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Exploring Alchemical Landscapes

Emilio Gallicchio

Dept. of Chemistry, Brooklyn College of CUNY

egallicchio@brooklyn.cuny.edu

www.compmolbiophysbc.org

​

​

Ron Levy’s 70+th birthday symposium

ACS National Meeting Spring 2023

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ACS New Orleans 2008

  1. Rajat Kumar Pal, Kamran Haider, Divya Kaur, William Flynn, Junchao Xia, Ronald M Levy, Tetiana Taran,* Lauren Wickstrom, Tom Kurtzman, Emilio Gallicchio. A Combined Treatment of Hydration and Dynamical Effects for the Modeling of Host-Guest Binding Thermodynamics: The SAMPL5 Blinded Challenge. J. Comp. Aided. Mol. Des. 31, 29 (2016). *Undergraduate student. Free access on SpringerNature doi:10.1007/s10822-016-9956-6
  2. Nanjie Deng, William F. Flynn, Junchao Xia, R. S. K. Vijayan, Baofeng Zhang, Peng He, Ahmet Mentes, Emilio Gallicchio and Ronald M. Levy. Blind Predictions of Protein-Ligand Binding Affinities by Large-Scale BEDAM Free Energy Calculations: the D3R Grand Challenge 2015. J. Comp. Aided. Mol. Des. 30.9 , 743-751 (2016). doi:10.1007/s10822-016-9952-x
  3. A Mentes, NJ Deng, RSK Vijayan, J Xia, E Gallicchio, RM Levy. Binding Energy Distribution Analysis Method: Hamiltonian Replica Exchange with Torsional Flattening for Binding Mode Prediction and Binding Free Energy Estimation. J. Chem. Theory Comput. 12, 2459-2470 (2016). doi:10.1021/acs.jctc.6b00134
  4. Lauren Wickstrom, Nanjie Deng, Peng He, Ahmet Mentes ,Crystal Nguyen, Michael K. Gilson, Tom Kurtzman, Emilio Gallicchio, and Ronald M. Levy. Parameterization of an effective potential for protein-ligand binding from host-guest affinity data. J. Molecular Recognition, 29.1, 10-21 (2016). doi:10.1002/jmr.2489
  5. Bin W Zhang, Wei Dai, Emilio Gallicchio, Peng He, Junchao Xia, Zhiqiang Tan, Ronald M Levy. Simulating Replica Exchange: Markov State Models, Proposal Schemes, and the Infinite Swapping Limit. J. Phys. Chem. B, 120, 8289–8301 (2016). doi:10.1021/acs.jpcb.6b02015
  6. Emilio Gallicchio, Junchao Xia, William F Flynn, Baofeng Zhang, Sade Samlalsingh,* Ahmet Mentes, Ronald M Levy. Asynchronous Replica Exchange Software for Grid and Heterogeneous Computing. Computer Physics Communications, 196, 236–246 (2015). doi:10.1016/j.cpc.2015.06.010 PubMed *Undergraduate student.
  7. Junchao Xia, William F. Flynn, Emilio Gallicchio, Bin W. Zhang, Peng He, Zhiqiang Tan, Ronald M. Levy. Large Scale Asynchronous and Distributed Multi-Dimensional Replica Exchange Molecular Simulations and Efficiency Analysis. J. Comp. Chem, 36, 1772-1785 (2015). doi:10.1002/jcc.23996
  8. Deng, Nan-jie; Forli, Stefano; He, Peng; Perryman, Alex; Wickstrom, Lauren; Vijayan, Suyambu Kesava Vijayan; Tiefenbrunn, Theresa; Stout, C.; Gallicchio, Emilio; Olson, Arthur; Levy, Ronald M. Distinguishing Binders from False Positives by Free Energy Calculations: Fragment Screening Against the Flap Site of HIV Protease. J. Phys. Chem B 119, 976–988 (2015). doi:10.1021/jp506376z
  9. Emilio Gallicchio, Haoyuan Chen, He Chen, Michael Fitzgerald, Yang Gao, Peng He, Malathi Kalyanikar, Chuan Kao, Beidi Lu, Yijie Niu, Manasi Pethe, Jie Zhu and Ronald M Levy. BEDAM Binding Free Energy Predictions for the SAMPL4 Octa-Acid Host Challenge. J. Comp. Aided Mol. Des. 29, 315-325 (2015), doi:10.1007%2Fs10822-014-9795-2
