Motivation: Automated machine learning (AutoML) solutions can bridge the gap between new computational advances and their real-world applications by enabling experimental scientists to build their own custom models. We examine different steps in the development life-cycle of peptide bioactivity binary predictors and identify key steps where automation cannot only result in a more accessible method, but also more robust and interpretable evaluation leading to more trustworthy models.
Results: We present a new automated method for drawing negative peptides that achieves better balance between specificity and generalization than current alternatives.
Background: Thiazolidinone derivatives show inhibitory activity (IC) against the Toxoplasma gondii parasite, as well as high selectivity with high therapeutic index. To disclose the target proteins of the thiazolidinone core in this parasite, we explored in silico the active sites of different T. gondii proteins and estimated the binding-free energy of reported thiazolidinone molecules with inhibitory effect on invasion and replication of the parasite inside host cells.
View Article and Find Full Text PDFWe performed a homology modeling of the structure of a non-mutated and mutated Ser83→Phe DNA gyrase of Porphyromonas gingivalis. The model presented structural features conserved in type II topoisomerase proteins. We designed and evaluated in silico structural modifications to the core of Moxifloxacin by molecular docking, predicted toxicity and steered molecular dynamics simulations (SMD).
View Article and Find Full Text PDFGalactose is an abundant monosaccharide found exclusively in mammals as galactopyranose (Gal p), the six-membered ring form of this sugar. In contrast, galactose appears in many pathogenic microorganisms as the five-membered ring form, galactofuranose (Gal f). Gal f biosynthesis begins with the conversion of UDP-Gal p to UDP-Gal f catalyzed by the flavoenzyme UDP-galactopyranose mutase (UGM).
View Article and Find Full Text PDFPrincipal component analysis is a technique widely used for studying the movements of proteins using data collected from molecular dynamics simulations. In spite of its extensive use, the technique has a serious drawback: equivalent simulations do not afford the same PC-modes. In this article, we show that concatenating equivalent trajectories and calculating the PC-modes from the concatenated one significantly enhances the reproducibility of the results.
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