Protein and Peptide Letters - Volume 18, Issue 9, 2011

CTX-M-15, an extended-spectrum β-lactamase emerging worldwide, hydrolyzes lactam ring of β-lactam antibiotics, and thus causes therapeutic failure and a lack of eradication of pathogenic bacteria by third-generation β-lactams. Therefore, the enzyme is a potential target for developing agents against pathogens isolated from patients suffering from nosocomial infections. The CTX-M-15 protein was purified and crystallized at 298 K. X-ray diffraction data from CTXM- 15 crystal have been collected to 1.46 Å resolution using synchrotron radiation. The crystal of CTX-M-15 belongs to space group P212121, with unit-cell parameters a = 45.50, b = 44.23, and c = 116.92 Å. Analysis of the packing density shows that the asymmetric unit probably contains two molecules with a solvent content of 41.26%.

AcrB is an inner membrane protein in Escherichia coli that is a component of a triplex AcrA-AcrB-TolC (AcrAB- TolC) multidrug efflux pump. The AcrAB-TolC complex and its homologues are highly conserved among Gramnegative bacteria and are major players in conferring multidrug resistance (MDR) in many pathogens. In this study we developed a disulfide trapping method that may reveal AcrB conformational changes under the native condition in the cell membrane. Specifically, we created seven disulfide bond pairs in the periplasmic domain of AcrB, which can be used as probes to determine local conformational changes. We have developed a rigorous protocol to measure the extent of disulfide bond formation in membrane proteins under the native condition. The rigorousness of the method was verified through examining the extent of disulfide bond formation in Cys pairs separated by different distances. The blockingreducing- labeling scheme combined with fluorescence labeling made the current method convenient, reliable, and quantitative.

G-protein coupled receptor (GPCR) is a protein family that is found only in the Eukaryotes. They are used for the interfacing of cell to the outside world and are involved in many physiological processes. Their role in drug development is evident. Hence, the prediction of GPCRs is very much demanding. Because of the unavailability of 3D structures of most of the GPCRs, the statistical and machine learning based prediction of GPCRs is much demanding. GPCRs are classified into family, sub family and sub-sub family levels in the proposed approach. We have extracted features using the hybrid combination of Pseudo amino acid, Fast Fourier Transform and Split amino acid techniques. The overall feature vector is then reduced using Principle component analysis. Mostly, GPCRs are composed of two or more sub units. The arrangement and number of sub units forming a GPCR are referred to as quaternary structure. The functions of GPCRs are closely related to their quaternary structure. The classification in the present research is performed using grey incidence degree (GID) measure, which can efficiently analyze the numerical relation between various components of GPCRs. The GID measure based classification has shown remarkable improvement in predicting GPCRs.

Catalase is an enzyme that occurs in almost all aerobic organisms. Its main metabolic function is to prevent oxidative damage to tissues induced by hydrogen peroxide which is a strong oxidizing agent. Catalase is very effective in performing this task, since it has the highest turnover rate among all the enzymes. The properties of catalase have been investigated extensively for many years; however, the role of the solvent molecules in the catalytic reaction of this enzyme has not yet been investigated. Therefore, the objective of this work was to investigate the contribution of the solvent molecules on the catalytic reaction of bovine liver catalase with its substrate H2O2 by the osmotic stress method. As a probe for protein structural changes in solution, the differential number of water molecules released during the transition from free to bound form of the enzyme was measured. These assays were correlated with protein structural data provided by the SAXS technique and crystallographic structures of free and CN?? bonded enzymes. The results showed that the difference in surface accessible area of the crystal structures does not reflect the variation that is observed in solution. Moreover, catalase is not influenced by the solvent during the catalytic reaction, which represents a lower energy barrier to be crossed in the overall energetics of the reaction, a fact that contributes to the high turnover rate of catalase.

In the biomimetic design two hydrophobic pentapetides Boc-Ile-Aib-Leu-Phe-Ala-OMe (I) and Boc-Gly-Ile- Aib-Leu-Phe-OMe (II) (Aib: α-aminoisobutyric acid) containing one Aib each are found to undergo solvent assisted selfassembly in methanol/water to form vesicular structures, which can be disrupted by simple addition of acid. The nanovesicles are found to encapsulate dye molecules that can be released by the addition of acid as confirmed by fluorescence microscopy and UV studies. The influence of solvent polarity on the morphology of the materials generated from the peptides has been examined systematically, and shows that fibrillar structures are formed in less polar chloroform/petroleum ether mixture and vesicular structures are formed in more polar methanol/water. Single crystal X-ray diffraction studies reveal that while β-sheet mediated self-assembly leads to the formation of fibrillar structures, the solvated β-sheet structure leads to the formation of vesicular structures. The results demonstrate that even hydrophobic peptides can generate vesicular structures from polar solvent which may be employed in model studies of complex biological phenomena.

Protein-protein interaction hot spots, as revealed by alanine scanning mutagenesis, make dominant contributions to the free energy of binding. Since mutagenesis experiments are expensive and time-consuming, the development of computational methods to identify hot spots is becoming increasingly important. In this study, by using a new combination of sequence, structure and energy features, we propose an iterative semi-supervised algorithm, SemiHS, to incorporate unlabeled data to improve the accuracy of hot spots prediction when sufficient training data is un-available and to overcome the imbalanced data problem. We evaluate the predictive power of SemiHS on a labeled set of 265 alaninemutated interface residues in 17 complexes and a large unlabeled set of 2465 interface residues with 10-fold cross validation, and get an AUC score of 0.85, with a sensitivity of 0.70 and a specificity of 0.87, which are better than those of the existing methods. Moreover, we validate the proposed method by an independent test and obtain encouraging results.

