Allomorphy as a mechanism of post-translational control of enzyme activity

Enzyme regulation is vital for metabolic adaptability in living systems. Fine control of enzyme activity is often delivered through post-translational mechanisms, such as allostery or allokairy. β-phosphoglucomutase (βPGM) from Lactococcus lactis is a phosphoryl transfer enzyme required for complete catabolism of trehalose and maltose, through the isomerisation of β-glucose 1-phosphate to glucose 6-phosphate via β-glucose 1,6-bisphosphate. Surprisingly for a gatekeeper of glycolysis, no fine control mechanism of βPGM has yet been reported. Herein, we describe allomorphy, a post-translational control mechanism of enzyme activity. In βPGM, isomerisation of the K145-P146 peptide bond results in the population of two conformers that have different activities owing to repositioning of the K145 sidechain. In vivo phosphorylating agents, such as fructose 1,6-bisphosphate, generate phosphorylated forms of both conformers, leading to a lag phase in activity until the more active phosphorylated conformer dominates. In contrast, the reaction intermediate β-glucose 1,6-bisphosphate, whose concentration depends on the β-glucose 1-phosphate concentration, couples the conformational switch and the phosphorylation step, resulting in the rapid generation of the more active phosphorylated conformer. In enabling different behaviours for different allomorphic activators, allomorphy allows an organism to maximise its responsiveness to environmental changes while minimising the diversion of valuable metabolites

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues

Succinic Semialdehyde Dehydrogenase Deficiency (SSADHD): Pathophysiological Complexity and Multifactorial Trait Associations in a Rare Monogenic Disorder of GABA Metabolism

Discovered some 35 years ago, succinic semialdehyde dehydrogenase deficiency (SSADHD) represents a rare, autosomal recessively-inherited defect in the second step of the GABA degradative pathway. Some 200 patients have been reported, with broad phenotypic and genotypic heterogeneity. SSADHD represents an unusual neurometabolic disorder in which two neuromodulatory agents, GABA (and the GABA analogue, 4-hydroxybutyrate), accumulate to supraphysiological levels. The unexpected occurrence of epilepsy in several patients is counterintuitive in view of the hyperGABAergic state, in which sedation might be expected. However, the epileptic status of some patients is most likely represented by broader imbalances of GABAergic and glutamatergic neurotransmission. Cumulative research encompassing decades of basic and clinical study of SSADHD reveal a monogenic disease with broad pathophysiological and clinical phenotypes. Numerous metabolic perturbations unmasked in SSADHD include alterations in oxidative stress parameters, dysregulation of autophagy and mitophagy, dysregulation of both inhibitory and excitatory neurotransmitters and gene expression, and unique subsets of SNP alterations of the SSADH gene (so-called ALDH5A1, or aldehyde dehydrogenase 5A1 gene) on the 6p22 chromosomal arm. While seemingly difficult to collate and interpret, these anomalies have continued to open novel pathways for pharmacotherapeutic considerations. Here, we present an update on selected aspects of SSADHD, the ALDH5A1 gene, and future avenues for research on this rare disorder of GABA metabolism