Welcome to MERITS

This website is free and open to all and there is no login requirement.


Data download

MERITS allows users to download the SQL file of the database, predicted PDB files and all the data in this database.

Entry page

Our entry web page organizes in a comprehensive 3D structure and biological information.

Long-term maintenance

MERITS is constantly being maintained and updated. You can find every update of our project and track prior versions on the Timeline page.

Data access

The entries can be searched through three different methods, including ID search, Keyword search and Advanced search.


The ECharts package was used to achieve as user interactive pie charts display of statistical results.

High-resolution Interface

Several high definition JavaScript packages are employed to enhance information visualization in MERITS.


A comprehensive 3D structure database for annotating and analyzing Mycobacterial PE/PPE proteins.

Here are the top 5 Proteins most viewed by users

MERITS is a reliable online resource for accessing 3D structural information on Mycobacterial PE/PPE proteins. Most protein structures in MERITS were predicted using Esmfold. In addition to providing information on the physicochemical properties of proteins, MERITS is equipped with DSSP and with the capacity to predict histidine phosphorylation sites, signal peptides, transmembrane helices, non-classical protein secretion.

MERITS utilizes a combination of Python, PHP, and JavaScript to create an interactive and speedy interface that enables efficient data retrieval and analysis with users having multiple means of exploring data browsing pages and query methods. Moreover, bioinformatics plug-ins such as PDBe and Echarts.js supplement MERITS to improve data visualization and provide extensive data annotation.

More Information

Publications & Tools

Publication and tools used in or contribute to our project.

Tools URLs References
ABCpred http://crdd.osdd.net/raghava/abcpred/ Saha and Raghava, 2006
AllerTOP v.2 https://www.ddg-pharmfac.net/AllerTOP/ Dimitrov et al, 2014
AlphaFold https://www.nature.com/articles/s41586-021-03819-2 Jumper et al.,2021
AlphaFold DB https://alphafold.ebi.ac.uk/ Varadi et al., 2022
DeepGOPlus https://deepgo.cbrc.kaust.edu.sa/deepgo/ Kulmanov and Hoehndorf, 2020
DSSP https://swift.cmbi.umcn.nl/gv/dssp/ Touw et al., 2014
ESM https://github.com/facebookresearch/esm Lin et al.,2022
FastTree http://www.microbesonline.org/fasttree/ Price et al.,2010
GeneCard https://www.genecards.org/ Stelzer et al., 2016
Mafft https://mafft.cbrc.jp/alignment/software/ Katoh et al.,2016
NetMHCIIpan-4.0 http://www.cbs.dtu.dk/services/NetMHCIIpan-4.0/ Reynisson et al., 2020
NetMHCpan-4.1 http://www.cbs.dtu.dk/services/NetMHCpan-4.1/ Reynisson et al., 2020
NetSurfP https://services.healthtech.dtu.dk/services/NetSurfP-3.0/ Høie et al., 2022
PDB https://www.rcsb.org/ Berman et al., 2000
Pdbe-molstar https://github.com/molstar/pdbe-molstar Sehnal et al., 2021
Prospect http://prospect.erc.monash.edu/ Chen et al,2020
ProtParam https://web.expasy.org/protparam/ E. et al.,2005
ProtScale https://web.expasy.org/protscale/ E. et al.,2005
SecretomeP https://services.healthtech.dtu.dk/services/SecretomeP-2.0/ Bendtsen et al.,2004
SignalP https://services.healthtech.dtu.dk/services/SignalP-6.0/ Teufel et al.,2022
TBpred http://crdd.osdd.net/raghava/tbpred/ Rashid et al., 2007
TMHMM https://services.healthtech.dtu.dk/services/TMHMM-2.0/ Möller et al.,2002
Toxinpred 2 https://github.com/raghavagps/toxinpred2 Sharma et al., 2022
UniProt https://www.uniprot.org/ The UniProt Consortium, 2016
VaxiJen http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html Doytchinova et al., 2007

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