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Optimizing structural modeling for a specific protein scaffold: knottins or inhibitor cystine knots.

Gracy J, Chiche L - BMC Bioinformatics (2010)

Bottom Line: This important variability is likely to arise from the highly diverse loops which connect the successive knotted cysteines.These average model deviations represent an improvement varying between 0.74 and 1.17 Å over a basic homology modeling derived from a unique template.In particular, we have shown that the accuracy of the models constructed at a low level of sequence identity can be improved by 1) a careful optimization of the modeling procedure, 2) the combination of multiple structural templates and 3) the use of conserved structural features as modeling restraints.

View Article: PubMed Central - HTML - PubMed

Affiliation: CNRS, UMR5048, Université Montpellier 1 et 2, Centre de Biochimie Structurale, 34090 Montpellier, France. Jerome.Gracy@cbs.cnrs.fr

ABSTRACT

Background: Knottins are small, diverse and stable proteins with important drug design potential. They can be classified in 30 families which cover a wide range of sequences (1621 sequenced), three-dimensional structures (155 solved) and functions (> 10). Inter knottin similarity lies mainly between 15% and 40% sequence identity and 1.5 to 4.5 Å backbone deviations although they all share a tightly knotted disulfide core. This important variability is likely to arise from the highly diverse loops which connect the successive knotted cysteines. The prediction of structural models for all knottin sequences would open new directions for the analysis of interaction sites and to provide a better understanding of the structural and functional organization of proteins sharing this scaffold.

Results: We have designed an automated modeling procedure for predicting the three-dimensionnal structure of knottins. The different steps of the homology modeling pipeline were carefully optimized relatively to a test set of knottins with known structures: template selection and alignment, extraction of structural constraints and model building, model evaluation and refinement. After optimization, the accuracy of predicted models was shown to lie between 1.50 and 1.96 Å from native structures at 50% and 10% maximum sequence identity levels, respectively. These average model deviations represent an improvement varying between 0.74 and 1.17 Å over a basic homology modeling derived from a unique template. A database of 1621 structural models for all known knottin sequences was generated and is freely accessible from our web server at http://knottin.cbs.cnrs.fr. Models can also be interactively constructed from any knottin sequence using the structure prediction module Knoter1D3D available from our protein analysis toolkit PAT at http://pat.cbs.cnrs.fr.

Conclusions: This work explores different directions for a systematic homology modeling of a diverse family of protein sequences. In particular, we have shown that the accuracy of the models constructed at a low level of sequence identity can be improved by 1) a careful optimization of the modeling procedure, 2) the combination of multiple structural templates and 3) the use of conserved structural features as modeling restraints.

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Main chain RMSD versus sequence identity percentage for all knottin structure pairs from the PDB.
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Figure 2: Main chain RMSD versus sequence identity percentage for all knottin structure pairs from the PDB.

Mentions: Figures 2 and 3 display sequence identity distributions over the whole knottin data set. Figure 2 indicates that the vast majority of known structure pairs share between 15% and 40% sequence identity (87% of all pairs) and 1.5 to 4.5 Å backbone deviation after geometrical superposition (90% of all pairs). This low level of average similarity clearly demonstrates the sequential and structural variability of the knottin superfamily. Knottins are indeed very diverse small proteins and the structural core of the whole family is actually limited to a few residues around the three knotted disulfide bridges.


Optimizing structural modeling for a specific protein scaffold: knottins or inhibitor cystine knots.

Gracy J, Chiche L - BMC Bioinformatics (2010)

Main chain RMSD versus sequence identity percentage for all knottin structure pairs from the PDB.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2984590&req=5

Figure 2: Main chain RMSD versus sequence identity percentage for all knottin structure pairs from the PDB.
Mentions: Figures 2 and 3 display sequence identity distributions over the whole knottin data set. Figure 2 indicates that the vast majority of known structure pairs share between 15% and 40% sequence identity (87% of all pairs) and 1.5 to 4.5 Å backbone deviation after geometrical superposition (90% of all pairs). This low level of average similarity clearly demonstrates the sequential and structural variability of the knottin superfamily. Knottins are indeed very diverse small proteins and the structural core of the whole family is actually limited to a few residues around the three knotted disulfide bridges.

Bottom Line: This important variability is likely to arise from the highly diverse loops which connect the successive knotted cysteines.These average model deviations represent an improvement varying between 0.74 and 1.17 Å over a basic homology modeling derived from a unique template.In particular, we have shown that the accuracy of the models constructed at a low level of sequence identity can be improved by 1) a careful optimization of the modeling procedure, 2) the combination of multiple structural templates and 3) the use of conserved structural features as modeling restraints.

View Article: PubMed Central - HTML - PubMed

Affiliation: CNRS, UMR5048, Université Montpellier 1 et 2, Centre de Biochimie Structurale, 34090 Montpellier, France. Jerome.Gracy@cbs.cnrs.fr

ABSTRACT

Background: Knottins are small, diverse and stable proteins with important drug design potential. They can be classified in 30 families which cover a wide range of sequences (1621 sequenced), three-dimensional structures (155 solved) and functions (> 10). Inter knottin similarity lies mainly between 15% and 40% sequence identity and 1.5 to 4.5 Å backbone deviations although they all share a tightly knotted disulfide core. This important variability is likely to arise from the highly diverse loops which connect the successive knotted cysteines. The prediction of structural models for all knottin sequences would open new directions for the analysis of interaction sites and to provide a better understanding of the structural and functional organization of proteins sharing this scaffold.

Results: We have designed an automated modeling procedure for predicting the three-dimensionnal structure of knottins. The different steps of the homology modeling pipeline were carefully optimized relatively to a test set of knottins with known structures: template selection and alignment, extraction of structural constraints and model building, model evaluation and refinement. After optimization, the accuracy of predicted models was shown to lie between 1.50 and 1.96 Å from native structures at 50% and 10% maximum sequence identity levels, respectively. These average model deviations represent an improvement varying between 0.74 and 1.17 Å over a basic homology modeling derived from a unique template. A database of 1621 structural models for all known knottin sequences was generated and is freely accessible from our web server at http://knottin.cbs.cnrs.fr. Models can also be interactively constructed from any knottin sequence using the structure prediction module Knoter1D3D available from our protein analysis toolkit PAT at http://pat.cbs.cnrs.fr.

Conclusions: This work explores different directions for a systematic homology modeling of a diverse family of protein sequences. In particular, we have shown that the accuracy of the models constructed at a low level of sequence identity can be improved by 1) a careful optimization of the modeling procedure, 2) the combination of multiple structural templates and 3) the use of conserved structural features as modeling restraints.

Show MeSH