BeAtMuSiC 1.0 – Prediction of Binding Affinity Changes upon Mutations

BeAtMuSiC 1.0

:: DESCRIPTION

The BeAtMuSiC program evaluates the change in binding affinity between proteins (or protein chains) caused by single-site mutations in their sequence.

::DEVELOPER

Service de Biomodélisation, Bioinformatique et Bioprocédés (3BIO)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Jul;41(Web Server issue):W333-9. doi: 10.1093/nar/gkt450. Epub 2013 May 30.
BeAtMuSiC: Prediction of changes in protein-protein binding affinity on mutations.
Dehouck Y, Kwasigroch JM, Rooman M, Gilis D.

mGPfusion – Predicting Stability Changes upon Single and Multiple Mutations

mGPfusion

:: DESCRIPTION

mGPfusion is a Gaussian process based method for predicting stability changes upon single and multiple mutations of proteins that complements the available experimental data with large amounts of simulated data.

::DEVELOPER

Computational systems biology group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Matlab

:: DOWNLOAD

mGPfusion

:: MORE INFORMATION

Citation

Bioinformatics, 34 (13), i274-i283 2018 Jul 1
mGPfusion: Predicting Protein Stability Changes With Gaussian Process Kernel Learning and Data Fusion
Emmi Jokinen , Markus Heinonen , Harri Lähdesmäki

DUET – Predicting Effects of Mutations on Protein Stability

DUET

:: DESCRIPTION

DUET is a web server for an integrated computational approach for studying missense mutations in proteins.

::DEVELOPER

Biosig Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach.
Pires DE, Ascher DB, Blundell TL.
Nucleic Acids Res. 2014 Jul;42(Web Server issue):W314-9. doi: 10.1093/nar/gku411.

MOAT v1.0 – Mutations Overburdening Annotations Tool

MOAT v1.0

:: DESCRIPTION

MOAT is a computational system for identifying significant mutation burdens in genomic elements with an empirical, nonparametric method. Taking a set of variant calls and a set of annotations, MOAT calculates which annotations have observed variant counts that are substantially elevated with respect to a distribution of expected variant counts determined by permutation of the input data.

::DEVELOPER

Gerstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

MOAT 

:: MORE INFORMATION

Citation:

Bioinformatics. 2018 Mar 15;34(6):1031-1033. doi: 10.1093/bioinformatics/btx700.
MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool.
Lochovsky L, Zhang J, Gerstein M.

DawnRank 1.2 – Discovering Personalized Driver Mutations in Cancer

DawnRank 1.2

:: DESCRIPTION

DawnRank is an R package that identifies personalized driver mutations for any given patient sample.

::DEVELOPER

Ma Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ MacOsX/Linux
  • R

:: DOWNLOAD

DawnRank

:: MORE INFORMATION

Citation:

Genome Med. 2014 Jul 31;6(7):56. doi: 10.1186/s13073-014-0056-8. eCollection 2014.
DawnRank: discovering personalized driver genes in cancer.
Hou JP, Ma J

TrackSig – Reconstructing Evolutionary Trajectories of Mutations in Cancer

TrackSig

:: DESCRIPTION

TrackSig is a method to estimate the evolutionary trajectories of signatures of somatic mutational processes. TrackSig uses cancer cell fraction (CCF) corrected by copy number to infer an approximate order in which the somatic mutations accumulate. TrackSig segments mutation ordering by CCF and fits signature exposures (activities) as a piece-wise constant function of the mutation ordering. TrackSig uses optimal segmentation to find the points of change in signature activities.

TrackSigFreq is an R package for TrackSig

::DEVELOPER

Morris Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R
  • Python

:: DOWNLOAD

TrackSig

:: MORE INFORMATION

Citation

TrackSig: reconstructing evolutionary trajectories of mutations in cancer
Yulia Rubanova, Ruian Shi, Roujia Li, Jeff Wintersinger, Nil Sahin, Amit Deshwar, Quaid Morris, PCAWG Evolution and Heterogeneity Working Group, PCAWG network
doi: https://doi.org/10.1101/260471

Cancer3D 2.0 – Patterns of Mutations in Cancer

Cancer3D 2.0

:: DESCRIPTION

Cancer3D database provides an open and user-friendly way to analyze cancer missense mutations in the context of structures of proteins they are found in and in relation to patients gender and age.

::DEVELOPER

Godzik Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser
:: DOWNLOAD

NO

:: MORE INFORMATION

Citation:

Cancer3D 2.0: interactive analysis of 3D patterns of cancer mutations in cancer subsets.
Sedova M, Iyer M, Li Z, Jaroszewski L, Post KW, Hrabe T, Porta-Pardo E, Godzik A.
Nucleic Acids Res. 2019 Jan 8;47(D1):D895-D899. doi: 10.1093/nar/gky1098.

CCMpred 0.3.2 – Prediction of Protein Residue-residue Contacts from Correlated Mutations

CCMpred 0.3.2

:: DESCRIPTION

CCMpred is a C implementation of a Markov Random Field pseudo-likelihood maximization for learning protein residue-residue contacts

::DEVELOPER

Söding Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C Compiler
:: DOWNLOAD

 CCMpred

:: MORE INFORMATION

Citation:

Bioinformatics. 2014 Jul 26. pii: btu500.
CCMpred-fast and precise prediction of protein residue-residue contacts from correlated mutations.
Seemayer S1, Gruber M1, Söding J

HotSpot3D 1.8.0 – 3D Hotspot Mutation Proximity Analysis tool

HotSpot3D 1.8.0

:: DESCRIPTION

HotSpot3D can be used to identify the mutation hotspots in the linear 1D sequence and correlates these hotspots with known or potential interacting domains based on both known intermolecular interactions and calculated proximity for potential intramolecular interactions.

::DEVELOPER

Ding Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 HotSpot3D

:: MORE INFORMATION

Citation

Nat Genet. 2016 Aug;48(8):827-37. doi: 10.1038/ng.3586. Epub 2016 Jun 13.
Protein-structure-guided discovery of functional mutations across 19 cancer types.
Niu B, Scott AD, Sengupta S, Bailey MH, Batra P, Ning J, Wyczalkowski MA, Liang WW, Zhang Q, McLellan MD, Sun SQ, Tripathi P, Lou C, Ye K, Mashl RJ, Wallis J, Wendl MC, Chen F, Ding L