The geomm library provides a collection of purely functional
computational routines that are especially suited for data related to
large and complex macromolecules such as proteins.
geomm is topology agnostic and instead different sets of atom
indices are used for all computations. The outsourcing of this complex
domain knowledge allows for extreme simplicity and specific focus on
calculating geometric properties with no ambiguity. This also makes
geomm only dependent upon standard data structures like the
numpy.ndarray.
The feature set is minimal at this point:
superimpose structures
wrangle those periodic boundary conditions
Cython implementation of Theobald-QCP for fast RMSD calculations
See the Motivation & Alternatives below for why the geomm approach
to software libraries is needed, and we encourage you to add your own
algorithms.
Once you have the package installed you can go straight to the API Overview or the Full API documentation.
There are also a few example to get you started in the info/examples
directory.
There are many (excellent) packages that provide similar facilities as
geomm like:
many others.
The question then arises “Why another one”?
While these are all excellent packages providing useful functionality there is a large mismatch between the in-memory representations they use. This means that integrating them with either with each other or with your own data means you will need a variety of conversion methods to go between the different libraries.
This may not be an issue for a small amount of data, but as structures get larger and the number of data points from simulations becomes larger then this conversion overhead can become very large.
This is made even worse by the fact that most of the conversions move through extremely underspecified, slow, and lossy formats like the Protein Data Bank (PDB) format.
To be clear, we are concerned with computational studies of molecular models consisting of atoms, bonds, and higher order structures therof and not on more “realistic” models involving electron densities like you would have from X-Ray crystallography. The PDB format was designed for the latter scenario where “topologies” are really just an afterthought and actually something that is inferred from the 3D geometries of unconnected atoms. It is tempting to view the PDB format as simply a CSV-like table format for storing information for tables of atoms. Unfortunately, it is not this and is highly unsuited for this task. Furthermore, using a format like PDB which requires a custom parser (of which there are many) and writer (of which there are more of) which is unlikely to be either: correct, consistent, and performant. This is partially due to the fact that inherently the format itself is not designed to be quickly parsed at all. Thus, beyond the complexity of mismatches in parsers using PDB as an interchange format is a bad idea. There are new formats like MMTF which are huge step forward in remediated deficiencies of the PDB format, however, in the end this format is intended to solve the needs of the crystallography community and not for all computational macromolecular researchers at large.
Thus, when using these different packages you must learn the details about each representation of the molecules. This is unfortunate because semantically are all essentially equivalent and only differ in details of syntax and aesthetics.
Furthermore, if you would like to run a series of computations in a pipeline over a single set of data using N different packages you will end up having to convert (and likely copy) your data multiple times to run the subroutines you need.
A better solution to using a different in-memory objects for each library with attached methods for each object is to use simple pure functions.
For example, most packages perform computations by first constructing an
object and then running a method inputting some hyperparameters. We will
pick on mdtraj since I am most familiar with it but all are equally
guilty.
First we must load from one of the supported formats, usually this is done from the disk as shown in the example but it is possible to load once into memory to avoid slow reads and writes to disks:
import mdtraj as mdj
# see examples for the structure files
traj = mdj.load('lysozyme_pxylene.pdb')
Once it is loaded into the traj object then we can perform
calculations on it with the packages functions, here we calculate the
solvent accessible surface area (SASA) using the Shrake-Rupley
algorithm:
sasas = mdj.shrake_rupley(traj)
Note that because we have an mdtraj.Trajectory object as an argument
there is functionally no difference in this being a method of the
object:
# not actually valid
sasas = traj.shrake_rupley()
Now if we want to find the hydrogen bonds from MDAnalysis we have to convert again since we cannot use the same representation:
from MDAnalysis import Universe
from MDAnalysis.analysis.hbonds import HydrogenBondAnalysis
uni = Universe('lysozyme_pxylene.pdb')
result = HydrogenBondAnalysis(
uni,
'protein',
'resname LIG',
distance=3.0,
angle=120.0
).run()
Notice that both of them take exactly the same information but have totally different concepts that they are even talking about: “trajectory” vs. “universe”.
Contrast this with the (not yet implemented) geomm version of
shrake-rupley:
from geomm.sasa import shrake_rupley
sasas = shrake_rupley(
coords,
radii,
probe_radius=0.14,
n_sphere_points=960
)
In this example we just pass in a set of coordinates assumed to be
atoms. For each atom there is an associated radii that you have obtained
by some means. Instead of hiding the magic of looking up the atomic
radii of your atoms we simply make it explicit so that you as the user
have complete control over it. In the mdtraj version you must alter
the source files to change the behavior.
