Multivalent binding in cell targeting and gene recognition

2019-12-11 09:49:00
报告人:Prof. Jure Dobnikar
Jure Dobnikar graduated in theoretical physics from University of Ljubljana in 2001. He worked as a postdoctoral fellow at University of Konstanz and University of Graz, and later as a senior researcher at Jozef Stefan Institute in Ljubljana and at University of Cambridge. In 2014, he moved to Beijing to manage the International Soft Matter Research Center at BUCT. In 2016, he accepted a permanent position at the Institute of Physics, Chinese Academy of sciences, where he is currently a full professor and the leader of the SM9 research group. His research interests are in physical modelling of soft and biological matter, particularly on colloidal interactions, self-assembly, non-equilibrium phenomena, modelling multivalent binding and activation processes, active matter, and bacterial motility. 


One of the key challenges in biomedicine and nanoscience is to design methods to selectively target or detect surfaces expressing specific cognate receptors. Such methods are key to achieve efficient drug delivery or to reliably detect DNA of bacterial pathogens in blood samples. Super-selective targeting is also an underlying mechanism of cellular activation based on molecular recognition, as we have shown in case of activation of TLR9 receptors by DNA-peptide complexes, responsible for certain autoimmune disorders.
We have demonstrated that multivalent carriers, i.e., nanoparticles that can form multiple weak reversible bonds to a large number of ligands simultaneously, can be designed to optimally target cells with specific composition of membrane receptors. We analytically derive the design rules for optimal targeting and show that ideal ligand-receptor binding strengths are of the order of kBT.
A similar concept has been employed to propose efficient and facile screening method for detecting pathogen DNA in blood samples containing a mixture of pathogen and non-pathogen DNA fragments. For instance, it is often required to distinguish between bacterial and viral infection, or between different strands of a given bacterium. We have developed a numerical scheme to identify optimal target sequences, and we have tested our approach in large-scale, coarse-grained simulations of the multivalent binding of entire bacterial genomes.

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