Catalytic contribution of flap-substrate hydrogen bonds in "HIV-1 protease" explored by chemical synthesis

An analogue of "HIV-1 protease" was designed in which the ability to donate important water-mediated hydrogen bonds to substrate was precisely and directly deleted. Chemical ligation of unprotected peptide segments was used to synthesize this "backbone-engineered" enzyme. The functionally relevant amide -CONH- linkage between residues Gly49-Ile50 in each flap of the enzyme was replaced by an isosteric thioester -COS- bond. The backbone-engineered enzyme had normal substrate specificity and affinity (Km). However, the catalytic activity (kcat) was reduced approximately 3000-fold compared to the native amide bond-containing enzyme. Inhibition by the reduced peptide bond substrate analogue MVT-101 was unaffected compared with native enzyme. By contrast, the normally tight-binding hydroxyethylamine inhibitor JG-365 bound to the backbone-engineered enzyme with an approximately 2500-fold reduction in affinity. The reduced catalytic activity of the -Gly49-psi(COS)-Ile50-backbone-engineered enzyme analogue provides direct experimental evidence to support the suggestion that backbone hydrogen bonds from the enzyme flaps to the substrate are important for the catalytic function of the HIV-1 protease.     

The electroactive species in the catalysis of cyclohexadiene to benzene by Rh2(TM4) 4 +2 (TM4=2,5-diisocyano-2,5-dimethylhexane)

Upon further investigation of the recently reported electrocatalytic oxidation of 1,4-cyclohexadiene to benzene by Rh₂(TM4) ₄ ⁺² (TM4=2,5-diisocyano-2,5-dimethylhexane), we have obtained data which strongly implicates the 2e^– oxidized d⁷-d⁷ complex as the electroactive species. This contrasts with the original report which suggested that the le^– oxidized d⁷-d⁸ radical acted as the key species via hydrogen atom abstraction from 1,4-cyclohexadiene. A possible mechanism for the catalysis is proposed.     

Espresso Reward Learning, Hold the Dopamine: Theoretical Comment on Robinson et al. (2005).

Question: Is dopamine needed for reward learning? Answer: No--at least, not in the brain of a caffeinated dopamine-deficient (DD) mutant mouse. That is the conclusion of an important paper in this issue by S. Robinson, S. M. Sandstrom, V. H. Denenberg, and R. D. Palmiter (see record 2005-01705-001). Those authors demonstrate that reward learning can proceed normally in the brains of DD mice, even though they contain no dopamine at the time of learning, if the mice are given caffeine just before learning. Caffeine activates the DD mice by a nondopaminergic mechanism, allowing them to learn where to obtain food reward in a T-maze runway. Their reward-learning-without-dopamine is revealed on a subsequent test day, when dopamine function is restored by L-dopa administration. Robinson et al. conclude that dopamine is not needed for normal learning about rewards, or for hedonic "liking" of rewards during learning, but rather specifically for a motivational "wanting" component of reward, such as incentive salience. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Condom Use Among High-Risk Adolescents: Testing the Influence of Alcohol Use on the Relationship of Cognitive Correlates of Behavior.

The goal of this study was the exploration of distal effects of alcohol use on condom use. Criminally involved adolescents completed an initial measure of attitudes, beliefs, and prior behavior. Of the 300 who completed the initial measurement, 267 (89%) completed a behavioral assessment 6 months later. Analyses validated a theoretical model of condom use intentions and indicated that intentions and attitudes measured at baseline were significant predictors of condom use behavior 6 months later. Neither alcohol use nor alcohol problems moderated relationships among model variables or the influence of intentions and attitudes on behavior. The findings do not support a distal role for alcohol use in altering the cognitive correlates of condom use intentions and behavior among high-risk adolescents. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Bidirectional Mapping Problem

Mappings between models and languages are essential to support model driven approaches to software development. Those mappings need to be supported by tools. There are different kinds of mapping appropriate for different stages in the development process. This paper focuses on bidirectional mappings between models used to capture software requirements and models used to capture software designs. The properties of such mappings are illustrated using a mapping between models used in the development of web-based, business systems. This motivates and helps identify a set of benchmarks against which approaches to the definition and tooling of such mappings can be judged. The paper concludes with pointers to one approach at addressing the bidirectional mapping problem.     

Efficient Total Chemical Synthesis of 13C=18O Isotopomers of Human Insulin for Isotope‐Edited FTIR

Isotope‐edited two‐dimensional Fourier transform infrared spectroscopy (2 D FTIR) can potentially provide a unique probe of protein structure and dynamics. However, general methods for the site‐specific incorporation of stable ¹³C=¹⁸O labels into the polypeptide backbone of the protein molecule have not yet been established. Here we describe, as a prototype for the incorporation of specific arrays of isotope labels, the total chemical synthesis—via a key ester insulin intermediate—of 97 % enriched [(1‐¹³C=¹⁸O)Phe^B24] human insulin: stable‐isotope labeled at a single backbone amide carbonyl. The amino acid sequence as well as the positions of the disulfide bonds and the correctly folded structure were unambiguously confirmed by the X‐ray crystal structure of the synthetic protein molecule. In vitro assays of the isotope labeled [(1‐¹³C=¹⁸O)Phe^B24] human insulin showed that it had full insulin receptor binding activity. Linear and 2 D IR spectra revealed a distinct red‐shifted amide I carbonyl band peak at 1595 cm^?1 resulting from the (1‐¹³C=¹⁸O)Phe^B24 backbone label. This work illustrates the utility of chemical synthesis to enable the application of advanced physical methods for the elucidation of the molecular basis of protein function.     

Assessing genomic prediction accuracy for Holstein sires using bootstrap aggregation sampling and leave-one-out cross validation

Since the introduction of genome-enabled prediction for dairy cattle in 2009, genomic selection has markedly changed many aspects of the dairy genetics industry and enhanced the rate of response to selection for most economically important traits. Young dairy bulls are genotyped to obtain their genomic predicted transmitting ability (GPTA) and reliability (REL) values. These GPTA are a main factor in most purchasing, marketing, and culling decisions until bulls reach 5 yr of age and their milk-recorded offspring become available. At that time, daughter yield deviations (DYD) can be compared with the GPTA computed several years earlier. For most bulls, the DYD align well with the initial predictions. However, for some bulls, the difference between DYD and corresponding GPTA is quite large, and published REL are of limited value in identifying such bulls. A method of bootstrap aggregation sampling (bagging) using genomic BLUP (GBLUP) was applied to predict the GPTA of 2,963, 2,963, and 2,803 young Holstein bulls for protein yield, somatic cell score, and daughter pregnancy rate (DPR), respectively. For each trait, 50 bootstrap samples from a reference population comprising 2011 DYD of 8,610, 8,405, and 7,945 older Holstein bulls were used. Leave-one-out cross validation was also performed to assess prediction accuracy when removing specific bulls from the reference population. The main objectives of this study were (1) to assess the extent to which current REL values and alternative measures of variability, such as the bootstrap standard deviation (SD) of predictions, could detect bulls whose daughter performance deviates significantly from early genomic predictions, and (2) to identify factors associated with the reference population that inform about inaccurate genomic predictions. The SD of bootstrap predictions was a mildly useful metric for identifying bulls whose future daughter performance may deviate significantly from early GPTA for protein and DPR. Leave-one-out cross validation allowed us to identify groups of reference population bulls that were influential on other reference population bulls for protein yield and observe their effects on predictions of testing set bulls, as a whole and individually.