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Ori Shachar

dblp:63/8713 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › protein design
multistate protein design
0.112010
SPRINT: side-chain prediction inference toolbox for multistate protein design · Bioinform. 2010
Bioinformatics and computational biology
protein design
0.112010
SPRINT: side-chain prediction inference toolbox for multistate protein design · Bioinform. 2010
Bioinformatics and computational biology › structural biology
protein structure and function
0.112010
SPRINT: side-chain prediction inference toolbox for multistate protein design · Bioinform. 2010
Bioinformatics and computational biology
protein structure prediction
0.112010
SPRINT: side-chain prediction inference toolbox for multistate protein design · Bioinform. 2010
Bioinformatics and computational biology › protein structure prediction
side-chain prediction
0.112010
SPRINT: side-chain prediction inference toolbox for multistate protein design · Bioinform. 2010

Methods — techniques the papers use, named apart from their topics

probabilistic graphical model · 0.1dead-end elimination · 0.1belief propagation · 0.1a* search · 0.1
YearPublicationVenuePosition
2010 SPRINT: side-chain prediction inference toolbox for multistate protein design
abstract
UNLABELLED: SPRINT is a software package that performs computational multistate protein design using state-of-the-art inference on probabilistic graphical models. The input to SPRINT is a list of protein structures, the rotamers modeled for each structure and the pre-calculated rotamer energies. Probabilistic inference is performed using the belief propagation or A* algorithms, and dead-end elimination can be applied as pre-processing. The output can either be a list of amino acid sequences simultaneously compatible with these structures, or probabilistic amino acid profiles compatible with the structures. In addition, higher order (e.g. pairwise) amino acid probabilities can also be predicted. Finally, SPRINT also has a module for protein side-chain prediction and single-state design. AVAILABILITY: The full C++ source code for SPRINT can be freely downloaded from http://www.protonet.cs.huji.ac.il/sprint.
Menachem Fromer, Chen Yanover, Amir Harel, Ori Shachar, Yair Weiss, Michal Linial
Bioinform.4