No abstract

A new class of mutants of E. coli exhibiting altered metabolism of ppGpp and pppGpp has been isolated, and mapped at a locus designated gpp, near min 83 on the genetic map. These mutants accumulate elevated levels of pppGpp during amino acid starvation or carbon source downshift, and exhibit a reduced rate of pppGpp degradation in vivo. The in vitro evidence suggests that the gpp mutants are defective in a 5'-nucleotidase, which specifically hydrolyzes pppGpp to ppGpp. Certain combinations of gpp and spoT mutations are inviable. A gpp spoT double mutant, constructed by employing a leaky spoT mutation, was found to have a slower rate of pppGpp degradation than the gpp mutant alone. This result indicates that spoT also participates in pppGpp degradation. The inviability of certain gpp spoT combinations is attributed to the inability of the double mutants to degrade pppGpp. This is supported by the observation that selection for increased growth rate on the double mutant results in the recovery of relA mutations. Various effects of the gpp mutation upon the pppGpp and ppGpp pools provide additional support for a scheme in which pppGpp is the major precursor of ppGpp.

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

Intraventricular hemorrhage (IVH) in preterm infants leads to cerebral inflammation, reduced myelination of the white matter, and neurological deficits. No therapeutic strategy exists against the IVH-induced white matter injury. AMPA-kainate receptor induced excitotoxicity contributes to oligodendrocyte precursor cell (OPC) damage and hypomyelination in both neonatal and adult models of brain injury. Here, we hypothesized that IVH damages white matter via AMPA receptor activation, and that AMPA-kainate receptor inhibition suppresses inflammation and restores OPC maturation, myelination, and neurologic recovery in preterm newborns with IVH. We tested these hypotheses in a rabbit model of glycerol-induced IVH and evaluated the expression of AMPA receptors in autopsy samples from human preterm infants. GluR1-GluR4 expressions were comparable between preterm humans and rabbits with and without IVH. However, GluR1 and GluR2 levels were significantly lower in the embryonic white matter and germinal matrix relative to the neocortex in both infants with and without IVH. Pharmacological blockade of AMPA-kainate receptors with systemic NBQX, or selective AMPA receptor inhibition by intramuscular perampanel restored myelination and neurologic recovery in rabbits with IVH. NBQX administration also reduced the population of apoptotic OPCs, levels of several cytokines (TNF␣, IL-␤, IL-6, LIF), and the density of Iba1 ϩ microglia in pups with IVH. Additionally, NBQX treatment inhibited STAT-3 phosphorylation, but not astrogliosis or transcription factors regulating gliosis. Our data suggest that AMPA-kainate receptor inhibition alleviates OPC loss and IVH-induced inflammation and restores myelination and neurologic recovery in preterm rabbits with IVH. Therapeutic use of FDA-approved perampanel treatment might enhance neurologic outcome in premature infants with IVH. Key words: AMPA; myelination; NBQX; oligodendrocyte; perampanel Significance StatementIntraventricular hemorrhage (IVH) is a major complication of prematurity and a large number of survivors with IVH develop cerebral palsy and cognitive deficits. The development of IVH leads to inflammation of the periventricular white matter, apoptosis and arrested maturation of oligodendrocyte precursor cells, and hypomyelination. Here, we show that AMPA-kainate receptor inhibition by NBQX suppresses inflammation, attenuates apoptosis of oligodendrocyte precursor cells, and promotes myelination as well as clinical recovery in preterm rabbits with IVH. Importantly, AMPA-specific inhibition by the FDA-approved perampanel, which unlike NBQX has a low side-effect profile, also enhances myelination and neurological recovery in rabbits with IVH. Hence, the present study highlights the role of AMPA-kainate receptor in IVH-induced white matter injury and identifies a novel strategy of neuroprotection, which might improve the neurological outcome for premature infants with IVH.

No abstract

Understanding accent is an issue which can derail any human-machine interaction. Accent classification makes this task easier by identifying the accent being spoken by a person so that the correct words being spoken can be identified by further processing, since same noises can mean entirely different words in different accents of the same language. In this paper, we present VFNet (Variable Filter Net), a convolutional neural network (CNN) based architecture which captures a hierarchy of features to beat the previous benchmarks of accent classification, through a novel and elegant technique of applying variable filter sizes along the frequency band of the audio utterances.

No abstract

No abstract