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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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Relative performance of different types of HMMs in assignment of structure to sequence probes, presented as a coverage vs. error plot. SLAHMM-CW refers to models built in the same way as SLAHMMs, but using only sequence information to align the SCOP domains rather than a structural alignment (see text). Iterative parameters used for construction of all models were from PS1 (Table 2). Values for correct assignments are truncated at 600 in order to emphasize differences between the various methods (no method other than SLAHMM-CW had errors below 600 correct assignments).
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Figure 2: Relative performance of different types of HMMs in assignment of structure to sequence probes, presented as a coverage vs. error plot. SLAHMM-CW refers to models built in the same way as SLAHMMs, but using only sequence information to align the SCOP domains rather than a structural alignment (see text). Iterative parameters used for construction of all models were from PS1 (Table 2). Values for correct assignments are truncated at 600 in order to emphasize differences between the various methods (no method other than SLAHMM-CW had errors below 600 correct assignments).

Mentions: When directly compared, single-master HMMs clearly outperformed SLAHMMs (Figure 2). This result is not surprising, as SLAHMMs attempt to incorporate all information about the superfamily in one model. In some cases, this may force the model away from sequences that would be easily annotated by a standard single-master HMM (i.e. SLAHMMs are designed to capture the most distant relationships, and therefore may miss easier ones).


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

Scheeff ED, Bourne PE - BMC Bioinformatics (2006)

Relative performance of different types of HMMs in assignment of structure to sequence probes, presented as a coverage vs. error plot. SLAHMM-CW refers to models built in the same way as SLAHMMs, but using only sequence information to align the SCOP domains rather than a structural alignment (see text). Iterative parameters used for construction of all models were from PS1 (Table 2). Values for correct assignments are truncated at 600 in order to emphasize differences between the various methods (no method other than SLAHMM-CW had errors below 600 correct assignments).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Relative performance of different types of HMMs in assignment of structure to sequence probes, presented as a coverage vs. error plot. SLAHMM-CW refers to models built in the same way as SLAHMMs, but using only sequence information to align the SCOP domains rather than a structural alignment (see text). Iterative parameters used for construction of all models were from PS1 (Table 2). Values for correct assignments are truncated at 600 in order to emphasize differences between the various methods (no method other than SLAHMM-CW had errors below 600 correct assignments).
Mentions: When directly compared, single-master HMMs clearly outperformed SLAHMMs (Figure 2). This result is not surprising, as SLAHMMs attempt to incorporate all information about the superfamily in one model. In some cases, this may force the model away from sequences that would be easily annotated by a standard single-master HMM (i.e. SLAHMMs are designed to capture the most distant relationships, and therefore may miss easier ones).

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