This project proposes to develop computational techniques for drug discovery based on accurate prediction of ligand-protein interactions using a novel polarizable potential energy function. The ability to predict binding affinity and specificity of a ligand to a macromolecular target is the key to structure-based drug discovery in silico. Currently available approaches are seriously limited by the quality of the underlying potential energy function that describes molecular interactions. Our goal is to achieve chemical accuracy in predicting ligand- protein binding by combining accurate physical based potentials with efficient computational algorithms. We will utilize and optimize a novel potential energy function with explicit polarization and atomic multipole electrostatics for predicting ligand-protein binding. We will develop a range of computational models and programs based on this polarizable potential function for lead discovery, including a drug-like fragment library prepared with atomic polarizability and multipole parameters;a program to automate ligand parameterization;a generalized Kirkwood implicit solvation model for polarizable atomic multipoles that can be applied to virtual screening of ligand library;a multiscale hybrid model that combines polarizable and fixed charge potential model for ligand-protein binding calculations;and parallel simulation programs to compute ligand-protein binding free energy with molecular dynamics. We will focus on metalloproteins that present a particular challenge for current approaches. Validation will be made via direct comparison to experimental binding affinities and structures of well studied systems including trypsin and matrix metalloproteinases. Our ultimate objectives are to develop efficient computational technology and software tools that enable computational drug discovery with chemical accuracy, and at the same time to advance our understanding of physical and chemical principles in molecular recognition. Computational models and algorithms developed in this work will also empower the broader molecular modeling community with a next-generation research tool. The resulting programs and parameters will be compatible with the existing drug discovery and molecular modeling software packages including TINKER, AMBER and AutoDock while documented source codes will be made freely available to the public.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM079686-05
Application #
8119763
Study Section
Special Emphasis Panel (ZRG1-BCMB-N (90))
Program Officer
Lyster, Peter
Project Start
2007-09-01
Project End
2013-07-31
Budget Start
2011-08-01
Budget End
2013-07-31
Support Year
5
Fiscal Year
2011
Total Cost
$216,602
Indirect Cost
Name
University of Texas Austin
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
170230239
City
Austin
State
TX
Country
United States
Zip Code
78712
Cisneros, G Andrés; Karttunen, Mikko; Ren, Pengyu et al. (2014) Classical electrostatics for biomolecular simulations. Chem Rev 114:779-814
Wu, Xue; Shi, Yue; Ren, Pengyu et al. (2013) Exploring the relationship between sequences, structures, dynamical behaviors and functions of new type protein drugs: DARPins. Curr Pharm Des 19:2308-17
Shi, Yue; Xia, Zhen; Zhang, Jiajing et al. (2013) The Polarizable Atomic Multipole-based AMOEBA Force Field for Proteins. J Chem Theory Comput 9:4046-4063
Fu, Ting; Ren, Hong; Zhang, Jiajing et al. (2013) Role of bivalent cations in structural stabilities of new drug targets--vaccinia-related kinases (VRK) from molecular dynamics simulations. Curr Pharm Des 19:2269-81
Zhang, Jiajing; Yang, Wei; Piquemal, Jean-Philip et al. (2012) Modeling Structural Coordination and Ligand Binding in Zinc Proteins with a Polarizable Potential. J Chem Theory Comput 8:1314-1324
Shi, Yue; Zhu, Crystal Z; Martin, Stephen F et al. (2012) Probing the effect of conformational constraint on phosphorylated ligand binding to an SH2 domain using polarizable force field simulations. J Phys Chem B 116:1716-27
Xiong, Rui; Cai, Xiao-Mei; Wei, Jing et al. (2012) Some insights into the binding mechanism of Aurora B kinase gained by molecular dynamics simulation. J Mol Model 18:4591-601
Schnieders, Michael J; Baltrusaitis, Jonas; Shi, Yue et al. (2012) The Structure, Thermodynamics and Solubility of Organic Crystals from Simulation with a Polarizable Force Field. J Chem Theory Comput 8:1721-1736
Wu, Johnny C; Chattree, Gaurav; Ren, Pengyu (2012) Automation of AMOEBA polarizable force field parameterization for small molecules. Theor Chem Acc 131:1138
Kaoud, Tamer S; Park, Heekwang; Mitra, Shreya et al. (2012) Manipulating JNK signaling with (--)-zuonin A. ACS Chem Biol 7:1873-83

Showing the most recent 10 out of 34 publications