Apprehensions and emerging solutions in ML-based protein structure prediction.

The three-dimensional structure of proteins determines their function in vital biological processes. Thus, when the structure is known, the molecular mechanism of protein function can be understood in more detail and obtained information utilized in biotechnological, diagnostics, and therapeutic applications. Over the past five years, machine learning (ML)-based modeling has pushed protein structure prediction to the next level with AlphaFold in the front line, predicting the structure for hundreds of millions of proteins. Further advances recently report promising ML-based approaches for solving remaining challenges by incorporating functionally important metals, co-factors, post-translational modifications, structural dynamics, and interdomain and multimer interactions in the structure prediction process.

Exogenous Trehalose Improves Growth, Glycogen and Poly-3-Hydroxybutyrate (PHB) Contents in Photoautotrophically Grown Arthrospira platensis under Nitrogen Deprivation.

Glycogen and poly-3-hydroxybutyrate (PHB) are excellent biopolymer products from cyanobacteria. In this study, we demonstrate that nitrogen metabolism is positively influenced by the exogenous application of trehalose (Tre) in Arthrospira platensis under nitrogen-deprived (-N) conditions. Cells were cultivated photoautotrophically for 5 days under -N conditions, with or without the addition of exogenous Tre. The results revealed that biomass and chlorophyll-a content of A. platensis experienced enhancement with the addition of 0.003 M and 0.03 M Tre in the -N medium after one day, indicating relief from growth inhibition caused by nitrogen deprivation. The highest glycogen content (54.09 ± 1.6% (w/w) DW) was observed in cells grown for 2 days under the -N + 0.003 M Tre condition (p < 0.05), while the highest PHB content (15.2 ± 0.2% (w/w) DW) was observed in cells grown for 3 days under the -N + 0.03 M Tre condition (p < 0.05). The RT-PCR analysis showed a significant increase in glgA and phaC transcript levels, representing approximately 1.2- and 1.3-fold increases, respectively, in A. platensis grown under -N + 0.003 M Tre and -N + 0.03 M Tre conditions. This was accompanied by the induction of enzyme activities, including glycogen synthase and PHA synthase with maximal values of 89.15 and 0.68 µmol min-1 mg-1 protein, respectively. The chemical structure identification of glycogen and PHB from A. platensis was confirmed by FTIR and NMR analysis. This research represents the first study examining the performance of trehalose in promoting glycogen and PHB production in cyanobacteria under nitrogen-deprived conditions.