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David Eisenberg

Researcher at Technion – Israel Institute of Technology

Publications -  719
Citations -  120468

David Eisenberg is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Amyloid & Protein structure. The author has an hindex of 156, co-authored 697 publications receiving 112460 citations. Previous affiliations of David Eisenberg include Howard Hughes Medical Institute & Hebrew University of Jerusalem.

Papers
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Journal ArticleDOI

Mining literature for protein–protein interactions

TL;DR: It is shown that the frequencies of words in Medline abstracts can be used to determine whether or not a given paper discusses protein-protein interactions, and the relevant information can be captured for the Database of Interacting Proteins.
Journal ArticleDOI

Crystal structure of a synthetic triple-stranded alpha-helical bundle

TL;DR: The x-ray crystal structure of a peptide designed to form a double-stranded parallel coiled coil shows that it is actually a triple- Stranded coiled Coil formed by three alpha-helices, suggesting that hydrophobic interactions are a dominant factor in the stabilization of coiled coils.
Book ChapterDOI

Oligomer Formation By 3D Domain Swapping: A Model For Protein Assembly And Misassembly

TL;DR: The chapter closes with a brief consideration of the possibility that 3D domain swapping is a mechanism for the formation of amyloid or other pathological aggregates of proteins.
Journal ArticleDOI

Transproteomic evidence of a loop-deletion mechanism for enhancing protein thermostability.

TL;DR: In this paper, a comparative analysis of 20 complete genomes for mesophilic, thermophilic and hyperthermophilic organisms was performed, showing a trend towards shortened thermophilicity relative to their mesophilous homologs.
Journal ArticleDOI

Toward rational protein crystallization: A Web server for the design of crystallizable protein variants

TL;DR: A Web server, the surface entropy reduction prediction server (SERp server), designed to identify mutations that may facilitate crystallization and an attempt to benchmark the server by comparing the server's predictions with successful SER structures.