Infrared absorbance spectroscopy of aqueous proteins: Comparison of transmission and ATR data collection and analysis for secondary structure fitting

Attenuated total reflectance (ATR) infrared absorbance spectroscopy of proteins in aqueous solution is much easier to perform than transmission spectroscopy, where short path‐length cells need to be assembled reproducibly. However, the shape of the resulting ATR infrared spectrum varies with the refractive index of the sample and the instrument configuration. Refractive index in turn depends on the absorbance of the sample. In this work, it is shown that a room temperature triglycine sulfate detector and a ZnSe ATR unit can be used to collect reproducible spectra of proteins. A simple method for transforming the protein ATR spectrum into the shape of the transmission spectrum is also given, which proceeds by approximating a Kramers‐Krönig–determined refractive index of water as a sum of four linear components across the amide I and II regions. The light intensity at the crystal surface (with 45° incidence) and its rate of decay away from the surface is determined as a function of the wave number–dependent refractive index as well as the decay of the evanescent wave from the surface. The result is a single correction factor at each wave number. The spectra were normalized to a maximum of 1 between 1600 cm^?1 and 1700 cm^?1 and a self‐organizing map secondary structure fitting algorithm, SOMSpec, applied using the BioTools reference set. The resulting secondary structure estimates are encouraging for the future of ATR spectroscopy for biopharmaceutical characterization and quality control applications.     

The MusIC method: a fast and quasi-optimal solution to the muscle forces estimation problem

The present paper aims at presenting a fast and quasi-optimal method of muscle forces estimation: the MusIC method. It consists in interpolating a first estimation in a database generated offline thanks to a classical optimization problem, and then correcting it to respect the motion dynamics. Three different cost functions – two polynomial criteria and a min/max criterion – were tested on a planar musculoskeletal model. The MusIC method provides a computation frequency approximately 10 times higher compared to a classical optimization problem with a relative mean error of 4% on cost function evaluation.     

Influence of temperature on energetics of hydrogen metabolism in homoacetogenic,methanogenic, and other anaerobic bacteria

Hydrogen consumption by various thermophilic, mesophilic and/or psychrotrophic homoacetogens and methanogens was measured at temperatures between 4 and 80°C. Within the tolerated temperature range H₂ was consumed until a final H₂ threshold partial pressure was reached. H₂ thresholds generally decreased with temperature, parallel to the values calculated from the thermodynamics prevailing under culture conditions, i.e. the Gibbs free energy (G) of H₂ oxidation corrected for temperature by both the free-energy form of the Nernst equation and the Van't Hoff equation. The difference between the observed and the calculated H₂ partial pressures gives the minimum energy required for H₂ utilization being about-5 to-6 kJ/mol H₂ for the homoacetogenes and-9 to-12 kJ/mol H₂ for methanogens. The temperature dependence of the standard Gibbs free energy (G⁰) as described by the Van't Hoff equation apparently became the more important for thermodynamics as well as H₂ thresholds the more the temperature deviated from standard conditions (i.e. 25°C). Correction factors for calculation of temperature-corrected G _infT ^sup0 are presented for various H₂-producing and H₂-consuming reactions.     

一测多评法测定番石榴叶中6种黄酮类成分的含量

为建立同时测定番石榴叶中6种黄酮类成分(金丝桃苷、异槲皮苷、瑞诺苷、番石榴苷、萹蓄苷、槲皮素)的一测多评法。本研究以金丝桃苷为内参物,采用HPLC法,确定金丝桃苷与另外5种成分的相对校正因子,并通过获得的校正因子计算后5种成分的量;同时采用外标法测定11批番石榴叶的含量。结果表明11批番石榴叶的QAMS法与外标法测定结果间无显著性差异,证明该法可用于番石榴叶6种黄酮类成分的定量分析。     

The interprotein scoring noises in glide docking scores

Small molecule drugs are rarely selective enough to interact solely with their designated targets. Unintended "off-target" interactions often lead to side effects, but also serendipitously lead to new therapeutic uses. Identification of the off-targets of a compound is therefore of significant value to the evaluation of its developmental potential. In computational biology, the strategy of "reverse docking" has been introduced to predict the targets of a compound, which uses a compound to virtually screen a library of proteins, reversing the bait and prey in "normal" docking screenings. The present study shows that, in reverse docking, additional optimization of the scoring function may help to improve the target prediction accuracy. In a case study with the Glide scores, we found that only 57% of the ligand-protein relationships could be correctly identified in a library of 58 complexes whose crystal binding conformations were all able to be accurately reproduced. This was likely a result of the constant over- or under-estimation of the scores for specific proteins. In other words, there were interprotein noises in the Glide scores. Introducing a correction term based on protein characteristics improved the target-prediction accuracy by 27% (57-72%). It is our hope that this focused discussion on the Glide scores would invite further efforts to characterize and normalize this type of interprotein noises in all docking scores, so that better target prediction accuracy can be achieved with the strategy of reverse docking.     

Quantification of blockiness in pectins—A comparative study using vibrational spectroscopy and chemometrics

The gelling properties of pectins are related not only to the degree of esterification (DE), but also to the distribution of the ester groups. In this study, we have examined an experimentally designed series of 31 pectins originating from the same mother pectin and de-esterified using combinations of two different enzymatic mechanisms. The potential of using infrared (IR), Raman, and near infrared (NIR) spectroscopies combined with chemometrics for reliable and rapid determination of the DE and distribution patterns of methyl ester groups in a designed set of pectin powders was investigated. Quantitative calibration models using partial least squares (PLS) regression were developed and compared. The calibration models for prediction of DE obtained on extended inverse signal correction (EISC)-treated spectra of all three spectroscopic methods yielded models with cross-validated prediction errors (RMSECV) between 1.1%p and 1.6%p DE and correlation coefficients of 0.99. A calibration model predicting degree of random de-esterification (R) and block de-esterification (B) was developed for each spectroscopic method, yielding RMSECV values between 4.4 and 6.7 and correlation coefficients (r) between 0.79 and 0.92. Variable selection using interval PLS (iPLS) significantly improved the prediction of R for IR spectroscopy, yielding RMSECV of 3.5 and correlation coefficients of 0.95. All three spectroscopic methods were able to distinguish the spectral patterns of pectins with different enzyme treatments in simple classification models by principal component analysis (PCA). Extended canonical variate analysis revealed one specific signal in the Raman (1045 cm⁻¹) spectrum and one significant area (1250-1400 cm⁻¹) in the IR spectrum which are able to classify the pectin samples according to the four different enzyme treatments. In both Raman and IR spectra, the signal intensity decreased in the sequence R-B > B > B-R > R > re-methylated pectin.     

Bias‐Corrected Diagonal Discriminant Rules for High‐Dimensional Classification

Summary Diagonal discriminant rules have been successfully used for high‐dimensional classification problems, but suffer from the serious drawback of biased discriminant scores. In this article, we propose improved diagonal discriminant rules with bias‐corrected discriminant scores for high‐dimensional classification. We show that the proposed discriminant scores dominate the standard ones under the quadratic loss function. Analytical results on why the bias‐corrected rules can potentially improve the predication accuracy are also provided. Finally, we demonstrate the improvement of the proposed rules over the original ones through extensive simulation studies and real case studies.