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Profiles of Natural and Designed Protein-Like Sequences Effectively Bridge Protein Sequence Gaps: Implications in Distant Homology Detection

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Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2449))

Abstract

Sequence-based approaches are fundamental to guide experimental investigations in obtaining structural and/or functional insights into uncharacterized protein families. Powerful profile-based sequence search methods rely on a sequence space continuum to identify non-trivial relationships through homology detection. The computational design of protein-like sequences that serve as “artificial linkers” is useful in identifying relationships between distant members of a structural fold. Such sequences act as intermediates and guide homology searches between distantly related proteins. Here, we describe an approach that represents natural intermediate sequences and designed protein-like sequences as HMM (Hidden Markov Models) profiles, to improve the sensitivity of existing search methods. Searches made within the “Profile database” were shown to recognize the parent structural fold for 90% of the search queries at query coverage better than 60%. For 1040 protein families with no available structure, fold associations were made through searches in the database of natural and designed sequence profiles. Most of the associations were made with the Alpha-alpha superhelix, Transmembrane beta-barrels, TIM barrel, and Immunoglobulin-like beta-sandwich folds. For 11 domain families of unknown functions, we provide confident fold associations using the profiles of designed sequences and a consensus from other fold recognition methods. For two DUFs (Domain families of Unknown Functions), we performed detailed functional annotation through comparisons with characterized templates of families of known function.

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Acknowledgments

This research is supported by Mathematical Biology program and FIST program, sponsored by the Department of Science and Technology and also by the Department of Biotechnology, Government of India, in the form of IISc-DBT partnership program. We also gratefully acknowledge support from Bioinformatics and Computational Biology Centre, funded by DBT and support from UGC, India—Centre for Advanced Studies and Ministry of Human Resource Development, India. NS is a J. C. Bose National Fellow. SS was supported as a post-doctoral fellow by the DBT-IISc partnership program and is currently affiliated with M.S. Ramaiah University of Applied Sciences, Bangalore this paper is dedicated to one of the authors of the paper, Prof N. Srinivasan, who passed away on September 03, 2021.

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  1. Sankaran Sandhya

    Present address: Department of Biotechnology, Faculty of Life and Allied Health Sciences, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India

Authors and Affiliations

  1. Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India

    Gayatri Kumar, Narayanaswamy Srinivasan & Sankaran Sandhya

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  1. Gayatri Kumar
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  2. Narayanaswamy Srinivasan
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Correspondence to Narayanaswamy Srinivasan or Sankaran Sandhya .

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Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Oliviero Carugo

  2. Genome Institute and Bioinformatics Institute, Singapore, Singapore

    Frank Eisenhaber

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Kumar, G., Srinivasan, N., Sandhya, S. (2022). Profiles of Natural and Designed Protein-Like Sequences Effectively Bridge Protein Sequence Gaps: Implications in Distant Homology Detection. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 2449. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2095-3_5

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  • DOI: https://doi.org/10.1007/978-1-0716-2095-3_5

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