CaSToRC, the HPC National Competence Centre,
 invites you to the EuroCC and SimEA Online Seminar Series

     

Abstract

In this webinar, the method development and applications of multiscale computational techniques for the modeling of materials and biomolecules ranging from atomistic and coarse-grained Molecular Dynamics (MD) simulations, Monte Carlo, Markov state models, and Machine Learning with applications in drug design and drug delivery design will be discussed. Biological membranes comprise fascinating examples of soft matter interfaces involved in a wide range of biological and industrial functions. Several aspects of the structure and dynamics of biomembranes as well as methodologies for targeting specific membrane interfaces for developing novel drug candidates and nanoparticle delivery systems will be presented.

Atomistic MD simulations of sterols in membranes shed light into the evolutionary advantage of cholesterol in higher vertebrates. To study longer time and length scales, coarse-grained molecular representations may be employed for observing spontaneous bilayer self-assembly and constructing phase diagrams of ternary lipid mixtures. These systems can be used to study the behavior of the partitioning and self-assembly of nanoparticles in lipid membranes. To study in atomic-level detail metal nanoparticles, a major hurdle is the creation of the nanoparticle crystal habit, which is required for accurate modeling of crystal nanoparticles. We discuss a novel implementation of the Wulff morphology from the crystallographic unit cell. We further employ this algorithm in the study of coated magnetic nanoparticles to investigate the model cell membrane nanoparticle interactions and the effect of their surface coating.

The cell membrane hosts up to two thirds of known druggable targets. Therefore, in order to design more efficient drugs and drug delivery systems, a better understanding of the physicochemical interactions that govern biomembrane interfaces is needed. We present a novel ensemble machine learning algorithm designed to predict protein-membrane interfaces, where potential drug targets can bind. We apply biased and unbiased MD simulations coupled with PCA, dynamical network analysis, binding site identification, virtual screening, lead optimization, and subsequent in vitro and in vivo assays, for understanding the oncogenic mechanisms of PI3Kα and to identify the first allosteric PI3Kα inhibitors selective for PI3Kα mutants. Finally, we use Markov State modeling to identify metastable states and kinetic networks for identifying biologically relevant molecular states.

About the Speaker

Dr. Cournia is a Researcher (Associate Professor level) at the Biomedical Research Foundation, Academy of Athens, where she works on anticancer drug and materials design using High Performance Computing (http://www.drugdesign.gr). She graduated from the Chemistry Department, University of Athens in 2001 and received her PhD at the University of Heidelberg in Germany in 2006.

She then worked as a postdoctoral researcher at the Chemistry Department, Yale University, USA, on computer-aided drug design and in 2009 she became a Lecturer at Yale College. She has been awarded with the American Association for Cancer Research Angiogenesis Fellowship (2008), the "Woman of Innovation 2009" Award from the Connecticut Technology Council, USA, the Marie Curie Fellowship from the European Union (2010), the "Outstanding Junior Faculty Award" from the American Chemical Society (2014) and the first "Ada Lovelace Award" from the "Partnership for Advanced Computing in Europe" (2016).

She is the Associate Editor of the Journal of Chemical information and Modeling, American Chemical Society, and a member of the Infrastructure Advisory Group (INFRAG) of the European High Performance Computing Joint Undertaking. She also teaches in the MSc program “Information Technologies in Technology and Medicine” at the Department of Informatics and Telecommunications, National University of Athens. She is the Founder of Ingredio, a mobile phone app that informs consumers on the potential hazards of chemical ingredients in food and cosmetic products using open, peer-reviewed data (http://www.ingred.io/android).


Download the Spring 2021 Online EuroCC & SimEA Seminar Series Programme here.

 

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