A DNN Biophysics Model with Topological and Electrostatic Features

• URL: http://arxiv.org/abs/2409.03658v1
• Date: Thu, 5 Sep 2024 16:11:40 GMT
• Title: A DNN Biophysics Model with Topological and Electrostatic Features
• Authors: Elyssa Sliheet, Md Abu Talha, Weihua Geng,
• Abstract summary: The model uses multi-scale and uniform topological and electrostatic features generated with protein structural information and force field. The machine learning simulation on over 4000 protein structures shows the efficiency and fidelity of these features. This model shows its potential as a general tool in assisting biophysical properties and function prediction for the broad biomolecules.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this project, we provide a deep-learning neural network (DNN) based biophysics model to predict protein properties. The model uses multi-scale and uniform topological and electrostatic features generated with protein structural information and force field, which governs the molecular mechanics. The topological features are generated using the element specified persistent homology (ESPH) while the electrostatic features are fast computed using a Cartesian treecode. These features are uniform in number for proteins with various sizes thus the broadly available protein structure database can be used in training the network. These features are also multi-scale thus the resolution and computational cost can be balanced by the users. The machine learning simulation on over 4000 protein structures shows the efficiency and fidelity of these features in representing the protein structure and force field for the predication of their biophysical properties such as electrostatic solvation energy. Tests on topological or electrostatic features alone and the combination of both showed the optimal performance when both features are used. This model shows its potential as a general tool in assisting biophysical properties and function prediction for the broad biomolecules using data from both theoretical computing and experiments.

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