  10. Emilio Gallicchio, Nanjie Deng, Peng He, Lauren Wickstrom, Alexander L. Perryman, Daniel N. Santiago, Stefano Forli, Arthur J. Olson and Ronald M. Levy. Virtual Screening of Integrase Inhibitors by Large Scale Binding Free Energy Calculations: the SAMPL4 Challenge. J Comp Aided Mol Design, 28, 475-490 (2014). doi:10.1007/s10822-014-9711-9
  11. Guohua Yi, Mauro Lapelosa, Rachel Bradley, Thomas M. Mariano, Denise Elsasser Dietz, Scott Hughes, Terri Wrin, Chris Petropoulos, Emilio Gallicchio, Ronald M. Levy, Eddy Arnold, Gail Ferstandig Arnold. Chimeric Rhinoviruses Displaying MPER Epitopes Elicit Anti-HIV Neutralizing Responses. PLoS ONE 8(9), e72205 (2013). doi:10.1371/journal.pone.0072205
  12. Brian K Radak, Tai-Sung Lee, Peng He, Melissa Romanus, Ole Weidner, Wei Dai, Emilio Gallicchio, Nan-Jie Deng, Darrin M York, Ronald M Levy, Shantenu Jha. A framework for flexible and scalable replica-exchange on production distributed CI. Proceedings of the Conference on Extreme Science and Engineering Discovery Environment. ACM (2013). doi:10.1145/2484762.2484830
  13. Wickstrom L, He P, Gallicchio E, Levy RM. Large scale affinity calculations of cyclodextrin host-guest complexes: Understanding the role of reorganization in the molecular recognition process. J Chem Theory Comput, 9, 3136–3150 (2013). doi:10.1021/ct400003r
  14. Tan Z, Gallicchio E, Lapelosa M, Levy RM. Theory of binless multi-state free energy estimation with applications to protein-ligand binding. J Chem Phys, 136, 144102 (2012). doi:10.1063/1.3701175
  15. Gallicchio E. Role of Ligand Reorganization and Conformational Restraints on the Binding Free Energies of DAPY Non-Nucleoside Inhibitors to HIV Reverse Transcriptase. Computational Molecular Bioscience, 2, 7-22 (2012).doi:10.4236/cmb.2012.21002
  16. Gallicchio E, Levy RM. Prediction of SAMPL3 Host-Guest Affinities with the Binding Energy Distribution Analysis Method (BEDAM). J Comp Aided Mol Design, 26, 505-516 (2012).doi:10.1007/s10822-012-9552-3
  17. Lapelosa M, Gallicchio E, Levy RM. Conformational Transitions and Convergence of Absolute Binding Free Energy Calculations. J Chem Theory Comput, 8, 44-60 (2012).doi:10.1021/ct200684b JCTC Most Read Article Q1 2012
  18. Bell JA, Cao Y, Gunn, JR, Day T, Gallicchio E, Zhou Z, Levy RM and Farid R. PrimeX and the SchrΓΆdinger Computational Chemistry Suite of Programs. International Tables for Crystallography, 534-538 (2012).html
  19. Wickstrom L, Gallicchio E and Levy RM. The Linear Interaction Energy Method for the Prediction of Stability Changes Upon Mutation. Proteins, 80, 111-125 (2011). doi:10.1002/prot.23168
  20. Emilio Gallicchio and Ronald M Levy, Recent Theoretical and Computational Advances for Modeling Protein-Ligand Binding Affinities. Advances in Protein Chemistry and Structural Biology, 85, 27-80, (2011). PubMed doi:10.1016/B978-0-12-386485-7.00002-8
  21. Deng, N., W. Zheng, E. Gallicchio, and R.M. Levy. Insights into the Dynamics of HIV-1 Protease: a Kinetic Network Model Constructed from Atomistic Simulations. J. Am. Chem. Soc., 133, 9387-9894 (2011). PubMed | doi:10.1021/ja2008032