Now consider the geomm (not yet implemented) version of the hbonds
algorithm:
from geomm.bonds import hbonds
matches, distances, angles = hbonds(
coords,
selection_a, # the protein if you wish, or anything else
selection_b, # the ligand perhaps
# the hyperparameters
distance=3.0,
angle=120.0,
)
No conversions necessary, and you are free to choose whatever sets of
atoms you want to compare. We will adress the drawbacks of the selection
language used in the MDAnalysis example below.
In both cases there is really only 3 different pieces of data that are common to all functions for macromolecules:
the 3D atomic coordinates
the periodic boundary conditions
the topology (bonds, residues, and types)
The 3D atomic coordinates and the periodic boundary conditions are essentially a very well known data structure, the humble array.
The only questions that need to be answered is whether the 3D atomic coordinates are a single structure (N x 3; N is number of atoms) or a “trajectory” (M x N x 3; M is the number of frames in the trajectory). However, these kinds of compositions idealy should be taken care of special purpose container types (a specialty of computer science). This is relevant for simulations that don’t follow a linear trajectory model but rather a branching tree that is used in importance sampling type simulations such as weighted ensemble (WE) (see wepy). But also implementors should be free to customize container types for performance optimization as well for different platforms, allowing extensions for out-of-memory access via HDF5 or other streaming data generators. In short the simple “frame” concept is really the only one that needs any community consensus anyways and is (amazingly) pretty much already standardized (i.e. an N x 3 numpy array in the python ecosystem).
As for (non-exotic) periodic boundary conditions there really is only one lossless format with is a a simple 3 x 3 matrix representing the x, y, and z unit vectors respectively. Any other representation (such as box lengths and angles) is easily computable from this and is fully general (linear algebra FTW).
With that out of the way 2/3 of the format standardization is already
taken care of. This actually covers a large number of useful routines
that you might want to perform. Currently this is all you need to use
geomm!!
Actually, geomm will always remain sans “topology”. Why? Because,
there is always a version of every computation on molecular structures
that can be expressed in terms of selections of the atoms in a given
frame. We saw this in the H-Bonds examples above where we simply
supplied the list of atoms for each countepart in place of ‘protein’ and
‘resname LIG’ used in MDAnalysis. As another example, take the
geomm superimpose function:
def superimpose(ref_coords, coords, idxs=None):
Here we only require two 3xN numpy arrays for the reference template, and the coordinates for which you want to transform (rotate).
Optionally, you can specify the idxs (read “indices”) for which you
actually want to minimize RMSD for the rotation matrix of the entire set
of coordinates. Imagine you have a simulation of a receptor protein and
a mobile ligand floating in and around your protein. If you want to see
the differences in the position and orientation of the ligand you really
just want to superimpose the protein structures. To geomm the
coords are just positions, it has no notion of what a “protein” is.
You supply this notion in terms of the semantics of the operation you
are trying to do, not in the domain language of protein structure.
Imagine geomm “guessed” what the protein structure was for you and
there was no way around this? This would be bad. What if you realized
that there is a floppy loop on the protein that makes your alignments
crooked with respect to the binding site. You would have no way of
working around this. What if you just want to use the heavy atoms to
align with.
Many other packages provide domain specific languages (DSLs) to help address this problem. However, there are many problems with these I will focus on three.
First of all is incompatibility. I know of at least 3 different languages for this purpose: VMD, MDtraj, and MDAnalysis. I am sure there are more. All of them are similar, none are the same semantically.
Second, is the lack of expressiveness and the assumption of complete domain knowledge. All of these DSLs essentially rely on a small database that maps keywords like “protein” to a lookup table of matching keywords. For instance ‘protein’ typically looks at the names of each residue and decides whether it considers that a protein residue or not. This works out maybe around 80% of the time, because most residues do have fairly standard abbreviations. However, many residues have valid alternative forms such as protonation states (not to mention other post-translational modifications like disulfide bridges etc.) that may sometimes be named differently. For instance histidine has many such forms: HIS, HSD, HSA, etc. From experience I can tell you that no one lookup table is going to be universal and capture all names produced by all softwares. And worse they are probably different between each implementation.
