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  • 标题:A Novel Soft Computing Technique For Fusion Protein Structure Prediction
  • 本地全文:下载
  • 作者:P.Bhargavi ; U.Subhashini ; S.Jyothi
  • 期刊名称:Webology
  • 印刷版ISSN:1735-188X
  • 出版年度:2022
  • 卷号:19
  • 期号:2
  • 页码:7685-7696
  • 语种:English
  • 出版社:University of Tehran
  • 摘要:Fusion protein structure prediction is one of the most significant and difficult issues in bioinformatics. Nowadays, structural bioinformatics majorly focuses on predicting the 3D structure of proteins by various test methods, for example, nuclear magnetic resonance (NMR), electron microscopy or X-ray diffraction. In this circumstance, fusion proteins are complex proteins because they have many structural domains. Determining the fusion protein structure utilizing experimental methods is costly because of the costs of NMR, electron microscopy or crystallography and time consumption. Machine learning methods were utilized to address the problem, and they've had a lot of success in this field. However, there is still opportunity for improvement in terms of approaching the limit. This chapter proposes a new technique for fusion protein structure forecast based on an Enhanced Fuzzy Logic (EFL) soft computing technique. Given a protein sequence database, this proposed work has to construct candidates of length equal to the query fusion protein sequence, find a overall figure of fuzzy matching techniques for query with provided fault tolerance, also applying this overall figure of matches to the secondary structure prediction. Experimental outcomes demonstrated that the EFL technique effectively predicts fusion protein structure.
  • 关键词:Peptide;Linker;Linker Flexibility;Fusion protein;Fuzzy logic
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