Protein folding and conformational change. Proteins are nature’s original “nanotechnology”–molecular machines built from chains of amino acids that have the ability to self-assemble into unique three-dimensional structures and perform a vast array of biochemical functions. Of broad impact to human biology and disease is understanding the physical principles that drive folding, binding and conformational allostery. We use high-performance and distributed computing along with state-of-the-art molecular simulation algorithms to model these processes. Click to see a simulation trajectory of protein folding for millisecond folder NTL9.  

Markov State Model (MSM) approaches to model long-timescale dynamics. MSMs describe conformational dynamics as a kinetic network of transitions between metastable states. The key advantages of MSMs are (1) they can be used to obtain long timescale dynamics from collections of much shorter trajectories, and (2) they are very helpful in extracting human-understandable mechanistic information about pathways and rates. 

Razavi AM, Voelz VA. Kinetic network models of tryptophan mutations in β-hairpins reveal the importance of non-native interactions. J Chem Theory Comput. 2015 Jun 9;11(6):2801-12.

Advancing MSM methodology toward molecular design. MSMs as a platform for efficient prediction and design of conformational populations and dynamics for molecules related by mutation (peptides and proteins) and/or chemical modification (e.g. cyclic peptides, peptidomimetics). Advancements from our lab extending MSM methods for this purpose include surprisal analysis and related adaptive sampling techniques, and maximum-caliber approaches to predict how mutations perturb MSM transition rates. The goal of these new methods is to enable efficient sampling of multiple designs, a task currently challenging for all-atom molecular simulation. 

Wan H, Zhou G, Voelz VA. A Maximum-Caliber Approach to Predicting Perturbed Folding Kinetics Due to Mutations. J Chem Theory Comput. 2016 Dec 13;12(12):5768-5776.

Voelz VA, Elman B, Razavi AM, Zhou G. Surprisal Metrics for Quantifying Perturbed Conformational Dynamics in Markov State Models. J Chem Theory Comput. 2014 Dec 9;10(12):5716-28

Prediction and design of ligand binding. With the development of new methods, our research interests have been increasingly focused on modeling ligand binding reactions, and its interplay with folding and conformational dynamics. Using p53+MDM2 as model system for protein/ligand binding, we are the studying molecular recognition mechanisms of intrinsically disordered peptides in cell signaling. We are also studying the the many applications of MSMs in computational drug design, such as modeling receptor flexibility for small-molecule docking, and screening and improving de novo designed protein binders.
Zhou G, Pantelopulos GA, Mukherjee S, Voelz VA. Bridging microscopic and macroscopic mechanisms of p53-MDM2 binding using molecular simulations and kinetic network models. Biophysical Journal, Accepted 

Mukherjee S, Pantelopulos GA, Voelz VA. Markov models of the apo-MDM2 lid region reveal diffuse yet two-state binding dynamics and receptor poses for computational docking. Sci Rep. 2016 Aug 19;6:31631

Prediction and design of foldable peptidomimetics. Towards the design of beta-hairpin mimics to disrupt protein-protein interactions, we are studying designed beta-hairpin peptides, including cyclic and beta-capped variants. We are working on several simulation-based strategies to screen and select designs with favorable conformational and binding properties. Peptoids (N-substituted oligoglycine) are sequence-specific bio-inspired polymers that can mimic many of the chemical and foldameric properties of proteins.  They can be synthesized easily using solid-phase techniques for combinatorial screening of bioactivity as well as for nanomaterial applications.  We are developing improved simulation models for peptoids, so we can predict and design their self-assembly and binding.

Razavi AM, Wuest WM, Voelz VA. Computational screening and selection of cyclic peptide hairpin mimetics by molecular simulation and kinetic network models. J Chem Inf Model. 2014 May 27;54(5):1425-32. 

Butterfoss GL, Yoo B, Jaworski JN, Chorny I, Dill KA, Zuckermann RN, Bonneau R, Kirshenbaum K, Voelz VA. De novo structure prediction and experimental characterization of folded peptoid oligomers. Proc Natl Acad Sci U S A. 2012 Sep 4;109(36):14320-5.