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Jarvas, Gabor,Guttman, Andras,Mię,kus, Natalia,Bą,czek, Tomasz,Jeong, Sunkyung,Chung, Doo Soo,Pä,toprstý,, Vladimir,Masá,r, Mariá,n,Hutta, Milan,Datinská,, Vladim Elsevier 2020 Trends in analytical chemistry Vol.122 No.-
<P><B>Abstract</B></P> <P>By coupling a sample pretreatment technique of sample clean up and enrichment power with capillary electrophoresis (CE) of high-performance separation, the task of analyzing trace analytes in a complex matrix such as a biological sample can be carried out successfully with ease. This review aims for providing an overview of strategies to couple sample pretreatment techniques with capillary and related microscale (e.g., microchip) electrophoresis, practically adoptable in an automatic manner, without requiring serious modification of existing instruments to install sophisticated interfaces. In-line sample pretreatment techniques based on liquid phase microextraction performed before sample injection and on-line sample preconcentration techniques performed during or after sample injection are discussed with emphasis on the applicability to samples of high conductivity, commonly encountered for biological samples. An overview of the recent developments in microfluidic immobilized enzymatic microreactors which fit excellently to microchip CE is also given.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Recent advances and major trends in sample pretreatment for capillary electrophoresis are summarized. </LI> <LI> In-line and on-line sample pretreatment techniques are discussed with emphasis on biological samples. </LI> <LI> We provide an overview of strategies to couple sample pretreatment techniques with capillary and microchip electrophoresis. </LI> </UL> </P>
Ž,uvela, Petar,Liu, J. Jay,Macur, Katarzyna,Bą,czek, Tomasz American Chemical Society 2015 ANALYTICAL CHEMISTRY - Vol.87 No.19
<P>In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (EPA), was compared in molecular descriptor selection for development of quantitative structure retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.</P>
Ž,uvela, Petar,Liu, J. Jay,Yi, Myunggi,Pomastowski, Paweł P.,Sagandykova, Gulyaim,Belka, Mariusz,David, Jonathan,Bą,czek, Tomasz,Szafrań,ski, Krzysztof,Ż,ołnowska, Beata,Sławi TaylorFrancis 2018 Journal of enzyme inhibition and medicinal chemist Vol.33 No.1
<P><B>Abstract</B></P><P>In this work, a target-based drug screening method is proposed exploiting the synergy effect of ligand-based and structure-based computer-assisted drug design. The new method provides great flexibility in drug design and drug candidates with considerably lower risk in an efficient manner. As a model system, 45 sulphonamides (33 training, 12 testing ligands) in complex with carbonic anhydrase IX were used for development of quantitative structure-activity-lipophilicity (property)-relationships (QSPRs). For each ligand, nearly 5,000 molecular descriptors were calculated, while lipophilicity (log<I>k</I><SUB>w</SUB>) and inhibitory activity (log<I>K</I><SUB>i</SUB>) were used as drug properties. Genetic algorithm-partial least squares (GA-PLS) provided a QSPR model with high prediction capability employing only seven molecular descriptors. As a proof-of-concept, optimal drug structure was obtained by inverting the model with respect to reference drug properties. 3509 ligands were ranked accordingly. Top 10 ligands were further validated through molecular docking. Large-scale MD simulations were performed to test the stability of structures of selected ligands obtained through docking complemented with biophysical experiments.</P>