Biomacromolecular structure analysis
Radka Svobodová
CEITEC, Masaryk University
MOLE and ChannelsDB – summary of past
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▪ MOLE – software tool for detection and
characterization of channels
▪ ChannelDB – interactive database of channels
with expected and annotated biological relevancy.
▪ Both include channel visualisation via LiteMol
Pravda L., Sehnal D., Svobodová
R., Navrátilová V., Toušek D.,
Berka K., ... & Koča J. (2017).
ChannelsDB: database of
biomacromolecular tunnels and
pores. Nucleic acids research,
46(D1), D399-D405.
MOLE and ChannelsDB – news
MOLEonline update finished & published
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MOLEonline: Web application for detection of channels
and pores and calculation of their geometrical and
physico-chemical properties
New features:
▪ Automatic detection of transmembrane pores
▪ Visualization of properties on the channel’s profile
▪ Interconnection with other bioinformatics tools (PDBe,
CSA, ChannelsDB, OPM, UniProt) and data transfer
from them
▪ Integration of LiteMol suite
.
Pravda L., Sehnal D., Toušek D., Navrátilová V., Bazgier V., Berka K., ... & Koča
J., Otyepka M. (2018). MOLEonline: a web-based tool for analyzing channels,
tunnels and pores (2018 update). Nucleic acids research.
MOLE and ChannelsDB – news
Use case: Transient receptor potential mucolipin 1
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(A) The channel (closed
conformation) of
TRPML1 (PDB ID:
5WJ5). The blue and
red dashed lines delimit
the membrane region.
The profiles depict
polarity and charge
along the pore.
(B) The channel in open
conformation (PDB ID:
5WJ9). The pore is
divided into three main
parts: Activation gate;
Central cavity and
Selectivity filter. The
figure was generated
using Pymol software.
PatternQuery: Description and detection of
fragments
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▪ Describes a fragment based on its topology and geometry
▪ Extracts the fragment from all structures in Protein Data
Bank
Sehnal D., Pravda L., Svobodová R., Ionescu C.-M., Koča J. (2015) PatternQuery: web application for fast detection of
biomacromolecular structural patterns in the entire Protein Data Bank. Nucleic Acids Res., 43, W383–W388.
http://ncbr.muni.cz/PatternQuery
Detection: Why to detect biomacromolecular parts?
Fragments:
• Patterns for drug design
• Comparison of biomacromolecules
• Understanding / discovery of biomaromolecule function
Channels:
• Key objects for biomacromolecule function
• Influence the binding site selectivity (only some substrate can path through)
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Detection: How to detect biomacromolecular fragments?
• Methodology:
• Describe a fragment via a defined expression (query)
• Find all suitable fragments
• Tools: PatternQuery, RASMOT-3D PRO, Promotif, Prosite, IMAAAGine,
PDBeMotif, SPRITE& ASSAM, 3Dfit, SPASM, Protein segment finder
Figure: Detection of testosterone (TES) and its 4 Å large surrounding
via PatternQuery: A query and a picture of the detected fragment.
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Detection: Example – binding pocket detection
Detection of fragments within 3U7Y
• Glycoprotein gp160 from Human immunodeficiency virus 1 in
complex with Homo sapiens immunoglobulins (PDB ID 3u7y).
• Goal: Detect a binding pocket of any residue containing a pyranose
• Results - via PatternQuery (http://ncbr.muni.cz/PatternQuery):
A) First, the query identifies a pyranose moiety (a ring composed of 5 carbons and
an oxygen atom). B) Then, all residues which include this pattern in their structure
are identified. C) Finally, all the residues that are at most 4Å from any of the
pyranose containing residues are detected as well.
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PatternQuery - Detecion of key parts
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Near(4,
Rings(5 * ["C"] + ["O"]),
Near(4, Atoms("Ca"), Atoms("Ca"))
)
.AmbientResidues(4)
Sugar binding site from Pseudomonas aeruginosa lectin
Results (38 entries from PDB):
• Pseudomonas aeruginosa (27 entries) or its synthetic construct (2 entries)
• Burkholderia cenocepacia (4 entries)
• Ralstonia solanacearum (2 entries)
• Chromobacterium violaceum (2 entries)
• Bacillus subtilis (1 entry)
Software PatternQuery:
Sehnal, Pravda, Svobodová, …, Koča, Nucl.
Acids Res. (2015)
Comparison
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Superimposition of sugar binding sites
from Pseudomonas aeruginosa lectin:
Results:
Two main types of binding sites:
• Pseudomonas aeruginosa: 2 SER
• Burkholderia cenocepacia and Ralstonia solanacearum: 2 ALA + HIS
Plus their combination:
• Chromobacterium violaceum, Pseudomonas aeruginosa mutants: 1 ALA, 1 SER
~
~
Software SiteBinder: Sehnal, Svobodová, ..., Koča, J. Chem. Inf. Model. (2012)
Detection: Example – sugar-binding site detection
Detection of Pseudomonas aeruginosa Lec-B sugar binding site
Motivation:
• Pseudomonas aeruginosa is an opportunistic pathogen associated with
a number of chronic infections.
• It forms a biofilm enabling it to survive both the response of the host
immune system, and antibiotic treatment.
• One of the cornerstones of biofilm formation is the presence of sugarbinding
protein LecB (PA-IIL).
• Its inhibition is considered to be a promising approach for antipseudomonadal
treatment.
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Description of the binding site:
• A pair of Ca atoms (distance < 4 Å)
• They are near to (< 4 Å) the pyran ring (= ring
containing 5 C and 1 O).
• Include its surrounding (residues closer than 4 Å)
Detection: Example – sugar-binding site detection
Detection of Pseudomonas aeruginosa Lec-B sugar binding site
Pattern: Near(4,
Rings(5 * ["C"] + ["O"]),
Near(4, Atoms("Ca"), Atoms("Ca"))
)
.AmbientResidues(4)
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Results:
The binding site is contained in 40 PDB entries from 4 organisms (bacteria):
• Pseudomonas aeruginosa
• Burkholderia cenocepacia
• Ralstonia solanacearum
• Chromobacterium violaceum
Detection: Example – sugar-binding site detection
Detection of Pseudomonas aeruginosa Lec-B sugar binding site
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Results: The binding site is very conserved and it has two structure variants:
Pseudomonas aeruginosa Burkholderia cenocepacia
Chromobacterium violaceum Ralstonia solanacearum
Conclusion: The anti-pseudomonadal treatment have to target the above
described binding sites (with higher affinity than the sugar).