Protein-protein interactions (PPIs) are crucial to most biochemical processes in human beings. Although many human PPIs have been identified by experiments, the number is still limited compared to the available protein sequences of human organisms. Recently, many computational methods have been proposed to facilitate the recognition of novel human PPIs. However the existing methods only concentrated on the information of individual PPI, while the systematic characteristic of protein-protein interaction networks (PINs) was ignored. In this study, a new method was proposed by combining the global information of PINs and protein sequence information. Random forest (RF) algorithm was implemented to develop the prediction model, and a high accuracy of 91.88% was obtained. Furthermore, the RF model was tested by using three independent datasets with good performances, suggesting that our method is a useful tool for identification of PPIs and investigation into PINs as well.

Hypoxia caused by waterlogging results in a severe loss of crop production. At the primary stage of wheat development, the seminal roots have strategies to survive under hypoxia through alternative metabolism coupling root anatomical modification. The present study used a model system of lysigenous aerenchymatous seminal roots from a representative seedling stage of wheat to elucidate the root physiology in response to soil hypoxia. Seminal roots characteristic with lysigenous aerenchyma tissues were developed in pot cultures for 7 days under two hypoxic conditions, water depths of 15 cm below and 3 cm above the soil surface. Proteins from the roots were separated using two-dimensional polyacrylamide gel electrophoresis and identified using mass spectrometry. The results showed that approximately 345 distinct protein spots were detected by 2-DE, 29 spots changed in the expression levels between the control and two hypoxic plants, and 10 spots exhibited a reproducible up- or down regulated fluctuation. The up-regulated proteins were thought to be involved in alteration in energy and redox status, defense responses and cell wall turnover. These results suggest the effects of soil hypoxia on the activity of the identified up-regulated proteins and their roles in alternative respiration and cell degeneration in wheat in order to gain metabolic adjustment under hypoxia stress.

One of the main challenges faced by biological applications is to predict protein subcellular localization in automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological systems into account. Traditionally, such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressed Sensing) theory, we propose the methodology which performs compressed learning with a sparseness criterion such that feature selection and dimension reduction are merged into one analysis. The proposed methodology decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse methods are quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.

Separate and simultaneous binding effects of paclitaxel (a drug with anti-tumor activity) and estradiol (used for treating multiple maladies) with human serum albumin (HSA) were investigated by fluorescence quenching, UV absorption, circular dichroism, zeta potential and molecular dynamic techniques. An extensive fluorescence quenching was observed during the reaction of drugs and HSA and was rationalized in terms of a static quenching mechanism. The molecular distances between the donor (HSA) and acceptors (paclitaxel or estradiol) in binary and ternary systems were estimated according to Forster's theory of dipole-dipole non-radiation energy transfer. The features of drug-induced structural disturbances of HSA have been studied in detail by synchronous fluorescence and circular dichroism (CD) analysis. The resonance Rayleigh scattering (RRS) intensities were proportional to the paclitaxel and estradiol concentrations in the range of respectively (0-8)×10-6 and (0-1)×10-4 mM in binary systems. The critical induced aggregation concentrations (CCIAC) of paclitaxel and estradiol for binary and ternary systems were determined by nonlinear relationships between the enhancement of the RRS intensities and the drug concentrations. A comparison between binary and ternary systems for two drugs allowed us to estimate the effect of a drug on the initial formation aggregation of the second drug. The zeta potential results were used to verify the existence of complexation and confirmed the CCIAC values obtained by the RRS technique. This phenomenon was supported by a progressive rise of the protein charge to a reversal point as a consequence of drug binding. The quantitative analysis data of circular dichroism (CD) spectra demonstrated that the binding of paclitaxel and/or estradiol to HSA induced conformational changes in HSA. Moreover, the α-helix content in HSA greatly decreased in the presence of paclitaxel as opposed when estradiol was present. Protein-ligand docking suggested that estradiol bound to residues situated in subdomain IIA of HSA. On the other hand, in the ternary system, the presence of the first drug decreased the binding affinity of the second drug to HSA. Therefore binding effects of paclitaxel and estradiol with HSA alone have different behavior than simultaneous interaction.

Two arginine side-chain protecting groups, NG-4-methoxy-2,3,6-trimethylbenzensulfonyl group (Mtr) and NG- 2,2,5,7,8-pentamethylchroman-6-sulfonyl (Pmc), have been investigated at both the Arg1 and/or Arg9 position of the bioactive peptide, Bradykinin using Fluorenylmethyloxycarbonyl (Fmoc) Solid Phase Peptide Synthesis. A more efficient synthesis of the peptide has been found when a combination of Arg(Mtr) is present at position 1 and Arg(Pmc) is present at position 9 giving a cleaved pure yield of 52%.

MHC-epitope binding plays a key role in the cellular immune response. Accurate prediction of MHC-epitope binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. In this paper, 13 T descriptors, which derived from 544 physicochemical properties of the natural amino acids, were used to characterize 4 MHC class I alleles epitope peptide sequences, the optimal QSAR models were constructed by using stepwise regression combines with multiple linear regression (STR-MLR). For HLA-A*0201, HLA-A*0203, HLA-A*0206 and HLAA*1101 alleles, the leave one out cross validation values (Q2 train) were 0.581, 0.553, 0.525 and 0.588, the correlation coefficients (R2 train) of training datasets were 0.607, 0.582, 0.556 and 0.606, the correlation coefficients (R2 test) of test datasets were 0.533, 0.506, 0.501 and 0.502, respectively. The results showed that all models can obtain good performance for prediction and explain the mechanism of interaction between MHC and epitope. The descriptors will be useful in structure characterization and activity prediction of peptide sequences.