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Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction.

Scheeff ED, Bourne PE - BMC Bioinformatics (2006)

Bottom Line: We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions.In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments.Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.

View Article: PubMed Central - HTML - PubMed

Affiliation: San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093-0537, USA. scheeff@salk.edu

ABSTRACT

Background: One of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences will presumably produce a more powerful profile, but distantly related sequences can be difficult to align accurately using only sequence information. Therefore, it would be expected that the use of protein structure alignments to improve the selection and alignment of diverse sequence homologs might yield improved profiles. However, the actual utility of such an approach has remained unclear.

Results: We explored several iterative protocols for the generation of profile hidden Markov models. These protocols were tailored to allow the inclusion of protein structure alignments in the process, and were used for large-scale creation and benchmarking of structure alignment-enhanced models. We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions. However, the results also revealed that the structure alignment-enhanced models were complimentary to the sequence-only models, particularly at the edge of the "twilight zone". When the two sets of models were combined, they provided improved results over sequence-only models alone. In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments. Our experiments with different iterative protocols for sequence-only models also suggested that simple protocol modifications were unable to yield equivalent improvements to those provided by the structure alignment-enhanced models. Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.

Conclusion: When attempting to predict the structure of remote homologs, we advocate a combined approach in which both traditional models and models incorporating structure alignments are used.

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Related in: MedlinePlus

Relative performance of single-master HMMs, SLAHMMs, and the combined models with differing iteration parameter sets (PS), presented as a coverage vs. theoretical errors per query (EPQ) plot. The different parameter sets are defined in Table 2 and explained in the text. Values for correct assignments are truncated at 600 in order to emphasize differences between the various methods (no method had an error below 600 correct assignments).
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Figure 1: Relative performance of single-master HMMs, SLAHMMs, and the combined models with differing iteration parameter sets (PS), presented as a coverage vs. theoretical errors per query (EPQ) plot. The different parameter sets are defined in Table 2 and explained in the text. Values for correct assignments are truncated at 600 in order to emphasize differences between the various methods (no method had an error below 600 correct assignments).

Mentions: When single-master HMMs were compared to each other for all four parameter sets (PS1-PS4), the difference in performance was extremely small (Figure 1). There was a slight advantage to a full heuristics methodology (PS1), which achieved more correct matches at a higher theoretical error per query (EPQ) level. However, this result indicates that traditional HMMs are surprisingly insensitive to the methods used to select and align sequences to the growing model.


Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction.

Scheeff ED, Bourne PE - BMC Bioinformatics (2006)

Relative performance of single-master HMMs, SLAHMMs, and the combined models with differing iteration parameter sets (PS), presented as a coverage vs. theoretical errors per query (EPQ) plot. The different parameter sets are defined in Table 2 and explained in the text. Values for correct assignments are truncated at 600 in order to emphasize differences between the various methods (no method had an error below 600 correct assignments).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1622756&req=5

Figure 1: Relative performance of single-master HMMs, SLAHMMs, and the combined models with differing iteration parameter sets (PS), presented as a coverage vs. theoretical errors per query (EPQ) plot. The different parameter sets are defined in Table 2 and explained in the text. Values for correct assignments are truncated at 600 in order to emphasize differences between the various methods (no method had an error below 600 correct assignments).
Mentions: When single-master HMMs were compared to each other for all four parameter sets (PS1-PS4), the difference in performance was extremely small (Figure 1). There was a slight advantage to a full heuristics methodology (PS1), which achieved more correct matches at a higher theoretical error per query (EPQ) level. However, this result indicates that traditional HMMs are surprisingly insensitive to the methods used to select and align sequences to the growing model.

Bottom Line: We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions.In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments.Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.

View Article: PubMed Central - HTML - PubMed

Affiliation: San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093-0537, USA. scheeff@salk.edu

ABSTRACT

Background: One of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences will presumably produce a more powerful profile, but distantly related sequences can be difficult to align accurately using only sequence information. Therefore, it would be expected that the use of protein structure alignments to improve the selection and alignment of diverse sequence homologs might yield improved profiles. However, the actual utility of such an approach has remained unclear.

Results: We explored several iterative protocols for the generation of profile hidden Markov models. These protocols were tailored to allow the inclusion of protein structure alignments in the process, and were used for large-scale creation and benchmarking of structure alignment-enhanced models. We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions. However, the results also revealed that the structure alignment-enhanced models were complimentary to the sequence-only models, particularly at the edge of the "twilight zone". When the two sets of models were combined, they provided improved results over sequence-only models alone. In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments. Our experiments with different iterative protocols for sequence-only models also suggested that simple protocol modifications were unable to yield equivalent improvements to those provided by the structure alignment-enhanced models. Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.

Conclusion: When attempting to predict the structure of remote homologs, we advocate a combined approach in which both traditional models and models incorporating structure alignments are used.

Show MeSH
Related in: MedlinePlus