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Combining physicochemical and evolutionary information for protein contact prediction.

Schneider M, Brock O - PLoS ONE (2014)

Bottom Line: The resulting contact predictions are highly accurate.As a result of combining two sources of information--evolutionary and physicochemical--we maintain prediction accuracy even when only few sequence homologs are present.We show that the predicted contacts help to improve ab initio structure prediction.

View Article: PubMed Central - PubMed

Affiliation: Robotics and Biology Laboratory, Department of Electrical Engineering and Computer Science, Technische Universit├Ąt Berlin, Berlin, Germany.

ABSTRACT
We introduce a novel contact prediction method that achieves high prediction accuracy by combining evolutionary and physicochemical information about native contacts. We obtain evolutionary information from multiple-sequence alignments and physicochemical information from predicted ab initio protein structures. These structures represent low-energy states in an energy landscape and thus capture the physicochemical information encoded in the energy function. Such low-energy structures are likely to contain native contacts, even if their overall fold is not native. To differentiate native from non-native contacts in those structures, we develop a graph-based representation of the structural context of contacts. We then use this representation to train an support vector machine classifier to identify most likely native contacts in otherwise non-native structures. The resulting contact predictions are highly accurate. As a result of combining two sources of information--evolutionary and physicochemical--we maintain prediction accuracy even when only few sequence homologs are present. We show that the predicted contacts help to improve ab initio structure prediction. A web service is available at http://compbio.robotics.tu-berlin.de/epc-map/.

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Comparison of ab initio structure prediction of 132 proteins from EPC-map_test with and without predicted contacts: each data point corresponds to the GDT_TS of the lowest-energy structure generated with and without the use of EPC-map predicted contacts.EPC-map increases the average prediction accuracy by 7.8% from 33.1 to 40.9 GDT_TS (paired Student's t-test p-value).
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pone-0108438-g008: Comparison of ab initio structure prediction of 132 proteins from EPC-map_test with and without predicted contacts: each data point corresponds to the GDT_TS of the lowest-energy structure generated with and without the use of EPC-map predicted contacts.EPC-map increases the average prediction accuracy by 7.8% from 33.1 to 40.9 GDT_TS (paired Student's t-test p-value).

Mentions: The prediction improvements are depicted in Figure 8. We used the GDTTS measure to quantify the quality of the predicted structures. The GDTTS measure ranges from 0 if two structures are completely dissimilar to 100 for a perfect structural match. At a GDT_TS of 50 or more, a prediction is considered to capture the native topology. The average GDT_TS of contact-guided Rosetta increases to 40.9 compared to 33.1 using standard Rosetta (paired Student -test -value), an absolute improvement of 7.8%. The GDT_TS increases by more than 10 for 41 of the 132 proteins. In 24 cases, the GDT_TS increase is higher than 20. In addition, for 21 proteins the GDT_TS transitions from well below 50 to 50 or higher. In these cases, the combination of EPC-map predicted contacts and Rosetta allows for the folding of proteins that could not be modeled with Rosetta alone. Thus, our results show that contact information from EPC-map readily enhances structure prediction performance.


Combining physicochemical and evolutionary information for protein contact prediction.

Schneider M, Brock O - PLoS ONE (2014)

Comparison of ab initio structure prediction of 132 proteins from EPC-map_test with and without predicted contacts: each data point corresponds to the GDT_TS of the lowest-energy structure generated with and without the use of EPC-map predicted contacts.EPC-map increases the average prediction accuracy by 7.8% from 33.1 to 40.9 GDT_TS (paired Student's t-test p-value).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4206277&req=5

pone-0108438-g008: Comparison of ab initio structure prediction of 132 proteins from EPC-map_test with and without predicted contacts: each data point corresponds to the GDT_TS of the lowest-energy structure generated with and without the use of EPC-map predicted contacts.EPC-map increases the average prediction accuracy by 7.8% from 33.1 to 40.9 GDT_TS (paired Student's t-test p-value).
Mentions: The prediction improvements are depicted in Figure 8. We used the GDTTS measure to quantify the quality of the predicted structures. The GDTTS measure ranges from 0 if two structures are completely dissimilar to 100 for a perfect structural match. At a GDT_TS of 50 or more, a prediction is considered to capture the native topology. The average GDT_TS of contact-guided Rosetta increases to 40.9 compared to 33.1 using standard Rosetta (paired Student -test -value), an absolute improvement of 7.8%. The GDT_TS increases by more than 10 for 41 of the 132 proteins. In 24 cases, the GDT_TS increase is higher than 20. In addition, for 21 proteins the GDT_TS transitions from well below 50 to 50 or higher. In these cases, the combination of EPC-map predicted contacts and Rosetta allows for the folding of proteins that could not be modeled with Rosetta alone. Thus, our results show that contact information from EPC-map readily enhances structure prediction performance.

Bottom Line: The resulting contact predictions are highly accurate.As a result of combining two sources of information--evolutionary and physicochemical--we maintain prediction accuracy even when only few sequence homologs are present.We show that the predicted contacts help to improve ab initio structure prediction.

View Article: PubMed Central - PubMed

Affiliation: Robotics and Biology Laboratory, Department of Electrical Engineering and Computer Science, Technische Universit├Ąt Berlin, Berlin, Germany.

ABSTRACT
We introduce a novel contact prediction method that achieves high prediction accuracy by combining evolutionary and physicochemical information about native contacts. We obtain evolutionary information from multiple-sequence alignments and physicochemical information from predicted ab initio protein structures. These structures represent low-energy states in an energy landscape and thus capture the physicochemical information encoded in the energy function. Such low-energy structures are likely to contain native contacts, even if their overall fold is not native. To differentiate native from non-native contacts in those structures, we develop a graph-based representation of the structural context of contacts. We then use this representation to train an support vector machine classifier to identify most likely native contacts in otherwise non-native structures. The resulting contact predictions are highly accurate. As a result of combining two sources of information--evolutionary and physicochemical--we maintain prediction accuracy even when only few sequence homologs are present. We show that the predicted contacts help to improve ab initio structure prediction. A web service is available at http://compbio.robotics.tu-berlin.de/epc-map/.

Show MeSH