M. Sandhu

Origin Of Evolutionary Bifurcation In An Enzyme

The paper describes how two distinct enzymes evolved from a common ancestor through different mutational paths, leading to divergent molecular and phenotypic outcomes.

Identifying the core genome of the nucleus-forming bacteriophage family and characterization of Erwinia phage RAY

The paper identifies a core genome of nucleus-forming bacteriophages and characterizes the replication mechanism of Erwinia phage RAY.

Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors

The paper characterizes the rugged fitness landscape of transcriptional repressors, showing rapid specificity switching and the role of regulatory crosstalk in shaping evolutionary paths.

Dissecting the allosteric networks governing agonist efficacy and potency in G protein-coupled receptors

The paper integrates residue-level pharmacology data with structural contact data to uncover allosteric networks that govern ligand efficacy and potency in GPCRs.

The chemotherapeutic CX-5461 primarily targets TOP2B and exhibits selective activity in high-risk neuroblastoma.

This study identifies CX-5461 as a TOP2B inhibitor with selective activity in high-risk neuroblastoma but highlights safety concerns due to TOP2B off-target effects.

Substrate dynamics contribute to enzymatic specificity in human and bacterial methionine adenosyltransferases

This study investigates how substrate conformational dynamics influence enzymatic specificity in human and bacterial methionine adenosyltransferases, revealing differences in catalytic promiscuity and identifying a new metabolite in cancer cells.

The evolution of human SIRT1 for multi-specificity is enabled by active-site adaptation

This study investigates the molecular basis and evolution of multi-specificity in SIRT1, finding that active-site adaptations enable the inclusion of p53 as a substrate.

Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors

The paper develops a machine learning framework to predict thermostable mutations for GPCRs, achieving higher success rates than systematic alanine scanning.