GithubHelp home page GithubHelp logo

dlpacker's Introduction


DLPacker



This repo contains the code from DLPacker paper DLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins.

Side chain restroration example


What can this code do?

  • Restore full-atom protein structure from backbone (packing)
  • Generate structures of point mutants (assumes the backbone has not changed)
  • Pack or refine parts of protein structure (e.g. after you modelled backbone of a missing loop)
  • Restore partially of fully missing side chains (to be implemented)
  • probably more

Input may contain any protein/protein complex/RNA/DNA/small molecules etc. Only water molecules are removed by default and MSE residues are renamed into MET, the rest will stay the same (except side chains of course).

Usage

As easy as three lines of code:

from dlpacker import DLPacker
dlp = DLPacker('my_structure.pdb')
dlp.reconstruct_protein(order = 'sequence', output_filename = 'my_structure_repacked.pdb')

Input stricture might or might not contain side chains, existing side chains, if present, will be ignored.

You can find more examples with explanations in the jupyter notebook DLPacker.ipynb.

Key dependencies:

  • tensorflow 2.x
  • biopython

Additional files

This repo does not contain neural network's weights due to their large size. You will need to download them from here.

Performance

The table below shows validation RMSD (ร…) for DLPacker as well as two other state of the art algorithms, SCWRL4 and Rosetta Packer (fixbb):

AA Name SCWRL4 Rosetta Packer DLPacker
Arg 2.07 1.84 1.44
Lys 1.54 1.40 1.21
Phe 0.67 0.53 0.32
Tyr 0.83 0.68 0.38
Trp 1.27 0.96 0.46
His 1.18 1.05 0.81
Glu 1.34 1.26 1.02
Gln 1.43 1.24 1.09
Met 1.08 0.91 0.76
Asn 0.88 0.80 0.65
Asp 0.68 0.65 0.47
Ser 0.59 0.52 0.36
Leu 0.49 0.45 0.36
Thr 0.36 0.33 0.27
Ile 0.40 0.36 0.31
Cys 0.40 0.30 0.24
Val 0.24 0.23 0.19
Pro 0.21 0.19 0.14

Citing our work

If you use our code in your work, please cite the DLPacker paper:

@article {Misiura2021.05.23.445347,
	author = {Misiura, Mikita and Shroff, Raghav and Thyer, Ross and Kolomeisky, Anatoly},
	title = {DLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins},
	elocation-id = {2021.05.23.445347},
	year = {2021},
	doi = {10.1101/2021.05.23.445347},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2021/05/25/2021.05.23.445347},
	eprint = {https://www.biorxiv.org/content/early/2021/05/25/2021.05.23.445347.full.pdf},
	journal = {bioRxiv}
}

dlpacker's People

Contributors

nekitmm avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.