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SENSAAS

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SENSAAS is a shape-based alignment software which allows to superimpose molecules. It is based on the publication SenSaaS: Shape-based Alignment by Registration of Colored Point-based Surfaces

This version of SENSAAS is entirely written in python.

example

Documentation: Full documentation is available at https://github.com/SENSAAS/sensaas-py/blob/main/docs/index.rst

Website: A web demo to use SENSAAS or SENSAAS-Flex is available at https://chemoinfo.ipmc.cnrs.fr/SENSAAS/index.html

Tutorial: These videos on YouTube provide tutorials

Requirements

SENSAAS relies on the open-source library Open3D. The current release of SENSAAS uses Open3D version 0.12.0 along with Python3.7.

Visit the following URL for using Python packages distributed via PyPI: http://www.open3d.org/docs/release/getting_started.html or conda: https://anaconda.org/open3d-admin/open3d/files. For example, for windows-64, you can download win-64/open3d-0.12.0-py37_0.tar.bz2

Virtual environment for python with conda (for Windows for example)

Install conda or Miniconda from https://conda.io/miniconda.html
Launch Anaconda Prompt, then complete the installation:

conda update conda
conda create -n sensaas
conda activate sensaas
conda install python=3.7 numpy

Once Open3D downloaded:

conda install open3d-0.12.0-py37_0.tar.bz2

(Optional) Additional packages for visualization with PyMOL:

conda install -c schrodinger pymol

If an error regarding PyQT occurs then:

conda install -c anaconda pyqt

Retrieve and unzip SENSAAS-PY repository in your desired folder. See below for running the program sensaas.py or meta-sensaas.py. The directory containing executables is called sensaas-py-main.

Linux

Install:

  1. Python3.7 and numpy
  2. Open3D version 0.12.0 (more information at http://www.open3d.org/docs/release/getting_started.html)

(Optional) Install additional packages for visualization with PyMOL:

  1. PyMOL (a molecular viewer; more information at https://pymolwiki.org)

Retrieve and unzip SENSAAS-PY repository. The directory containing executables is called sensaas-py-main.

MacOS

Not tested

Run Sensaas

To align a Source molecule on a Target molecule, the syntax is:

sensaas.py sdf molecule-target.sdf sdf molecule-source.sdf slog.txt optim

Example:

sensaas.py sdf examples/IMATINIB.sdf sdf examples/IMATINIB_mv.sdf slog.txt optim

You may have to run the script as follows:

python sensaas.py sdf examples/IMATINIB.sdf sdf examples/IMATINIB_mv.sdf slog.txt optim

Don't worry if you get the following warning from Open3D: "Open3D WARNING KDTreeFlann::SetRawData Failed due to no data.". It is observed with conda on windows.

Here, the source file IMATINIB_mv.sdf is aligned (moved) on the target file IMATINIB.sdf (that does not move). The output tran.txt contains the transformation matrix allowing the alignment of the source file (result in Source_tran.sdf). The slog.txt file details results with final scores of the aligned molecule (Source) on the last line. In the current example, the last line must look like:

gfit= 1.000 cfit= 0.999 hfit= 0.996 gfit+hfit= 1.996

There are three different fitness scores but we only use 2 of them, gfit and hfit, to calculate gfit+hfit. More about Fitness scores

  • gfit score estimates the geometric matching of point-based surfaces - it ranges between 0 and 1

  • hfit score estimates the matching of colored points representing pharmacophore features - it ranges between 0 and 1

Thus, we calculate a hybrid score = gfit + hfit scores - gfit+hfit ranges between 0 and 2

  • A gfit+hfit score close to 2.0 means a perfect superimposition.
  • A gfit+hfit score close to 0.0 means that there are no similarities between molecular structures.

