An essential histidine in bacterial cytochrome c peroxidases

The cytochrome c peroxidase from the bacterium Pciracoccus denitrificans is a relative of the extensively characterised enzyme from Pseudomonas aeruginosa. This study investigates the role of an essential histidine residue in the enzyme mechanism of bacterial peroxidases.Cytochrome c peroxidase from Paracoccus denitrificans was modified with the histidine-specific reagent diethylpyrocarbonate. The reaction can be followed spectroscopically and, at low excess of reagent, one mol of histidine was modified in the oxidised enzyme. The agreement between the spectrophotometric measurement of histidine modification and radioactive incorporation using a radiolabeled reagent indicated little modification of other amino acids. Modification of this easily modifiable histidine was associated with loss of the enzyme's ability to form the active state. With time, the modification reversed and the ability to form the active mixed valence state was recovered. However the reversal of histidine modification observed spectrophotometrically was not matched by loss of radioactivity and a slow transfer of the ethoxyformyl group to another amino acid is proposed. The presence of CN" bound to the active peroxidatic site of the enzyme completely protected the essential histidine from modification.In its active form cytochrome c peroxidase is a dimer, with Ca2+ situated at the interface between the two monomers. Under conditions where the dimer is the dominant species modification of only 0.5 mol histidine abolishes enzyme activityLimited subtilisin treatment of the native enzyme resulted in cleavage at a single peptide bond. Although the two fragments remain tightly associated, the cleaved enzyme is inactive. Modification with radiolabeled diethylpyrocarbonate and subsequent subtilisin treatment, followed by tryptic digestion of a 9k fragment, showed that radioactivity was located in a peptide containing a single histidine 275.With the benefit of four homologous sequences and the use of secondary structure prediction analysis we can determine that histidine 275 is indeed conserved in the four sequences and is preceded by a remarkably unvaried a-helical region suggestive of functional importance. It is proposed that this conserved residue acts as both a catalytic active site residue and a conduit for intermolecular electron transfer in the active mixed-valence high spin-state

Bio-Inspired Approach to Modelling Retinal Ganglion Cells using System Identification Techniques

The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power, and performance. A key aspect to modeling the human visual system is the ability to accurately model the behavior and computation within the retina. In particular, we focus on modeling the retinal ganglion cells (RGCs) as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within RGCs can be derived by quantitatively fitting the sets of physiological data using an input–output analysis where the input is a known stimulus and the output is neuronal recordings. Currently, these input–output responses are modeled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this paper, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behavior, and are a viable alternative to traditional linear–nonlinear approaches

Discovery of AZD-2098 and AZD-1678, two potent and bioavailable CCR4 receptor antagonists

N-(5-Bromo-3-methoxypyrazin-2-yl)-5-chlorothiophene-2-sulfonamide 1 was identified as a hit in a CCR4 receptor antagonist high throughput screen (HTS) of a sub-set of the AstraZeneca compound bank. As a hit with a lead-like profile, it was an excellent starting point for a CCR4 receptor antagonist program and enabled the rapid progression through the Lead Identification and Lead Optimization phases resulting in the discovery of two bioavailable CCR4 receptor antagonist candidate drugs

Computational modelling of salamander retinal ganglion cells using machine learning approaches

Artificial vision using computational models that can mimic biological vision is an area of ongoing research. One of the main themes within this research is the study of the retina and in particular, retinal ganglion cells which are responsible for encoding the visual stimuli. A common approach to modelling the internal processes of retinal ganglion cells is the use of a linear - non-linear cascade model, which models the cell's response using a linear filter followed by a static non-linearity. However, the resulting model is generally restrictive as it is often a poor estimator of the neuron's response. In this paper we present an alternative to the linear - non-linear model by modelling retinal ganglion cells using a number of machine learning techniques which have a proven track record for learning complex non-linearities in many different domains. A comparison of the model predicted spike rate shows that the machine learning models perform better than the standard linear - non-linear approach in the case of temporal white noise stimuli