Furthermore, these DSLs don’t cover domain knowledge really outside of the protein centric world. What about lipid head groups and tails? Sugars? Disulfide bonds? Ions and solutes? Small molecule functional groups? They don’t even cover the richness of protein secondary, tertiary, and quaternary structure. There is no such query language that can cover all of these domains. I would argue that these restricted languages actually has an effect on the kinds of questions that researchers are willing to ask given the limited exressiveness.
Thirdly, is the unfounded implicit assumption on a particular domain model for macromolecules. To not beat around the bush this means the prevalent model that all molecules follow the chain, residue, atom model (with bonds as an afterthought). This is likely due to the use of the PDB format which has this structure. Despite the concept of a “reside” coming from the initial discovery of the heterogeneous polymeric properties of proteins it lives on and even now small molecules, lipids, sugars, waters, ions, and just about every other distinct chemical species now having to bear “residue” designations. This is entirely absurd in practice outside of the context of proteins. Much more practically should there be concepts of “molecule types” and some of those molecule types being polymers which can then be expressed as sequences of residues.
That is the residue is really only a convenient method for indexing sub-selections of larger structures and as a compression mechanism so that proteins can be expressed as a simple sequence of abbreviations. This abbreviation makes a lot of sense in some situations (to say a geneticist, who is essentially computing residues from DNA sequences) but not really at all to someone interested in the fine-detail of molecules that only atoms and bond graphs can give. For our purposes a “reside” really only is a designation for a set of atoms (i.e. indices of atoms in the coordinates table).
So in the end the purpose of all these DSLs really is to just allow for quick selections of subsets of atoms. They do this in a way which is very easy to get wrong unexpected results and that is restricted only to the semantics of their small and poorly designed language. In the right context with proper standardization of domain models, atom names/types, and residue names/types this might work. However, today the absence of such standards results only in chaos, we highly recommend against using these languages, unless necessary or where reproducibility and correctness don’t matter (for exploratoray analysis perhaps).
A much better approach is to understand the meaning of names in your own data (and to change them if necessary) and use well established mechanisms for querying data in deterministic ways. That is the use of algorithms (for tree and graph traversal like algorithms) and query languages, similar to SQL.
To this end packages like mdtraj do provide methods for generating
standard tables which are either chain, residue, atoms, or bond oriented
with columns relating to entity subsets, positions, and other
characteristics like types and charges.
This is all to say that geomm doesn’t want to say anything about
this process and it is up to you to use whatever algorithmic and domain
specific models you need to generate sets of atom indices.
Do I have a solution? No. Many of the failed solutions above are trying to fix inherently difficult problems about data modelling. Its my opinion that you should just understand your domain problem very well and do it yourself. Alternatively, researchers need to actually agree upon specifications for both ontologies (e.g. what is a residue and what does it actually apply to) as well as vocabularies (e.g. what are the canonical names and abbreviations for atoms and residues).
I do have some suggestions for some useful structures that do not attempt to solve consensus problems above but do provide a collection of serialization formats to avoid the pain and suffering associated with PDBs.
First, tables. The table is a massively under-utilized tool in this field.
You only need a few to remain at feature parity with all of the ecosystem:
molecule/chain table
residue table
atom table
bond table
The Python pandas library has excellent support for reading and
writing to a variety of interchange formats as well as the ability to
query in it’s own way plus standard ones like SQL.
This collection of files (which you could zip into an archive file)
would be the “topology”. For any other level of resolution (say for
protein domains), just add another table. This could easily be adapted
to a standard single-file SQL database like SQlite, HDF5, or
even pages of an excel spreadsheet (shudder).
Another workable alternative is the JSON format used internally in the
mdtraj HDF5 format and in wepy. wepy provides tools for
reading and writing this format into mdtraj.Topology objects, but
JSON parsers are fast and ubiquitous and the resulting dicts-and-lists
datastructure is very easy to manually manipulate.
As for actual numerical data we just defer to the numpy ecosystem.
They know what they are doing in terms of arrays of numbers.
The only other thing I can really think of is perhaps a simple FASTA format for residue sequences. This could be useful for comparative analysis between different structures.
Thats it! It really isn’t hard once you take a step back and look at the wreckage thats been continuously piled on for the last few decades. Its just data, ours isn’t really that special.
I even wrote an example (info/examples/format_conversion) that does
all of this starting from, an admittedly well-behaved, PDB starting at
mdtraj.