Characterization of two transketolases encoded on the chromosome and the plasmid pBM19 of the facultative ribulose monophosphate cycle methylotroph Bacillus methanolicus

Markert B, Stolzenberger J, Brautaset T, Wendisch VF. Characterization of two transketolases encoded on the chromosome and the plasmid pBM19 of the facultative ribulose monophosphate cycle methylotroph Bacillus methanolicus. BMC Microbiology. 2014;14(1): 7.Background Transketolase (TKT) is a key enzyme of the pentose phosphate pathway (PPP), the Calvin cycle and the ribulose monophosphate (RuMP) cycle. Bacillus methanolicus is a facultative RuMP pathway methylotroph. B. methanolicus MGA3 harbors two genes putatively coding for TKTs; one located on the chromosome (tktC) and one located on the natural occurring plasmid pBM19 (tktP). Results Both enzymes were produced in recombinant Escherichia coli, purified and shown to share similar biochemical parameters in vitro. They were found to be active as homotetramers and require thiamine pyrophosphate for catalytic activity. The inactive apoform of the TKTs, yielded by dialysis against buffer containing 10 mM EDTA, could be reconstituted most efficiently with Mn2+ and Mg2+. Both TKTs were thermo stable at physiological temperature (up to 65°C) with the highest activity at neutral pH. Ni2+, ATP and ADP significantly inhibited activity of both TKTs. Unlike the recently characterized RuMP pathway enzymes fructose 1,6-bisphosphate aldolase (FBA) and fructose 1,6-bisphosphatase/sedoheptulose 1,7-bisphosphatase (FBPase/SBPase) from B. methanolicus MGA3, both TKTs exhibited similar kinetic parameters although they only share 76% identical amino acids. The kinetic parameters were determined for the reaction with the substrates xylulose 5-phosphate (TKTC: kcat/KM: 264 s-1 mM-1; TKTP: kcat/KM: 231 s-1 mM) and ribulose 5-phosphate (TKTC: kcat/KM: 109 s-1 mM; TKTP: kcat/KM: 84 s-1 mM) as well as for the reaction with the substrates glyceraldehyde 3-phosphate (TKTC: kcat/KM: 108 s-1 mM; TKTP: kcat/KM: 71 s-1 mM) and fructose 6-phosphate (TKTC kcat/KM: 115 s-1 mM; TKTP: kcat/KM: 448 s-1 mM). Conclusions Based on the kinetic parameters no major TKT of B. methanolicus could be determined. Increased expression of tktP, but not of tktC during growth with methanol [J Bacteriol 188:3063–3072, 2006] argues for TKTP being the major TKT relevant in the RuMP pathway. Neither TKT exhibited activity as dihydroxyacetone synthase, as found in methylotrophic yeast, or as the evolutionary related 1-deoxyxylulose-5-phosphate synthase. The biological significance of the two TKTs for B. methanolicus methylotrophy is discussed

Transaldolase in Bacillus methanolicus: Biochemical Characterization and Biological Role in Ribulose Monophosphate Cycle

Pfeifenschneider J, Markert B, Stolzenberger J, Brautaset T, Wendisch VF. Transaldolase in Bacillus methanolicus: Biochemical Characterization and Biological Role in Ribulose Monophosphate Cycle. BMC Microbiology. 2020;20: 63.Background The Gram-positive facultative methylotrophic bacterium Bacillus methanolicus uses the sedoheptulose-1,7-bisphosphatase (SBPase) variant of the ribulose monophosphate (RuMP) cycle for growth on the C1 carbon source methanol. Previous genome sequencing of the physiologically different B. methanolicus wild-type strains MGA3 and PB1 has unraveled all putative RuMP cycle genes and later, several of the RuMP cycle enzymes of MGA3 have been biochemically characterized. In this study, the focus was on the characterization of the transaldolase (Ta) and its possible role in the RuMP cycle in B. methanolicus. Results The Ta genes of B. methanolicus MGA3 and PB1 were recombinantly expressed in Escherichia coli, and the gene products were purified and characterized. The PB1 Ta protein was found to be active as a homodimer with a molecular weight of 54 kDa and displayed KM of 0.74 mM and Vmax of 16.3 U/mg using Fructose-6 phosphate as the substrate. In contrast, the MGA3 Ta gene, which encodes a truncated Ta protein lacking 80 amino acids at the N-terminus, showed no Ta activity. Seven different mutant genes expressing various full-length MGA3 Ta proteins were constructed and all gene products displayed Ta activities. Moreover, MGA3 cells displayed Ta activities similar as PB1 cells in crude extracts. Conclusions While it is well established that B. methanolicus can use the SBPase variant of the RuMP cycle this study indicates that B. methanolicus possesses Ta activity and may also operate the Ta variant of the RuMP

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc