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Because of the complexity of enzyme catalysis, it’s difficult to predict point mutation improving protein activity accurately. How AEMD work?<br> | Because of the complexity of enzyme catalysis, it’s difficult to predict point mutation improving protein activity accurately. How AEMD work?<br> | ||
Firstly,the development team of AEMD recently described a method which is able to identify desired mutations by analyzing the coevolution information of protein sequences (Liu, et al., 2016). In the AEMD-web, some point mutations are suggested by this method. Besides, AEMD’s analysis generated some residues close to active center and transport tunnels which are recommended to saturated mutation to improve activity (Fig. 1C). For the input of target protein sequence, AEMD first obtain the PDB file using RosettaCM (Song, et al., 2013). Next, the substrate of template PDB was mapped into target PDB using the “struct_align” funciton of Schrodinger software (QikProp, 2015). The spatial location of substrate in target PDB can help to determine the ligand-binding pocket of target enzyme. If all potential template PDB had no substrate in the PDB file, AEMD predicted the ligand-binding pocket by a Rosetta script (gen_apo_grids.linuxgccrelease) (Zanghellini, et al., 2006). After the determination of ligand-binding pocket, AEMD generated the possible catalytic sites by search local Catalytic Site Atlas (Furnham, et al., 2014); the residues within 5Å distance from ligands by calculating the minimum distance between residue and substrate; and the residues located within 3 Å distance from transport tunnels by CAVER (Chovancova, et al., 2012).(see the Fig.1 (C)) <br> | Firstly,the development team of AEMD recently described a method which is able to identify desired mutations by analyzing the coevolution information of protein sequences (Liu, et al., 2016). In the AEMD-web, some point mutations are suggested by this method. Besides, AEMD’s analysis generated some residues close to active center and transport tunnels which are recommended to saturated mutation to improve activity (Fig. 1C). For the input of target protein sequence, AEMD first obtain the PDB file using RosettaCM (Song, et al., 2013). Next, the substrate of template PDB was mapped into target PDB using the “struct_align” funciton of Schrodinger software (QikProp, 2015). The spatial location of substrate in target PDB can help to determine the ligand-binding pocket of target enzyme. If all potential template PDB had no substrate in the PDB file, AEMD predicted the ligand-binding pocket by a Rosetta script (gen_apo_grids.linuxgccrelease) (Zanghellini, et al., 2006). After the determination of ligand-binding pocket, AEMD generated the possible catalytic sites by search local Catalytic Site Atlas (Furnham, et al., 2014); the residues within 5Å distance from ligands by calculating the minimum distance between residue and substrate; and the residues located within 3 Å distance from transport tunnels by CAVER (Chovancova, et al., 2012).(see the Fig.1 (C)) <br> | ||
− | We submitted the amino acid sequence and PDB file of ceaS2 online and got the prediction result in half an | + | We submitted the amino acid sequence and PDB file of ceaS2 online and got the prediction result in half an hour.As shown below, you can |
+ | also <a href="https://static.igem.org/mediawiki/2017/5/57/CeaS2_analysis_report.pdf">download PDF version</a>. | ||
+ | <br> | ||
+ | <img src="https://static.igem.org/mediawiki/2017/e/e2/Model_result.png" class="img-responsive"> | ||
+ | <br><br> | ||
<h3 style="text-align:center">Result</h3> | <h3 style="text-align:center">Result</h3> | ||
<br> | <br> |
Revision as of 20:03, 1 November 2017