ACGA a Novel Biomimetic Hybrid Optimisation Algorithm based on a HP Protein Visualizer: An Interpretable Web-Based Tool for 3D Protein Folding based on the Hydrophobic-Polar Model

In this study, we used the Hydrophobic-Polar (HP) two-dimensional square and three-dimensional cubic lattice models for the problem of Protein structure prediction (PSP). This kind of lattice reduces computational time and calculations, and the conformational space from 9n to 4n−2, even in this context, it is an appropriate benchmark problem for genetic algorithms. The contributions of the paper consist in: (1) implementation of a high-performing novel Genetic algorithm (GA); instead of considering only the self- avoiding walk (SAW) conformations approached in other work, we decided to allow any conformation to appear in the population at all stages of the proposed all conformations biomimetic Genetic Algorithm (ACGA). This increases the probability of achieving good native conformations (SAW ones), with the lowest energy that has the minimum number of contacts in the lattice, the one with the maximum topological neighbors. In addition to classical crossover and mutation operators, (2) we introduced specific translation operators for these two operations. We have proposed and implemented an HP Protein Visualizer tool which offers interpretability, a hybrid approach in that the visualizer give some insight to the algorithm, that analyse and optimise protein structures HP model. The program resulted based on performed research, provides a molecular modeling tool for studying protein folding using technologies such as React, Next.js, and Three.js for 3D rendering, and includes optimization algorithms to simulate protein folding.