# Sign Gradient Descent Algorithms for Kinetostatic Protein Folding

arXiv: https://arxiv.org/abs/2308.07453 · HTML: https://mlsanity.com/paper/2308.07453/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2023.

## Built on by (most cited first, 1 of 1)

- [SPGD: Steepest Perturbed Gradient Descent Optimization](https://mlsanity.com/paper/2411.04946/) (2024)

## Abstract (arXiv)

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.