Abstract
Owing to its clinical significance, modulation of functionally relevant amino acids in protein-protein complexes has attracted a great deal of attention. To this end, many approaches have been proposed to predict the partner-selecting amino acid positions in evolutionarily close complexes. These approaches can be grouped into sequence-based machine learning and structure-based energy-driven methods. In this work, we assessed these methods’ ability to map the specificity-determining positions of Axl, a receptor tyrosine kinase involved in cancer progression and immune system diseases. For sequence-based predictions, we used SDPpred, Multi-RELIEF, and Sequence Harmony. For structure-based predictions, we utilized HADDOCK refinement and molecular dynamics simulations. As a result, we observed that (i) sequence-based methods overpredict partner-selecting residues of Axl and that (ii) combining Multi-RELIEF with HADDOCK-based predictions provides the key Axl residues, covered by the extensive molecular dynamics simulations. Expanding on these results, we propose that a sequence-structure-based approach is necessary to determine specificity-determining positions of Axl, which can guide the development of therapeutic molecules to combat Axl misregulation.
Highlights
The functional identification of proteins is essential to understand the grounds of innate cellular processes
In the Axl:growth arrest-specific protein 6 (Gas6) structure, two Ig-like domains of Axl interact with two laminin G (LG)-like domains of Gas6, without involving any receptor-receptor or ligandligand interactions (Figure 1B)
Axl and Gas6 interact through two symmetric copies of major and minor interfaces, burying 2366 Å2 and 765 Å2 surface areas, respectively (Figures 1B,C)
Summary
The functional identification of proteins is essential to understand the grounds of innate cellular processes. Several computational tools have been deployed to annotate protein function from everaccumulating protein sequences (Friedberg, 2006). These approaches aim to define functionally important residues through comparative sequence analysis (Whisstock and Lesk, 2004). Resolving the functionally key amino acids is interesting, as modulation of these residues holds a great potential to design protein-based therapeutics (Moll et al, 2016). Such key amino acids can be identified upon searching for conserved positions across different species. Some mutations are evolved to act as specificity-determining positions
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