A novel optimization algorithm (Lion-AYAD) to find optimal DNA protein synthesis

In this paper, we present a new algorithm to find the optimal proteins generated through DNA synthesis. The algorithm executes in five stages: in the first stage, it takes a DNA sequences and consider it as the initial populations of lions, determined the main positions of each lion and the main distances among lions and goal point then consider this distance as fitness of that lions, after that sort the lions based on their fitness to preparing it to the second stage. The second stage develops lion optimization algorithm (LOA) by adding four new features on it, each feature performance one task, a replacing the kernel of LOA (i.e., searching machnizam) by spirally searching & Bubble net searching to increase the accuracy, at the same time reduce the execution time to reach of the goal achieve by A Smart feature. The main purpose of the third stage is determining lion active or more yauld where each lion in population need update the positions and fitness after each move in searching space to reach of their goal., this achieved through Yauld feature. The fourth stage applies the Cooperative features to convert the active sequence of DNA (i.e., Yauld lion) into mRNA after that built tRNA from it after splitting it into triplet to start to generate the proteins. Synthesis of all triplet of tRNA to generated final proteins result by new optimization algorithm achieved based on deep composite that satisfies the four rules, this feature called Deep feature and represent the final stage of the algorithm. The new algorithm appears as a pragmatic optimization model, it proves their robust to work with dynamic length of DNA sequence. It increases accuracy and reduces execution times. (C) 2022 THE AUTHORS. Published by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University.

Automated summary

This paper presents a new algorithm for finding optimal proteins generated through DNA synthesis. The algorithm consists of five stages, incorporating features such as spiral searching and bubble net searching to enhance accuracy and reduce execution time. It successfully achieves protein synthesis based on deep composite and demonstrates robustness in handling dynamic DNA sequence lengths.