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hoDCA: higher order direct-coupling analysis

Schmidt, Michael ; Hamacher, Kay (2022)
hoDCA: higher order direct-coupling analysis.
In: BMC Bioinformatics, 2018, 19
doi: 10.26083/tuprints-00012863
Article, Secondary publication, Publisher's Version

Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

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Item Type: Article
Type of entry: Secondary publication
Title: hoDCA: higher order direct-coupling analysis
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2018
Publisher: Springer Nature
Journal or Publication Title: BMC Bioinformatics
Volume of the journal: 19
Collation: 5 Seiten
DOI: 10.26083/tuprints-00012863
Corresponding Links:
Origin: Secondary publication

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.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-128630
Additional Information:

Keywords: Contact prediction, Proteins, DCA

Classification DDC: 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie
Divisions: 10 Department of Biology > Computational Biology and Simulation
Date Deposited: 01 Mar 2022 13:28
Last Modified: 27 Feb 2023 09:27
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/12863
PPN: 505325896
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