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OPUS-PSP specifications

Information


Unique identifier OMICS_16799
Name OPUS-PSP
Software type Package/Module
Interface Command line interface
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Source code URL http://ma-lab.rice.edu/MaLab/dist/opus-psp/OPUS_PSP_1.0.tgz
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Jianpeng Ma

Publication for OPUS-PSP

OPUS-PSP citations

 (11)
library_books

OPUS‐CSF: A C‐atom‐based scoring function for ranking protein structural models

2017
PMCID: 5734313
PMID: 29047165
DOI: 10.1002/pro.3327

[…] he Pearson's correlation coefficients between CSF score and TM‐score in all decoy sets. The results are shown in Table . OPUS‐CSF has comparable average correlation coefficient with those of GOAP and OPUS‐PSP despite the fact that OPUS‐CSF is highly coarse‐grained and the other two are all‐atom potentials.For further analysis of the method, we use 5‐residue segment case as an example, Figure show […]

library_books

Structure based cross docking analysis of antibody–antigen interactions

2017
Sci Rep
PMCID: 5557897
PMID: 28811664
DOI: 10.1038/s41598-017-08414-y

[…] C curves for the complexes using four other widely-used scoring potentials computed using the CCharPPI web server: DFIRE2 interaction energy, total FireDock energy (antibody–antigen energy function), OPUS-PSP all-atom potential, and ZRANK2 scoring function. We considered the native cognate antibody–antigen interaction pairs as the only true positives.Figure 8 In ROC curves, the area under the curv […]

call_split

An allosteric role for receptor activity modifying proteins in defining GPCR pharmacology

2016
PMCID: 4869360
PMID: 27462459
DOI: 10.1038/celldisc.2016.12
call_split See protocol

[…] the CTR alone and the CTR ECD docked to the RAMP1 ECD were based on the CLR/RAMP1 ECD structure 3N7R using a Clustal Omega alignment. With Modeller v9.10, 1 000 models were generated and scored using OPUS_PSP. The best scoring model was visually inspected before minimization using the Amber99sb ILDN force-field as implemented in GROMACS. For MD simulations, the minimized protein was placed central […]

library_books

A large scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11

2015
BMC Bioinformatics
PMCID: 4619059
PMID: 26493701
DOI: 10.1186/s12859-015-0775-x

[…] , ProQ2 [], Pcons [], APOLLO [], ModelEvaluator [], ModelCheck2 – an improved version of ModelEvaluator, QApro – a weighted combination of ModelEvaluator and APOLLO, SELECTpro [], Dope [], DFIRE2 [], OPUS_PSP [], RWplus [], RF_CB_SRS_OD []). MULTICOM-CONSTRUCT used the consensus (average) ranking of the individual rankings produced by these methods to select top five models as predictions [, ]. MU […]

library_books

Large scale model quality assessment for improving protein tertiary structure prediction

2015
Bioinformatics
PMCID: 4553833
PMID: 26072473
DOI: 10.1093/bioinformatics/btv235

[…] , RWplus score (), dope score () and RF_CB_SRS_OD score (); ProQ2 (); Model check2 method produced by an improved version of ModelEvaluator (); a recalibrated SELECTpro energy (); Dope (); DFIRE2 (); OPUS_PSP (); Rwplus (); ModelEvaluator () and RF_CB_SRS_OD (). The multi-model QA methods include ModFOLDclust2 (); Pcons (); APPOLLO (); QApro—a weighted combination of ModelEvaluator and APOLLO (). […]

call_split

Determining Effects of Non synonymous SNPs on Protein Protein Interactions using Supervised and Semi supervised Learning

2014
PLoS Comput Biol
PMCID: 4006705
PMID: 24784581
DOI: 10.1371/journal.pcbi.1003592
call_split See protocol

[…] clash, backbone clash, helix dipole, water bridge, disulfide, electrostatic kon, partial covalent bonds, Energy Ionisation, Entropy Complex . The second group of three features includes energy terms (OPUS-PSP terms 1–3) calculated in OPUS-PSP . Accessible surface area of the mutant amino acid residue is computed by NACCASS , as a descriptor to measure the changes on solvent accessibility during th […]

Citations

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OPUS-PSP institution(s)
Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA; Department of Bioengineering, Rice University, Houston, TX, USA
OPUS-PSP funding source(s)
This work was supported by a grant from the National Institutes of Health (R01-GM067801), a grant from the National Science Foundation CAREER Award (MCB-0237796), partially by a pre-doctoral fellowship from the Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia, and by a pre-doctoral fellowship from the Keck Center through the National Library of Medicine Computational Biology and Medicine Training Program (NLM Grant No. 5T15LM07093).

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