Schmidt, Michael ; Hamacher, Kay (2018)
hoDCA: higher order direct-coupling analysis.
In: BMC bioinformatics, 19 (1)
Artikel, Bibliographie
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: | 2018 |
| Autor(en): | Schmidt, Michael ; Hamacher, Kay |
| Art des Eintrags: | Bibliographie |
| Titel: | hoDCA: higher order direct-coupling analysis. |
| Sprache: | Englisch |
| Publikationsjahr: | 29 Dezember 2018 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | BMC bioinformatics |
| Jahrgang/Volume einer Zeitschrift: | 19 |
| (Heft-)Nummer: | 1 |
| 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. |
| ID-Nummer: | pmid:30594145 |
| Fachbereich(e)/-gebiet(e): | 10 Fachbereich Biologie 10 Fachbereich Biologie > Computational Biology and Simulation |
| Hinterlegungsdatum: | 07 Jan 2019 07:18 |
| Letzte Änderung: | 07 Jan 2019 07:18 |
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