  22. Zheng, W., E. Gallicchio, N. Deng, M. Andrec, and R.M. Levy. Kinetic Network Study of the Diversity and Temperature Dependence of TRP-Cage Folding Pathways: Combining Transition Path Theory with Stochastic Simulations. J. Phys. Chem. B, 115, 1512-1523 (2011). PubMed | doi:10.1021/jp1089596
  23. Gallicchio E and Levy RM. Advances in all atom sampling methods for modeling protein-ligand binding affinities . Curr Op Struct Biol, 21, 161-166 (2011). PubMed | doi:10.1016/j.sbi.2011.01.010
  24. Gallicchio E, Lapelosa M, Levy RM. Binding energy distribution analysis method (BEDAM) for estimation of protein-ligand binding affinities. J Chem Theory Comput, 6, 2961-2977 (2010). PubMed | doi:10.1021/ct1002913
  25. Lapelosa M, Ferstandig Arnold G, Gallicchio E, Arnold E, and Levy RM. Antigenic characteristics of rhinovirus chimeras designed in silico for enhanced presentation of HIV-1 gp41 epitopes. J Mol Biol, 397, 752-766 (2010). PubMed | doi:10.1016/j.jmb.2010.01.064
  26. Okumura H, Gallicchio E, and Levy RM. Conformational populations of ligand-sized molecules by replica exchange molecular dynamics and temperature reweighting. J Comp Chem, 31, 1357-1367 (2009). PubMed | doi:10.1002/jcc.21419
  27. Gallicchio E, Paris K, and Levy RM. The AGBNP2 Implicit Solvent Model. J Chem Theory Comput, 5, 2544-2564 (2009). PubMed | doi:10.1021/ct900234u
  28. Zheng, W., M. Andrec, E. Gallicchio, and R.M. Levy. Recovering Kinetics from a Simplified Protein-Folding Model Using Replica Exchange Simulations, a Kinetic Network and Effective Stochastic Dynamics. J. Phys. Chem. B., 113, 11702-11709 (2009). PubMed | doi:10.1021/jp900445t
  29. Frenkel, Y.V., E. Gallicchio, K. Das, R.M. Levy, and E. Arnold. Molecular dynamics study of non-nucleoside HIV-1 RT inhibitor TMC278/rilpivirine aggregates: correlation between amphiphilic properties of the drug and oral bioavailability. J. Med. Chem., 52, 5896-5905 (2009). PubMed | doi:10.1021/jm900282z
  30. Lapelosa, M., E. Gallicchio, G. Ferstandig Arnold, E. Arnold and R.M. Levy. In silico vaccine design based on molecular simulations of rhinovirus chimeras presenting HIV-1 gp41 epitopes. J. Mol. Biol., 385, 675-691 (2009). PubMed | doi:10.1016/j.jmb.2008.10.089
  31. Felts, A.K., M. Andrec, E. Gallicchio, and R.M. Levy. Protein Folding and Binding: Effective Potentials, Replica Exchange Simulations, and Network Models, in "Water and Biomolecules - Physical Chemistry of Life Phenomena", Springer Science (2008).
  1. Felts, A.K., K. Paris, E. Gallicchio, R.A. Friesner, and R.M. Levy. Predicting Long Loops with the AGBNP Implicit Solvent Model using Hierarchical Torsion Angle Sampling and Protein Local Optimization. J. Chem. Theor. Comp., 4, 855-858 (2008). PubMed | doi:10.1021/Fct800051k
  2. Zheng, W., M. Andrec, E. Gallicchio, and R.M. Levy. Simple Continuous and Discrete Models for Simulating Replica Exchange Simulations of Protein Folding. J. Phys. Chem. B, 112, 6083-6093 (2008). PubMed | doi:10.1021/jp076377+
  3. Gallicchio, E., R.M. Levy, and M. Parashar. Asynchronous Replica Exchange for Molecular Simulations. J. Comp. Chem., 29, 788-794 (2008). PubMed | doi:10.1002/jcc.20839
  4. Knight, J.L., Z. Zhou, E. Gallicchio, D.M. Himmel, R.A. Friesner, E. Arnold, and R.M. Levy. Exploring structural variability in X-ray crystallographic models using protein local optimization by torsion angle sampling. Acta. Cryst., D64, 383-396 (2008). PubMed | doi:10.1107/S090744490800070X
