Lynx1 and the family of endogenous mammalian neurotoxin-like proteins and their roles in modulating nAChR function.

The promise of nicotinic receptors as a therapeutic target has yet to be fully realized, despite solid data supporting their involvement in neurological and neuropsychiatric diseases. The reasons for this are likely complex and manifold, having to do with the widespread action of the cholinergic system and the biophysical mechanism of action of nicotinic receptors leading to fast desensitization and down-regulation. Conventional drug development strategies tend to focus on receptor subtype-specific action of candidate therapeutics, although the broad agonist, nicotine, is being explored in the clinic. The potential negative effects of nicotine make the search for alternate strategies warranted. Prototoxins are a promising yet little-explored avenue of nicotinic receptor drug development. Nicotinic receptors in the brain belong to a complex of proteins, including those that bind to the extracellular face of the receptor, as well as chaperones that bind the intracellular domain, etc. Lynx prototoxins have allosteric modularity effects on receptor function and number and have been implicated in complex in vivo processes such as neuroplasticity, learning, and memory. Their mechanism of action and binding specificity on sets of nAChR subtypes present intriguing possibilities for more efficacious and nuanced therapeutic targeting than nicotinic receptor subtypes alone. An allosteric drug may restrict its actions to physiologically relevant time points, which tend to be correlated with salient events which would be encoded into long-term memory storage. Rather than blanketing the brain with a steady and prolonged elevation of agonist, an allosteric nAChR compound could avoid side effects and loss of efficacy over time. This review details the potential strengths and challenges of prototoxin proteins as therapeutic targets, and some of the utility of such therapeutics based on the emerging understanding of cholinergic signaling in a growing number of complex neural processes.

Abnormal response to chronic social defeat stress and fear extinction in a mouse model of cholinergic dysregulation.

Cholinergic signaling is critical for an individual to react appropriately and adaptably to salient stimuli while navigating a complex environment. The cholinergic neurotransmitter system drives attention to salient stimuli, such as stressors, and aids in orchestrating the proper neural and behavioral response. Fine-tuned regulation of the cholinergic system has been linked to appropriate stress responses and subsequent mood regulation while dysregulation has been implicated in mood disorders. Among the multiple layers of regulation are cholinergic protein modulators. Here, we use validated models of experiential-based affective disorders to investigate differences in responses to stress in a genetic mouse model of cholinergic dysregulation based on the loss of protein modulator. The lynx2 nicotinic receptor modulatory protein provides negative cholinergic regulation within the amygdala, medial prefrontal cortex, and other brain regions. We discovered here that lynx2 knockout (KO) mice demonstrate an inability to update behavior with an inability to extinguish learned fear during a fear extinction test. We also observed, under an increased stress load following exposure to chronic social defeat stress (CSDS) paradigm, there was a unified resilience phenotype in lynx2KO mice, as opposed to the wild-type cohort which was split between resilience and susceptible phenotypes. Furthermore, we provide evidence for the functional role of α7 nicotinic receptor subtypes by phenotypic rescue with MLA or crossing with an α7 null mutant mouse (e.g. lynx2/α7 double KO mice). We demonstrate a direct physical interaction between lynx2 and α7 nAChR by co-immunoprecipitation of complexes from mouse BLA extracts. The genetic predisposition to heightened basal anxiety-like behavior and altered cholinergic signaling impairs individual behavior responses stressors. Together, these data indicate that the effects of social stress can be influenced by baseline genetic factors involved in anxiety regulation.

Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction.

Protein-protein interactions often involve a complex system of intermolecular interactions between residues and atoms at the binding site. A comprehensive exploration of these interactions can help reveal key residues involved in protein-protein recognition that are not obvious using other protein analysis techniques. This paper presents and extends DiffBond, a novel method for identifying and classifying intermolecular bonds while applying standard definitions of bonds in chemical literature to explain protein interactions. DiffBond predicted intermolecular bonds from four protein complexes: Barnase-Barstar, Rap1a-raf, SMAD2-SMAD4, and a subset of complexes formed from three-finger toxins and nAChRs. Based on validation through manual literature search and through comparison of two protein complexes from the SKEMPI dataset, DiffBond was able to identify intermolecular ionic bonds and hydrogen bonds with high precision and recall, and identify salt bridges with high precision. DiffBond predictions on bond existence were also strongly correlated with observations of Gibbs free energy change and electrostatic complementarity in mutational experiments. DiffBond can be a powerful tool for predicting and characterizing influential residues in protein-protein interactions, and its predictions can support research in mutational experiments and drug design.

DiffBond: A Method for Predicting Intermolecular Bond Formation.

Many tools that explore models of protein complexes are also able to analyze interactions between specific residues and atoms. A comprehensive exploration of these interactions can often uncover aspects of protein-protein recognition that are not obvious using other protein analysis techniques. This paper describes DiffBond, a novel method for searching for intermolecular interactions between protein complexes while differentiating between three different types of interaction: hydrogen bonds, ionic bonds, and salt bridges. DiffBond incorporates textbook definitions of these three interactions while contending with uncertainties that are inherent in computational models of interacting proteins. We used it to examine the barnase-barstar, Rap1a-raf, and Smad2-Smad4 complexes, as well as a subset of protein complexes formed between three-finger toxins and nAChRs. Based on electrostatic interactions established by previous experimental studies, DiffBond was able to identify ionic and hydrogen bonds with high precision and recall, and identify salt bridges with high precision. In combination with other electrostatic analysis methods, DiffBond can be a useful tool in helping predict influential amino acids in protein-protein interactions and characterizing the type of interaction.