New Generation of General AMBER Force Field for Biomedical Research

University Of Pittsburgh At Pittsburgh, Pittsburgh PA

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Abstract

Project Summary Molecular simulation plays an essential role in biochemical and biophysical research. Its major application is to decipher molecular interactions between small molecule ligands and biomolecules, especially protein receptors, so that highly potent agonists or antagonists can be discovered to enhance or eradicate target functions. Despite tremendous efforts spent on development, it is still very challenging to accurately predict protein-ligand binding. To overcome the challenge, we need (1) to develop a high-quality molecular mechanics force field (MMFF) to accurately describe the energetics of the simulation systems, and (2) to apply a sampling strategy which can effectively sample “hidden” events that are coupled with state transitions. The major goal of this project is to develop and test the 3rd generation of the general-purpose AMBER force field of GAFF (GAFF3) to significantly improve the quality of the AMBER small molecule force field. GAFF3 will be critically evaluated in studying biomolecule-ligand interactions using a novel GPU-accelerated 𝜆-dynamics based orthogonal space tempering (OST) algorithm. The advanced sampling technique will guarantee that our macromolecule-ligand binding free energy calculations are not complicated by existing sampling issues so that GAFF3 can be objectively evaluated. The study includes three specific goals: (1) to develop GAFF3 utilizing ABCG2, a new physical charge model which has demonstrated its superior performance in large scale solvation free energy calculations. Large-scale ab initio calculations will be performed for drugs and druglike screening compounds to derive force field parameters; (2) to critically evaluate the GAFF3 performance in studying biomolecule-ligand interactions using both pathway-based and endpoint free energy methods using GPU computing. The OST sampling method will be continuously developed and implemented for this evaluation effort; and (3) to apply a variety of strategies to handle “difficult” molecules identified by us or our users. The strategies will include fine atom typing and introduction of new functional forms. We believe that these efforts will allow GAFF3 to approach the performance limit an additive model could have. The successful pursuit of these research aims will facilitate us to surmount the challenges in accurately modeling protein-ligand and nucleic acid-ligand binding.