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Lukasz Goldschmidt

Researcher at University of California, Los Angeles

Publications -  17
Citations -  3137

Lukasz Goldschmidt is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Protein structure & Protein folding. The author has an hindex of 13, co-authored 17 publications receiving 2466 citations. Previous affiliations of Lukasz Goldschmidt include University of Washington & Howard Hughes Medical Institute.

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Identifying the amylome, proteins capable of forming amyloid-like fibrils

TL;DR: The amylome is the universe of proteins that are capable of forming amyloid-like fibrils and the presence in the protein of a segment that can form a tightly complementary interface with an identical segment permits the formation of a steric zipper.
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Macromolecular modeling and design in Rosetta: recent methods and frameworks

Julia Koehler Leman, +117 more
- 01 Jul 2020 - 
TL;DR: This Perspective reviews tools developed over the past five years in the Rosetta software, including over 80 methods, and discusses improvements to the score function, user interfaces and usability.
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Molecular basis for amyloid-beta polymorphism.

TL;DR: To elucidate Aβ polymorphism in atomic detail, eight new microcrystal structures of fiber-forming segments of A β are determined, all of short, self-complementing pairs of β-sheets termed steric zippers, revealing a variety of modes of self-association of Aβ.
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Atomic structures of low-complexity protein segments reveal kinked β sheets that assemble networks

TL;DR: The atomic structures of five segments from protein low-complexity domains associated with membraneless assemblies, found in proteins as diverse as RNA binders, nuclear pore proteins, and keratins, are determined, which suggest that short segments of two such domains can bind weakly to each other by forming a pair of kinked β-sheets.
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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.