A protein folding robot driven by a self-taught agent

• DOI: 10.1016/j.biosystems.2020.104315
• Publication Date: 2020-12-29T18:09:27Z
• Authors: Oscar Chang; Fernando A. Gonza...; Luis Zhinin-Vera; Rafael Valencia-R...; Israel Pineda; Antonio Diaz-Barrios
• Keywords: Models, Molecular; 0301 basic medicine; Protein Folding; Systems Analysis; Protein Conformation; Systems Biology; Hemagglutinins, Viral; Robotics; Article; Coronavirus; Machine Learning; Viral Proteins; 03 medical and health sciences; Humans; Computer Simulation; Amino Acid Sequence; Neural Networks, Computer; Viral Fusion Proteins; Algorithms

This paper presents a computer simulation of a virtual robot that behaves as a peptide chain of the Hemagglutinin-Esterase protein (HEs) from human coronavirus. The robot can learn efficient protein folding policies by itself and then use them to solve HEs folding episodes. The proposed robotic unfolded structure inhabits a dynamic environment and is driven by a self-taught neural agent. The neural agent can read sensors and control the angles and interactions between individual amino acids. During the training phase, the agent uses reinforcement learning to explore new folding forms that conduce toward more significant rewards. The memory of the agent is implemented with neural networks. These neural networks are noise-balanced trained to satisfy the look for future conditions required by the Bellman equation. In the operating phase, the components merge into a wise up protein folding robot with look-ahead capacities, which consistently solves a section of the HEs protein.