Biology subjects

Damodaran, A. R.

Publications and source records attributed to Damodaran, A. R..

4 recordsLinked to original sources

bioRxiv2025

Rational design of tertiary coordination sphere of a heme-based sensor for two-orders enhanced oxygen affinity

Biological O2 sensing is crucial for diverse physiological functions across all forms of life. Heme-containing proteins achieve this by binding O2 to their iron center and have been found to display O2 affinities spanning several orders of magnitude. Despite decades of investigation into the structure and function of heme-based O2 sensors, the molecular mechanisms that enable the tuning of O2 affinity to match specific physiological roles remain unclear. Here, we utilize the O2 sensing mycobacterial DosS protein as a model system to explore the role of heme irons tertiary coordination sphere in controlling its O2 affinity. By rationally and systematically modifying the tertiary coordination sphere to promote the formation of a Trp-Tyr-Asn H-bond triad within the hemes distal pocket, we have enhanced the O2 affinity of WT DosS by over 150-fold. The rationally designed DosS exhibited a Kd value of 3 {+/-} 1 nM, compared to 460 {+/-} 80 nM for WT DosS. Employing a combination of structural, biochemical, spectroscopic, and computational studies, our analysis of WT and designed DosS variants highlights how the interplay between distal H-bond networks and heme-pocket electrostatics drives large differences in their O2 sensing capabilities. Ultimately, our work shows how metalloenzymes can dramatically alter their sensitivity to diatomic signaling molecules by tuning the tertiary coordination sphere, broadly impacting how we understand related biological sensing and signaling.

biochemistry↗

bioRxiv2024

Machine learning guided rational design of a non-heme iron-based lysine dioxygenase improves its total turnover number

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based machine-learning algorithm with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40% improvement in the TTN (218{+/-}3) as compared to WT LDO (TTN = 160{+/-}2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

biochemistry↗

bioRxiv2024

Oxygen affinities of DosT and DosS sensor kinases with implications for hypoxia adaptation in Mycobacterium tuberculosis

DosT and DosS are heme-based kinases involved in sensing and signaling O2 tension in the microenvironment of Mycobacterium tuberculosis (Mtb). Under conditions of low O2, they activate >50 dormancy-related genes and play a pivotal role in the induction of dormancy and associated drug resistance during tuberculosis infection. In this work, we reexamine the O2 binding affinities of DosT and DosS to show that their equilibrium dissociation constants are 3.3{+/-}1 M and 0.46{+/-}0.08 M respectively, which are six to eight-fold stronger than what has been widely referred to in literature. Furthermore, stopped-flow kinetic studies reveal association and dissociation rate constants of 0.84 M-1s-1 and 2.8 s-1, respectively for DosT, and 7.2 M-1s-1 and 3.3 s-1, respectively for DosS. Remarkably, these tighter O2 binding constants correlate with distinct stages of hypoxia-induced non-replicating persistence in the Wayne model of Mtb. This knowledge opens doors to deconvoluting the intricate interplay between hypoxia adaptation stages and the signal transduction capabilities of these important heme-based O2 sensors.

biochemistry↗

bioRxiv2023

Gas tunnel engineering of an iron-based oxygen sensor reprograms cellular hypoxia signaling

Cells have evolved intricate mechanisms for recognizing and responding to changes in oxygen (O2) concentrations. Here, we have reprogrammed cellular hypoxia (low O2) signaling via gas tunnel engineering of prolyl hydroxylase 2 (PHD2), a non-heme iron dependent O2 sensor. Using computational modeling and protein engineering techniques, we identify a gas tunnel and critical residues therein that limit the flow of O2 to PHD2s catalytic core. We show that systematic modification of these residues can open the constriction topology of PHD2s gas tunnel. Using kinetic stopped-flow measurements with NO as a surrogate diatomic gas, we demonstrate up to 3.5-fold enhancement in its association rate to the iron center of tunnel-engineered mutants. Our most effectively designed mutant displays 9-fold enhanced catalytic efficiency (kcat/KM = 830 {+/-} 40 M-1 s-1) in hydroxylating a peptide mimic of hypoxia inducible transcription factor HIF-1, as compared to WT PHD2 (kcat/KM = 90 {+/-} 9 M-1 s-1). Furthermore, transfection of plasmids that express designed PHD2 mutants in HEK-293T mammalian cells reveal significant reduction of HIF-1 and downstream hypoxia response transcripts under hypoxic conditions of 1% O2. Overall, these studies highlight activation of PHD2 as a new pathway to reprogram hypoxia responses and HIF signaling in cells.

biochemistry↗