Brian Radak

Home Department: Beckman Institute for Advanced Science and Technology

Office Address: Beckman Institute, 405 N. Mathews Avenue, Urbana, IL 61801

Email Address: bradak@ks.uiuc.edu

Personal Webpages: ResearchGate GitHub ORCID

CV


Education

  • PhD Chemical Physics, University of Minnesota (2014)
  • MS Chemistry, Northwestern University (2008)
  • BA Integrated Science and Chemistry, Northwestern University (2008)

Research Interests

My research focuses on the use of fundamental physical laws to create detailed (atomic scale) models of biological systems. Even though such models are often simplified, the sheer scale of biomolecules means that they are often difficult to understand because there are so many possibilities for how the atoms and molecules can be arranged. It is therefore necessary to have computational techniques that can accurately relate the microscopic statistical behavior of such systems to their macroscopic (quasi-)deterministic behavior. This is a two-fold exercise. First, we need robust simulation protocols for reliably collecting statistics, especially for rare events, which are frequently the most biologically relevant. Second, we need a reduced model that only includes the essential parts of the phenomenon and thus provides a deeper understanding of its true nature. My research goals encompass both of these pursuits by developing new, efficient methods for simulation as well as advanced statistical methods for extracting the most important information (along with estimates of statistical uncertainty).

Biochemical and Biophysical Phenomena

  • pH effects in biological systems, especially as they impact structure and function of proteins and small molecules at membrane interfaces
  • Coupled chemical and conformational transformations, such as pH switches and biological phenomena triggered by ligand binding

New Simulation Techniques and Analysis Methods for Complex Systems

  • Hybrid molecular dynamics (MD) and Monte Carlo (MC) methods
    • Nonequilibrium MD/MC (neMD/MC) methods which use artificial forces to study rare events
    • Serial and parallel expanded ensemble methods (e.g. replica exchange MD)
  • Free energy methods based on alchemical transformations
  • Enhanced sampling techniques to accelerate rare conformational changes in biomolecules
  • Machine learning methods using maximum likelihood (e.g. WHAM, MBAR)

Publications

  • See my ResearchGate page for links and preprint access (where available).