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Garcia Garcia, C.

Publications and source records attributed to Garcia Garcia, C..

2 recordsLinked to original sources

Prebiotic aqueous reactions catalyzed by native nickel without hydrogen

Compared to iron, nickel is comparatively rare as a transition metal in enzymes. But it is essential in several enzymes of carbon and energy metabolism in acetogens (bacteria) and methanogens (archaea), which use the acetyl-CoA pathway of H2-dependent CO2 fixation. Nickel containing enzymes of acetogens and methanogens include FeNi hydrogenase, carbon monoxide dehydrogenase, acetyl-CoA synthase and, in methanogens, methyl-CoM reductase in the last step of methane synthesis. Several lines of evidence implicate the acetyl-CoA pathway as the most ancient pathway of CO2 fixation, most notably recent findings that the overall reaction of the enzymatic pathway from H2 (E0' = -414 mV) and CO2 to pyruvate can be replaced by Ni0 alone in water as the lone catalyst. Here we studied the ability of Ni0 to serve as catalyst and reductant for nonenzymatic redox reactions that require only a mild reductant, as the midpoint potential of Ni0 oxidation to Ni2+ is E0' = -260 mV. We show that Ni0 in water can convert 2-oxo acids to 2-hydroxy acids and, in the presence of NH3, to amino acids at 25-100{degrees}C without addition of H2, and that it will function as catalyst and reductant for the fumarate reductase reaction. The findings expand the repertoire of ancient metabolic reactions that Ni0 can catalyze without proteins, cofactors, or sulfur, shedding light on the broad catalytic activity and substrate specificity of Ni0 at metabolic origin.

biochemistry↗

A cautionary tale on organelle proteome prediction algorithms: limits and opportunities

Mitochondria and plastids import thousands of proteins. Their experimental localisation remains a frequent task, but can be resource-intensive and sometimes impossible. Hence, hundreds of studies make use of algorithms that predict a localisation based on a proteins sequence. Their reliability across evolutionary diverse species is unknown. Here, we evaluate the performance of common algorithms (TargetP, Localizer and WoLFPSORT) for four photosynthetic eukaryotes for which experimental plastid and mitochondrial proteome data is available, and 171 eukaryotes using orthology inferences. The match between predictions and experimental data ranges from 75% to as low as 2%. Results worsen as the evolutionary distance between training and query species increases, especially for plant mitochondria for which performance borders on random sampling. Specificity, sensitivity and precision analyses highlight cross-organelle errors and uncover the evolutionary divergence of organelles as the main driver of current performance issues. The results encourage to train the next generation of neural networks on an evolutionary more diverse set of organelle proteins for optimizing performance and reliability.

plant biology↗