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Frank Eisenhaber

Researcher at Agency for Science, Technology and Research

Publications -  211
Citations -  17972

Frank Eisenhaber is an academic researcher from Agency for Science, Technology and Research. The author has contributed to research in topics: Protein structure & Peptide sequence. The author has an hindex of 61, co-authored 202 publications receiving 16816 citations. Previous affiliations of Frank Eisenhaber include Humboldt University of Berlin & University of Innsbruck.

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Regulation of chromatin structure by site-specific histone H3 methyltransferases

TL;DR: A functional interdependence of site-specific H3 tail modifications is revealed and a dynamic mechanism for the regulation of higher-order chromatin is suggested.
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Fat Mobilization in Adipose Tissue Is Promoted by Adipose Triglyceride Lipase

TL;DR: It is reported that a second enzyme, adipose triglyceride lipase (ATGL), catalyzes the initial step in triglyceride hydrolysis, and it is interesting that ATGL contains a “patatin domain” common to plant acyl-hydrolases.
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The double cubic lattice method: Efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies

TL;DR: The double cubic lattice method (DCLM) is an accurate and rapid approach for computing numerically molecular surface areas and the volume and compactness of molecular assemblies and for generating dot surfaces, and is the method of choice, especially for large molecular complexes and high point densities.
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The Tudor domain 'Royal Family': Tudor, plant Agenet, Chromo, PWWP and MBT domains.

TL;DR: This finding has been extended, using a combination of sequence- and structure-dependent approaches, to show that the three beta-stranded core regions of Tudor, PWWP, chromatin-binding (Chromo) and MBT domains are homologous because they originate from a common ancestor.
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Prediction of potential GPI-modification sites in proprotein sequences.

TL;DR: A new prediction technique locating potential GPI-modification sites in precursor sequences has been applied for large-scale protein sequence database searches and has been implemented in the prototype software "big-Pi predictor" which may find application as a genome annotation and target selection tool.