Snapshots during the catalytic cycle of a histidine acid phytase reveal an induced fit structural mechanism

Highly engineered phytases, which sequentially hydrolyze the hexakisphosphate ester of inositol known as phytic acid, are routinely added to the feeds of monogastric animals to improve phosphate bioavailability. New phytases are sought as starting points to further optimize the rate and extent of dephosphorylation of phytate in the animal digestive tract. Multiple inositol polyphosphate phosphatases (MINPPs) are clade 2 histidine phosphatases (HP2P) able to carry out the stepwise hydrolysis of phytate. MINPPs are not restricted by a strong positional specificity making them attractive targets for development as feed enzymes. Here, we describe the characterization of a MINPP from the Gram-positive bacterium Bifidobacterium longum (BlMINPP). BlMINPP has a typical HP2P-fold but, unusually, possesses a large a-domain polypeptide insertion relative to other MINPPs. This insertion, termed the U-loop, spans the active site and contributes to substrate specificity pockets underpopulated in other HP2Ps. Mutagenesis of U-loop residues reveals its contribution to enzyme kinetics and thermostability. Moreover, four crystal structures of the protein along the catalytic cycle capture, for the first time in an HP2P, a large ligand-driven a-domain motion essential to allow substrate access to the active site. This motion recruits residues both downstream of a molecular hinge and on the U-loop to participate in specificity subsites, and mutagenesis identified a mobile lysine residue as a key determinant of positional specificity of the enzyme. Taken together, these data provide important new insights to the factors determining stability, substrate recognition, and the structural mechanism of hydrolysis in this industrially important group of enzymes

A conserved amino acid residue critical for product and substrate specificity in plant triterpene synthases

Triterpenes are structurally complex plant natural products with numerous medicinal applications. They are synthesized through an origami-like process that involves cyclization of the linear 30 carbon precursor 2,3-oxidosqualene into different triterpene scaffolds. Here, through a forward genetic screen in planta, we identify a conserved amino acid residue that determines product specificity in triterpene synthases from diverse plant species. Mutation of this residue results in a major change in triterpene cyclization, with production of tetracyclic rather than pentacyclic products. The mutated enzymes also use the more highly oxygenated substrate dioxidosqualene in preference to 2,3-oxidosqualene when expressed in yeast. Our discoveries provide new insights into triterpene cyclization, revealing hidden functional diversity within triterpene synthases. They further open up opportunities to engineer novel oxygenated triterpene scaffolds by manipulating the precursor supply

Structure of a cereal purple acid phytase provides new insights to phytate degradation in plants

Grain phytate, a mixed metal ion salt of inositol hexakisphosphate, accounts for 60%–80% of stored phosphorus in plants and is a potent antinutrient of non-ruminant animals including humans. Through neofunctionalization of purple acid phytases (PAPhy), some cereals such as wheat and rye have acquired particularly high mature grain phytase activity. As PAPhy activity supplies phosphate, liberates metal ions necessary for seedling emergence, and obviates antinutrient effects of phytate, its manipulation and control are targeted crop traits. Here we show the X-ray crystal structure of the b2 isoform of wheat PAPhy induced during germination. This high-resolution crystal structure suggests a model for phytate recognition that, validated by molecular dynamics simulations, implicates elements of two sequence inserts (termed PAPhy motifs) relative to a canonical metallophosphoesterase (MPE) domain in forming phytate-specific substrate specificity pockets. These motifs are well conserved in PAPhys from monocot cereals, enzymes which are characterized by high specificity for phytate. Tested by mutagenesis, residues His229 in PAPhy motif 4 and Lys410 in the MPE domain, both conserved in PAPhys, are found to strongly influence phytase activity. These results explain the observed phytase activity of cereal PAPhys and open the way to the rational engineering of phytase activity in planta

Rapamycin‐mediated lifespan increase in mice is dose and sex dependent and metabolically distinct from dietary restriction

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107367/1/acel12194.pd

Randomness Concerns When Deploying Differential Privacy

The U.S. Census Bureau is using differential privacy (DP) to protect confidential respondent data collected for the 2020 Decennial Census of Population & Housing. The Census Bureau's DP system is implemented in the Disclosure Avoidance System (DAS) and requires a source of random numbers. We estimate that the 2020 Census will require roughly 90TB of random bytes to protect the person and household tables. Although there are critical differences between cryptography and DP, they have similar requirements for randomness. We review the history of random number generation on deterministic computers, including von Neumann's "middle-square" method, Mersenne Twister (MT19937) (previously the default NumPy random number generator, which we conclude is unacceptable for use in production privacy-preserving systems), and the Linux /dev/urandom device. We also review hardware random number generator schemes, including the use of so-called "Lava Lamps" and the Intel Secure Key RDRAND instruction. We finally present our plan for generating random bits in the Amazon Web Services (AWS) environment using AES-CTR-DRBG seeded by mixing bits from /dev/urandom and the Intel Secure Key RDSEED instruction, a compromise of our desire to rely on a trusted hardware implementation, the unease of our external reviewers in trusting a hardware-only implementation, and the need to generate so many random bits.Comment: 12 pages plus 2 pages bibliograph

Brain age as a biomarker for pathological versus healthy ageing – a REMEMBER study

Objectives: This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. Methods: The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict ‘brain age’ and ‘brain predicted age difference’ (BPAD = brain age–chronological age) for every subject. Results: MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers. Conclusions: Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health

Model Convolution: A Computational Approach to Digital Image Interpretation

Digital fluorescence microscopy is commonly used to track individual proteins and their dynamics in living cells. However, extracting molecule-specific information from fluorescence images is often limited by the noise and blur intrinsic to the cell and the imaging system. Here we discuss a method called “model-convolution,” which uses experimentally measured noise and blur to simulate the process of imaging fluorescent proteins whose spatial distribution cannot be resolved. We then compare model-convolution to the more standard approach of experimental deconvolution. In some circumstances, standard experimental deconvolution approaches fail to yield the correct underlying fluorophore distribution. In these situations, model-convolution removes the uncertainty associated with deconvolution and therefore allows direct statistical comparison of experimental and theoretical data. Thus, if there are structural constraints on molecular organization, the model-convolution method better utilizes information gathered via fluorescence microscopy, and naturally integrates experiment and theory