Simulating protein-ligand binding with neural network potentials

Lahey, Shae-Lynn (2021) Simulating protein-ligand binding with neural network potentials. Masters thesis, Memorial University of Newfoundland.

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Computational methods have been developed to predict the structures and energetics of protein-ligands complexes. However these methods are limited by the accuracy and transferability of the molecular mechanical (MM) models used to calculate the potential energy. Neural network potentials (NNPs) eliminate the need for parameterization and avoid many of the limiting assumptions of MM models. We evaluated the accuracy of ANI-type NNP models for predicting the potential energy surface of biaryl torsions. The ANI-2X and ANI-1ccX NNPs were found to be more accurate and reliable than popular molecular mechanical models. We then developed a new method where the NNP is used to describe the intramolecular terms of a ligand while a conventional MM model is used to describe the environment. This method was found to be effective for predicting the binding pose of ligands bound to proteins and could be used to calculate the conformational component of the binding energy. We also show that these methods can be used to re�ne low-resolution cryo-EM structures of protein-ligand complexes.

Item Type: Thesis (Masters)
Item ID: 15260
Additional Information: Includes bibliographical references (pages 78-91).
Keywords: neural network, ANI, protein-ligand binding
Department(s): Science, Faculty of > Chemistry
Date: August 2021
Date Type: Submission
Digital Object Identifier (DOI):
Library of Congress Subject Heading: Neural networks (Computer science); Ligands (Biochemistry); Computational chemistry; Chemistry, Physical and theoretical.

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