Molecular Dynamics and Toenail Fungus
I had been plagued by this complaint for many years and nothing I
did had any effect. I decided to look at my own treatment using
these computing techniques.
The basic process that is adopted is:
1. Find and select the protein , based upon medical research data ,
which is causing the problem, which we will call a "target".
2. Create a molecular model of it.
3. Select a "drug" , based upon any data you can find , to try and
disable the "target".
4. Create a molecular model of it.
5. Dock the "drug" with the "target" to see what its effect might
Athletes foot fungus and toenail fungus are usually the fungus
If we search the literature on the Pubmed site
with "trichophyton rubrum secretion targets" we find a paper
"Expression dynamics of secreted protease genes in Trichophyton rubrum induced
by key host's proteinaceous components." This paper tells us
"Our results suggest that Sub3, Sub4, and Mep4 may be the
dominant secreted proteases responsible for invasion and the use
of host proteins as nutrients during infection by T. rubrum."
Searching a little further the paper "Gene Expression Profiling in
the Human Pathogenic Dermatophyte Trichophyton rubrumduring Growth
on Proteins" is found which quotes:
"Notably, a sequence that encodes an Hsp70 protein (TrMZE08ACQ)
was found to be strongly upregulated during growth in both
protein-containing media." and "Since the
induced T. rubrum HSP70 gene is concomitantly expressed with genes
that encode major secreted proteases, the putative Hsp70 chaperone
could be involved in the folding and/or secretion of these
This leads me to , at least as a first attempt, to drug the HSP70
protein in an attempt to disable it and "starve" the fungus.
We need two sets of data; the construction and shape of the target
and the drug. Which drugs? I have built up a drug database on
my system and this is covered in Selecting
Now we need the target which is going to be the HSP70 protein.
You want the crystal structure often found on the RCSB PDB site, but
it is not always there. All proteins can be found on the Uniprot site. Searching
for HSP70.and selecting human leads you to the entry Q9NZL4;
select it and under format select fasta and you have a page with:
- >sp|Q9NZL4|HPBP1_HUMAN Hsp70-binding protein 1 OS=Homo
sapiens GN=HSPBP1 PE=1 SV=1
Copy and save that as a file, say hsp70.fasta.txt. This is the
construction of that protein as a string of aminoacids; which does
not give us the shape
To get the shape ( or at least as good an estimate as possible we
can use the Swiss Model
where you click "Start Model" and then paste into the "Target Box"
the content of your fasta file; then click "Build Model". A
little while later you will be presented with the best estimate of
the shape of that protein. If it is not at least 90% then you
are on the wrong path and must re-evaluate the situation. In
this case Model 1 should show 99.64% alignment and if you click it
and then PDB file you are shown a PDB data file. Select it all
and save it as say hsp70-swiss.pdb. Load that into VegaZZ and
you should see:
It can be rotated , zoomed etc with the mouse.
This protein must later be defined as a target in Autodock Tools but
for now we need to make sure it is correct in bonds and atoms.
In Vega edit , add , hydrogens , protein and you will see
hydrogen atoms being added. Then edit , coordinates ,
normalize to make sure it is on a common reference
grid. Then edit , change , bond type , find bond type
completes initial corrections. Then calculate ,
charge and pot , CHARM22_PROT with Gasteiger MUST
end up with an integer for every correct protein; in this case -9.
Now to best find the shape in blood we edit , add ,
cluster , water set x=100 for water box size
and click the "Same" button then OK. Your screen then
has lots of water molecules added in. Now calculate , ammp
, minimisation letting it run for 3000 steps. You can
see small shape changes as the energy is balanced. It may take
a while -- on my PC about 2 to 5 minutes.
Now edit , remove , water then file , save
as , pdb2.2 and call it hsp70-swiss-min.pdb or similar.
Load that file into (drag and drop) Autodock Tools (ADT). Here
it must be defined as a target (know as macromolecule)
so grid , macromolecule , choose , select and the
program initializes. If non-bonded atoms are found you have an
error. The new file is then saved as a PDBQT file , say
hsp70-swiss-min.pdbqt. Before closing ADT we need to know how
big that molecule is to tell the docking system what range it must
examine. Use grid , grid box ,and set the
spacing to 1 angstrom. You will see it does not quite cover
the molecule; red needs to go up to 55, green to 50 and blue
to 80. Remember those numbers.
