Depletion of high-abundance proteins from human plasma using a combination of an affinity and pseudo-affinity column

Human serum albumin (HSA) and immunoglobulin G (IgG) represent over 75% of all proteins present in human plasma. These high-abundance proteins prevent the detection of low-abundance proteins which are potential markers for various diseases. The depletion of HSA and IgG is therefore essential for further proteome analysis. In this paper we describe the optimization of conditions for selective depletion of HSA and IgG using affinity and pseudo-affinity chromatography. A BIA Separations CIM (convective interaction media) Protein G disk was applied for the removal of IgG and the Mimetic Blue SA A6XL stationary phase for the removal of HSA. The binding and the elution buffer for CIM Protein G disk were chosen on the basis of the peak shape. The dynamic binding capacity was determined. It was shown to be dependent on the buffer system used and independent of the flow rate and of the concentration of IgG. Beside the binding capacity for the IgG standard, the binding capacity was also determined for IgG in human plasma. The Mimetic Blue SA A6XL column was characterized using human plasma. The selectivity of the depletion was dependent on the amount of human plasma that was loaded on the column. After the conditions on both supports had been optimized, the Mimetic Blue SA A6XL stationary phase was combined with the CIM Protein G disk in order to simultaneously deplete samples of human plasma. A centrifuge spin column that enables the removal of IgG and HSA from 20 μL of human plasma was designed. The results of the depletion were examined using sodium dodecyl sulfate polyacrylamide gel electrophoresis and two-dimensional gel electrophoresis.     

Identification of proteins interacting with ammodytoxins in Vipera ammodytes ammodytes venom by immuno-affinity chromatography

In order to perform their function, proteins frequently interact with other proteins. Various methods are used to reveal protein interacting partners, and affinity chromatography is one of them. Snake venom is composed mostly of proteins, and various protein complexes in the venom have been found to exhibit higher toxicity levels than respective components separately. Complexes can modulate envenomation activity of a venom and/or potentiate its effect. Our previous data indicate that the most toxic components of the Vipera ammodytes ammodytes (Vaa) venom isolated so far—ammodytoxins (Atxs)—are contributing to the venom’s toxicity only moderately; therefore, we aimed to explore whether they have some interacting partner(s) potentiating toxicity. For screening of possible interactions, immuno-affinity chromatography combined with identification by mass spectrometry was used. Various chemistries (epoxy, carbonyldiimidazole, ethylenediamine) as well as protein G functionality were used to immobilize antibodies on monolith support, a Convective Interaction Media disk. Monoliths have been demonstrated to better suit the separation of large biomolecules. Using such approach, several proteins were indicated as potential Atx-binding proteins. Among these, the interaction of Atxs with a Kunitz-type inhibitor was confirmed by far-Western dot-blot and surface plasmon resonance measurement. It can be concluded that affinity chromatography on monolithic columns combined with mass spectrometry identification is a successful approach for screening of protein interactions and it resulted with detection of the interaction of Atx with Kunitz-type inhibitor in Vaa venom for the first time.     

Empirical kernel map approach to nonlinear underdetermined blind separation of sparse nonnegative dependent sources: pure component extraction from nonlinear mixture mass spectra

Nonlinear underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed nonlinearly mixed signals into a greater number of original nonnegative and dependent component (source) signals. This hard problem is practically relevant for contemporary metabolic profiling of biological samples, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of nonlinear multicomponent mixtures. This paper presents a method for nonlinear underdetermined blind separation of nonnegative dependent sources that comply with a sparse probabilistic model, that is, sources are constrained to be sparse in support and amplitude. This model is validated on experimental pure component mass spectra. Under a sparse prior, a nonlinear problem is converted into an equivalent linear one comprised of original sources and their higher‐order, mostly second‐order, monomials. The influence of these monomials, which stand for error terms, is reduced by preprocessing a matrix of mixtures by means of robust principal component analysis and hard, soft and trimmed thresholding. Preprocessed data matrices are mapped in high‐dimensional reproducible kernel Hilbert space (RKHS) of functions by means of an empirical kernel map. Sparseness‐constrained nonnegative matrix factorizations in RKHS yield sets of separated components. They are assigned to pure components from the library using a maximal correlation criterion. The methodology is exemplified on demanding numerical and experimental examples related respectively to extraction of eight dependent components from three nonlinear mixtures and to extraction of 25 dependent analytes from nine nonlinear mixture mass spectra recorded in nonlinear chemical reaction of peptide synthesis. Copyright © 2014 John Wiley & Sons, Ltd.     

