Brian E. Weiner
Vanderbilt University
17 Papers
204 Citations
Brian E. Weiner is an academic researcher from Vanderbilt University. The author has contributed to research in topics: Membrane protein & Protein structure prediction. The author has an hindex of 10, co-authored 17 publications.
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Papers
Structural mechanism of RPA loading on DNA during activation of a simple pre-replication complex
Xiaohua Jiang,Vitaly Klimovich,Alphonse I. Arunkumar,Erik B. Hysinger,Yingda Wang,Robert D. Ott,Gulfem D. Guler,Brian E. Weiner,Walter J. Chazin,Ellen Fanning +9 more
TL;DR: A mechanistic model is proposed in which the ternary complex is a key intermediate that directly couples origin DNA unwinding to RPA loading on emerging ssDNA.
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NMR analysis of the architecture and functional remodeling of a modular multidomain protein, RPA.
Chris A. Brosey,Marie-Eve Chagot,Mark Ehrhardt,Dalyir I. Pretto,Brian E. Weiner,Walter J. Chazin +5 more
TL;DR: The first direct evidence is obtained for the remodeling of RPA upon binding ssDNA, including an alteration in the availability of the RPA32N domain that may help explain its damage-dependent phosphorylation.
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BCL::Fold--de novo prediction of complex and large protein topologies by assembly of secondary structure elements.
TL;DR: The algorithm performs a Monte Carlo Metropolis simulated annealing folding simulation and optimizes a knowledge-based potential that analyzes radius of gyration, β-strand pairing, secondary structure element (SSE) packing, aminoacid pair distance, amino acid environment, contact order,secondary structure prediction agreement and loop closure.
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BCL::Score--knowledge based energy potentials for ranking protein models represented by idealized secondary structure elements.
TL;DR: A tailored knowledge-based energy function is introduced that evaluates arrangement of secondary structure elements only and significantly enriches for native-like models in three different databases of 10,000–12,000 protein models in 80–94% of the cases.
BCL::MP-fold: folding membrane proteins through assembly of transmembrane helices.
TL;DR: Modifications to the de novo protein structure prediction method BCL::Fold are described, which demonstrate that the algorithm can accurately predict protein topology without the need for large multiple sequence alignments, homologous template structures, or experimental restraints.
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