LVukovic-01
Characterizing DNA-nanotube hybrid structures with high-affinity binding to molecular targets
Health, Human Disease & Diagnostics
Preferred major field of study or minimum required skills
Studying towards chemistry, biochemistry, or chemical engineering degree. The project may also potentially be suitable for a student pursuing computer science degree. Required: strong interest in becoming skilled in computational techniques, programming and chemical modeling to solve chemistry and biochemistry problems. Good knowledge of physical chemistry concepts.
Scholarly significance/intellectual merit
The goal of the proposed work is to discover and characterize DNA-carbon nanotube conjugates for optical sensing of important target analytes that currently cannot be easily detected at present. Towards this goal, we propose to combine the results of the experimental DNA sequence screening methods (obtained by our experimental collaborators) with the artificial intelligence (AI)-based methodology and atomistic modeling techniques. Our research aims are to: 1) determine short DNA sequences and sequence motifs with high affinity for target analytes on nanotube surfaces by artificial intelligence methods; and 2) determine with atomistic simulations the molecular basis of analyte recognition by the identified DNA sequences and sequence motifs.
Research question(s)
The specific research objective for the student participating in summer research is to examine with atomistic simulations the molecular basis of analyte recognition by several DNA sequences and sequence motifs on nanotube surfaces. This objective is a part of a larger ongoing project in my research group.
Methods/techniques/instruments to be learned/utilized
Computational modeling. Setting up and running molecular dynamics simulations of complex chemical systems and systems at the interface of biomolecules and materials. Working in Linux operating system, programming with python and tcl, bash scripting. Writing/presenting skills, preparing professional publication-quality figures. Artificial intelligence methods (if interested). Use of supercomputers (if interested).