TorchProteinLibrary version 0.15

This library pytorch layers for working with protein structures in a differentiable way. We are working on this project and it's bound to change: there will be interface changes to the current layers, addition of the new ones and code optimizations.

Requirements

• GCC >= 7
• CUDA >= 10.0
• PyTorch >= 1.1
• Python >= 3.5
• Biopython
• setuptools

Installation

Clone the repository:

git clone https://github.com/lupoglaz/TorchProteinLibrary.git

then run the following command:

python setup.py install

Contents

The library is structured in the following way:

FullAtomModel

This module deals with full-atom representation of a protein. Layers:

• Angles2Coords: computes the coordinates of protein atoms, given dihedral angles
• Coords2TypedCoords: rearranges coordinates according to predefined atom types
• CoordsTransform: implementations of translation, rotation, centering in a box, random rotation matrix, random translation
• PDB2Coords: loading of PDB atomic coordinates

ReducedModel

The coarse-grained representation of a protein.

• Angles2Backbone: computes the coordinates of protein backbone atoms, given dihedral angles

RMSD

For now, only contains implementation of differentiable least-RMSD. Layers:

• Coords2RMSD: computes minimum RMSD by optimizing wrt translation and rotation of input coordinates

Volume

Deals with volumetric representation of a protein.

• TypedCoords2Volume: computes 3d density maps of coordinates with assigned types
• Select: selects cells from a set of volumes at scaled input coordinates
• VolumeConvolution: computes correlation of two volumes of equal size

Additional useful function in C++ extension _Volume:

_Volume.Volume2Xplor: saves volume to XPLOR format

General design decisions

The library is structured in the following way:

• Layers directory contains c++/cuda implementations
• Each layer has _ interface.h and .cpp files, that have implementations of functions that are exposed to python
• Each python extension has main.cpp file, that contains macros with definitions of exposed functions

We found that these principles provide readability and overall cleaner design.