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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

Venn diagram describing coverage overlap of the three primary model sets from PS1, when using a strict cutoff of 80 incorrect assignments (theoretical EPQ ~0.05). The numbers shown in parentheses near each model type designation refer to the total number of correct matches made by that model type prior to the cutoff point. Identical matches of the same probes by some or all of the three different methods are provided by the numbers in the set diagram. The completely unique matches by single-master HMMs and SLAHMMs are color coded to match the circle for that model type. "All Models" denotes the assignments made by the combined database of SLAHMMs and single-master HMMs used together in a single search.
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Figure 3: Venn diagram describing coverage overlap of the three primary model sets from PS1, when using a strict cutoff of 80 incorrect assignments (theoretical EPQ ~0.05). The numbers shown in parentheses near each model type designation refer to the total number of correct matches made by that model type prior to the cutoff point. Identical matches of the same probes by some or all of the three different methods are provided by the numbers in the set diagram. The completely unique matches by single-master HMMs and SLAHMMs are color coded to match the circle for that model type. "All Models" denotes the assignments made by the combined database of SLAHMMs and single-master HMMs used together in a single search.

Mentions: Assessment of the specific probe assignments made by SLAHMMs vs. single-master HMMs confirmed that SLAHMMs made unique assignments. At a strict cutoff of 80 incorrect assignments (EPQ ~0.05), the two types of HMMs shared 839 correct assignments of the same probes (out of a total number of 1575 possible correct assignments, see methods). However, while single-master HMMs correctly assigned an additional set of 224 probes uniquely, SLAHMMs also correctly assigned 42 probes uniquely (Figure 3). The unique assignments made by SLAHMMs are summarized in Table 1; they come from a variety of superfamilies from all four major SCOP structural classes, indicating that the results are not simply the result of a few atypical superfamilies.


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

Scheeff ED, Bourne PE - BMC Bioinformatics (2006)

Venn diagram describing coverage overlap of the three primary model sets from PS1, when using a strict cutoff of 80 incorrect assignments (theoretical EPQ ~0.05). The numbers shown in parentheses near each model type designation refer to the total number of correct matches made by that model type prior to the cutoff point. Identical matches of the same probes by some or all of the three different methods are provided by the numbers in the set diagram. The completely unique matches by single-master HMMs and SLAHMMs are color coded to match the circle for that model type. "All Models" denotes the assignments made by the combined database of SLAHMMs and single-master HMMs used together in a single search.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Venn diagram describing coverage overlap of the three primary model sets from PS1, when using a strict cutoff of 80 incorrect assignments (theoretical EPQ ~0.05). The numbers shown in parentheses near each model type designation refer to the total number of correct matches made by that model type prior to the cutoff point. Identical matches of the same probes by some or all of the three different methods are provided by the numbers in the set diagram. The completely unique matches by single-master HMMs and SLAHMMs are color coded to match the circle for that model type. "All Models" denotes the assignments made by the combined database of SLAHMMs and single-master HMMs used together in a single search.
Mentions: Assessment of the specific probe assignments made by SLAHMMs vs. single-master HMMs confirmed that SLAHMMs made unique assignments. At a strict cutoff of 80 incorrect assignments (EPQ ~0.05), the two types of HMMs shared 839 correct assignments of the same probes (out of a total number of 1575 possible correct assignments, see methods). However, while single-master HMMs correctly assigned an additional set of 224 probes uniquely, SLAHMMs also correctly assigned 42 probes uniquely (Figure 3). The unique assignments made by SLAHMMs are summarized in Table 1; they come from a variety of superfamilies from all four major SCOP structural classes, indicating that the results are not simply the result of a few atypical superfamilies.

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