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There are two types of features which can be extracted from protein sequences. It is not possible to known which, if any, of these features would be useful in classification tasks. It may not be possible to build a classifier at all, or it may be very straight forward. To know this, a feature selection technique must be used, such as e.g. forward feature ...


3

It is a crystallisation artefact. Namely, the protein are placed in a condition where they fall out of solution without aggregating a gloop (as happens in most well in xstal trial) and to pack as a crystal they need to be placed nice and orderly. It is coordinated by water so is not relevant. A lot of structures have these —DMSO and ions are the most ...


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CellPhoneDB is a publicly available repository of curated receptors, ligands and their interactions. The interactions can be searched or downloaded.


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You can calculate gene ontology similarity with the GOSemSim R package (paper)(disclaimer I'm a contributor to this package). You have several similarity scores implemented, some of them are across the three subontologies. In python you have the GOATOOLS (paper). I think there was another Python package but now I don't remember its name.


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You can run a Molecular Dynamics simulation to see if the pose is stable and whether the protein changes/adapts it’s conformation. You can calculate the ligand efficiency, the docking score divided by the number if heavy atoms, and rank the compounds accordingly. You can also compare the scores of known ligands to the experimental values and see if the ...


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Have you tried STRING? It's a publicly available protein-protein interaction database, so it won't be a complete picture. Ingenuity Pathways Analysis (IPA) is really nice but is a paid service. Ask your institution if they have a license for one.


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Oligopeptide The lipophilicity of an oligopeptide or a small molecule can be calculated with RDKit, the classic compChem python toolkit. The measure is logP or partition coefficient, namely the magnitude of the ratio of the concentration in octanol over than in water, so the higher it is, the more it partitions in the octanol phase. However, with a compound ...


2

As others have said, there is no short cut and you will need to spend some time with a good book. I would say there are 3 broad areas of understanding you have to have as a structural bioinformatician: Solid understanding of the basic science. This includes molecular biology but also chemistry, which is often overlooked. Knowledge of the databases and data ...


2

So you can install PyMOL as a standalone, but you can also install it as a bona fide Python 3 module via conda: conda install -c schrodinger -c conda-forge pymol-bundle It can be installed in other ways —apt-get or brew or even compiled, but the latter is excruciatingly painful. In your python notebook you can do: import pymol2 with pymol2.PyMOL() as pymol: ...


1

First, I'll observe that Freeman centrality as you define it is the reciprocal of the Barycenter centrality as you define. It looks like people often use the reciprocal instead of the original definition, so in that sense they do look the same. I found the following comparison here (though it doesn't name it as Freeman): There are 2 types of distance ...


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Thanks a lot to all who commented/replied, the author of the scripts provided a response. It was down to a syntax change in PyMol of which I wasn't aware that caused my selection to come up empty.


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Based on my rudimentary understanding, PPIs are constructed from gene co-expression networks Not really, no. PPI stands for protein-protein interaction. In PPI networks, each node is a protein and each edge (line) connecting it to another node represents an interaction. A very common example is that an edge represents a physical interaction between these ...


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So, as the first reply said, there's sort of multiple questions here. For filtering things out from a table, I would absolutely use pandas. https://swcarpentry.github.io/python-novice-gapminder/08-data-frames/ Pandas is a way of managing dataframes in python. It looks like what you want from your table is to remove certain rows, if the content of those rows ...


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A good starting point could be this book by Gu and Bourne: Structural Bioinformatics, 2nd Edition


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It is probably necessary to correct for the degree distribution of the network, I am not sure that a tool such as the Mantel test will quite do this (as suggested by user Michael G.). In naturally occurring networks degree distribution is a strong confounder. Then again I am not totally sure what shape your data is in or what the question is. You mention a ...


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