Nathan Alexander
Vanderbilt University
22 Papers
178 Citations
Nathan Alexander is an academic researcher from Vanderbilt University. The author has contributed to research in topics: Protein structure prediction & Site-directed spin labeling. The author has an hindex of 15, co-authored 21 publications.
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Papers
Bcl::Cluster: A method for clustering biological molecules coupled with visualization in the Pymol Molecular Graphics System
Nathan Alexander,Nils Woetzel,Jens Meiler +2 more
- 03 Feb 2011
TL;DR: The current work presents a hierarchical agglomerative clustering method termed bcl::Cluster, which utilizes the Pymol Molecular Graphics System to graphically depict dendrograms in three dimensions and allows simultaneous display of relevant biological molecules as well as additional information about the clusters and the members comprising them.
De Novo High-Resolution Protein Structure Determination from Sparse Spin labeling EPR Data
TL;DR: Here, distances between spin labels are converted into distance ranges between beta carbons by using a "motion-on-a-cone" model, and a linear-correlation model links spin-label accessibility to the number of neighboring residues.
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RosettaEPR: an integrated tool for protein structure determination from sparse EPR data.
TL;DR: RosettaEPR is introduced, which has been designed to improve de novo high-resolution protein structure prediction using sparse SDSL-EPR distance data and yields a 1.7Å model of T4-lysozyme, indicating that atomic detail models can be achieved by combining sparse EPR data with Rosetta.
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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::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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