Despite numerous existing potent antibiotics and other antimicrobial means, bacterial infections are still a major cause of morbidity, implant rejection, and mortality. Moreover, the need to develop additional bactericidal means has significantly increased due to the growing concern regarding multidrug-resistant bacterial strains and biofilm associated infections. Bacterial adhesion onto solid surfaces is the onset of biofilm formation, which is of critical importance in a wide spectrum of problems including many modern industrial applications, especially in microbiology and biomedical research. The mechanism of bacterial adhesion to solid surfaces is a complex process affected by multiple factors. The most important property of bacterial adhesion mechanism is influenced by physicochemical interaction of the bacterial cells. The microscopic understanding of bacteria cell envelope interaction with its surroundings is one of the corner stones of rational design of novel antibiotics and antimicrobial coatings. Unfortunately, the complexity of bacterial cell envelopes prevented until now development of multiscale models for these systems, and only recently progress had been made in coarse grain molecular dynamic simulations of cell envelopes of gram-negative bacteria. In this project, we will capitalize on these achievements, and extend existing state of arts coarse grain models of cell envelope to be able describe interactions between cell envelopes and titanium oxide (TiO2) surfaces with or without imbedded metallic nanoparticles. The developed model will be used to carry out molecular dynamics simulations of interfaces between cell envelope of gram-negative bacteria and TiO2 surfaces. The obtained results will be used not only to gain microscopic understanding of atomistic factors governing interaction between cell envelope and TiO2 surfaces, but also to guide experimental efforts to fabricate antimicrobial coatings using vacuum deposition methods. The aim of this project is to develop and implement machine learning based model for prediction of the formed structures’ surface antimicrobial activity for targeted gram-negative bacteria. The development of this model would lead to the advanced use of antimicrobial coatings and bacterial control.
This research project is funded by the European Social Fund according to the 2014–2020 Operational Programme for the European Union Funds’ Investments under measure’s No. 09.3.3-LMT-K-712 activity “Improvement of Researchers’ Qualification by Implementing World-class R&D Projects”.
The main project result will be machine learning based model for prediction of the TiO2 structures’ surface antimicrobial activity for targeted gram-negative bacteria and related software. The research results will be published in international journals indexed in Clarivate Analytics Web of Science with Impact Factor ranked in Q1 quartile (6 articles) and presented at six international conferences. Also, it is planned to submit an application under the calls of the international programmes (Horizon H2020, etc.) as principal investigator, partner or implementer.
Period of project implementation: 2018-10-02 - 2022-06-30
Project coordinator: Kaunas University of Technology