Petar Žuvela

Molecular Descriptor Subset Selection in Theoretical Peptide Quantitative Structure–Retention Relationship Model Development Using Nature-Inspired Optimization Algorithms

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 (FPA), 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 models error were identified, thus allowing for further application of the developed methodology in proteomics.

Silver-Lactoferrin Nanocomplexes as a Potent Antimicrobial Agent

The process of silver immobilization onto and/or into bovine lactoferrin (LTF), the physicochemical properties of bovine lactoferrin and obtained silver-lactoferrin complexes, as well as antibacterial activity of silver-lactoferrin complexes were investigated in this work. Kinetic study of the silver immobilization into lactoferrin was carried out using batch sorption techniques. Spectrometric (MALDI-TOF/TOF-MS, ICP-MS), spectroscopic (FTIR, SERS), electron microscopic (TEM) and electrophoretic (I-DE) techniques, as well as zeta potential measurements, were applied for characterization of LTF and binding nature of silver in Ag-LTF complexes. On the basis of the results of the kinetics study, it was established that the silver binding to LTF is a heterogeneous process involving two main stages: (i) internal diffusion and sorption onto external surface of lactoferrin globules; and (ii) internal diffusion and binding into lactoferrin globule structure. Spectroscopic techniques combined with TEM analysis confirmed the binding process. Molecular dynamics (MD) analysis was carried out in order to simulate the mechanism of the binding process, and locate potential binding sites, as well as complement the experimental findings. Quantum mechanics (QM) simulations were performed utilizing density functional theory (DFT) in order to support the reduction mechanism of silver ions to elemental silver. Antimicrobial activity of synthesized lactoferrin complexes against selected clinical bacteria was confirmed using flow cytometry and antibiograms.

Assessment of column selection systems using Partial Least Squares

Column selection systems based on calculation of a scalar measure based on Euclidean distance between chromatographic columns, suffer from the same issue. For diverse values of their parameters, identical or near-identical values can be calculated. Proper use of chemometric methods can not only provide a remedy, but also reveal underlying correlation between them. In this work, parameters of a well-established column selection system (CSS) developed at Katholieke Universiteit Leuven (KUL CSS) have been directly correlated to parameters of selectivity (retention time, resolution, and peak/valley ratio) toward pharmaceuticals, by employing Partial Least Squares (PLS). Two case studies were evaluated, separation of alfuzosin, lamotrigine, and their impurities, respectively. Within them, comprehensive correlation structure was revealed, which was thoroughly interpreted, confirming a causal relationship between KUL parameters and parameters of column performance. Furthermore, it was shown that the developed methodology can be applied to any distance-based column selection system.

On feature selection for supervised learning problems involving high-dimensional analytical information

Several computational methods were applied to feature selection for supervised learning problems that can be encountered in the field of analytical chemistry. Namely, Genetic Algorithm (GA), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Least Absolute Shrinkage and Selection Operator (LASSO), Least Angle Regression Algorithm (LARS), interval Partial Least Squares (iPLS), sparse PLS (sPLS), and Uninformative Variable Elimination-PLS (UVE-PLS). Methods were compared in two case studies which cover both supervised learning cases; (i) regression: multivariate calibration of soil carbonate content using Fourier transform mid-infrared (FT-MIR) spectral information, and (ii) classification: diagnosis of prostate cancer patients using gene expression information. Beside quantitative performance measures: error and accuracy often used in feature selection studies, a qualitative measure, the selection index (SI), was introduced to evaluate the methods in terms of quality of selected features. Robustness was evaluated introducing artificially generated noise variables to both datasets. Results of the first case study have shown that in order of decreasing predictive ability and robustness: GA > FA ≈ PSO > LASSO > LARS (errors of 1.775, 4.504, 4.055 mg g−1, 10.085, and 10.510 mg g−1) are recommended for application in regression involving spectral information. In the second case study, the following trend: GA > PSO > FA ≈ LASSO > LARS (accuracies of 100, 95.12 and 90.24%) has been observed. Strong robustness has been observed in the regression case with no decrease in SI for GA, and SI decreasing from 28.85 to 10.26, and 36.11 to 21.05%, for FA and PSO, respectively. In the classification case, only LARS exhibited a considerable decrease in accuracy upon introduction of noise features. Major sources of errors were identified and mostly originated from the analytical methods themselves, which confirmed strong applicability of the evaluated feature selection methods.

Non-target analysis of phospholipid and sphingolipid species in egg yolk using liquid chromatography/triple quadrupole tandem mass spectrometry

In this work, phospholipids extracted from egg yolk (control group, experimental group) were identified using high performance liquid chromatography coupled with electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS). Combinations of fatty acyls occurring in 11 classes of phospholipids from egg yolk were investigated. Differences between the profile of fatty acyls from hens fed traditionally and the ones that received special diet supplementation were observed. Experimental findings were complemented with multivariate chemometric analysis. Multiple reaction monitoring mass spectrometry mode was utilized and 123 distinct combinations of fatty acyls occurring in phospholipids were identified. From these, large portions are polyunsaturated fatty acyls from the omega-3 and omega-6 family. HPLC MS/MS analysis allows for quick, accurate and precise determination of biologically active compounds, found in low concentrations within the tested material.

Target-based drug discovery through inversion of quantitative structure-drug-property relationships and molecular simulation: CA IX-sulphonamide complexes

Abstract 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 (logkw) and inhibitory activity (logKi) 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. Graphical Abstract