Skip to main content

Vilseck Lab

The Vilseck Laboratory, led by Jonah Vilseck, PhD, is focused on the development and application of state-of-the-art computer simulations to provide atomistic insights into the mechanisms and thermodynamics of protein–ligand and protein–protein binding. This information is then used to guide the discovery and design of novel small molecule or peptide-based therapeutics to address a variety of infectious and proliferative diseases, especially those with known drug resistance.

Active Research

The Vilseck lab is pursuing three main areas of research.

Research Updates

To stay up-to-date on the medical research work at IU School of Medicine, follow the IU School of Medicine research blog and/ or newsroom.

Research Updates


Develop Novel and Innovative λ-Dynamics Based Methodologies

Molecular simulations, including molecular dynamics simulations and free energy-based calculations, have become robust tools to investigate thermodynamic and structural properties for a variety of chemical and biochemical systems. Atomistic insights into solvation, complexation, conformational rearrangements, and structure-function relationships are possible with these techniques. Traditional free energy calculations, however, suffer from poor scalability and high computational costs. To improve calculation efficiency and reduce costs, the Vilseck lab is exploring new methodological developments of a technique called λ-dynamics. This approach allows us to explore large combinatorial chemical spaces collectively within a single molecular dynamics simulation and is a promising new approach for in silico structure-based drug design.

Perform in silico Structure-Based Drug Design

One promising area of application for λ-dynamics based computer simulations is in the discovery of future therapeutics. In rational drug design, prior knowledge of the three-dimensional structure of a disease target, such as a receptor or enzyme, is used to engineer favorable electrostatic and dispersive interactions into novel drug candidates. This can be performed computationally with λ-dynamics to inform and direct experimental design strategies. By investigating a variety of new substituent modifications of a drug candidate, λ-dynamics can identify the most promising modifications to pursue experimentally, thus accelerating drug discovery with reducing costs. In collaboration with esteemed colleagues at IU School of Medicine and elsewhere, the Vilseck lab is employing λ-dynamics based techniques and molecular simulations to discover and design new therapeutic agents to address a variety of proliferative and infectious diseases.

Investigate Aberrant Protein Side Chain Mutations in Disease Targets

Protein side chain mutations are fundamental to a variety of biological processes, including evolution, protein-engineering, and disease. The Vilseck lab uses λ-dynamics based techniques and molecular simulations to better understand the structural and functional effects of aberrant protein side chain mutations in disease targets and design novel pharmaceutical agents capable of addressing the ongoing healthcare concerns they cause. A major goal of the Vilseck lab is to understand drug resistance in cancer, specifically in multiple myeloma (MM) and in triple negative breast cancer (TNBC), essential pillars in the Precision Health Initiative at IU School of Medicine.

Recent Publications

  • 2019
    Vilseck, J. Z.; Sohail, N.; Hayes, R. L.; Brooks, C. L., III Overcoming Challenging Substituent Perturbations with Multisite λ-Dynamics: A Case Study Targeting β-Secretase 1. J. Phys. Chem. Lett. 2019, 10, 4875-4880.

    Ding, X.; Vilseck, J. Z.; Brooks, C. L. III A Fast Solver for Large Scale Multistate Bennett Acceptance Ratio Equations. J. Chem. Theory Comput. 2019, 15, 799-802.

  • 2018
    Hayes, R. L.; Vilseck, J. Z.; Brooks, C. L. III Approaching Protein Design with Multisite λ Dynamics: Accurate and Scalable Mutational Folding Free Energies in T4 Lysozyme. Protein Sci. 2018, 27, 1910-1922.

    Vilseck, J. Z.; Armacost, K. A.; Hayes, R. L.; Goh, G. B.; Brooks, C. L. III Predicting Binding Free Energies in a Large Combinatorial Chemical Space Using Multisite λ Dynamics. J. Phys. Chem. Lett. 2018, 9, 3328-3332.

    Cabeza de Vaca, I.; Qian, Y.; Vilseck, J. Z.; Tirado-Rives, J.; Jorgensen, W. L., Enhanced Monte Carlo Methods for Modeling Proteins Including Computation of Absolute Free Energies of Binding. J. Chem. Theory Comput. 2018, 14, 3279-3288.

  • 2017
    Ding, X.; Hayes, R. L.; Vilseck, J. Z.; Charles, M. K.; Brooks, C. L., III CDOCKER and λ-​dynamics for prospective prediction in D3R Grand Challenge 2. J. Comput.-Aided Mol. Des. 2017, 32, 89-102.

    Ding, X.; Vilseck, J. Z.; Hayes, R. L.; Brooks, C. L., III Gibbs Sampler-Based λ-Dynamics and Rao-Blackwell Estimator for Alchemical Free Energy Calculations. J. Chem. Theory Comput. 2017, 13, 2501-2510.

Research Team

44747-Vilseck, Jonah

Jonah Vilseck

Assistant Professor of Biochemistry & Molecular Biology

Read Bio