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Hypothesis: Electrosynthesis of the MIP nano-film after binding of the separated domains or holocytochrome BM3 via an engineered anchor should result in domain-specific cavities in the polymer layer. Experiments: Both the two domains and the holo P450 BM3 have been bound prior polymer deposition via a N-terminal engineered his6-anchor to the electrode surface. Each step of MIP preparation was characterized by cyclic voltammetry of the redox-marker ferricyanide. Rebinding after template removal was evaluated by quantifying the suppression of the diffusive permeability of the signal for ferricyanide and by the NADH-dependent reduction of cytochrome c by the reductase domain (BMR). Findings: The working hypothesis is verified by the discrimination of the two domains by the respective MIPs: The holoenzyme P450 BM3 was ca. 5.5 times more effectively recognized by the film imprinted with the oxidase domain (BMO) as compared to the BMR-MIP or the non-imprinted polymer (NIP). Obviously, a cavity is formed during the imprinting process around the hiss-tag-anchored BMR which cannot accommodate the broader BMO or the P450 BM3. The affinity of the MIP towards P450 BM3 is comparable with that to the monomer in solution. The hiss-tagged P450 BM3 binds (30 percent) stronger which shows the additive effect of the interaction with the MIP and the binding to the electrode.
Background Aversive stimuli in the environment influence human actions. This includes valence-dependent influences on action selection, e.g., increased avoidance but decreased approach behavior. However, it is yet unclear how aversive stimuli interact with complex learning and decision-making in the reward and avoidance domain. Moreover, the underlying computational mechanisms of these decision-making biases are unknown. Methods To elucidate these mechanisms, 54 healthy young male subjects performed a two-step sequential decision-making task, which allows to computationally model different aspects of learning, e.g., model-free, habitual, and model-based, goal-directed learning. We used a within-subject design, crossing task valence (reward vs. punishment learning) with emotional context (aversive vs. neutral background stimuli). We analyzed choice data, applied a computational model, and performed simulations. Results Whereas model-based learning was not affected, aversive stimuli interacted with model-free learning in a way that depended on task valence. Thus, aversive stimuli increased model-free avoidance learning but decreased model-free reward learning. The computational model confirmed this effect: the parameter lambda that indicates the influence of reward prediction errors on decision values was increased in the punishment condition but decreased in the reward condition when aversive stimuli were present. Further, by using the inferred computational parameters to simulate choice data, our effects were captured. Exploratory analyses revealed that the observed biases were associated with subclinical depressive symptoms. Conclusion Our data show that aversive environmental stimuli affect complex learning and decision-making, which depends on task valence. Further, we provide a model of the underlying computations of this affective modulation. Finally, our finding of increased decision-making biases in subjects reporting subclinical depressive symptoms matches recent reports of amplified Pavlovian influences on action selection in depression and suggests a potential vulnerability factor for mood disorders. We discuss our findings in the light of the involvement of the neuromodulators serotonin and dopamine.