Now we have the construction and shape for the target protein.
For drug selection you can search blogs , medical papers and / or
try a few well known remedies or just guess. I usually start
with aspirin first and then try curcumin next.
Often a Google search for say "aspirin pdb" will lead you to a file
for aspirin. In my case the file was found ( as is often the
case ) on the RCSB Protein Data Bank under the code of 1oxr. This is
a file of aspirin already prepared and docked with something
else. We will download it and delete the "something
else". It downloads as 1oxr.pdb. Save that file and load it
into Chimera where you will see:
In Chimera zoom in so the aspirin is clear and then select a part of
it (CNTL click) - it goes green. holding CNTL press the UP
ARROW key and the whole molecule goes green. Now on the menu select
Select / Invert all models and everything else turns
green. Select Actions /
Atoms and bonds / delete and all the green is gone
and you are left with aspirin alone. Select File / Save PDB and call it
asp.pdb. Close Chimera.
Open the asp.pdb in Vegazz to make sure the previous step worked
OK. At this stage Edit
/ coordinates / normalize is a good idea to make sure
it is in full central view for the next stages. Save the file and
Open Autodock Tools and drag in the saved file. Now we process
that as a "ligand". So select ligand / input /
choose and then ligand / output / save as pdbqt.
We now have the construction and shape for the drug ( now called a
We must prepare a configuration file (called lets say hsp70-asp-vina.config)
to tell the docking program Vina exactly what to do.
The file looks like this:
We have told it where the drug file is (ligand); the target
protein (receptor) and where we want the output placing (out)
-- full file locations must be used, which on your system may
be different depending on where you saved the files. The sizes
have been set to the size of our hsp70 protein, and exhaustiveness=8
means "do a good job but don't take too long"
out = d:\temp\hsp70-asp-vina-out.pdbqt
ligand = d:\temp\asp.pdbqt
receptor = d:\temp\hsp70-swiss-min.pdbqt
exhaustiveness = 8
Save that file as vina.config and open a command prompt in that
folder. The Vina program (vina.exe) has been installed on your
system and you should have placed its path into the PATH variable
such that it can be executed from other locations.
At command prompt type in:
vina.exe --config hsp70-asp-vina.config -- log
The system should find all your CPUs and run a docking in a minute
or so , telling you the best score was -4.9. With a log file
You have performed a molecular docking analysis!
However you should repeat it because Vina should only
really be used for regions of docking in a box smaller than
40X40X40. Loading the output from your docking above analyze
, dockings , open result (load as single molecules)
pulls in the positions of the drug molecules when docked. Load
in the target molecule (which was called hsp70-swiss-min.pdbqt) by
"drag and drop" then analyze . macromolecule , choose. Now
by selecting the colours and fill of the two loaded molecules you
can see where they interacted. Using analyze , dockings ,
interactions and you will see exactly where the dockings
are taking place. Use the grid , grid box to
manipulate the grid box to the area of interest. On my system
the 40X40X40 box needed moving with offsets Red=0 , Green=5 and
Alter these values in the configuration file i.e.
40 , 40 , 40 , 0 , 5 , 9 and rerun. The new
run score will be more accurate and reliable than the first one.
What do these scores mean? They are the energy loss which
occurs when docking takes place, and without going into great detail
(most of which I can not claim to fully understand) it would appear
that any scores above -6 mean that the interaction has little or no
effect ; ranging down to -10 which indicates very profound
What that run tells you is that aspirin will not cure toenail
fungus; a score of -4 to -5 will have no effect, you need one below
-7. Try other drugs.
This process can be repeated using other drugs of your
choosing. I eventually selected a mixture of several drugs
with good scores ( below -7 ) which, after further processing to
increase water solubility, I applied to my toe as a paste under a
plaster for 2 days. The effects take a long time to appear but now I
do seem to be free from the fungus that has pestered me for years.
This example below used curcumin in its SD formulation during
2015. The fungus returned in 2018 as no doubt my blood flow to
my feet is getting worse with old age hence my feet are not being
kept well above the 30C needed to suppress fungal growth. I
have now tried curcumin in its SOL formulation with DMSO whilst
adding a little N-M-2-pyrollidone for permeation enhancement.
The results appear just as good, and it is much easier to do.
It would appear to me that molecular docking does give
real indications as to how molecules behave.