Bromination of aromatic ring of tetrahydro-1,5-benzodiazepin-2-ones

Novel mono- or dibromo-substituted tetrahydro-1,5-benzodiazepinones were obtained by direct bromination of the corresponding 2,3,4,5-tetrahydro-(1H)-1,5-benzodiazepin-2-ones and 5-N-alkyl (or formyl) derivatives with bromine. Substituent effects and the orientation of the entering groups in the bromination reaction are discussed.Institute of Biochemistry, Vilnius LT-2600, Lithuania; c-mail: apalaima@bchi.lt. Translated from Khimiya Geterotsiklicheskikh Socdinenii, No. 6, pp. 799–805, June, 2000.     

Nonlinear mixture‐wise expansion approach to underdetermined blind separation of nonnegative dependent sources

Underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed mixed signals into greater number of original nonnegative and dependent component (source) signals. That is an important problem for which very few algorithms exist. It is also practically relevant for contemporary metabolic profiling of biological samples, such as biomarker identification studies, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of complex multicomponent mixtures. This paper presents a method for underdetermined blind separation of nonnegative dependent sources. The method performs nonlinear mixture‐wise mapping of observed data in high‐dimensional reproducible kernel Hilbert space (RKHS) of functions and sparseness‐constrained nonnegative matrix factorization (NMF) therein. Thus, the original problem is converted into new one with increased number of mixtures, increased number of dependent sources, and higher‐order (error) terms generated by nonlinear mapping. Provided that amplitudes of original components are sparsely distributed, which is the case for mass spectra of analytes, sparseness‐constrained NMF in RKHS yields, with significant probability, improved accuracy relative to the case when the same NMF algorithm is performed on the original problem. The method is exemplified on numerical and experimental examples related respectively to extraction of 10 dependent components from five mixtures and to extraction of 10 dependent analytes from mass spectra of two to five mixtures. Thereby, analytes mimic complexity of components expected to be found in biological samples. Copyright © 2013 John Wiley & Sons, Ltd.     

Methodologies and applications for resilient global development from the aspect of SDI-SOR special issues of CJOR

Central European Journal of Operations Research - In this paper, we present the results of a bibliometric analysis of papers published in six SDI-SOR special issues that have been published since...     

TiO2 assisted photocatalytic degradation of macrolide antibiotics

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Chemical Profiling and Antimicrobial Properties of Honey Bee (Apis mellifera L.) Venom

The incidence of antibiotic resistance in pathogenic bacteria has become an alarming clinical and social problem. Therefore, the demand for alternative antimicrobial compounds has increased. In this study, a chemical profile of honey bee (Apis mellifera L.) venom (HBV) has been determined by HPLC and FTIR-ATR spectroscopy, and tested for antibacterial activity, as well as efficiency with regard to conventional antibiotics. The investigated HBV was of high quality with melittin and total protein contents of 70.10 ± 7.01%, and 84.44 ± 3.12 g/100 g, respectively. The purity of HBV was confirmed by FTIR-ATR spectral profiling, which revealed a unique pattern of absorption bands that are characteristic of its major fractions. In addition, HBV showed a broad spectrum of activity against all three tested biomasses of potentially pathogenic Gram-positive and Gram-negative bacteria with MIC values ranging between 12.5 and 200 µg/mL, and MBC between 12.5 and 400 µg/mL. When compared to conventional antibiotics, HBV (400 µg) showed up to 27.8% efficiency of tetracycline (30 µg), 52.2% erythromycin (15 µg), 21.2% ciprofloxacin (5 µg), and 34.6% of ampicillin-sulbactam (20 µg). The overall results demonstrate the therapeutic potential of the analyzed HBV.     

Point estimation of simultaneous methods for solving polynomial equations: A survey (II)

The construction of computationally verifiable initial conditions which provide both the guaranteed and fast convergence of the numerical root-finding algorithm is one of the most important problems in solving nonlinear equations. Smale's “point estimation theory” from 1981 was a great advance in this topic; it treats convergence conditions and the domain of convergence in solving an equation f(z)=0$f(z)=0$ using only the information of f   at the initial point _z0$z_0$. The study of a general problem of the construction of initial conditions of practical interest providing guaranteed convergence is very difficult, even in the case of algebraic polynomials. In the light of Smale's point estimation theory, an efficient approach based on some results concerning localization of polynomial zeros and convergent sequences is applied in this paper to iterative methods for the simultaneous determination of simple zeros of polynomials. We state new, improved initial conditions which provide the guaranteed convergence of frequently used simultaneous methods for solving algebraic equations: Ehrlich–Aberth's method, Ehrlich–Aberth's method with Newton's correction, Börsch-Supan's method with Weierstrass’ correction and Halley-like (or Wang–Zheng) method. The introduced concept offers not only a clear insight into the convergence analysis of sequences generated by the considered methods, but also explicitly gives their order of convergence. The stated initial conditions are of significant practical importance since they are computationally verifiable; they depend only on the coefficients of a given polynomial, its degree n and initial approximations to polynomial zeros.