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hoDCA: higher order direct-coupling analysis.

Schmidt, Michael and Hamacher, Kay (2018):
hoDCA: higher order direct-coupling analysis.
In: BMC bioinformatics, 19 (1), p. 546, ISSN 1471-2105,
[Article]

Abstract

BACKGROUND

Direct-coupling analysis (DCA) is a method for protein contact prediction from sequence information alone. Its underlying principle is parameter estimation for a Hamiltonian interaction function stemming from a maximum entropy model with one- and two-point interactions. Vastly growing sequence databases enable the construction of large multiple sequence alignments (MSA). Thus, enough data exists to include higher order terms, such as three-body correlations.

RESULTS

We present an implementation of hoDCA, which is an extension of DCA by including three-body interactions into the inverse Ising problem posed by parameter estimation. In a previous study, these three-body-interactions improved contact prediction accuracy for the PSICOV benchmark dataset. Our implementation can be executed in parallel, which results in fast runtimes and makes it suitable for large-scale application.

CONCLUSION

Our hoDCA software allows improved contact prediction using the Julia language, leveraging power of multi-core machines in an automated fashion.

Item Type: Article
Erschienen: 2018
Creators: Schmidt, Michael and Hamacher, Kay
Title: hoDCA: higher order direct-coupling analysis.
Language: English
Abstract:

BACKGROUND

Direct-coupling analysis (DCA) is a method for protein contact prediction from sequence information alone. Its underlying principle is parameter estimation for a Hamiltonian interaction function stemming from a maximum entropy model with one- and two-point interactions. Vastly growing sequence databases enable the construction of large multiple sequence alignments (MSA). Thus, enough data exists to include higher order terms, such as three-body correlations.

RESULTS

We present an implementation of hoDCA, which is an extension of DCA by including three-body interactions into the inverse Ising problem posed by parameter estimation. In a previous study, these three-body-interactions improved contact prediction accuracy for the PSICOV benchmark dataset. Our implementation can be executed in parallel, which results in fast runtimes and makes it suitable for large-scale application.

CONCLUSION

Our hoDCA software allows improved contact prediction using the Julia language, leveraging power of multi-core machines in an automated fashion.

Journal or Publication Title: BMC bioinformatics
Volume: 19
Number: 1
Divisions: 10 Department of Biology
10 Department of Biology > Computational Biology and Simulation
Date Deposited: 07 Jan 2019 07:18
Identification Number: pmid:30594145
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