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arXiv · 2308.07453

Sign Gradient Descent Algorithms for Kinetostatic Protein Folding

Alireza Mohammadi·Mohammad Al Janaideh
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Abstract

This paper proposes a sign gradient descent (SGD) algorithm for predicting the three-dimensional folded protein molecule structures under the kinetostatic compliance method (KCM). In the KCM framework, which can be used to simulate the range of motion of peptide-based nanorobots/nanomachines, protein molecules are modeled as a large number of rigid nano-linkages that form a kinematic mechanism under motion constraints imposed by chemical bonds while folding under the kinetostatic effect of nonlinear interatomic force fields. In a departure from the conventional successive kinetostatic fold compliance framework, the proposed SGD-based iterative algorithm in this paper results in convergence to the local minima of the free energy of protein molecules corresponding to their final folded conformations in a faster and more robust manner. KCMbased folding dynamics simulations of the backbone chains of protein molecules demonstrate the effectiveness of the proposed algorithm.

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Alireza Mohammadi, Mohammad Al Janaideh. 2023-08-14. Sign Gradient Descent Algorithms for Kinetostatic Protein Folding. https://arxiv.org/abs/2308.07453

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