If you need to process multiple files, you could use Biopython to parse a PDB structure.
from Bio.PDB import PDBParser
# create parser
parser = PDBParser()
# read structure from file
structure = parser.get_structure('PHA-L', '1fat.pdb')
model = structure
chain = model['A']
# this example uses only the first residue of a single chain.
# it is easy to ...
There is a very nice database, pdbcull (also known as the PISCES server in the literature). It filters the PDB for high resolution and reduced sequence identity. It also seems to be updated regularly. Depending on the cut-offs, you get between 3000 and 35000 structures.
If you are specifically interested in rotamers, you may want to look at top8000 instead, ...
You could try one of these tools to predict protein-protein interactions:
Given two protein sequences, the structure-based interaction prediction technique threads these two sequences to all the protein complexes in the PDB and then chooses the best potential match. Based on this match, the method generates alignment scores, z-scores, and an ...
To make it crystal clear (and make than pun):
Assignment: a 3D structure is known and the residues are assigned a secondary structure
Prediction: an algorithm predicts from linear (primary) sequence what the secondary structure may be —often incorrectly. This can be homology based, covariance, deep learning etc. But most commonly just the vector norm of ...
A popular (and I would say, respected and trusted) website is PDBsum https://www.ebi.ac.uk/pdbsum (which also has a Wikipedia article about it: https://en.wikipedia.org/wiki/PDBsum)
They measure protein-protein (prot-prot) contacts as any Nitrogen, Carbon, or Oxygen element atoms (N, C, O) within 4.0 Angstroms (Å) when measuring the 3d euclidean distance.
Some of this information (at least some domains, active sites, etc) is available from UniProt.
If you want to download their whole database, you can search without specifying any terms and then click the Download button.
Sulfur atoms are shown in yellow.
The molecular viewer that you use is JSMol (JMol ported to the web).
Atoms are colored by element: grey C, blue N, red O and yellow S.
If you wonder how other atoms would be colored, see JMol's Default element colors, by periodic table.
If you choose to perform your own culling of the PDB, resolution is probably the first thing you'll want to look at, which as Davidmh mentions is the main selection criteria for PISCES. High quality structures will also have better R-factor values. You can also give preference based on experimental technique, in descending order of quality:
I'm less familiar with Phyre, but I-TASSER is a really sophisticated system that takes the results of a search using multiple threaders and plugs them into an ab initio simulation which tries to minimize the energy of the models by sampling many possible 3D conformations, which I don't think Phyre does.
Tried looking for an explicit database? i.e.
ComSin: database of protein structures in bound (complex) and unbound (single) states in relation to their intrinsic disorder: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2808974/ or
LigASite: a database of biologically relevant binding sites in proteins with known apo-structures https://www.ncbi.nlm.nih.gov/...
TL;DR: docking is much slower than any ML approach, but the ML approach can be constrained by pharmacophores dictated by the active site.
Side note: Scale
The scale for ligand space exploration is generally is generally several orders of magnitude higher than "hundreds": Zinc DB lists 750 million enumerated compounds, GDB13 has enumerate all possible ...
You can use one of the UniProt Protein APIs.
As you said you have your pdb entries in a text file line by line you can, like this example.txt containing:
Using the commandline, you can use a little script like this to download the name, if it is available for the given pdb entry.
while read line;
curl -X GET --header 'Accept:application/...
Different criteria give different rankings.
Mol* (read molstar with trilled rhotic R according to the given IPA) is the newest, is used by the RCSB PDB and can support huge complexes. It is less implemented and has a tricker documentation.
NGL is the former viewer from RCSB PDB. It is good and the switch to Mol∗ was driven by an effort for uniformity with ...
Are you aware of BRENDA? I was just introduced to it today for a completely separate reason (looking at carbohydrate enzyme families in the Nippostrongylus brasiliensis proteome), and it seems to be a fairly comprehensive database. There is at least a literature link there for the ascorbate-complexed crystalisation of Ascorbate peroxidase on that site. ...
