the fast and accurate prediction of biochemical redox potentials and its large-scale application to 315,000 redox reactions

The paper develops a hybrid quantum chemistry/machine learning method to predict redox potentials and applies it to 315,000 reactions, outperforming group contribution methods with lower cost.

Quantum chemistry reveals the thermodynamic principles of redoxbiochemistry

This study uses quantum chemistry to calculate redox potentials of biochemical reactions, revealing thermodynamic trends and explaining the role of NAD/NADP in cellular metabolism.