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3DRobot specifications

Information


Unique identifier OMICS_10819
Name 3DRobot
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Native structure
Input format PDB
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Yang Zhang

Information


Unique identifier OMICS_10819
Name 3DRobot
Interface Web user interface
Restrictions to use None
Input data Native structure
Input format PDB
Computer skills Basic
Stability Stable
Maintained Yes

Maintainer


  • person_outline Yang Zhang

Publication for 3DRobot

3DRobot citations

 (3)
library_books

Diverse effects of distance cutoff and residue interval on the performance of distance dependent atom pair potential in protein structure prediction

2017
BMC Bioinformatics
PMCID: 5723101
PMID: 29221443
DOI: 10.1186/s12859-017-1983-3

[…] 100 structure decoys (a total of 5858 structures for 58 proteins). the moulder decoy sets [] include 20 protein and their comparative models generated by the homology-modeling tool modeller. the 3drobot decoy sets were generated by the fragment assembly method we previously developed [], which include 200 non-redundant proteins and a total of 60,200 structures. the casp10 and casp11 decoy […]

library_books

A Template Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning

2016
PMCID: 5658031
PMID: 29081613
DOI: 10.4172/jpb.1000419

[…] protein structure prediction using only the 3d structural coordinates of homologous template proteins as input. the templates were identified for a target protein by a psi-blast search. 3drobot (a program that automatically generates diverse and well-packed protein structure decoys) was used to generate initial decoy models for the target from the templates. a stacked denoising […]

library_books

Sorting protein decoys by machine learning to rank

2016
Sci Rep
PMCID: 4987638
PMID: 27530967
DOI: 10.1038/srep31571

[…] we develop a single-model method mqaprank based on the learning-to-rank algorithm firstly, and then implement a quasi single-model method quasi-mqaprank. the proposed methods are benchmarked on the 3drobot and casp11 dataset. the five-fold cross-validation on the 3drobot dataset shows the proposed single model method outperforms other methods whose outputs are taken as features of the proposed […]


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3DRobot institution(s)
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan, China; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
3DRobot funding source(s)
The work was supported in part by the National Institute of General Medical Sciences (GM083107, GM084222), the National Natural Science Foundation of China (No.11175068 and No.11474117), and China Scholarship Council (201206770004).

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