  5. Zheng, W., M. Andrec, E. Gallicchio, and R.M. Levy. Simulating replica exchange simulations of protein folding with a kinetic network model. Proc. Natl. Acad. Sci. USA, 104, 15340-15345 (2007). PubMed | doi:10.1073/pnas.0704418104
  6. Krishna Pratap Ravindranathan, Emilio Gallicchio, Ann E. McDermott, and Ronald M. Levy Conformational Dynamics of Substrate in the Active Site of Cytochrome P450 BM-3/NPG Complex: Insights from NMR Order Parameters. J. Am. Chem. Soc. 129, 474-475 (2007). doi:10.1021/ja0672371
  7. Su, Y., E. Gallicchio, K. Das, E. Arnold, and R.M. Levy. Linear Interaction Energy (LIE) Models for Ligand Binding in Implicit Solvent: Theory and Application to the Binding of NNRTIs to HIV-1 Reverse Transcriptase, J. Chem. Theory Comput., 3, 256-277 (2007). doi:10.1021/ct600258e
  8. Ravindranathan, K.P., E. Gallicchio, R.A. Friesner, A.E. McDermott, and R.M. Levy. Conformational equilibrium of cytochrome P450 BM-3 complexed with N-Palmitoylglycine: A replica exchange molecular dynamics study. J. Am. Chem. Soc., 128, 5786-5791 (2006). doi:10.1021/ja058465i
  9. Zhang, L., M. Parashar, E. Gallichio and R.M. Levy. Salsa: Scalable Asynchronous Replica Exchange for Parallel Molecular Dynamics Applications. Proceedings of the 35th International Conference on Parallel Processing (ICPP 2006), Columbus, OH, USA, IEEE Computer Society Press, pp. 127 - 134, August 2006. doi:10.1109/ICPP.2006.63
  10. Ravindranathan, K.P., E. Gallicchio, and R.M. Levy. Conformational Equilibria and Free Energy Profiles for the Allosteric Transition of the Ribose Binding Protein. J. Mol. Biol., 353, 196-210 (2005). doi:10.1016/j.jmb.2005.08.009
  11. Banks, J.L., H.S. Beard, Y. Cao, A.E. Cho, W. Damm, R. Farid, A.K. Felts, T.A. Halgren, D.T. Mainz, J.R. Maple, R. Murphy, D.M. Philipp, M.P. Repasky, L.Y. Zhang, B.J. Berne, R.A. Friesner, E. Gallicchio, and R.M. Levy. Integrated Modeling Program, Applied Chemical Theory (IMPACT). J. Comp. Chem., 26, 1752 (2005). doi:10.1002/jcc.20292
  12. Andrec, M., A.K. Felts, E. Gallicchio, and R.M. Levy. Protein Folding Pathways from Replica Exchange Simulations and a Kinetic Network Model. Proceedings Natl. Acad. Sci. USA, 102, 6801-6806 (2005). doi:10.1073/pnas.0408970102
  13. Gallicchio, E., M. Andrec, A.K. Felts, and R.M. Levy. Temperature Weighted Histogram Analysis Method, Replica Exchange, and Transition Paths. J. Phys. Chem. B, 109, 6722-6731 (2005). doi:10.1021/jp045294f
  14. Felts, A.K., Y. Harano, E. Gallicchio, R.M. Levy. Free energy surfaces of beta-hairpin and alpha-helical peptides generated by replica exchange molecular dynamics with the AGNP implicit solvent model. PROTEINS: Structure, Function, and Bioinformatics, 56,310-321 (2004). doi:10.1002/prot.20104
  15. Y. Su and E. Gallicchio, The Non-polar Solvent Potential of Mean Force for the Dimerization of Alanine Dipeptide: The Role of Solute-Solvent van der Waals Interactions. Biophysical Chemistry. 109:251-260 (2004).doi:10.1016/j.bpc.2003.11.007
  16. Gallicchio E., and R.M. Levy. AGBNP, an analytic implicit solvent model suitable for molecular dynamics simulations and high-resolution modeling, J. Comp. Chem. 25, 479-499 (2004).doi:10.1002/jcc.10400
