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Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks.

Walsh I, Baù D, Martin AJ, Mooney C, Vullo A, Pollastri G - BMC Struct. Biol. (2009)

Bottom Line: We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates.Furthermore, we test both ab-initio and template-based 8 A predictions on the CASP7 targets using a pre-CASP7 PDB, and find that both predictors are state-of-the-art, with the template-based one far outperforming the best CASP7 systems if templates with sequence identity to the query of 10% or better are available.Although this is not the main focus of this paper we also report on reconstructions of C alpha traces based on both ab initio and template-based 4-class map predictions, showing that the latter are generally more accurate even when homology is dubious.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. ian.walsh@ucd.ie

ABSTRACT

Background: Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3-4 A from the native structure). Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure.

Results: We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that C alpha trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10%) yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 A threshold (as per CASP assessment) from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8 A predictions on the CASP7 targets using a pre-CASP7 PDB, and find that both predictors are state-of-the-art, with the template-based one far outperforming the best CASP7 systems if templates with sequence identity to the query of 10% or better are available. Although this is not the main focus of this paper we also report on reconstructions of C alpha traces based on both ab initio and template-based 4-class map predictions, showing that the latter are generally more accurate even when homology is dubious.

Conclusion: Accurate predictions of multi-class maps may provide valuable constraints for improved ab initio and template-based prediction of protein structures, naturally incorporate multiple templates, and yield state-of-the-art binary maps. Predictions of protein structures and 8 A contact maps based on the multi-class distance map predictors described in this paper are freely available to academic users at the url http://distill.ucd.ie/.

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Related in: MedlinePlus

8 Å prediction for sequence separation of 24 and greater. On the x axis the sequence identity between the query and the best template. The bins' height is proportional to the average F1 for the contact class. Red bins represent ab initio predictions, while blue ones are template-based. Results for sequence separations of 24 and greater.
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Figure 3: 8 Å prediction for sequence separation of 24 and greater. On the x axis the sequence identity between the query and the best template. The bins' height is proportional to the average F1 for the contact class. Red bins represent ab initio predictions, while blue ones are template-based. Results for sequence separations of 24 and greater.

Mentions: If one focusses only on the contact class, and in particular on contacts for sequence separations of [6, 11], [12, 23] and [24, ∞) residues (Figures 1, 2 and 3 report F1, or harmonic mean of Accuracy and Coverage, as a function of template identity for the three sequence separations), 8AI performs slightly better than 8TE if the best template shows a [0,10)% identity to the query, for sequence separations of [6, 11] and [12, 23] residues, but almost identically to 8TE for the largest sequence separation class. It is important to point out that approximately half of all proteins in this identity range have in fact no templates at all. For all other template identity ranges and sequence separations 8TE outperforms 8AI. For template identities of [20,30)% 8TE's F1 is roughly 50% compared to just over 10% for the ab initio predictor.


Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks.

Walsh I, Baù D, Martin AJ, Mooney C, Vullo A, Pollastri G - BMC Struct. Biol. (2009)

8 Å prediction for sequence separation of 24 and greater. On the x axis the sequence identity between the query and the best template. The bins' height is proportional to the average F1 for the contact class. Red bins represent ab initio predictions, while blue ones are template-based. Results for sequence separations of 24 and greater.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654788&req=5

Figure 3: 8 Å prediction for sequence separation of 24 and greater. On the x axis the sequence identity between the query and the best template. The bins' height is proportional to the average F1 for the contact class. Red bins represent ab initio predictions, while blue ones are template-based. Results for sequence separations of 24 and greater.
Mentions: If one focusses only on the contact class, and in particular on contacts for sequence separations of [6, 11], [12, 23] and [24, ∞) residues (Figures 1, 2 and 3 report F1, or harmonic mean of Accuracy and Coverage, as a function of template identity for the three sequence separations), 8AI performs slightly better than 8TE if the best template shows a [0,10)% identity to the query, for sequence separations of [6, 11] and [12, 23] residues, but almost identically to 8TE for the largest sequence separation class. It is important to point out that approximately half of all proteins in this identity range have in fact no templates at all. For all other template identity ranges and sequence separations 8TE outperforms 8AI. For template identities of [20,30)% 8TE's F1 is roughly 50% compared to just over 10% for the ab initio predictor.

Bottom Line: We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates.Furthermore, we test both ab-initio and template-based 8 A predictions on the CASP7 targets using a pre-CASP7 PDB, and find that both predictors are state-of-the-art, with the template-based one far outperforming the best CASP7 systems if templates with sequence identity to the query of 10% or better are available.Although this is not the main focus of this paper we also report on reconstructions of C alpha traces based on both ab initio and template-based 4-class map predictions, showing that the latter are generally more accurate even when homology is dubious.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. ian.walsh@ucd.ie

ABSTRACT

Background: Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3-4 A from the native structure). Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure.

Results: We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that C alpha trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10%) yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 A threshold (as per CASP assessment) from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8 A predictions on the CASP7 targets using a pre-CASP7 PDB, and find that both predictors are state-of-the-art, with the template-based one far outperforming the best CASP7 systems if templates with sequence identity to the query of 10% or better are available. Although this is not the main focus of this paper we also report on reconstructions of C alpha traces based on both ab initio and template-based 4-class map predictions, showing that the latter are generally more accurate even when homology is dubious.

Conclusion: Accurate predictions of multi-class maps may provide valuable constraints for improved ab initio and template-based prediction of protein structures, naturally incorporate multiple templates, and yield state-of-the-art binary maps. Predictions of protein structures and 8 A contact maps based on the multi-class distance map predictors described in this paper are freely available to academic users at the url http://distill.ucd.ie/.

Show MeSH
Related in: MedlinePlus