An Insight of RuBisCO Evolution through a Multilevel Approach

The phylogeny of Acetobacteraceae: photosynthetic traits and deranged respiratory enzymes

IMPORTANCE

Acetobacteraceae are one of the best known and most extensively studied groups of bacteria, which nowadays encompasses a variety of taxa that are very different from the vinegar-producing species defining the family. Our paper presents the most detailed phylogeny of all current taxa classified as Acetobacteraceae, for which we propose a taxonomic revision. Several of such taxa inhabit some of the most extreme environments on the planet, from the deserts of Antarctica to the Sinai desert, as well as acidic niches in volcanic sites like the one we have been studying in Patagonia. Our work documents the progressive variation of the respiratory chain in early branching Acetobacteraceae into the different respiratory chains of acidophilic taxa such as Acidocella and acetous taxa such as Acetobacter. Remarkably, several genomes retain remnants of ancestral photosynthetic traits and functional bc 1 complexes. Thus, we propose that the common ancestor of Acetobacteraceae was photosynthetic.

Predicting plant Rubisco kinetics from RbcL sequence data using machine learning

Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) is responsible for the conversion of atmospheric CO2 to organic carbon during photosynthesis, and often acts as a rate limiting step in the later process. Screening the natural diversity of Rubisco kinetics is the main strategy used to find better Rubisco enzymes for crop engineering efforts. Here, we demonstrate the use of Gaussian processes (GPs), a family of Bayesian models, coupled with protein encoding schemes, for predicting Rubisco kinetics from Rubisco large subunit (RbcL) sequence data. GPs trained on published experimentally obtained Rubisco kinetic datasets were applied to over 9000 sequences encoding RbcL to predict Rubisco kinetic parameters. Notably, our predicted kinetic values were in agreement with known trends, e.g. higher carboxylation turnover rates (Kcat) for Rubisco enzymes from C4 or crassulacean acid metabolism (CAM) species, compared with those found in C3 species. This is the first study demonstrating machine learning approaches as a tool for screening and predicting Rubisco kinetics, which could be applied to other enzymes.

Interspecies Comparison of Interaction Energies between Photosynthetic Protein RuBisCO and 2CABP Ligand

Ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCO) functions as the initial enzyme in the dark reactions of photosynthesis, catalyzing reactions that extract CO2 from the atmosphere and fix CO2 into organic compounds. RuBisCO is classified into four types (isoforms I–IV) according to sequence-based phylogenetic trees. Given its size, the computational cost of accurate quantum-chemical calculations for functional analysis of RuBisCO is high; however, recent advances in hardware performance and the use of the fragment molecular orbital (FMO) method have enabled the ab initio analyses of RuBisCO. Here, we performed FMO calculations on multiple structural datasets for various complexes with the 2′-carboxylarabinitol 1,5-bisphosphate (2CABP) ligand as a substrate analog and investigated whether phylogenetic relationships based on sequence information are physicochemically relevant as well as whether novel information unobtainable from sequence information can be revealed. We extracted features similar to the phylogenetic relationships found in sequence analysis, and in terms of singular value decomposition, we identified residues that strongly interacted with the ligand and the characteristics of the isoforms for each principal component. These results identified a strong correlation between phylogenetic relationships obtained by sequence analysis and residue interaction energies with the ligand. Notably, some important residues were located far from the ligand, making comparisons among species using only residues proximal to the ligand insufficient.