  17. R. M. Levy, L. Y. Zhang, E. Gallicchio, A. K. Felts, On the Non-Polar Hydration Free Energy of Proteins: Surface Area and Continuum Solvent Models for the Solute-Solvent Interaction Energy, J. Am. Chem. Soc., 125, 9523-9530 (2003).
  18. Felts, A.K., E. Gallicchio, A. Wallqvist, and R.M. Levy. Distinguishing Native Conformations of Proteins from Decoys with an Effective Free Energy Estimator based on the OPLS All-Atom Force Field and the Surface Generalized Born Solvent Model. Proteins, 48, 404-422 (2002).
  19. E. Gallicchio, L.Y. Zhang, and R.M. Levy. The SGB/NP Hydration Free Energy Model Based on the Surface Generalized Born Solvent Reaction Field and Novel Non-Polar Hydration Free Energy Estimators. J. Comp. Chem., 23, 517-529 (2002).
  20. Wallqvist, A., E. Gallicchio, A.K. Felts, and R.M. Levy. Detecting Native Protein Folds Among Large Decoy Sets With the OPLS All-Atom Potential and Surface Generalized Born Solvent Model, in "Computational Methods for Protein Folding: A Special Volume of Advances in Chemical Physics," Vol. 120, R. Friesner, editor, I. Prigogine and S.A. Rice, series editors, John Wiley & Sons, 459-486 (2002).
  21. Wallqvist, A., E. Gallicchio, and R.M. Levy. A Model for Studying Drying at Hydrophobic Interfaces: Structural and Thermodynamic Properties. J. Phys. Chem.,105, 6745-6753 (2001).
  22. Felts, A.K., A. Wallqvist, E. Gallicchio, D. Bassolino, S.R. Krystek and R.M. Levy. Fold Recognition using the OPLS All-Atom Potential and the Surface Generalized Born Solvent Model, in "Lecture Notes in Computational Science and Engineering (LNCSE)", Vol. 24, Springer-Verlag, Berlin, 2002.
  23. Chernyavsky, B., E. Gallicchio, D. Knight, R. Levy, and A. Page. The Rutgers Computational Grid: A Distributed Linux PC Cluster. Cluster Computing (2002)
  24. Zhang, L., E. Gallicchio, R. Friesner, and R.M. Levy. Solvent Models for Protein-Ligand Binding: Comparison of Implicit Solvent Poisson and Surface Generalized Born Models with Explicit Solvent Simulations. J. Comp. Chem., 22, 591-607 (2001).
  25. Gallicchio, E., M. Kubo, and R.M. Levy. Enthalpy-Entropy and Cavity Decomposition of Alkane Hydration Free Energies: Numerical Results and Implications for Theories of Hydrophobic Solvation. J. Phys. Chem., 104, 6271-6285 (2000).
  26. Zhang, L., E. Gallicchio, and R.M. Levy. Implicit Solvent Models for Protein-Ligand Binding: Insights Based on Explicit Solvent Simulations. AIP Conference Proceedings (Simulation and Theory of Electrostatic Interactions in Solutions), 492, 451-472 (1999).
  27. Matubayasi, N., E. Gallicchio, and R.M. Levy. On the local and nonlocal components of solvation thermodynamics and their relation to solvation shell models. J. Chem. Phys., 109, 4864-4872 (1998).
  28. Gallicchio, E., M.M. Kubo, and R.M. Levy. Entropy-Enthalpy Compensation in Solvation and Ligand Binding Revisited. J. Am. Chem Soc., 120, 4526-4527 (1998).
  29. Levy, R.M., and E. Gallicchio. Computer Simulations with Explicit Solvent: Recent Progress in the Thermodynamic Decomposition of Free Energies, and in Modeling Electrostatic Effects. Annual Review of Physical Chemistry, 49, 531-567 (1998).
  30. Kubo, M.M., E. Gallicchio, and R.M. Levy. Thermodynamic Decomposition of Hydration Free Energies by Computer Simulation: Application to Amines, Oxides, and Sulfides. J. Phys. Chem., 101, 10527-10534 (1997).

