Ron Levy group


Levy Group Software and Related Projects


The first molecular dynamics simulation of a protein was reported in 1977. Martin Karplus published (Karplus, M. Biopolymers 2003, 68, 350) a brief history of molecular dynamics simulations of biological macromolecules, and of the period in his laboratory, during which time one of us (Levy) was a postdoctoral student in the group. It was difficult at the time to carry out molecular dynamics simulations of proteins using programs then available, and several members of the Karplus group at Harvard University discussed their ideas about how the situation might be improved.

The development of IMPACT began in the Levy group at Rutgers University, in 1985, starting with a core set of molecular mechanics energy subroutines used to carry out molecular dynamics simulations of proteins. Early research using IMPACT focused on the relation between simulations and NMR experimental studies of protein structure and dynamics, and on protein solvation. In 1992, the Center for Theoretical Simulation of Biological Systems was established by Richard Friesner at Columbia University and Friesner, Levy and Bruce Berne began to collaborate using IMPACT as a platform for methods development. In 1997, a strategic development partnership involving Columbia, Rutgers, Yale, and Schrödinger LLC was formed to provide a path for commercializing some of the new methods being developed for simulations of protein structural changes and interactions with ligands. Basic research projects have benefited from this partnership, particularly by the development of tools to automate the preparation of ligand–protein complexes, by the expanded coverage of the force field, and by the increased coordination between various modeling packages.

Molecular simulations of protein structural changes and ligand binding are built upon two foundations: (1) the design of effective potentials that are matched to the requirements of accuracy and speed appropriate to particular modeling problems; and (2) the design of algorithms to sample the effective potentials in highly efficient ways so as to facilitate the convergence of the simulations in a thermodynamic sense and/or the coverage over large databases containing structures for which effective potential energy calculations are required. Developing algorithms to satisfy the competing goals of accuracy and speed is at the heart of the problem when considering computational models for use in structural biology.

Since 2004 algorithm development within IMPACT has focused on the AGBNP series of implicit solvation models and a physics based model for estimating the free energy of binding of ligands to a protein receptor, the Binding Energy Distribution Analysis Method (BEDAM). The development of advanced sampling methods within IMPACT based on replica exchange has been central to this effort (see ASyncRE below). These projects have been carried out as a collboration between Levy, Emilio Gallicchio, with contributions from several of the students in the Levy lab during the period of 2004-2014.

For more information about the development of the molecular mechanics core technologies in Academic IMPACT, see:

Banks, J. L., Beard, H. S., Cao, Y., Cho, A. E., Damm, W., Farid, R., Felts, A. K., Halgren, T. A., Mainz, D. T., Maple, J. R., Murphy, R., Philipp, D. M., Repasky, M. P., Zhang, L. Y., Berne, B. J., Friesner, R. A., Gallicchio, E. and Levy, R. M. (2005), Integrated Modeling Program, Applied Chemical Theory (IMPACT). J. Comput. Chem., 26: 1752–1780. DOI: 10.1002/jcc.20292. PMCID: PMC2742605.
Gallicchio E., Paris K., Levy R.M. (2009), The AGBNP2 Implicit Solvation Model. J. Chem. Theory Comput., 5: 2544–2564. DOI:10.1021/ct900234u. PMCID: PMC2857935.
Gallicchio E., Lapelosa M., Levy R.M. (2010), The Binding Energy Distribution Analysis Method (BEDAM) for the Estimation of Protein-Ligand Binding Affinities. J. Chem. Theory Comput., 6: 2961–2977. DOI:10.1021/ct1002913. PMCID: PMC2992355.


Asynchronous replica exchange (or ASyncRE for short) is an implementation of the popular parallel replica exchange conformational sampling algorithm. Unlike traditional synchronous implementations, ASyncRE can scale to many hundreds of replicas over heterogeneous grids of unreliable compute nodes for long running times.

The software can be found at the following GitHub repository:

ASyncRE code on GitHub

For more information, see:

Xia, Junchao, William F. Flynn, Emilio Gallicchio, Bin W. Zhang, Peng He, Zhiqiang Tan, and Ronald M. Levy (2015). Large-scale asynchronous and distributed multidimensional replica exchange molecular simulations and efficiency analysis. J. Comput. Chem., 36 (23), 1772-1785. DOI: 10.1002/jcc.23996. PMCID: PMC4512903.
Gallicchio, Emilio, Junchao Xia, William F. Flynn, Baofeng Zhang, Sade Samlalsingh, Ahmet Mentes, and Ronald M. Levy (2015). Asynchronous replica exchange software for grid and heterogeneous computing. Comput. Phys. Commun., Web publication. DOI: 10.1016/j.cpc.2015.06.010.


