Zebra2: advanced and easy-to-use web-server for bioinformatic analysis of subfamily-specific and conserved positions in diverse protein superfamilies

Abstract

Zebra2 is a highly automated web-tool to search for subfamily-specific and conserved positions (i.e. the determinants of functional diversity as well as the key catalytic and structural residues) in protein superfamilies. The bioinformatic analysis is facilitated by Mustguseal—a companion web-server to automatically collect and superimpose a large representative set of functionally diverse homologs with high structure similarity but low sequence identity to the selected query protein. The results are automatically prioritized and provided at four information levels to facilitate the knowledge-driven expert selection of the most promising positions on-line: as a sequence similarity network; interfaces to sequence-based and 3D-structure-based analysis of conservation and variability; and accompanied by the detailed annotation of proteins accumulated from the integrated databases with links to the external resources. The integration of Zebra2 and Mustguseal web-tools provides the first of its kind out-of-the-box open-access solution to conduct a systematic analysis of evolutionarily related proteins implementing different functions within a shared 3D-structure of the superfamily, determine common and specific patterns of function-associated local structural elements, assist to select hot-spots for rational design and to prepare focused libraries for directed evolution. The web-servers are free and open to all users at https://biokinet.belozersky.msu.ru/zebra2, no login required.

Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies

“…Finally, it can be noted that Zebra3D is not a competitor to sequence-based strategies to identify SSPs/SDPs. The output of Zebra3D and Zebra2 [14] was qualitatively different even when the same alignments from the case-studies were used as input. This was due to the fact that the two bioinformatic tools are focused on mutually exclusive parts of the alignment: sequence-based methods assess specificity in structurally similar regions (i.e.…”

“…The superimposed 3D-entries and corresponding sequence representation of alignment in FASTA format can be used as input to a local installation of Zebra3D to further improve bioinformatic analysis by fine-tuning the parameters. The Mustguseal protocol [39] , a step-by-step practical guide to its use and parameter selection [52] , as well as a discussion of various case-studies [14] , [41] , [53] , [54] , [55] are available in our recent publications. An illustrated user guide for the input preparation is available on-line at https://biokinet.belozersky.msu.ru/Zebra3D-input .…”

“…This was due to the fact that the two bioinformatic tools are focused on mutually exclusive parts of the alignment: sequence-based methods assess specificity in structurally similar regions (i.e. columns with a low content of gaps [11] , [13] , [14] ) while 3D-specificity is a characteristic of regions that feature high structural diversity usually leading to a high content of gaps ( Fig. 1 ).…”

“…Interest in the analysis of functionally important specific positions is a long-standing trend in computational biology: the concept was introduced in late 1990s [9] , [10] , the first systematic approach to identify such positions/residues in protein sequences was published in 2002 [11] , followed by a variety of improvements to increase the accuracy of predictions and facilitate the ease-of-use in the daily routine [3] , [5] , [7] , [12] , [13] . In particular, the original Zebra/Zebra2 approach [14] to identify SSPs/SDPs in multiple sequence alignments was recognized as a tool [15] , [16] to help studying structure–function relationship in protein superfamilies [17] , [18] , [19] , and used to assist experimental design of improved enzymes [20] and ligand binding specificity [21] .…”

“…With the growing interest in 3D-structure analysis in the last few years, it became increasingly common to use 3D-record(s) of reference protein(s) to assist bioinformatic analysis, e.g. to map sequence alignment statistics onto such 3D-reference(s) to visualize results, or to improve accuracy of corresponding predictions by considering spatial features of a representative/query protein [14] , [41] , [42] , [43] . These tools are interesting and practically useful, but in most cases consideration of 3D-data is focused on just one homolog or serves supplementary/illustrative purposes.…”