Principal Investigator

Alex
Alex Dickson
Email: alexrd@msu.edu

Education:
Hon. B. Sc. Chemical Physics, University of Toronto (2006)
M. Sc. Chemistry, University of Chicago (2007)
Ph. D. Chemistry, University of Chicago (2011)

Prof. Dickson has extensive experience developing new methods for the simulation of rare events, and is driven to apply these tools to the study of ligand binding processes that are relevant to human health. Alex is cross-appointed in the Department of Computational Mathematics, Science and Engineering, where he currently teaches graduate courses in computational modeling. He loves computational research, and still gets his hands dirty in the lab, assisting in the development of sampling tools and persuing his own research projects.

Before MSU, Alex was a Postdoctoral Researcher at the University of Michigan with Prof. Charles L. Brooks, III. There he worked on a wide range of projects:

  • Development of a new sampling method, WExplore, and its application to several biomolecular rare events such as the unfolding of the chignolin protein, and large loop motions in HIV-1 TAR RNA
  • Development of new tools for the network visualization of protein dynamics, where entire free energy landscapes are visualized in a single 2D plot, without the specification of specific visualization axes, as in principal component analysis
  • Utilization of coarse-grained models in conjuction with enhanced sampling methods to observe and characterize the coupled unfolding and binding of the HdeA homodimer: a periplasmic bacterial protein that helps respond to acid shock
  • Modeling higher-order biological reaction networks to study protein chaperone activity in E. coli

Alex obtained his Ph.D. in Chemistry from the University of Chicago in 2011 in Prof. Aaron Dinner's group. His work was mostly focused on the development of another enhanced sampling method: Nonequilibrium Umbrella Sampling, and these ideas helped form the foundation of the WExplore method. In Chicago Alex also worked on projects related to the large deviation theory of driven oscillators, and Ising models of lattice gases.

Postdoctoral Researchers

Arzu
Arzu Uyar
Email: uyararzu@msu.edu

Education:
B. Sc. Chemical Engineering, Istanbul University (2004)
M. Sc. Chemical Engineering, Bogazici University (2008)
Ph. D. Chemical Engineering, Bogazici University (2014)

Arzu focused on the analysis of large conformational transitions of biomolecules during her Ph.D. studies at the Bogazici University in the group of Pemra Doruker. The main aim of her studies was to develop computationally fast and efficient hybrid tecnhiques and to apply them to different types of proteins such as hinge-bending, shear, DNA-binding proteins, and enzymes showing local motions. Those techniques can be summarized as:

  • ANM-MC: a combination of Elastic Network Model (ENM/ANM) and Monte-Carlo (MC) methods to generate conformational transition pathways between open and closed conformations of biomolecules. For more information, please visit our database involving different type of proteins such as adenylate kinase, GroEL, calmodulin (http://safir.prc.boun.edu.tr/anmmc/).
  • Rg-ANM-MC: a fast hybrid technique for coarse-grained closed structure prediction of hinge-bending type proteins, where radius of gyration (Rg) was used as a constraint during the selection of direction/mode.

She also worked on the dynamics of G-protein coupled receptors (GPCRs) and protein-DNA complexes using molecular dynamics simulations and elastic network model calculations.

The aim of her recent projects is to understand the biophysics of ligand binding using the WExplore method and pharmacophore-based virtual screening.

Graduate Students

Nazanin
Nazanin Donyapour
Email: nazanin@msu.edu

Education: B. Sc. Computer Engineering, Hamedan University, Iran (2005) M. Sc. Information Technology (IT) Engineering - Computer Networks, Urmia University, Iran (2013)

I am a PhD student in the CMSE department at MSU. I have bachelors degree in Computer Engineering and MS in Information Technology. I was always passionate about modeling and simulation. I believe if we conquer challenges with well-engineered simulation can have a big impact in the real-world. More importantly, it is through simulation that we can be try out new ideas.

My interest now is in biomolecular simulation. Specifically, I am developing new algorithms to effectively run molecular simulations and unravel the mystery of protein-drug binding problems. Molecular simulations are often very resource-hungry experiments. Thanks to recent advancement in computation, specifically GPUs, researchers have the ability to conduct large-scale molecular simulation experiments. However, even with this new hardware in hand, efficient and well-designed parallel algorithms is still a must. It is my job to design such algorithms.

Meanwhile, I am also interested in machine learning algorithms (ML) in big data. Specifically, I am studying how can we leverage ML in biomolecular simulation and experiment.

Thomas
Thomas M. Dixon
Email: dixonth1@msu.edu

Education: B. Sc. Chemical Engineering, Kettering University (2015)

I am a CMSE graduate student working on pharmacokinetic modeling for protein-drug interactions.

Previously, I worked on optimizing reactor conditions, such as temperature, pressure, and reaction time, to extract polyphenolic compounds from plant waste using supercritical fluid extraction. Additionally, I took the extracted compounds and tested to see if they could protect DNA against radical cleavage by hydroxyl radicals.

Samuel
Samuel D. Lotz
Email: lotzsamu@msu.edu

Education:
B. Sc. Biology, Slippery Rock University (2014)

The main goals of my research are to understand the structural factors that determine the kinetics of ligand unbinding and to develop Quantitative Structure Kinetics Relationships (QSKRs) for customizing kinetic properties in drug design. This involves applying machine learning techniques to biological macromolecular dynamics data to predict kinetic properties of ligands in virtual screening. Data mining of large molecular dynamics (MD) datasets from enhanced sampling for information on intermolecular interactions, water hydrogen bonding networks, transition state pharmacophores, etc. is of great importance, motivating the development of mastic which is a hackable/extensible python library for doing analyses on complex macromolecular systems to accomplish this.

In collaboration with Dr. Kin Sing Lee at MSU we are currently focused on inhibitors of soluble epoxide hydrolase (sEH), which has been of intense interest as a drug target for a variety of diseases including diabetic neuropathic pain. Kinetics and /in vitro/ assays have shown that increasing the residence time of inhibitors show a marked increase in efficacy, motivating an understanding of the the unbinding transition state. We plan to make predictions of new high residence time inhibitors of sEH.

I am also interested in a more general understanding of the physical principles underlying dynamic processes of biological molecules, their evolution, and the tools used to understand them. Including Max Entropy (MaxEnt) and Maximum Caliber (MaxCal) methods for equilibrium and dynamics respectively, Geometric Algebra (Clifford Algebras), network flow theory on markov state models, interactive molecular and network visualization, and software development.

For a list of works:
ORCID: 0000-0001-6159-615X
Google Scholar: Samuel D. Lotz

github: @salotz
twitter: @real_salotz
linkedin: in/salotz

Undergraduate Students

Chris
Chris Bailey
Email: baile286@msu.edu

I am a sophomore majoring in Biochemistry and Molecular Biology and Physics.

I am studying small molecule binding and unfolding in the BioA transaminase of Mycobacterium tuberculosis using Window Exchange Umbrella Sampling and coarse-grained sampling methods.