Schmidt, Michael ; Hamacher, Kay (2022)
hoDCA: higher order direct-coupling analysis.
In: BMC Bioinformatics, 2018, 19
doi: 10.26083/tuprints-00012863
Artikel, Zweitveröffentlichung, Verlagsversion
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Schmidt, Michael ; Hamacher, Kay |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | hoDCA: higher order direct-coupling analysis |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Publikationsdatum der Erstveröffentlichung: | 2018 |
| Verlag: | Springer Nature |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | BMC Bioinformatics |
| Jahrgang/Volume einer Zeitschrift: | 19 |
| Kollation: | 5 Seiten |
| DOI: | 10.26083/tuprints-00012863 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/12863 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung |
| Kurzbeschreibung (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. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-128630 |
| Zusätzliche Informationen: | Keywords: Contact prediction, Proteins, DCA |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie |
| Fachbereich(e)/-gebiet(e): | 10 Fachbereich Biologie 10 Fachbereich Biologie > Computational Biology and Simulation |
| Hinterlegungsdatum: | 01 Mär 2022 13:28 |
| Letzte Änderung: | 02 Mär 2022 07:09 |
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| Zugehörige Links: | |
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