Limits...
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/.

Show MeSH

Related in: MedlinePlus

Quality of 3D models from 4-class maps vs. TM-score of the best template. On the x axis is the fraction of residues in the query which are within 5 Å of the template. The bins' height is proportional to the average fraction of residues in either the 3D model (red bins) or the best template (blue bins) that are within 5 Å of the native structure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2654788&req=5

Figure 8: Quality of 3D models from 4-class maps vs. TM-score of the best template. On the x axis is the fraction of residues in the query which are within 5 Å of the template. The bins' height is proportional to the average fraction of residues in either the 3D model (red bins) or the best template (blue bins) that are within 5 Å of the native structure.

Mentions: In Figure 8 we report the quality of reconstructions as a function of the TM-score between the best template and the query. We measure quality as the fraction of the native structure's residues that are modelled within 5 Å. When the template is perfect to near-perfect (TM-score above 0.7) the reconstruction from the map is, on average, very slighly worse (-1%) than the template. This is not surprising, as even from native maps model quality levels off at a TM-score of 0.83. When the TM-score between the best template and the native structure is between 0.4 and 0.7, models built from 4-class maps are slightly better than the templates (covering 4% more residues within 5 Å), and substantially better (+17%) when the best template has a TM-score under 0.4. If instead of residue coverage at 5 Å we measure the TM-score of the model and of the best template vs. the native structure (only focussing on the area covered by the best template) we obtain broadly similar results, but slightly less favourable for the models, with results now undistinguishable in the 0.4–0.7 region and still slightly worse in the 0.7–1 one. We can broadly conclude that, if good templates are available, reconstructions from 4-class maps are only about as good as them in the area covered by the template.


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)

Quality of 3D models from 4-class maps vs. TM-score of the best template. On the x axis is the fraction of residues in the query which are within 5 Å of the template. The bins' height is proportional to the average fraction of residues in either the 3D model (red bins) or the best template (blue bins) that are within 5 Å of the native structure.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Quality of 3D models from 4-class maps vs. TM-score of the best template. On the x axis is the fraction of residues in the query which are within 5 Å of the template. The bins' height is proportional to the average fraction of residues in either the 3D model (red bins) or the best template (blue bins) that are within 5 Å of the native structure.
Mentions: In Figure 8 we report the quality of reconstructions as a function of the TM-score between the best template and the query. We measure quality as the fraction of the native structure's residues that are modelled within 5 Å. When the template is perfect to near-perfect (TM-score above 0.7) the reconstruction from the map is, on average, very slighly worse (-1%) than the template. This is not surprising, as even from native maps model quality levels off at a TM-score of 0.83. When the TM-score between the best template and the native structure is between 0.4 and 0.7, models built from 4-class maps are slightly better than the templates (covering 4% more residues within 5 Å), and substantially better (+17%) when the best template has a TM-score under 0.4. If instead of residue coverage at 5 Å we measure the TM-score of the model and of the best template vs. the native structure (only focussing on the area covered by the best template) we obtain broadly similar results, but slightly less favourable for the models, with results now undistinguishable in the 0.4–0.7 region and still slightly worse in the 0.7–1 one. We can broadly conclude that, if good templates are available, reconstructions from 4-class maps are only about as good as them in the area covered by the template.

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