A Novel Population-based Optimization for Multiple Sequence Alignment in Protein Sequencing

Abstract

This paper proposes a novel Population-based optimization (PBO) approach to use in conjunction with an upgraded migration operator. It can tackle multiple sequence alignment (MSA) problems which can be implemented to solve the protein sequences problem. In the present state of development, it is a unique population-based algorithm based on genetic algorithms. Further, the lack of population variety and the sluggish convergence pace can be ascribed to the lack of population variety. This improved migration operator absorbs more information from various habitat types, maintains population variation, and maintains its ability to exploit new possibilities. The performance of the proposed approach has been compared to that of many current techniques, including VDGA, MOMSA, and GAPAM, using publicly available benchmark datasets, with the results showing that it outperforms them all (such as the Bali base). It has been observed that in most cases, the suggested approach outperforms and/or is competitive with the existing techniques.

Polymer-ceramic composites for bone challenging applications: Materials and manufacturing processes

“…They can be derived from various biological sources, including meat, dairy, and plant-based foods. [28][29][30][31] Collagen, for instance, is a crucial natural biopolymer found in animal connective tissues, contributing to the structural support and elasticity of tissues such as skin, bone, and cartilage. 32 Cellulose, on the other hand, is a key biopolymer present in plant cell walls, providing structural support and protection to plant cells.…”

Enhanced Genetic Method for Optimizing Multiple Sequence Alignment

“…MSA can be achieved globally [7], where the similarity over the entire sequence length is generally considered, or locally [8], in which the local best-scoring parts of similar characters are considered. An alignment generally considers scoring functions to measure the alignment quality [9,10]. However, in MSA, it is a challenging task to identify an optimum scoring function since the statistically optimized functions are not biologically optimal [11].…”