A protein folding robot driven by a self-taught agent. (March 2021) - Chang, Oscar; Gonzales-Zubiate, Fernando A.; Zhinin-Vera, Luis; Valencia-Ramos, Rafael; Pineda, Israel; Diaz-Barrios, Antonio
Abstract: 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. Graphical abstract: Image 1 Highlights: Reinforcement learning agents learn to fold a section of Hemagglutinin-Esterase . The knowledge about protein folding is stored as a set of connectivity weights. The problem is solved with sparse data, molecular interactions and internal state. The system is scalable to high complexity and parallel computing.