TRAP 3.05 – Transcription factor Affinity Prediction

TRAP 3.05

:: DESCRIPTION

TRAP (Transcription factor Affinity Prediction) calculates the affinity of transcription factors for DNA sequences on the basis of a biophysical model. This method has proven to be useful for several applications, including for determining the putative target genes of a given factor. This protocol covers two other applications: (i) determining which transcription factors have the highest affinity in a set of sequences (illustrated with chromatin immunoprecipitation–sequencing (ChIP-seq) peaks), and (ii) finding which factor is the most affected by a regulatory single-nucleotide polymorphism.

::DEVELOPER

TRAP Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
C++ compiler
R Package

:: DOWNLOAD

TRAP

:: MORE INFORMATION

Citation

Morgane Thomas-Chollier, Andrew Hufton, Matthias Heinig, Sean O’Keeffe, Nassim El Masri, Helge G Roider, Thomas Manke and Martin Vingron.
Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs.
Nature Protocols, 3;6(12):1860-9. (2011)

mmCSM-NA – Predicting Effects of Single and Multiple Mutations on Protein-nucleic Acid Binding affinity

mmCSM-NA

:: DESCRIPTION

mmCSM-NA is the first scalable method capable of quantitatively and accurately predicting the effects of multiple-point mutations on nucleic acid binding affinities.

::DEVELOPER

Biosig Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Nguyen TB, Myung Y, de Sá AGC, Pires DEV, Ascher DB.
mmCSM-NA: accurately predicting effects of single and multiple mutations on protein-nucleic acid binding affinity.
NAR Genom Bioinform. 2021 Nov 17;3(4):lqab109. doi: 10.1093/nargab/lqab109. PMID: 34805992; PMCID: PMC8600011.

CSM-AB / mCSM-AB / mCSM-AB2 / mmCSM-AB- Predicting Antibody-antigen Binding Affinity

CSM-AB / mCSM-AB / mCSM-AB2 / mmCSM-AB

:: DESCRIPTION

CSM-AB is a machine learning method capable of predicting antibody-antigen binding affinity by modelling interaction interfaces as graph-based signatures.

mCSM-AB is a web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures

mCSM-AB2 is an updated and refined version approach, capable of accurately modelling the effects of mutations on antibody-antigen binding affinity, through the inclusion of evolutionary and energetic terms.

mmCSM-AB is a tool for analysing the effects of introducing multiple point mutations on antibody-antigen binding affinity.

::DEVELOPER

Biosig Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation:

Myung Y, Pires DEV, Ascher DB.
CSM-AB: graph-based antibody-antigen binding affinity prediction and docking scoring function.
Bioinformatics. 2021 Nov 4:btab762. doi: 10.1093/bioinformatics/btab762. Epub ahead of print. PMID: 34734992.

mCSM-AB: a web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures.
Pires DE, Ascher DB.
Nucleic Acids Res. 2016 May 23. pii: gkw458.

Myung Y, Rodrigues CHM, Ascher DB, Pires DEV.
mCSM-AB2: guiding rational antibody design using graph-based signatures.
Bioinformatics. 2020 Mar 1;36(5):1453-1459. doi: 10.1093/bioinformatics/btz779. PMID: 31665262.

Myung Y, Pires DEV, Ascher DB.
mmCSM-AB: guiding rational antibody engineering through multiple point mutations.
Nucleic Acids Res. 2020 Jul 2;48(W1):W125-W131. doi: 10.1093/nar/gkaa389. PMID: 32432715; PMCID: PMC7319589.

PreDBA 1.1 – Prediction of Protein-DNA Binding Affinity

PreDBA 1.1

:: DESCRIPTION

PreDBA is a computational method that can effectively predict Protein-DNA Binding Affinity using Machine Learning Algorithm.

::DEVELOPER

DLab (Data Mining and Bioinformatics Lab)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

PreDBA

:: MORE INFORMATION

Citation

Yang W, Deng L.
PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity.
Sci Rep. 2020 Jan 28;10(1):1278. doi: 10.1038/s41598-020-57778-1. PMID: 31992738; PMCID: PMC6987227.

PANDA – Protein Function Prediction Using Domain Architecture and Affinity Propagation on the GO Graph

PANDA

:: DESCRIPTION

PANDA (Propagation of Affinity and Domain Architecture) is a web server to predict protein functions in the format of Gene Ontology (GO) terms.

::DEVELOPER

Z. WANG LAB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Wang Z, Zhao C, Wang Y, Sun Z, Wang N.
PANDA: Protein function prediction using domain architecture and affinity propagation.
Sci Rep. 2018 Feb 22;8(1):3484. doi: 10.1038/s41598-018-21849-1. PMID: 29472600; PMCID: PMC5823857.

CRAPome v.0 – Contaminant Repository for Affinity Purification – Mass Spectrometry data

CRAPome 2.0

:: DESCRIPTION

CRAPome is a database of annotated negative controls contributed by the proteomics research community. It addresses the common problem of distinguishing real interactions from the non-specific background (also known as ‘contaminants’). The database and associated computational tools to score protein interactions are available online.

::DEVELOPER

Proteomics & Integrative Bioinformatics Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Nat Methods. 2013 Aug;10(8):730-6. doi: 10.1038/nmeth.2557. Epub 2013 Jul 7.
The CRAPome: a contaminant repository for affinity purification-mass spectrometry data.
Mellacheruvu D et al.

MAPSD – Markov Affinity-based Proteogenomic Signal Diffusion

MAPSD

:: DESCRIPTION

MAPSD is a multi-omic signal diffusion algorithm designed to identify the disease susceptibility scores in unknown genes (or proteins) in complex and polygenic diseases such as schizophrenia.

::DEVELOPER

Wang Genomics Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
R

:: DOWNLOAD

MAPSD

:: MORE INFORMATION

Citation

Doostparast Torshizi A, Duan J, Wang K.
Cell type-specific proteogenomic signal diffusion for integrating multi-omics data predicts novel schizophrenia risk genes.
bioRxiv, 2020.

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

:: 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.

APEG – Affinity Prediction by Epigenome and Genome

APEG

:: DESCRIPTION

APEG uses a biophysical model to analyze transcription (TF)-DNA binding data, such as ChIP-seq data by incorporating epigenomic modifications and genome sequence data. This model can learn synergistic and antagonistic interactions between specific TFs and epigenomic modifications from genome-wide TF binding and epigenomic data.

::DEVELOPER

Zhong Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

APEG

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2013;9(12):e1003367. doi: 10.1371/journal.pcbi.1003367.
Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.
Chen CC, Xiao S, Xie D, Cao X, Song CX, Wang T, He C, Zhong S.

APCluster 1.4.8 – Affinity Propagation Clustering

APCluster 1.4.8

:: DESCRIPTION

The apcluster package implements Frey’s and Dueck’s Affinity Propagation clustering in R. The algorithms are largely analogous to the Matlab code published by Frey and Dueck. The package further provides an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results

::DEVELOPER

Institute of Bioinformatics, Johannes Kepler University Linz

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / Mac OsX
R Package / MatLab

:: DOWNLOAD

APCluster for R

:: MORE INFORMATION

Citation

U. Bodenhofer, A. Kothmeier, and S. Hochreiter (2011).
APCluster: an R package for affinity propagation clustering.
Bioinformatics 27:2463-2464