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mdciao: Analysis of Molecular Dynamics Simulations Using Residue Neighborhoods

Project description

mdciao: Analysis of Molecular Dynamics Simulations Using Residue Neighborhoods

Python Package Coverage DOI License

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mdciao is a Python module that provides quick, “one-shot” command-line tools to analyze molecular simulation data using residue-residue distances. mdciao tries to automate as much as possible for non-experienced users while remaining highly customizable for advanced users, by exposing an API to construct your own analysis workflow.

Under the hood, the module mdtraj is doing most of the computation and handling of molecular information, using BioPython for sequence alignment, pandas for many table and IO related operations, and matplotlib for visualizaton. It tries to automatically use the

consensus-nomenclature schemes by either using local files or on-the-fly lookups of the GPCRdb and/or https://www.mrc-lmb.cam.ac.uk/CGN/

Licenses

Status

mdciao is in its initial development, with versions 0.Y.Z. Anything MAY change at any time. The public API SHOULD NOT be considered stable.

Documentation

Currently, docs are hosted at http://proteinformatics.org/mdciao/, but this can change in the future.

TODOs

This is an informal list of known issues and TODOs:
  • keeping vs reporting contacts: a design choice has to be made wrt to the effect of ctc_cutoff_Ang on a ContactGroup: If a given cutoff makes a ContactPair have freq=0, should the CP be kept in the CG, simply not reported? There’s now a branch for that: https://github.com/gph82/mdciao/tree/buffer_neighborhood
  • overhaul the “printing” system with proper logging and warnings (perhaps use loguru)
  • progressbar not very informative for one chunked trajectory or parallel runs
  • the “consensus” fragmentation sometimes breaks automatic flareplot labelling #should no longer be the case after https://github.com/gph82/mdciao/pull/21 (check)
  • Building the docs with sphinx v>2.4.4 doesn’t autogen a class’ method’s linked doc
  • the affiliation of a residue to a fragment is done as “res@frag” on the string output and res^frag in figures, this implementation is simply using replace(“@”,”^”), could be better
  • heuristics for proper font-sizing of flareplots could be optimized
  • parallel execution with memory mdtraj.Trajectory objects should be better
  • harmonize documentation API cli methods (mdciao.cli) and the CLI scripts (mdc_*)
  • The interface between API methods and cli scripts could be better, using sth like click
  • The API-cli methods (interface, neighborhoods, sites, etc) have very similar flows but a lot of code repetition, I am sure some patterns/boilerplate could be outsourced/refactored even more.
  • color handling of the flare-plots is buggy because it tries to guess too many things. Undecided about best decision.
  • Most of the tests were written against a very rigid API that mimicked the CLI closely. Now the API is more flexible and many tests could be re-written or deleted , like those needing mock-input or writing to tempdirs because writing figures or files could not be avoided.
  • There’s some inconsistencies in private vs public attributes of classes. An attribute might’ve “started” as private and is exceptionally used somewhere else until the number of exceptions is enough for it to make sense to be public, documented and well tested. I’m working on it.
  • neighborlists could be computed much more efficiently
  • The labelling names should be harmonized (ctc_label, anchor_res…) and the logic of how/where it get’s constructed (short_AA vs AA_format) is not obvious sometimes
  • There’s many other TODOs spread throughout the code

System Requirements

At the moment, mdciao is CI-tested only for GNU/Linux and Python versions 3.6, 3.7, 3.8, 3.9. MacOS CI-tests are failing currently because of a segmentation error (139 and 11) when calling mdtraj.dsssp.

Authors

mdciao is written and maintained by Guillermo Pérez-Hernández (ORCID) currently at the Institute of Medical Physics and Biophysics in the Charité Universitäsmedizin Berlin.

Please cite “mdciao, G. Pérez-Hernández and P.W. Hildebrand, 2021 (in preparation)”

Project details


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