Effect of cyclization of N-terminal glutamine and carbamidomethyl-cysteine (residues) on the chromatographic behavior of peptides in reversed-phase chromatography

N-terminal loss of ammonia is a typical peptide modification chemical artifact observed in bottom-up proteomics experiments. It occurs both in vivo for N-terminal glutamine and in vitro following enzymatic cleavage for both N-terminal glutamine and cysteine alkylated with iodoacetamide. In addition to a mass change of −17.03 Da, modified peptides exhibit increased chromatographic retention in reversed-phase (RP) HPLC systems. The magnitude of this increase varies significantly depending on the peptide sequence and the chromatographic condition used. We have monitored these changes for extensive sets (more than 200 each) of tryptic Gln and Cys N-terminated species. Peptides were separated on 100 Å pore size C18 phases using identical acetonitrile gradient slopes with 3 different eluent compositions: 0.1% trifluoroacetic acid; 0.1% formic acid and 20 mM ammonium formate at pH 10 as ion-pairing modifiers. The observed effect of this modification on RP retention is the product of increased intrinsic hydrophobicity of the modified N-terminal residue, lowering or removing the effect of ion-pairing formation on the hydrophobicity of adjacent residues at acidic pHs; and possibly the increased formation of amphipathic helical structures when the positive charge is removed. Larger retention shifts were observed for Cys terminated peptides compared to Gln, and for smaller peptides. Also the size of the retention increase depends on the eluent conditions: pH 10 ? trifluoroacetic acid < formic acid. Different approaches for incorporation these findings in the peptide retention prediction models are discussed.     

An evaluation of thermodynamic models for the prediction of drug and drug-like molecule solubility in organic solvents

Prediction of solubility of active pharmaceutical ingredients (API) in different solvents is one of the main issue for crystallization process design. Experimental determination is not always possible because of the small amount of product available in the early stages of a drug development. Thus, one interesting perspective is the use of thermodynamic models, which are usually employed for predicting the activity coefficients in case of Vapour-Liquid equilibria or Liquid-Liquid equilibria (VLE or LLE). The choice of the best thermodynamic model for Solid-Liquid equilibria (SLE) is not an easy task as most of them are not meant particularly for this. In this paper, several models are tested for the solubility prediction of five drugs or drug-like molecules: Ibuprofen, Acetaminophen, Benzoic acid, Salicylic acid and 4-aminobenzoic acid, and another molecule, anthracene, a rather simple molecule. The performance of predictive (UNIFAC, UNIFAC mod., COSMO-SAC) and semi-predictive (NRTL-SAC) models are compared and discussed according to the functional groups of the molecules and the selected solvents. Moreover, the model errors caused by solid state property uncertainties are taken into account. These errors are indeed not negligible when accurate quantitative predictions want to be performed. It was found that UNIFAC models give the best results and could be an useful method for rapid solubility estimations of an API in various solvents. This model achieves the order of magnitude of the experimental solubility and can predict in which solvents the drug will be very soluble, soluble or not soluble. In addition, predictions obtained with NRTL-SAC model are also in good agreement with the experiments, but in that case the relevance of the results is strongly dependent on the model parameters regressed from solubility data in single and mixed solvents. However, this is a very interesting model for quick estimations like UNIFAC models. Finally, COSMO-SAC needs more developments to increase its accuracy especially when hydrogen bonding is involved. In that case, the predicted solubility is always overestimated from two to three orders of magnitude. Considering the use of the most accurate equilibrium equation involving the ΔC_p term, no benefits were found for drug predictions as the models are still too inaccurate. However, in function of the molecules and their solid thermodynamic properties, the ΔC_p term can be neglected and will not have a great impact on the results.     

Improving protein structural class prediction using novel combined sequence information and predicted secondary structural features

Protein structural class prediction solely from protein sequences is a challenging problem in bioinformatics. Numerous efficient methods have been proposed for protein structural class prediction, but challenges remain. Using novel combined sequence information coupled with predicted secondary structural features (PSSF), we proposed a novel scheme to improve prediction of protein structural classes. Given an amino acid sequence, we first transformed it into a reduced amino acid sequence and calculated its word frequencies and word position features to combine novel sequence information. Then we added the PSSF to the combine sequence information to predict protein structural classes. The proposed method was tested on four benchmark datasets in low homology and achieved the overall prediction accuracies of 83.1%, 87.0%, 94.5%, and 85.2%, respectively. The comparison with existing methods demonstrates that the overall improvements range from 2.3% to 27.5%, which indicates that the proposed method is more efficient, especially for low-homology amino acid sequences.     

