About

Professor Michael R. Shirts is a member of the Shirts Research Group at the University of Colorado Boulder, within the Department of Chemical and Biological Engineering. His research focuses on designing and characterizing new materials at the nanoscale through the use of theory and computation. The group emphasizes drug design by predicting physical properties and binding affinities, as well as the development of novel biomimetic materials. A key aspect of his work involves creating computational tools that enhance molecular design by making searches through chemical and configuration space more predictive, reliable, and efficient.

Research topics

• Computer Science
• Physics
• Quantum mechanics
• Statistical physics
• Reliability engineering
• Classical mechanics
• Engineering
• Chemistry
• Thermodynamics
• Telecommunications
• Algorithm
• Computational chemistry
• Statistics
• Mathematics
• Chemical physics
• Environmental science

Selected publications

• Development and Benchmarking of Open Force Field v1.0.0—the Parsley Small-Molecule Force Field

  Journal of Chemical Theory and Computation · 2021 · 167 citations

  • Computer Science
  • Computer Science
  • Physics

  We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.

  DOI

• Best Practices for Alchemical Free Energy Calculations [Article v1.0].

  Living Journal of Computational Molecular Science · 2020 · 240 citations

  • Computer Science
  • Statistical physics
  • Computer Science

  Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing alchemical intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations performed with equilibrium simulations, in particular relative and absolute small molecule binding free energy calculations to biomolecular targets.

  DOI

• The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

  Journal of Computer-Aided Molecular Design · 2020 · 137 citations

  • Computer Science
  • Reliability engineering
  • Environmental science

  Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

  DOI

Recent grants

• NIH Grant F32GM074384

  NIH · $137k · 2008

• CAREER: Understanding the thermodynamics of crystalline materials using advanced molecular simulation sampling methods

  NSF · $450k · 2014–2016

• Hardening and Development of the GROMACS Molecular Simulation Engine

  NIH · $1.1M · 2016–2022

• Highly multidimensional thermodynamic property prediction for chemical design using atomistic simulations

  NSF · $414k · 2012–2016

• Collaborative Research: NSCI Framework: Software: SCALE-MS - Scalable Adaptive Large Ensembles of Molecular Simulations

  NSF · $287k · 2019–2023

Frequent coauthors

• David L. Mobley

  University of California, Irvine

  84 shared

• John D. Chodera

  64 shared

• Simon Boothroyd

  55 shared

• Jeffrey Wagner

  Open Geospatial Consortium

  39 shared

• Peter M. Kasson

  Georgia Institute of Technology

  38 shared

• Michael K. Gilson

  38 shared

• Roland Schulz

  Intel (United States)

  36 shared

• Jeremy C. Smith

  University of Tennessee at Knoxville

  36 shared

Education

• Ph.D., Chemistry

  Stanford University

  2005

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