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The Computational Molecular Biophysics Laboratory @BC

3

Sheenam Khuttan

Solmaz Azimi

Joe Wu

Dr. Rajat K. Pal

TandemAI

Current and Former Lab Members

Collaborators

Lauren Wickstrom

Tom Kurtzman

Nanje Deng

Ron Levy

Peter Eastman

Ryan Murelli

Wayne Harding

Software

Gianni De Fabritiis

Eric Chen

Huafeng Xu

Yujie Wu

Woody Sherman

Brian Radak

Sebastian Dick

Chuanjie Wu

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Alchemical Transformation for Binding

4

Alchemical Potential

Unbound State

Bound State

+

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Alchemical Transformation for Relative Binding

5

Alchemical Potential

Bound to Ligand 1

+

Bound to Ligand 2

+

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Alchemical Transformation for Relative Binding

6

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The Alchemical Transfer Method (ATM)

  • Based on a coordinate displacement perturbation of the ligand.
  • Supports absolute and relative binding free energy calculations.
  • Uses a single solvent box: no vacuum intermediate.
  • Applicable to scaffold-hopping and charge-changing transformations.

​

7

  • Does not require:
    • soft-core pair potentials.
    • van der Waals/electrostatics splitting.
    • modifications of energy/forces routines.
  • Force-field agnostic
    • applicable to PME, polarizable, QM/MM, ML potentials, implicit solvent, etc.
  • Fully open-source: AToM-OpenMM.

+h

-h

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AToM-OpenMM: Tutorials and Workflows

8

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Recent Large-Scale Benchmark Tests

9

Schrodinger’s β€œJACS” set

Francesc SabanΓ©s Zariquiey, AdriΓ‘n PΓ©rez, Maciej Majewski, Emilio Gallicchio, and Gianni De Fabritiis

KRAS and Merck Benchmark Set

Eric Chen, Ana Silvera, Woody Sherman, Yujie Wu, Chuanjie Wu, Huafeng Xu, Emilio Gallicchio

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Statistical View of Computational Alchemy

  • Alchemical potential (linear):

perturbation energy

  • Zwanzig’s FEP formula:

probability density of the perturbation energy in the decoupled ensemble. All follows from it.

Decoupled (vacuum) state

The field is mostly about numerical results. It would benefit from a deeper statistical understanding of alchemical transformations.

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The Beginning: the Binding Energy Distribution Analysis Method

The main idea was to estimate p0(u) using WHAM from histograms of u collected at Ξ»

Binding with implicit solvation

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Early Push toward Free Energy-Based Virtual Screening

pre-GPUs, 285-ligands computational tour de force

HIV integrase

8,000 binding free energy calculations

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Understanding Binding Free Energy Profiles

TI/DDM: Amprenavir Binding to HIV Protease

(receptor decoupling leg)

Nanjie Deng et al., JPC B, 115, 11902 (2011)

​

decoupled

coupled

Lennard-Jones

electrostatic

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Understanding Binding Free Energy Profiles

Alchemical Transfer Method (AToM)

Estrogen Receptor 𝛼

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Understanding Binding Free Energy Profiles

