Hi Carl,
Curt is right, theres no structural information in fasta files. I'm not sure
what it is exactly you want to do or hope to achieve. RDkit can give you a
molfile
(http://rdkit.org/Python_Docs/rdkit.Chem.rdmolfiles-module.html#MolFromFASTA),
but if you want to have a 3D protein structure from sequence, you'll need to do
some homology modelling by using Salilabs modeller as an example
(https://salilab.org/modeller/), or failing that theres a homologous protein
structure available, some ab initio protein structure prediction software (I've
seen Rosetta be successful once). Esben Jannik Bjerrum
cand.pharm, Ph.D
/Sent from my Ubuntu Touch Phone
Phone +45 2823 8009
http://dk.linkedin.com/in/esbenbjerrum
http://www.wildcardconsulting.dk
On Sunday, December 4, 2016 7:26 PM,
"[email protected]"
<[email protected]> wrote:
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Today's Topics:
1. File Conversion? (Carl MacGentey)
2. Re: File Conversion? (Curt Fischer)
3. Re: comparing two or more tables of molecules (Matthew Swain)
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1.0in 1.0in;}#yiv6217805229 div.yiv6217805229WordSection1 {}-->Dear RDKit
Discussion Group- Is it possible to convert fasta files (DNA nucleotide
sequences) into PDB files? I am wanting to view strands of DNA and full length
genes in three dimensions. Sent from Mail for Windows 10 This is not
really possible. Fasta files contain only sequence information, not 3D
structural information.
Curt
On Sun, Dec 4, 2016 at 7:00 AM, Carl MacGentey <[email protected]> wrote:
Dear RDKit Discussion Group- Is it possible to convert fasta files (DNA
nucleotide sequences) into PDB files? I am wanting to view strands of DNA and
full length genes in three dimensions. Sent from Mail for Windows 10
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Sorry Steve, there was a bug in MolVS that you encountered. Should now be fixed.
"pip install -U molvs" to get the update (v0.0.7).
Matt
On 1 Dec 2016, at 15:52, Stephen O'hagan <[email protected]> wrote:
Thanks for the interesting links. MolVS looks good, but failed on
‘NC(CC(=O)O)C(=O)[O-].O.O.[Na+]’ which isn’t that extraordinary… Couldn’t get
Standardise to work at all, even on the example given; API not intuitive or
docs wrong or out of date. I will have a look at the info in the UniChem
paper, though not inclined to use a web service for what I want to do.
Cheers,Steve. From: George Papadatos [mailto:[email protected]]
Sent: 01 December 2016 14:26
To: Greg Landrum <[email protected]>
Cc: Stephen O'hagan <[email protected]>;
[email protected]; Francis Atkinson <[email protected]>
Subject: Re: [Rdkit-discuss] comparing two or more tables of molecules HI
Stephen, Further to Greg's excellent reply, see this paper on how InChI
strings and keys can be used in practice to map together tautomer (ones covered
by InChI at least), isotope, stereo and parent-salt variants.
http://rd.springer.com/article/10.1186/s13321-014-0043-5 Francis (cc'ed) has a
nice notebook somewhere illustrating these nice InChI splits to find these
variants. For educational purposes, there have been other approaches like
the NCI's identifiers - discussion here:
http://acscinf.org/docs/meetings/237nm/presentations/237nm17.pdf For pure
structure standardization using RDKit see here:
https://github.com/flatkinson/standardiserand https://github.com/mcs07/MolVS
Cheers, George On 29 November 2016 at 17:02, Greg Landrum
<[email protected]> wrote:
Wow, this is a great question and quite a fun thread. It's hard to really make
much of a contribution here without writing a book/review article (something
that I'm really not willing to do!), but I have a few thoughts. Most of this is
repeating/rephrasing things others have already said. I'm going to propose
some things as facts. I think that these won't be controversial:fact 1: if the
structures are coming from different sources, they need to be
standardized/normalized before you compare them. This is true regardless of how
you want to compare them. The details of the standardization process are not
incredibly important, but it does need to take care of the things you care
about when comparing molecules. For example, if you don't care about
differences between salts, it should strip salts. If you don't care about
differences between tautomers, it should normalize tautomers.fact 2: The InChI
algorithm includes a standardization step that normalizes some tautomers, but
does not remove salts.fact 3: The InChI representation contain a number of
layers defining the structure in increasing detail (this isn't strictly true,
because some of the choices about how layers are ordered are arbitrary, but
it's close).fact 4: canonicalization, the way I define it, produces a canonical
atom numbering for a given structure, but it does *not* standardizefact 5: the
RDKit has essentially no well-documented standardization code fact X: we don't
have any standard, broadly accepted approach for standardization,
canonicalization or representation that is fool-proof or that works for even
all of organic chemistry, never mind organometallics. InChI, useful as it is
for some things, completely fails to handle things like atropisomers (they are
working on this kind of thing, but it's not out yet). Given all of this, if I
wanted to have flexible duplicate checking *right* now, I think I would use the
AvalonTools struchk functionality that the RDKit provides (the new pure-RDKit
version still needs a bit more testing) to handle basic standardization and
salt stripping and then produce a table that includes the InChI in a couple of
different forms. I'd want to be able to recognize molecules that differ only by
stereochemistry, molecules that differ only by location of tautomeric Hs, and
molecules that differ only by the location of isotopic labels. You can do this
with various clever splits of the InChI (how to do it is left as an exercise
for the reader and/or a future RDKit blog post). I think there's something
fun to be done here with SMILES variants, borrowing heavily from some of the
things that Roger has written
about:https://nextmovesoftware.com/blog/2013/04/25/finding-all-types-of-every-mer/here's
a more recent application of that from Noel:
https://nextmovesoftware.com/blog/2016/06/22/fishing-for-matched-series-in-a-sea-of-structure-representations/
If I didn't really care about details and just wanted something that I could
explain easily to others, I'd skip all the complication and just use InChIs (or
InChI keys) to recognize duplicates. There would be times when that would be
the wrong answer, but it would be a broadly accepted kind of wrong.[1]
Regardless of the approach, I would not, under most any circumstances, discard
the original input structures that I had. It's really good to be able to figure
out what the original data looked like later. -greg[1] I'm crying as I write
this... On Mon, Nov 28, 2016 at 5:25 PM, Stephen O'hagan
<[email protected]> wrote:
Has anyone come up with fool-proof way of matching structurally equivalent
molecules? Unique Smiles or InChI String comparisons don’t appear to work
presumable because there are different but equivalent structures, e.g. explicit
vs non-explicit H’s, Kekule vs Aromatic, isomeric forms vs non-isomeric form,
tautomers etc. I also expect that comparing InChI strings might need something
more than just a simple string comparison, such as masking off stereo
information when you don’t care about stereo isomers. I assume there are
suitable tools within RDKit that can do this? N.B. I need to collate tables
from several sources that have a mix of smiles / InChI / sdf molecular
representations. I usually use RDKit via Python and/or Knime. Cheers,Steve.
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