Multidrug resistance (MDR) is a common problem when fighting cancer with chemotherapy. P-glycoprotein (P-gp, or MDR1) is an active pump responsible for the efflux of xenobiotics out of the cell, including anti-cancer drugs. It is a validated target against MDR. No crystal structure of the human P-gp is available to date, and only recently several cryo-EM structures have been solved. In this paper, we present a comprehensive computational approach that includes constructing the full-length three-dimensional structure of the human P-gp and its refinement using molecular dynamics. We assessed its flexibility and conformational diversity, compiling a dynamical ensemble that was used to dock a set of lignan compounds, previously reported as active P-gp inhibitors, and disclose their binding modes. Based on the statistical analysis of the docking results, we selected a system for performing the structure-based virtual screening of new potential P-gp inhibitors. We tested the method on a library of 87 natural flavonoids described in the literature, and 10 of those were experimentally assayed. The results reproduced the theoretical predictions only partially due to various possible factors. However, at least two of the predicted natural flavonoids were demonstrated to be effective P-gp inhibitors. They were able to increase the accumulation of doxorubicin inside the human promyelocytic leukemia HL60/MDR cells overexpressing P-gp and potentiate the antiproliferative activity of this anti-cancer drug.

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In our previous work, we designed and implemented a synthetic metabolic pathway for 1,2,3-trichloropropane (TCP) biodegradation in Escherichia coli. Significant effects of metabolic burden and toxicity exacerbation were observed on single cell and population levels. Deeper understanding of mechanisms underlying these effects is extremely important for metabolic engineering of efficient microbial cell factories for biotechnological processes. In this paper, we present a novel mathematical model of the pathway. The model addresses for the first time the combined effects of toxicity exacerbation and metabolic burden in the context of bacterial population growth. The model is calibrated with respect to the real data obtained with our original synthetically modified E. coli strain. Using the model, we explore the dynamics of the population growth along with the outcome of the TCP biodegradation pathway considering the toxicity exacerbation and metabolic burden. On the methodological side, we introduce a unique computational workflow utilising algorithmic methods of computer science for the particular modelling problem.

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