We use molecular simulations to characterize, predict, and engineer the behavior of synthetic and biological soft materials
The overall goal of Van Lehn group research is to gain molecular-scale insight into the behavior of synthetic and biological soft materials for applications relevant to human health and biotechnology, sustainability, advanced manufacturing, and energy. Our research is entirely computational and employs a variety of simulation techniques, including all-atom and coarse-grained molecular dynamics simulations, enhanced sampling techniques, and implicit solvent thermodynamics methods. A key focus of our approach is the use of chemically specific simulations that enable numerous collaborations with experimental groups at UW-Madison and around the world. We further develop and apply data-centric and machine learning techniques to complement simulation analysis and accelerate both computational and experimental workflows. We promote an interdisciplinary research environment by recruiting students from the Department of Chemical and Biological Engineering, Department of Chemistry, and Biophysics Training Program, participating in the National Institutes of Health Chemistry-Biology Interface and Biotechnology Training Programs, and contributing to research in two Department of Energy centers – the Center for the Chemical Upcycling of Waste Plastics and the Great Lakes Bioenergy Research Center. Current research areas are detailed below.
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Designing synthetic materials to interact with biological molecules and materials
Synthetic materials are used in numerous applications, such as drug delivery or biosensing, that require finely tuned interactions with biological molecules or materials. For example, synthetic nanomaterials might ideally adsorb to bacterial membranes to achieve high antimicrobial activity while minimizing interactions with red blood cell membranes to avoid hemolysis. Meeting such criteria is challenging because subtle differences in material properties can manifest as large changes in biological behaviors that are difficult to predict. Moreover, fabrication can be highly laborious and time-consuming, limiting the ability to experimentally screen biomaterial libraries. We address these challenges by utilizing molecular dynamics simulations to understand interactions at the interface between synthetic materials and biosystems and use machine learning methods to uncover structure-activity relationships for the design of new materials, with a particular focus on ligand-functionalized nanoparticles.
- A. K. Chew, J. A. Pedersen, and R. C. Van Lehn. “Predicting the physicochemical properties and biological activities of monolayer-protected gold nanoparticles using simulation-derived descriptors.” ACS Nano, 2022, 16 (4), 6282-6292. [Link]
- C. A. Lochbaum, A. K. Chew, X. Zhang, V. M. Rotello, R. C. Van Lehn*, and J.A. Pedersen*. “The lipophilicity of cationic ligands promotes irreversible adsorption of nanoparticles to lipid bilayers.” ACS Nano, 2021, 15, 6562-6572. [Link].
- A. K. Chew, B. C. Dallin, and R. C. Van Lehn. “The interplay of ligand properties and core size dictates the hydrophobicity of monolayer-protected gold nanoparticles.” ACS Nano, 2021, 15 (3), 4534-4545. [Link]
Screening solvents for (bio)polymer dissolution and depolymerization
Understanding the behavior of synthetic and biological polymers in single- and multicomponent solvent systems is necessary to design processes that depend upon polymer solubility or solvent-mediated depolymerization. For example, we (with the Huber group at UW-Madison) have recently developed a plastic recycling process called Solvent-Targeted Recovery and Precipitation (STRAP) in which polymers are recovered from mixed plastic waste by selectively dissolving them in carefully chosen solvent systems. To guide STRAP process design, we are developing and applying high-throughput molecular and data-driven models to identify solvent systems suitable for the selective dissolution of common polymers while also considering challenges associated with process costs and safety. We are using similar methods to predict solvent effects on the dissolution and depolymerization of lignin. Together, these methods are leading to new, sustainable methods for recycling plastic waste and upcycling biomass.
