Statistics-BactMentha

On this page, you can explore graphical statistical summaries of the BactMentha database.
You can also display a table by selecting a specific statistics for a taxon (or for all taxa)..

The BactMentha database stores 12544 bacteria-host protein-protein interactions (PPIs) distributed as follows:
• 11612 interactions (human),
• 897 interactions (mouse),
• 35 interactions (rat).

These interactions involve 5088 distinct host proteins (respectively 4554 for human, 521 for mice and 13 for rat) and 2970 distinct bacterial proteins (2914 interacting with human proteins, 98 with mouse proteins, and 12 with rat proteins).

In total, 102 bacterial strains from 39 distinct families are represented in BactMentha:
• 97 strains from 38 families (human),
• 27 strains from 16 families (mouse),
• 6 strains from 4 families (rat).

Number of bacteria-host protein-protein interactions per host

Number of full annotated, partially annotated or unannotated interactions per host

Global

Human

Mouse

Rat

BactMentha provides different types of additionnal annotations about the protein-protein interactons (PPIs) that can be displayed in the "Annotation" column of the result tables:

• bacterial protein annotations (PA):
Bacterial proteins have been annotated as virulence factors or effectors after perfomring BLASTp sequences seraches against Virulence Factor Database (VFDB) and BastionHub databases. More details about the bacterial protein annotations can be found in the documentation "Data Sources" section.

• binding regions (BR):
Available experimental interaction binding regions have been retreived from IMEx databases for either the host or the bacterial protein (or both).

• mimicINT interfaces (MI):
The mimicINT was used to infer interaction interfaces for bacteria-host interactions using known interaction templates (domain-domain or motif-domain). More details about the mimicINT workflow can be found in the documentation "Data Sources" section.

The graphs on the left reports statistics on three subsets of BactMentha interaction data:
• Fully annotated interactions correspond to interactions for which the three possible annotations are present (the bacterial protein is annotated and there are both experimental and inferred binding regions).
• Partially annotated interactions correspond to interactions for which at least one of the three types of possible annotations is present (the bacterial protein is annotated OR there are both experimental OR inferred regions of interaction OR any combination of two of these annotation types).
• Unannotated interactions correspond to interactions for which none of the three possible annotations is present (the bacterial protein isn't annotated and there is no interaction region description).

N.B.: mimicINT only runs for the Human host.

On the right, the plots display the different proportions of interaction annotation combinations in the whole dataset and for each host.

Legend:
• ___ mimicINT interfaces (MI) prediction only.
• ___ MI + BR
• ___ Experimental Binding regions (BR) only.
• ___ BR + PA
• ___ Bacterial Protein Annotation (PA) only.
• ___ MI + PA
• ___ MI + BR + PA

Enhancing Interaction Annotations with mimicINT Interfaces (MI):

Integrating mimicINT Interfaces (MI) inference has importantly increased the annotation of protein interactions in BactMentha. Specifically, MI-based predictions have led to over 10% more annotated interactions (MI alone), Additionally, when MI predictions overlap with known binding tegions (BR), they help refine existing interaction data.

This improvement is valuable for two key reasons:
• Increased resolution: MI annotations often pinpoint defined interaction determinants within experimentally identified binding regions. While BR annotations encompass larger protein segments necessary or sufficient for interaction, MI provide annotations of specific domains or short linear motifs (SLiMs), thus suggesting a potential interaction molecular mechanism.
• Guiding experimental validation: The agreement between MI predictions and BR suggests that MI-based annotations are reliable. These insights can help design new experiments to validate predicted interactions, especially when no experimental data is available yet.

Proportions of interactions annotation types (among annotated interactions) per host

Global

Human

Mouse

Rat

Number of interactions (and their proportion of bacterial protein annotations) for the 10 first bacterial taxa

Global

Human

Mouse

Rat

Bacterial Protein Annotation generation:

To better understand how bacterial proteins interact with host proteins, we annotated bacterial proteins using data from the Virulence Factor Database (VFDB) and BastionHub. These annotations were assigned through BLASTp similarity searches between each bacterial protein in BactMentha and the proteins in these databases.

A given annotation was assigned to a bacterial protein if it meets the following criteria:
• Sequence Identity ≥ 30%: The bacterial protein needed to be at least 30% identical to a known protein in the database.
• Alignment Coverage ≥ 75%: At least 75% of the bacterial protein sequence should be aligned with the reference protein.

If a bacterial protein meetst both conditions, it is annotated with the given functional category such as secreted effectors, immune modulation, adherence, or motility.

BactMentha Database Statistics

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