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Title: Sign Gradient Descent Algorithms for Kinetostatic Protein Folding
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. KCM-based folding dynamics simulations of the backbone chains of protein molecules demonstrate the effectiveness of the proposed algorithm.  more » « less
Award ID(s):
2153744
PAR ID:
10523223
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3503-3039-7
Page Range / eLocation ID:
1 to 6
Format(s):
Medium: X
Location:
2023 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS 2023)
Sponsoring Org:
National Science Foundation
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