Retention in coupled capillary column supercritical fluid chromatography with lipids as model compounds

Summary We have investigated to which extent retention data, acquired on single capillary columns, can be used for predicting retention factors in a coupled column system. For this purpose we utilized a model mixture of 18 lipid components with widely different vapor pressures and polarities. The sample was chromatographed on two columns, SB-biphenyl-30 (70% methyl-30% biphenylpolysiloxane) and SB-cyanopropyl-50 (50% methyl-50% cyanopropylsiloxane). Experimental retention factors, acquired in coupled column systems with two columns connected in different order, were thus compared with values calculated from runs on each single column. The agreement between calculated and experimental values generally was better than 5% without any pressure drop correction.To study the possibility of predicting retention behavior in a wide pressure range from a limited number of experiments, we also investigated the relation between solute retention and mobile phase density. We found that all data could be fitted to second order equations, which gives the possibility to optimize the resolution with respect to pressure from a limited number of runs at different pressures.     

人工神经网络研究硝基苯化合物结构与毒性的关系

基于简单的化学基团描述符,应用人工神经网络研究了硝基苯类化合物对黑呆头鱼的毒性构效关系,并与多元线性回归相比较,结果显示了人工神经网络处理非线性问题的优越性.     

Determination of 12 potential nephrotoxicity biomarkers in rat serum and urine by liquid chromatography with mass spectrometry and its application to renal failure induced by Semen Strychni

In previous nephrotoxicity metabonomic studies, several potential biomarkers were found and evaluated. To investigate the relationship between the nephrotoxicity biomarkers and the therapeutic role of Radix Glycyrrhizae extract on Semen Strychni‐induced renal failure, 12 typical biomarkers are selected and a simple LC–MS method has been developed and validated. Citric acid, guanidinosuccinic acid, taurine, guanidinoacetic acid, uric acid, creatinine, hippuric acid, xanthurenic acid, kynurenic acid, 3‐indoxyl sulfate, indole‐3‐acetic acid, and phenaceturic acid were separated by a Phenomenex Luna C₁₈ column and a methanol/water (5 mM ammonium acetate) gradient program with a runtime of 20 min. The prepared calibration curves showed good linearity with regression coefficients all above 0.9913. The absolute recoveries of analytes from serum and urine were all more than 70.4%. With the developed method, analytes were successfully determined in serum and urine samples within 52 days. Results showed that guanidinosuccinic acid, guanidinoacetic acid, 3‐indoxyl sulfate, and indole‐3‐acetic acid (only in urine) were more sensitive than the conventional renal function markers in evaluating the therapeutic role of Radix Glycyrrhizae extract on Semen Strychni‐induced renal failure. The method could be further used in predicting and monitoring renal failure cause by other reasons in the following researches.     

Correlation of antioxidant depletion and mechanical performance during thermal degradation of an HTPB elastomer

Thermal degradation studies of a stabilized HTPB based elastomer were conducted at temperatures from 50 °C to 110 °C. The concentration of extractable antioxidant (AO2246) in the polymer was quantified via AO extraction and a gas chromatography-based method using internal standards. The decrease in extractable AO levels as a function of time and temperature was evaluated and correlated with mechanical property changes. Most importantly, AO depletion features were found to be temperature dependent. At elevated temperatures (>80 °C) extractable AO levels decreased rapidly and faster than the concurrent loss in mechanical properties. While extractable AO concentrations decrease quickly, the material is able to maintain some useful mechanical properties, perhaps via non-extractable or grafted AO species formed during degradation providing additional protection. At lower aging temperatures extractable or free AO levels decreased more slowly than the mechanical properties. Therefore, for condition monitoring purposes a universal correlation between AO levels and aging state or material condition could not be established. Most importantly, however, loss of mechanical properties and oxidative degradation is observed at lower temperatures despite significant levels of free antioxidant in the material. The antioxidant appears to be limited in its effectiveness to completely prevent degradation reactions, or only fractions of the total AO available are actually involved in the inhibition process.     

Membrane protein native state discrimination by implicit membrane models

Four implicit membrane models [IMM1, generalized Born (GB)‐surface area‐implicit membrane (GBSAIM), GB with a simple switching (GBSW), and heterogeneous dielectric GB (HDGB)] were tested for their ability to discriminate the native conformation of five membrane proteins from 450 decoys generated by the Rosetta‐Membrane program. The energy ranking of the native state and Z‐scores were used to assess the performance of the models. The effect of membrane thickness was examined and was found to be substantial. Quite satisfactory discrimination was achieved with the all‐atom IMM1 and GBSW models at 25.4 Å thickness and with the HDGB model at 28.5 Å thickness. The energy components by themselves were not discriminative. Both van der Waals and electrostatic interactions contributed to native state discrimination, to a different extent in each model. Computational efficiency of the models decreased in the order: extended‐atom IMM1 > all‐atom IMM1 > GBSAIM > GBSW > HDGB. These results encourage the further development and use of implicit membrane models for membrane protein structure prediction. © 2012 Wiley Periodicals, Inc.