Biology subjects

Greenhalgh, J. C.

Publications and source records attributed to Greenhalgh, J. C..

3 recordsLinked to original sources

bioRxiv2021

Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production

Fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for the microbial production of fatty alcohols. Many existing metabolic engineering strategies utilize these reductases to produce fatty alcohols from intracellular acyl-CoA pools; however, acting on acyl-ACPs from fatty acid biosynthesis has a lower energetic cost and could enable more efficient production of fatty alcohols. Here we engineer FARs to preferentially act on acyl-ACP substrates and produce fatty alcohols directly from the fatty acid biosynthesis pathway. We implemented a machine learning-driven approach to iteratively search the protein fitness landscape for enzymes that produce high titers of fatty alcohols in vivo. After ten design-test-learn rounds, our approach converged on engineered enzymes that produce over twofold more fatty alcohols than the starting natural sequences. We further characterized the top identified sequence and found its improved alcohol production was a result of an enhanced catalytic rate on acyl-ACP substrates, rather than enzyme expression or KM effects. Finally, we analyzed the sequence-function data generated during the enzyme engineering to identify sequence and structure features that influence fatty alcohol production. We found an enzymes net charge near the substrate-binding site was strongly correlated with in vivo activity on acyl-ACP substrates. These findings suggest future rational design strategies to engineer highly active enzymes for fatty alcohol production.

synthetic biology↗

bioRxiv2019

In silico screening of GMQ-like compounds reveals guanabenz and sephin1 as new allosteric modulators of acid-sensing ion channel 3

Acid-sensing ion channels (ASICs) are voltage-independent cation channels that detect decreases in extracellular pH. Dysregulation of ASICs underpins a number of pathologies. Of particular interest is ASIC3, which is recognised as a key sensor of acid-induced pain and is instrumental in the establishment of pain arising from inflammatory conditions, such as rheumatoid arthritis. Thus, the identification of new ASIC3 modulators and the mechanistic understanding of how these compounds modulate ASIC3 could be important for the development of new strategies to counteract the detrimental effects of dysregulated ASIC3 activity in inflammation. Here, we report the identification of novel ASIC3 modulators based on the ASIC3 agonist, 2-guanidine-4-methylquinazoline (GMQ). Through a GMQ-guided in silico screening of Food and Drug administration (FDA)-approved drugs, 5 compounds were selected and tested for their modulation of rat ASIC3 (rASIC3) using whole-cell patch-clamp electrophysiology. Of the chosen drugs, guanabenz (GBZ), an 2-adrenoceptor agonist, produced similar effects to GMQ on rASIC3, activating the channel at physiological pH (pH 7.4) and potentiating its response to mild acidic (pH 7) stimuli. Sephin1, a GBZ derivative that lacks 2-adrenoceptor activity, has been proposed to act as a selective inhibitor of a regulatory subunit of the stress-induced protein phosphatase 1 (PPP1R15A) with promising therapeutic potential for the treatment of multiple sclerosis. However, we found that like GBZ, sephin1 activates rASIC3 at pH 7.4 and potentiates its response to acidic stimulation (pH 7), i.e. sephin1 is a novel modulator of rASIC3. Furthermore, docking experiments showed that, like GMQ, GBZ and sephin1 likely interact with the nonproton ligand sensor domain of rASIC3. Overall, these data demonstrate the utility of computational analysis for identifying novel ASIC3 modulators, which can be validated with electrophysiological analysis and may lead to the development of better compounds for targeting ASIC3 in the treatment of inflammatory conditions.

pharmacology and toxicology↗

bioRxiv2019

Proposed model of the Dictyostelium cAMP receptors bound to cAMP

3,5-cyclic adenosine monophosphate (cAMP) is well known as a ubiquitous intracellular messenger regulating a diverse array of cellular processes. However, for a group of social amoebae or Dictyostelia undergoing starvation, intracellular cAMP is secreted in a pulsatile manner to their exterior. This then uniquely acts as a first messenger, triggering aggregation of the starving amoebae followed by their developmental progression towards multicellular fruiting bodies formation. Such developmental signalling for extracellularly-acting cAMP is well studied in the popular dictyostelid, Dictyostelium discoideum, and is mediated by a distinct family ( class E) of G protein-coupled receptors (GPCRs) collectively designated as the cAMP receptors (cARs). Whilst the biochemical aspects of these receptors are well characterised, little is known about their overall 3D architecture and structural basis for cAMP recognition and subtype-dependent changes in binding affinity. Using a ligand docking-guided homology modelling approach, we hereby present for the first time, plausible models of active forms of the cARs from D. discoideum. Our models highlight some structural features that may underlie the differential affinities of cAR isoforms for cAMP binding and also suggest few residues that may play important roles for the activation mechanism of this GPCR family.

biochemistry↗