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 ...
Getting a crystal structure is hard work for crystallographers, even with a high throughput systems, so they make constructs with only known domains or regions of particular interest. What happened here is that only two parts have been cloned, expressed and crystallised. The rest may be crystallisable or solvable by cryoEM, but may be too disordered.
So binaryCIF is newer and is heavily inspired by MMTF.
NGL, the most "loved" JS library (IMO) to show protein fetches proteins as MMTF, while Mol* uses bCIF —Mol* has the official repo of the bCIF format. Alex Rose, developed the former at the RCSB PDB, and the latter in collaboration with the PDBe. He also worked with Antony Bradley, the main ...
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 ...
Here is a page with several suggested methods. I agree that Rampage looks the best of the options, but it seems that it provides some suggested workflows for drawing it in R so that it is very customizable and you can probably get it to show any features you want if you're willing to customize a little.
Here is a Python tool for drawing Ramachandran plots.
If you want to share a PDB file, you could try Michelanglo. It allows you to upload a PDB file (among other things) edit a description panel (which can feature special links that control the protein view and representation) and share the link with whomever —without needing any installations on either side.
Here is the page of shared protein: gallery
Asking how to visualise the disordered region of a protein is a bit like asking how to visualise the location of an electron.
An intrinsically disordered protein, by definition, has regions that are disordered. In other words, their location is variable under observation, so they cannot be precisely placed in a 3D model.
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,...
The difference between different force-fields is not going to be major, it is the side steps which are.
If you are starting from a SMILES string, optimisation is a must obviously.
If you are using a 3D conformer from PubChem or even an actual sub-1 Å crystal structure from CSD, optimisation is nice for consistency.
MMF94 is a solid choice. ...
There is some confusion in the question.
Solvent accessibility of a residue, i.e. the area accessible to the solvent (water), depends on the 3D structure of the protein. It's not a property of an amino-acid (chemical compound such as alanine, leucine, ...).
There may be a correlation -- amino-acids with hydrophobic side chains are more likely to be buried,...
Adding to Devon's correct answer: Depending on the amount of images you got, there is also the possibility to avoid the feature engineering by building a two level architecture:
The first level identifies the regions of interest in your image and scales these regions to the same size.
In the second level, the recognized areas are fed to a (multilayer) ...
Whether to quantify things at all with depend on the technique you want to use. An end-to-end CNN (convolutional neural network) for example would just be fed the 3D image for training and predictions. However the major downside to CNNs is that you need a LOT of data to train them, though perhaps you can get lucky and find an existing CNN that you can use ...
You cant do this in Chimera, but in pymol you can write a script to do this.
In terms of the view, you can use get_view on the command line. This returns a section that you can copy and paste in - which will give you the same view every time. I recommend labelling the residue you want, then finding the position you want to view it from. Then using get_view ...
Dan is correct but it doesn't answer the question. After much thought, I have finally figured out how to do it, only thanks to Dan's answer.
The problem is that a subsequence is one-dimensional and does not therefore include any of the additional information which is extractable from the three-dimensional structure (when available). Obviously, having the ...
No, this doesn't makes sense (I think).
The hydrophobicity of an AA is the hydrophobicity of the AA, in whatever context, just like it's molecular weight. It's weight is it's weight, in a structure or in isolation.
I know this seems physically unrealistic, but so is an abstract measure of 'hydrophobicity', it's just the best we can do based on partition ...
IBIS reports protein interaction with other biomolecules. Your protein or homolog must exist in the Protein Data Bank.
IBIS @ NCBI: https://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi
"For a given protein sequence or structure query, IBIS reports protein-protein, protein-small molecule, protein nucleic acids and protein-ion interactions observed in ...
I wrote a very quick and dirty script to handle conversion between file types using BioJava.
Download the jar file here
To run: java -jar BioUtils.jar $FILE $TYPE
where \$FILE is a PDB or mmCIF file you'd like to convert and \$TYPE is the format of the output file [PDB, CIF, MMTF].
You can do this with BioStructures.jl in Julia. All the 6 transformations between PDB/mmCIF/MMTF are possible.
For example, PDB to MMTF:
struc = read(in_filepath, PDB)
mmCIF to MMTF:
struc = read(in_filepath, MMCIF)
Each of the three PDB sites had the same problem: using mmCIF files in web-based viewers is inefficient. To solve this problem three new file formats were introduced:
MMTF at RCSB,
BinaryCIF at PDBe,
mmJSON at PDBj.
MMTF has the smallest files – it's aggressively optimized for size.
It is based on MessagePack, but on top of it uses a ...
I am not good as you are and needed to use a parser for the pdb file
# -*- coding: utf-8 -*-
Created on Thu Nov 7 17:40:03 2020
import matplotlib.pyplot as plt #matplotlib
from pdbx.reader.PdbxReader import PdbxReader #...
Answer from @o-laprevote, converted from comment:
Your protein includes intrinsically disordered regions (https://www.uniprot.org/uniprot/Q12802#family_and_domains). Showing a predicted structure may give a wrong idea of what it looks like: I'd consider simply showing the ordered part (of which a structure already exists) plus a "spaghetti" ...
It is a glitch in PyMOL.
Caveat. I would say that, whereas cmd.label is great for adding labels en-mass for internal figures, it is not great for figures for dissemination (which require few strategically placed labels possibly with a faint white outer glow —cf. your D330): most figures in papers I would say are Powerpoint or Photoshop labelled.
You called ...