We are interested in developing a variety of computational methods in multiple resolutions for IDPs, including:
1. Polymer models: SAW-ν JCP 2018, SHD JPCL 2020, SAW-ν-tr JPCB 2023;
2. Coarse-grained models: HPS PLOS Comput. Biol. 2018, T-HPS ACS Cent. Sci. 2019, HPS-salt JPCL 2021;
3. All-atom models: ff03ws JCTC 2014, KBFFs JCTC 2015, JPCB 2020.
Intracellular compartmentalization is essential for normal physiological activity. This is commonly accomplished through isolation by lipid membranes or vesicles, but can also be achieved without the use of a membrane via membraneless organelles. The process of liquid-liquid phase separation (LLPS) allows these organelles to spontaneously coalesce and disperse, and is important for biological functions requiring spatial organization and biochemical regulation. LLPS has also been implicated as a precursor to the formation of fibrillar aggregates, suggesting possible relevance to the pathogenesis of diseases.
Please see our recent publications for examples: PLOS Comput. Biol. 2018, PNAS 2018, JPCB 2020.
Molecular dynamics simulation packages: GROMACS, LAMMPS, and HOOMD-Blue
Machine learning methods for data analysis: dimensionality reduction, and neural network
Programming language: Python/bash script, and C++
We appreciate the support from the National Science Foundation and the National Institutes of Health, and computational resources from Anton2 and ASU Research Computing.