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Protein remote homology detection combining PCA and multiobjective optimization tools

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Abstract

Protein remote homology detection (PRHD) is one of the intense researched and a complex problem in computational biology. The objective of PRHD is to predict the structural and functional characteristics of un-annotated protein sequences by means of homologies. Two sequences are said to be homologous if there exists a shared ancestry between them. It aims to detect the evolutionary relationships among protein sequences, that are distantly located, via computational techniques. Prediction of functions of protein sequences are computationally intensive. Features extracted from protein sequences are too high and presence of insignificant redundant features degrades the performance of computational model with respect to time and cost. Hence, using Principal Component Analysis (PCA, the high dimensional input matrix was transformed into a lower dimension matrix. In the first stage, for each protein sequence, 531 physico-chemical properties from AAIndex database are considered as features for homology detection. The protein sequences are taken from protein families from UniProtKB database. A set of 185 representative features have been extracted from 531 features using PCA. In the second stage, NSGA II and NSGA III, two powerful optimization techniques are used to efficiently search the non-zero eigen space and retrieve distinguishable eigen vectors. This work is tested on widely used Uniprot and SCOP benchmark datasets. To check the efficiency of both the algorithms, the controlling parameters are kept same and numerical simulations were performed. NSGA-III outperformed NSGA-II in the results.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, International Institute of Information Technology, Odisha, Bhubaneswar, India

    Mukti Routray & Swati Vipsita

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  1. Mukti Routray
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  2. Swati Vipsita
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Correspondence to Mukti Routray.

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Routray, M., Vipsita, S. Protein remote homology detection combining PCA and multiobjective optimization tools. Evol. Intel. 16, 67–76 (2023). https://doi.org/10.1007/s12065-021-00642-6

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  • DOI: https://doi.org/10.1007/s12065-021-00642-6

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