Next-generation integrated quantum force ?elds for biomedical applications PI: Darrin M. York, Rutgers University, Piscataway, NJ 08854-8087 USA. We have recently developed novel framework for next-generation quantum mechanical force ?elds (QMFFs) designed to meet the challenges of biomolecular simulations and drug discovery applications. QMFFs have tremendous computational advantages relative to their fully QM counterparts, being inherently parallelizable and linearly scaling, offering tremendous computational speedup, and promising quantitative accuracy potentially superior to full QM methods. QMFFs accurately model multipolar electrostatics, charge penetration effects, and non-linear polarization response. QMFFs thus offer a transformative technology for drug discovery applications, in particular, for advancing the predictive capability of free energy simulations in lead re?nement. These are critically important for the diverse chemical space of drug molecules, including halogen bonding, cation- and metal-ligand interactions. Further, QMFFs offer a mechanism for modeling covalent inhibitors. Speci?cally, we propose to: I. Develop new QMFFs for drug discovery. QMFFs will be developed based on both semiempirical and ab initio density-functional methods in the following stages: 1) determination of multipolar mapping parameters enhancing the DFTB electrostatic potential to reach greater accuracy, 2) augmentation of electronic response terms using chemical potential equalization (CPE) corrections using an orthogonal perturbation-response approach to solve the under-polarization problem of DFTB methods, 3) parameterization of non-electrostatic non-bonded interac- tion parameters using realistic potentials that capture many-body exchange and dispersion interactions, and 4) exploration of statistical potentials, using machine learning approaches applied to quantum data sets, to correct internal conformational energies and short-range interactions. II. Develop new free energy methods to enable protein-ligand binding predictions using QMFFs. We will develop a novel integrated free energy pipeline to pre- dict alchemical binding free energies for ligands and inhibitors. This will include new GPU-accelerated methods for -space self-adaptive mixture sampling ( -SAMS) and 2D-vFEP analysis, coupled with conformational space enhanced sampling methods for alchemical steps of the thermodynamic cycle, and advancements in free en- ergy ?book-ending? methods (BBQm) to ef?ciently connect molecular mechanical force ?eld and QMFF model representations. III. Test and validate QMFFs and free energy methods, and apply to MIF inhibitor binding. The methods will be broadly tested against established data sets for solvation free energies, and a drug discovery data set. More in-depth validation studies will be conducted by examining the relative binding free energies of inhibitors of the macrophage inhibitory factor (MIF). Finally, exploratory applications will examine mechanisms, characterize transition states and predict rates for covalent inhibition for a series of MIF inhibitors.

Public Health Relevance

Next-generation integrated quantum force ?elds for biomedical applications PI: Darrin M. York, Laboratory for Biomolecular Simulation Research, Rutgers University, Piscat- away, NJ 08854-8087 USA. We propose a novel strategy to develop a class of integrated quantum mechanical force ?elds (QMFFs) that create highly accurate physical models for complex biomolecular simulations. Com- bined with recently developed high-precision free energy simulation and analysis tools, the pro- posed QMFFs will overcome current critical barriers to progress for drug discovery and deliver accurate and precise predictions for drug binding af?nity to enhance lead optimization. The pro- posed work will be applied to understand ligand-protein binding in the macrophage inhibitory factor (MIF), and provide insight that may guide the design of new non-covalent and targeted covalent inhibitors to MIF and other systems.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM107485-06
Application #
10005389
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Lyster, Peter
Project Start
2015-08-01
Project End
2023-06-30
Budget Start
2020-07-01
Budget End
2021-06-30
Support Year
6
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Rutgers University
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
001912864
City
Piscataway
State
NJ
Country
United States
Zip Code
08854
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Lee, Tai-Sung; Cerutti, David S; Mermelstein, Dan et al. (2018) GPU-Accelerated Molecular Dynamics and Free Energy Methods in Amber18: Performance Enhancements and New Features. J Chem Inf Model 58:2043-2050
Giese, Timothy J; York, Darrin M (2018) A GPU-Accelerated Parameter Interpolation Thermodynamic Integration Free Energy Method. J Chem Theory Comput 14:1564-1582
Lee, Tai-Sung; Hu, Yuan; Sherborne, Brad et al. (2017) Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration. J Chem Theory Comput 13:3077-3084
Huang, Ming; Dissanayake, Thakshila; Kuechler, Erich et al. (2017) A Multidimensional B-Spline Correction for Accurate Modeling Sugar Puckering in QM/MM Simulations. J Chem Theory Comput 13:3975-3984
Giese, Timothy J; York, Darrin M (2017) Quantum mechanical force fields for condensed phase molecular simulations. J Phys Condens Matter 29:383002
Kuechler, Erich R; Giese, Timothy J; York, Darrin M (2016) VR-SCOSMO: A smooth conductor-like screening model with charge-dependent radii for modeling chemical reactions. J Chem Phys 144:164115
Giese, Timothy J; York, Darrin M (2016) Ambient-Potential Composite Ewald Method for ab Initio Quantum Mechanical/Molecular Mechanical Molecular Dynamics Simulation. J Chem Theory Comput 12:2611-32
Hu, Yuan; Sherborne, Brad; Lee, Tai-Sung et al. (2016) The importance of protonation and tautomerization in relative binding affinity prediction: a comparison of AMBER TI and Schrödinger FEP. J Comput Aided Mol Des 30:533-9

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