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Navigating Among Known Structures in Protein Space

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Computational Methods in Protein Evolution

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1851))

Abstract

Present-day protein space is the result of 3.7 billion years of evolution, constrained by the underlying physicochemical qualities of the proteins. It is difficult to differentiate between evolutionary traces and effects of physicochemical constraints. Nonetheless, as a rule of thumb, instances of structural reuse, or focusing on structural similarity, are likely attributable to physicochemical constraints, whereas sequence reuse, or focusing on sequence similarity, may be more indicative of evolutionary relationships. Both types of relationships have been studied and can provide meaningful insights to protein biophysics and evolution, which in turn can lead to better algorithms for protein search, annotation, and maybe even design.

In broad strokes, studies of protein space vary in the entities they represent, the similarity measure comparing these entities, and the representation used. The entities can be, for example, protein chains, domains, supra-domains, or smaller protein sub-parts denoted themes. The measures of similarity between the entities can be based on sequence, structure, function, or any combination of these. The representation can be global, encompassing the whole space, or local, focusing on a particular region surrounding protein(s) of interest. Global representations include lists of grouped proteins, protein networks, and maps. Networks are the abstraction that is derived most directly from the similarity data: each node is the protein entity (e.g., a domain), and edges connect similar domains. Selecting the entities, the similarity measure, and the abstraction are three intertwined decisions: the similarity measures allow us to identify the entities, and the selection of entities influences what is a meaningful similarity measure. Similarly, we seek entities that are related to each other in a way, for which a simple representation describes their relationships succinctly and accurately. This chapter will cover studies that rely on different entities, similarity measures, and a range of representations to better understand protein structure space. Scholars may use publicly available navigators offering a global representation, and in particular the hierarchical classifications SCOP, CATH, and ECOD, or a local representation, which encompass structural alignment algorithms. Alternatively, scholars can configure their own navigator using existing tools. To demonstrate this DIY (do it yourself) approach for navigating in protein space, we investigate substrate-binding proteins. By presenting sequence similarities among this large and diverse protein family as a network, we can infer that one member (pdb ID 4ntl; of yet unknown function) may bind methionine and suggest a putative binding mechanism.

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Notes

  1. 1.

    Notice that the terms used here characterize the similarity measure, not the style of navigation in protein space, to use the same terms as in the Needleman–Wunsch and Smith–Waterman sequence alignment algorithms.

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Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel

    Aya Narunsky & Nir Ben-Tal

  2. Department of Computer Science, University of Haifa, Haifa, Israel

    Rachel Kolodny

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  1. Aya Narunsky
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  2. Nir Ben-Tal
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  3. Rachel Kolodny
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Correspondence to Rachel Kolodny .

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  1. GlaxoSmithKline, Cellzome – a GSK company Meyerhofstrasse 1, Heidelberg, Baden-Württemberg, Germany

    Tobias Sikosek

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Narunsky, A., Ben-Tal, N., Kolodny, R. (2019). Navigating Among Known Structures in Protein Space. In: Sikosek, T. (eds) Computational Methods in Protein Evolution. Methods in Molecular Biology, vol 1851. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8736-8_12

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