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Linear Response

  • Alper, Howard E. and Ronald M. Levy (1989). Computer simulations of the dielectric properties of water: Studies of the simple point charge and transferrable intermolecular potential models. J. Chem. Phys., 91(2), 1242
  • Levy, Ronald M. and Emilio Gallicchio (1998). Computer Simulations with Explicit Solvent: Recent Progress in the Thermodynamic Decomposition of Free Energies and in Modeling Electrostatic Effects. Annu. Rev. Phys. Chem., 49(1), 531-567.
  • Su, Yang, Emilio Gallicchio, Kalyan Das, Eddy Arnold, and Ronald M. Levy (2007). Linear Interaction Energy (LIE) Models for Ligand Binding in Implicit Solvent: Theory and Application to the Binding of NNRTIs to HIV-1 Reverse Transcriptase. J. Chem. Theory Comput., 3(1), 256-277.

If the medium responds linearly to the introduction of charges

the free energy varies quadratically

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Linear Response: Statistical View

Linear response implies a normal distribution of the electrostatic interaction energy in the absence of charge:

The distribution shifts linearly as the charge of the solute is turned on. The width stays the same

(central limit theorem)

(potential distribution theorem)

The alchemical transformation is fully described by only two parameters:

the mean and variance of p0(u)

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Looking for a General Expression for p0(u)

p0(u) contains all of the information of the alchemical process

It provides both the free energy profile and the binding energy distributions at all Ξ»

Ξ»

p0(u) serves a similar role in alchemical theory as the density of states in standard equilibrium statistical mechanics

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Analytical Model for p0(u): Collision + Attraction

p0(u) results from the convolution of two random processes:

  1. A background attractive interaction energy component uB
  2. A repulsive interaction energy uC whenever atoms collide

The model distinguishes the two contributions using their distinct statistical behaviors:

β€œcollision”: unfavorable energy, dominated by closest interaction:

max statistics

β€œbackground”: weaker interaction energies, sum of many interactions:

central limit statistics

(linear response)

Kilburg & Gallicchio JCTC (2018)

The overall behavior is a convolution of the components:

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20

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Applications: Classification of Binding Modes

Kilburg & Gallicchio JCTC (2018)

AToM-OpenMM

alchemical simulations

TensorFlow

Maximum Likelihood Parameterization

Parameters of p0(u)

u kcal/mol

Solmaz Azimi

POSTER!

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Applications: Optimization of Alchemical Protocols

Pal & Gallicchio JCP (2019); Khuttan, Azimi, Wu, Gallicchio JCP (2021); Wu, Azimi, Khuttan, Gallicchio JCTC (2021)

  • Optimization of alchemical Ξ» schedules
  • Optimization of non-linear alchemical potential functions
  • Optimization of soft-core representations

Avoidance of Alchemical Phase Transitions

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Applications: Modeling Transfer from Double-Decoupling (and viceversa)

Solmaz Azimi

POSTER!

  • Alchemical transfer perturbation energy distributions are the convolution of hydration free energy distributions

: decoupling from solvent

recoupling to receptor:

alchemical transfer:

  • Models for double-decoupling transformations can be used to interpret alchemical transfer perturbation energy distributions
  • Conversely, transfer of a solute from one place to another in the solvent yields information about hydration.

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Conclusions

  • Alchemical processes can be understood in terms of an analytical statistical model
  • The parameters of the model have physical significance
  • The model is useful in practical and theoretical applications
  • The roots of the model trace back to Ron Levy’s pioneering work.

Happy Birthday, Ron!

  • There is a place for analytical and theoretical work in the results-driven world of alchemical free energy modeling.

25 of 25

The Computational Molecular Biophysics Laboratory @BC

25

Sheenam Khuttan

Solmaz Azimi

Joe Wu

Dr. Rajat K. Pal

TandemAI

Current and Former Lab Members

Collaborators

Lauren Wickstrom

Tom Kurtzman

Nanje Deng

Ron Levy

Peter Eastman

Ryan Murelli

Wayne Harding

Software

Gianni De Fabritiis

Eric Chen

Huafeng Xu

Yujie Wu

Woody Sherman

Brian Radak

Sebastian Dick

Chuanjie Wu