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Abstract MicroRNAs have been linked to synaptic plasticity and memory function and are emerging as potential biomarkers and therapeutic targets for cognitive diseases. Most of these data stem from the analysis of model systems or postmortem tissue from patients which mainly represents an advanced stage of pathology. Due to the in-accessibility of human brain tissue upon experimental manipulation, it is still challenging to identify microRNAs relevant to human cognition, which is however a key step for future translational studies. Here, we employ exercise as an experimental model for memory enhancement in healthy humans with the aim to identify microRNAs linked to memory function. By analyzing the circulating smallRNAome we find a cluster of 18 microRNAs that are highly correlated to cognition. MicroRNA-409-5p and microRNA-501-3p were the most significantly regulated candidates. Functional analysis revealed that the two microRNAs are important for neuronal integrity, synaptic plasticity, and morphology. In conclusion, we provide a novel approach to identify microRNAs linked to human memory function.

The emergence of novel catalytic functions in ancient proteins likely played a role in the evolution of modern enzymes. Here, the authors use protein sequences from Precambrian beta-lactamases and demonstrate that a single hydrophobic-to-ionizable amino acid mutation can lead to substantial Kemp eliminase activity.

The distance-dependent knowledge-based DrugScore(PPI) potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking "unbound perturbation" ("unbound docking") decoys generated by Baker and coworkers a 4-fold (1.5-fold) enrichment of acceptable docking solutions in the top ranks compared to a random selection is found. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScore(PPI)/FRODOCK finds up to 10% (15%) more high accuracy solutions in the top 1 (top 10) predictions than the original FRODOCK implementation. When used as an objective function for global unbound protein-protein docking, fair docking success rates are obtained, which improve by ∼ 2-fold to 18% (58%) for an at least acceptable solution in the top 10 (top 100) predictions when performing knowledge-driven unbound docking. This suggests that DrugScore(PPI) balances well several different types of interactions important for protein-protein recognition. The results are discussed in view of the influence of crystal packing and the type of protein-protein complex docked. Finally, a simple criterion is provided with which to estimate a priori if unbound docking with DrugScore(PPI)/FRODOCK will be successful.