A fast, open source implementation of adaptive biasing potentials uncovers a ligand design strategy for the chromatin regulator BRD4.

In this communication we introduce an efficient implementation of adaptive biasing that greatly improves the speed of free energy computation in molecular dynamics simulations. We investigated the use of accelerated simulations to inform on compound design using a recently reported and clinically relevant inhibitor of the chromatin regulator BRD4 (bromodomain-containing protein 4). Benchmarking on our local compute cluster, our implementation achieves up to 2.5 times more force calls per day than plumed2. Results of five 1 μs-long simulations are presented, which reveal a conformational switch in the BRD4 inhibitor between a binding competent and incompetent state. Stabilization of the switch led to a -3 kcal/mol improvement of absolute binding free energy. These studies suggest an unexplored ligand design principle and offer new actionable hypotheses for medicinal chemistry efforts against this druggable epigenetic target class.

[1]  Volodymyr Babin,et al.  Adaptively biased molecular dynamics for free energy calculations. , 2007, The Journal of chemical physics.

[2]  Bradley M Dickson,et al.  μ-tempered metadynamics: Artifact independent convergence times for wide hills. , 2015, The Journal of chemical physics.

[3]  Daniel Gooch,et al.  Communications of the ACM , 2011, XRDS.

[4]  Xinyi Huang,et al.  Fluorescence polarization competition assay: the range of resolvable inhibitor potency is limited by the affinity of the fluorescent ligand. , 2003, Journal of biomolecular screening.

[5]  G. de Fabritiis,et al.  Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations , 2011, Proceedings of the National Academy of Sciences.

[6]  P. Sandy,et al.  Identification of a Benzoisoxazoloazepine Inhibitor (CPI-0610) of the Bromodomain and Extra-Terminal (BET) Family as a Candidate for Human Clinical Trials. , 2016, Journal of medicinal chemistry.

[7]  Duncan Poole,et al.  Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald. , 2013, Journal of chemical theory and computation.

[8]  M. Artola,et al.  NATURE cHEmIcAL BIOLOGy , 2018 .

[9]  October I.  Physical Review Letters , 2022 .

[10]  Bradley M Dickson.  Approaching a parameter-free metadynamics. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Albert C. Pan,et al.  Pathway and mechanism of drug binding to G-protein-coupled receptors , 2011, Proceedings of the National Academy of Sciences.

[12]  Jeffrey K. Hollingsworth.  Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis , 2017, SC.

[13]  R. Williams,et al.  Journal of American Chemical Society , 1979 .

[14]  Wayne A Hendrickson,et al.  What is 'current opinion' in structural biology? , 2011, Current opinion in structural biology.

[15]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

[16]  VINCENT ZOETE,et al.  SwissParam: A fast force field generation tool for small organic molecules , 2011, J. Comput. Chem..

[17]  Eric T. Kim,et al.  How does a drug molecule find its target binding site? , 2011, Journal of the American Chemical Society.

[18]  Joseph A Morrone,et al.  Advances in free-energy-based simulations of protein folding and ligand binding. , 2016, Current opinion in structural biology.

[19]  R. Copeland.  The drug–target residence time model: a 10-year retrospective , 2015, Nature Reviews Drug Discovery.

[20]  Matthieu Hamel,et al.  Journal of Medicinal Chemistry , 2010 .

[21]  M. Parrinello,et al.  Well-tempered metadynamics: a smoothly converging and tunable free-energy method. , 2008, Physical review letters.

[22]  Michele Parrinello,et al.  Well-tempered metadynamics converges asymptotically. , 2014, Physical review letters.

[23]  Ross C. Walker,et al.  An overview of the Amber biomolecular simulation package , 2013 .

[24]  Juan de Pablo,et al.  A boundary correction algorithm for metadynamics in multiple dimensions. , 2013, The Journal of chemical physics.

[25]  S. Frye,et al.  The structure-activity relationships of L3MBTL3 inhibitors: flexibility of the dimer interface. , 2013, MedChemComm.

[26]  J. Herskowitz,et al.  Proceedings of the National Academy of Sciences, USA , 1996, Current Biology.

[27]  M. Parrinello,et al.  Funnel metadynamics as accurate binding free-energy method , 2013, Proceedings of the National Academy of Sciences.

[28]  J. P. Grossman,et al.  Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer , 2014, SC14: International Conference for High Performance Computing, Networking, Storage and Analysis.

[29]  O. Bagasra,et al.  Proceedings of the National Academy of Sciences , 1914, Science.

[30]  M. Plotkin.  Nature as medicine. , 2005, Explore.

[31]  Rui Sun,et al.  Exploring Valleys without Climbing Every Peak: More Efficient and Forgiving Metabasin Metadynamics via Robust On-the-Fly Bias Domain Restriction , 2015, Journal of chemical theory and computation.

[32]  Massimiliano Bonomi,et al.  PLUMED 2: New feathers for an old bird , 2013, Comput. Phys. Commun..

[33]  Carsten Kutzner,et al.  GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. , 2008, Journal of chemical theory and computation.

[34]  A. Chaidos,et al.  Inhibition of bromodomain and extra-terminal proteins (BET) as a potential therapeutic approach in haematological malignancies: emerging preclinical and clinical evidence , 2015, Therapeutic advances in hematology.

[35]  Nancy Cheng,et al.  A cellular chemical probe targeting the chromodomains of Polycomb Repressive Complex 1 , 2015, Nature chemical biology.

[36]  Journal of Chemical Physics , 1932, Nature.

[37]  M. Moyer.  First drugs found to inhibit elusive cancer target , 2011, Nature Medicine.