Muscle-specific knockout of general control of amino acid synthesis 5 (GCN5) does not enhance basal or endurance exercise-induced mitochondrial adaptation

Objective  Lysine acetylation is an important post-translational modification that regulates metabolic function in skeletal muscle. The acetyltransferase, general control of amino acid synthesis 5 (GCN5), has been proposed as a regulator of mitochondrial biogenesis via its inhibitory action on peroxisome proliferator activated receptor-γ coactivator-1α (PGC-1α). However, the specific contribution of GCN5 to skeletal muscle metabolism and mitochondrial adaptations to endurance exercise in vivo remain to be defined. We aimed to determine whether loss of GCN5 in skeletal muscle enhances mitochondrial density and function, and the adaptive response to endurance exercise training.  Methods  We used Cre-LoxP methodology to generate mice with muscle-specific knockout of GCN5 (mKO) and floxed, wildtype (WT) littermates. We measured whole-body energy expenditure, as well as markers of mitochondrial density, biogenesis, and function in skeletal muscle from sedentary mice, and mice that performed 20 days of voluntary endurance exercise training.  Results  Despite successful knockdown of GCN5 activity in skeletal muscle of mKO mice, whole-body energy expenditure as well as skeletal muscle mitochondrial abundance and maximal respiratory capacity were comparable between mKO and WT mice. Further, there were no genotype differences in endurance exercise-mediated mitochondrial biogenesis or increases in PGC-1α protein content.  Conclusion  These results demonstrate that loss of GCN5 in vivo does not promote metabolic remodeling in mouse skeletal muscle

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery