Prediction of Protein Folding Pathways under Entropy-Loss Constraints using Quadratic Programming-Based Nonlinear Control - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Prediction of Protein Folding Pathways under Entropy-Loss Constraints using Quadratic Programming-Based Nonlinear Control

Résumé

This paper investigates the problem of prediction of protein molecule folding pathways under entropy-loss constraints by formulating a control synthesis problem whose solutions are obtained by solving large-scale quadratic programming (QP) optimizations with nonlinear constraints. The utilized non-iterative and computationally efficient algorithm, which is based on solving generalized eigenvalue problems, prevents an unpredictable and potentially large number of iterations at each protein conformation for computing the folding control inputs. The synthesized control inputs remain close to the renowned kinetostatic compliance method (KCM) reference vector field while satisfying proper quadratic inequality constraints that limit the rate of molecule entropy-loss during folding.
Fichier principal
Vignette du fichier
ACC2023_MohammadiSpong.pdf (738.99 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04176926 , version 1 (03-08-2023)

Identifiants

  • HAL Id : hal-04176926 , version 1

Citer

Alireza Mohammadi, Mark W Spong. Prediction of Protein Folding Pathways under Entropy-Loss Constraints using Quadratic Programming-Based Nonlinear Control. 2023 American Control Conference, May 2023, San Diego (California), United States. ⟨hal-04176926⟩

Collections

TDS-MACS
41 Consultations
45 Téléchargements

Partager

Gmail Facebook X LinkedIn More