The BEDAM Binding Energy Distribution Analysis Method is an absolute binding free energy estimation and analysis methodology based on a statistical mechanics theory of molecular association and efficient computational strategies built upon parallel Hamiltonian replica exchange, implicit solvation and multi-state statistical inference. It has been implemented into a python workflow that works within the Schrödinger environment with the IMPACT MD engine.

For more information, see:

Gallicchio, Emilio, Mauro Lapelosa, and Ronald M. Levy (2010). Binding Energy Distribution Analysis Method (BEDAM) for Estimation of Protein-Ligand Binding Affinities. J. Chem. Theory Comput., 6, 2961-2977. DOI: 10.1021/ct1002913. PMCID: PMC2992355.

The BEDAM Workflow is available for download on Github.

The workflow takes as input .mae files of receptor and ligand plus a definition of the binding site region. Analysis of the results produce, among other things, the estimated values of the binding free energy. See the documentation and example provided with the source code.


The software can be found at the following GitHub repository:

UWHAM and SWHAM code on GitHub

The {UWHAM} and {SWHAM} Software Package is available here:

{UWHAM} and {SWHAM} Software Package

You can get more information from the following publications:

Tan, Zhiqiang, Emilio Gallicchio, Mauro Lapelosa, and Ronald M. Levy (2012). Theory of binless multi-state free energy estimation with applications to protein-ligand binding. J. Chem. Phys., 136, 144102. DOI: 10.1063/1.3701175. PMCID: PMC3339880.
Zhang, Bin W., Junchao Xia, Zhiqiang Tan, Ronald M. Levy (2015). A Stochastic Solution to the Unbinned WHAM Equations. J. Phys. Chem. Lett. , 6, 3834-3840. DOI: 10.1002/jmr.2489. PMCID : PMC4715590.
Tan, Zhiqiang, Junchao Xia, Bin W. Zhang, and Ronald M. levy (2016) Locally weighted histogram analysis and stochastic solution for large-scale multi-state free energy estimation. J. Chem. Phys., 144(3). DOI: 10.1063/1.4939768. PMCID: PMC4729400.
Zhang, Bin W., Nanjie Deng, Zhiqiang Tan, and Ronald M. Levy (2017) Stratified UWHAM and Its Stochastic Approximation for Multicanonical Simulations Which are Far from Equilibrium. J. Chem. Theory Comput. , 13, 10, 4660-4674. DOI: 10.1021/acs.jctc.7b00651.
Zhang, Bin W., Shima Arasteh, and Ronald M. Levy (2019) The {UWHAM} and {SWHAM} Software Package. Scientific Reports , 9(1). DOI: 10.1038/s41598-019-39420-x. PMCID: PMC6391495.

FightAIDS@Home, Phase2

Phase 2 of the FightAIDS@Home project has launched! Please visit the main page for the project for more details.


Mi3-GPU ("Mee-three", for Markov-Chain Inverse Ising Inference) solves the inverse Ising problem for application in protein covariation analysis. The goal is to infer "coupling" parameters between positions in a Multiple Sequence Alignment (MSA) of a protein family, with many applications including protein-contact prediction and fitness prediction. Mi3-GPU solves the inverse Ising problem with few approximations using Markov-Chain Monte-Carlo methods with Quasi-Newton optimization, and the implementation is highly parallelized using GPUs with ~250x speedup vs CPU on typical problems. This enables the construction of "generative" models which reproduce the covariation patterns of the observed MSA with very high statistical precision, particularly suited for studying sequence variation on a sequence-by-sequence basis and MSA statistics, but can also be used in other common applications of covariation analysis.

This package also provides tools for analysis and preparation of protein-family MSAs to account for finite-sampling issues, which are a major source of error or bias in inverse Ising inference.

Available with user-guide at: Mi3-GPU on Github

Main publications:

Allan Haldane and Ronald M. Levy (2020). Mi3-GPU: MCMC-based Inverse Ising Inference on GPUs for protein J.Computer Physics CommunicationsDOI: 10.1016/j.cpc.2020.107312
Allan Haldane and Ronald M. Levy (2019). Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation Phys. Rev. Evol. 99 no. 3, pp. 032405. DOI: 10.1103/PhysRevE.99.032405.