An antifungal active fraction (AAF) from the coelomic fluid (CF) of the earthworm Dendrobaena veneta was isolated. The aim of the study was to analyze the antifungal activity of the AAF and to carry out chemical characterization of the fraction. The active fraction showed antifungal activity against a clinical C. albicans isolate, C. albicans ATCC 10231, and C. krusei ATCC 6258. It effectively reduced the metabolic activity of C. albicans cells and influenced their morphology after 48 hours of incubation. Scanning electron microscopy (SEM) images revealed loss of integrity of the cell wall induced by the active fraction. Calcofluor White staining showed changes in the structure of the C. albicans cell wall induced by the AAF. The fungal cells died via apoptosis and necrosis after the treatment with the studied fraction. Electrophoresis under native conditions revealed the presence of two compounds in the AAF, while SDS/PAGE gel electrophoresis showed several protein and carbohydrate compounds. The active fraction was analyzed using Raman spectroscopy, MALDI TOF/TOF, and ESI LC-MS. The Raman analysis confirmed the presence of proteins and determined their secondary structure. The MALDI TOF/TOF analysis facilitated detection of four main compounds with a mass of 7694.9 m/z, 12292.3 m/z, 21628.3 m/z, and 42923.2 m/z in the analyzed fraction. The presence of carbohydrate compounds in the preparation was confirmed by nuclear magnetic resonance (NMR) and gas chromatography (GC-MS). The ATR-FTIR spectrum of the AAF exhibited high similarity to the spectrum of egg white lysozyme. The AAF showed no endotoxicity and cytotoxicity towards normal skin fibroblasts (HSF); therefore, it can be used for the treatment of skin and mucous membrane candidiasis in the future. Given its efficient and selective action, the fraction seems to be a promising preparation with antifungal activity against C. albicans.

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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 model's error were identified, thus allowing for further application of the developed methodology in proteomics.

Human follicular fluid (hFF) is a natural environment of oocyte maturation, and some components of hFF could be used to judge oocyte capability for fertilization and further development. In our pilot small-scale study three samples from four donors (12 samples in total) were analyzed to determine which hFF proteins/peptides could be used to differentiate individual oocytes and which are patient-specific. Ultrafiltration was used to fractionate hFF to high-molecular-weight (HMW) proteome (>10 kDa) and low-molecular-weight (LMW) peptidome (<10 kDa) fractions. HMW and LMW compositions were analyzed using LC-MS in SWATH data acquisition and processing methodology. In total we were able to identify 158 proteins, from which 59 were never reported before as hFF components. 55 (45 not reported before) proteins were found by analyzing LMW fraction, 67 (14 not reported before) were found by analyzing HMW fraction, and 36 were identified in both fractions of hFF. We were able to perform quantitative analysis for 72 proteins from HMW fraction of hFF. We found that concentrations of 11 proteins varied substantially among hFF samples from single donors, and those proteins are promising targets to identify biomarkers useful in oocyte quality assessment.

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