Yes you can modify the reference PDB file and look for the changes and for this purpose you need visualizers. One among these is Chimera. You can easily carry out the energy minimization steps using Chimera, first mutate your PDB file manually and then provide it as an input for Chimera and the steps for doing energy minimization are fairly simple as given ...
Could you use CCP4's NCONT program? There's a GUI and a command line interface, whatever suits. You can specify which chains you want to target and interact with and set a cut off for distance. The bonus here is once you're in you have a nice suite of other structural tools to use.
If you're just doing it once, the GUI is friendly enough to work things out, ...
There are three initiatives I know of to have a go at this:
PConsFam, which collects data from this paper.
The Baker group's metagenomic study which you mention in the question.
The recent DMPfold work from our lab. This compares its results to the above two studies and discusses the effect on various model organisms.
The second and third of these don't ...
With pymol as a python module (conda install -c schrodinger pymol, not the GUI one), it is very easy.
with pymol2.PyMOL() as pymol:
pymol.cmd.load('protein.pdbqt', 'protein') # the second is the name within pymol instance.
pymol.cmd.save('neighbours.pdb','ligand expand 3') # get atoms 3 Å ...
You can use Gemmi.
ccp4_map = gemmi.read_ccp4_map('my.map')
ccp4_map.setup() # optional
np_array = numpy.array(ccp4_map.grid, copy=False)
The setup() call expands data to the whole unit cell.
The numpy array above accesses the data through Python's Buffer Protocol (it does not copy the data). So ...
Protein sequences from the same family form a sequence alignment. Some positions in the alignment will be conserved, i.e. the same in all sequences. These are often associated with function, e.g. a catalytic residue at position 50.
However there is a more subtle signal in sequence alignments called covariation. Two positions, say residue 30 and residue 60, ...
In general, if you simply want to extract that part of the PDB file, you could loop over it (it's plain text) and check the fields you're interested in:
with open('2ly4.pdb') as pdb:
for line in pdb:
if line[:4] == 'ATOM':
chain = line
res_idx = int(line[23:26])
if chain == 'A' and 1 <= res_idx <= 30:
EnsEMBL also has this. Search for your gene of interest, choose your transcript, go to the page of its protein product(s), and select "Domains & Features" from the right-hand menu (using human p53 as an example):
Domain source Start End Description Accession InterPro
PANTHER 3 331 - ...
The CATH database classifies protein by fold: https://www.cathdb.info/
So the value from that is probably the most useful for you.
Reducing Agent Concentration
Your crystallising conditions do not mean much. They are a solution that is close to precipitating your protein but slowly enough for it ...
Glycine has a single hydrogen atom as its side chain:
All the six bond angles with the CA atom in the middle are about 109°
(C-CA-N, C-CA-HA3, C-CA-HA2, N-CA-HA3, N-CA-HA2 and HA3-CA-HA2 using the CCD naming convention). This defines the rough direction of the hydrogen atoms.
But which of the two hydrogens is the side chain?
If it is a left-handed protein,...
As of 2021, the EBI alphaFold2 is the best resource. There are some caveats, which I have outlined in this blog post.
But majorly, it contains only monomeric predictions and not oligomers and complexes. As a result, for protein with a close homologue with a solved structure, it may be more handy to use a threaded structure as found in the SwissModel ...
My answer is mainly based on molecular dynamics,but in this context it shouldn't make much of a difference.
The main reason has to do with the force-field-water model used during the simulation. Examples of water models are SPC, TIP3P or TIP4P; same molecule (water) but different topology.
A pdb usually comes from different sources and it can contain ...
Most crystallographic structures are X-ray and the hydrogens are absent. In the case of neutron diffraction models, there may be many missing high b-factor ones (example in PDB:5MOP). As a consequence models are reprotonated. Also missing carbons and missing loop residues are added for some in silico applications too (not docking).
However, it is ...
In cases when you cannot find answer through the web interface, you can download all the data and search it locally.
Relying on the PDB annotations of SS bonds, you could search for PDB files that have exactly one SSBOND record:
$ cd $PDB_DIR/structures/divided/pdb/
$ zgrep -c ^SSBOND aa/pdb*.ent.gz | grep :1$