- P. Zhou, J. Yu, K. L. Sanchez-Rivera, G. W. Huber, and R. C. Van Lehn. “Large-scale computational polymer solubility predictions and applications to dissolution-based plastic recycling.” Green Chemistry, 2023, 45, 4002-4014. [Link]
- Z. Sumer and R. C. Van Lehn. “Heuristic computational model for predicting lignin solubility in tailored organic solvents.” ACS Sustainable Chemistry & Engineering, 2023, 11 (1), 187-198. [Link]
- S. Qin, S. Jiang, J. Li, P. Balaprakash, R. C. Van Lehn, and V. M. Zavala. “Capturing molecular interactions in graph neural networks: A case study in multi-component phase equilibrium.” Digital Discovery, 2023, 2, 138-151. [Link]
Uncovering the physicochemical rules governing interactions with lipid membranes
The lipid membrane is a thin, amphiphilic, soft membrane that regulates transport into and out of the cell. Interactions between synthetic or biological materials and the membrane can induce various processes, including adsorption to the membrane surface, perturbations to membrane structure, or translocation across the membrane, that dictate critical outcomes such as cellular uptake or cytotoxicity. However, these behaviors are difficult to predict and challenging to model computationally as they occur over long timescales. To address this challenge, we perform atomistic and coarse-grained simulations, coupled with advanced sampling techniques, to model membrane adsorption, pore formation, and translocation for a range of materials. Our goal is to uncover physicochemical rules governing these behaviors that are relevant to both understanding biological processes (e.g., bacterial signaling, membrane protein production) and designing synthetic materials (e.g., drug delivery vehicles, antimicrobial agents).
- J. Morstein, A. Capecchi, K. Hinnah, B. Park, J. Petit-Jacques, R. C. Van Lehn, J-L Reymond, and D. Trauner. “Medium-chain lipid conjugation facilitates cell-permeability and bioactivity.” Journal of the American Chemical Society, 2022, 144 (40), 18532-18544. [Link]
- C. G. Gahan, S. J. Patel, L. M. Chen, D. E. Manson, Z. J. Ehmer, H. E. Blackwell, R. C. Van Lehn, and D. M. Lynn. “Bacterial quorum sensing signals promote large-scale remodeling of lipid membranes.” Langmuir, 2021, 35 (30), 9120-9136. [Link]
- S. J. Patel and R. C. Van Lehn. “Analysis of charged peptide loop-flipping across a lipid bilayer using the string method with swarms-of-trajectories.” Journal of Physical Chemistry B, 2021, 125 (22), 5862-5873. [Link]
Understanding structure formation and perturbations in complex liquid environments
Liquids exhibit structure over molecular length scales that is particularly pronounced in systems with strong interactions (e.g., hydrogen bonds). Perturbations to liquid structure can lead to solvent-mediated interactions, such as hydrophobic interactions that emerge from the disruption of interfacial water structure. Preferential interactions between components in liquid mixtures can further drive structure formation to create unique solvation environments that influence macroscopic behaviors like ion transport. These behaviors are challenging to study in part because structural order parameters may be difficult to define. To address these challenges, we combine molecular dynamics simulations with data-centric analysis techniques to analyze structure in a variety of systems, including ionic liquids, deep eutectic solvents, organic solvent mixtures, liquid crystals, and interfacial water. This analysis provides new insight relevant to biomaterial interfaces, electrolytes for energy storage, liquid crystal sensors, and solvent effects in liquid-phase catalysis.
- A. D. Smith, S. Runde, A. K. Chew, A. S. Kelkar, U. Maheshwari, R. C. Van Lehn, and V. M. Zavala. “Topological analysis of molecular dynamics simulations using the Euler Characteristic.” Journal of Chemical Theory and Computation, 2023, 19(5), 1553-1567. [Link]
- B. C. Dallin, A. S. Kelkar, and R. C. Van Lehn. “Structural features of interfacial water predict the hydrophobicity of chemically heterogeneous surfaces.” Chemical Science, 2023, 14 (5), 1308-1319. [Link]
- Z. Sumer and R. C. Van Lehn. “Data-centric development of lignin structure – solubility relationships in deep eutectic solvents using molecular simulations.” ACS Sustainable Chemistry & Engineering, 2022, 10 (31), 10144-10156. [Link]