FFT-Mutant kit: A novel library for the design of de-novo mutations using mathematical modeling techniques, data mining and machine learning
David Medina, Álvaro Olivera‐Nappa · 2019
Designing mutations to get desirable biological activities is one of the most recurrent problems in biotechnology. Experimental methods imply large time, economic costs and limit the search space for mutants. Computational tools appear as a powerful solution for this challenge. Nevertheless, the latent problem still persists. We propose FFT-Mutant Kit, a novel tool that allows to design mutations from linear sequences, digitizing their physicochemical properties, considering for their evaluation, structural and phylogenetic information and the application of techniques of data mining and pattern recognition. Tool trains models through meta-learning techniques. The descriptors are based on frequency spectra of the protein obtained from the coding of the residues and digitized through Fourier Transforms. Dataset is composed of homolog proteins whose characteristics are known. New mutations and to evaluate their potential effect, physicochemical, thermodynamic and phylogenetic properties are considered as a pre-filter stage. The tool applies trained models to associate mutations and their expected effect on the target variable and the relevant physicochemical properties for describing the suggested mutations. Finally, it is believed that this tool will be a significant contribution when designing mutants with desirable biological activities and a powerful means of studying proteins from the digitalization of their physicochemical properties.