Run meta-sensaas.py

1. Virtual Screening

This script is suited for performing virtual screenings of sdf files containing several molecules (database mode). For example, if you want to process a sdf file containing several conformers for Target and/or Source. A similarity matrix is provided along with a sdf file that contains all aligned Sources. The syntax is:

meta-sensaas.py molecules-target.sdf molecules-source.sdf

Example

The following example works with 2 files from the directory examples/

meta-sensaas.py examples/IMATINIB.sdf examples/IMATINIB_parts.sdf

You may have to run the script as follows:

python meta-sensaas.py examples/IMATINIB.sdf examples/IMATINIB_parts.sdf

Here, the source file IMATINIB_parts.sdf contains 3 substructures that are aligned (moved) on the target file IMATINIB.sdf (that does not move). Outputs are:

  • the file bestsensaas.sdf that contains the best ranked aligned Source
  • the file catsensaas.sdf that contains all aligned Sources
  • the file matrix-sensaas.txt that contains gfit+hfit scores (rows=Targets and columns=Sources)

Option -l

When executing meta-sensaas.py, you can iterate the alignment by using the option -l:

meta-sensaas.py molecules-target.sdf molecules-source.sdf -l 2

here the alignment will be evaluated twice and the best of the two will be kept.

Post-processing

Then, to ease the analysis of the results, the script utils/ordered-catsensaas.py can be used to generate files in descending order of score.

utils/ordered-catsensaas.py matrix-sensaas.txt catsensaas.sdf

You may have to run the script as follows:

python utils/ordered-catsensaas.py matrix-sensaas.txt catsensaas.sdf

or if you want to only retrieve solutions having a gfit+hfit score above a defined cutoff (here the cutoff 1.1 is chosen):

python utils/ordered-catsensaas.py matrix-sensaas.txt catsensaas.sdf 1.1
  • the file ordered-catsensaas.sdf contains all aligned Sources in descending order of score
  • the file ordered-scores.txt contains gfit+hfit scores in descending order

Option -s

When executing meta-sensaas.py, you can also select the score type by using the option -s (default is the score of the Source (-s source)):

meta-sensaas.py molecules-target.sdf molecules-source.sdf -s mean

here the mean of the score of the target and of the aligned source will be used to rank solutions and to fill matrix-sensaas.txt. The option '-s mean' is interesting to favor source molecules that have the same size of the Target. More about Options

2. Finding alternative alignments and Clustering

This option allows to repeat in order to find alternative alignments when they exist as for example when aligning a fragment on a large molecule. The syntax is:

meta-sensaas.py target.sdf source.sdf -r 10

here 10 alignments of the Source will be generated and clustered. Outputs are:

  • the file sensaas-1.sdf with the best ranked alignment - it contains 2 molecules: first is Target and second the aligned Source
  • the file sensaas-2.sdf (if exists) with the second best ranked alignment - it contains 2 molecules: first is Target and second the aligned Source
  • ...
  • file cat-repeats.sdf that contains all aligned Sources

Example

The following example works with 2 files from the directory examples/

meta-sensaas.py examples/VALSARTAN.sdf examples/tetrazole.sdf -r 20

You may have to run the script as follows:

python meta-sensaas.py examples/VALSARTAN.sdf examples/tetrazole.sdf -r 20

As described in the publication, outputs are:

  • sensaas-1.sdf contains the self-matching superimposition
  • sensaas-2.sdf contains the bioisosteric superimposition
  • sensaas-3.sdf contains the geometric-only superimposition

Visualization

You can use any molecular viewer. For instance, you can use PyMOL if installed (see optional packages) to load the Target and the aligned Source(s):

after aligning IMATINIB_mv.sdf on IMATINIB.sdf using sensaas.py:

pymol examples/IMATINIB.sdf Source_tran.sdf

or after executing meta-sensaas.py with several molecules:

pymol examples/IMATINIB.sdf bestsensaas.sdf catsensaas.sdf

or after the post-processing:

pymol examples/IMATINIB.sdf ordered-catsensaas.sdf

or after executing meta-sensaas.py with the repeat option (State 1 is Target and State 2 is the aligned Source):

pymol examples/VALSARTAN.sdf sensaas-1.sdf

Licenses

SENSAAS code is released under the 3-Clause BSD License

Copyright

Copyright (c) 2018-2021, CNRS, Inserm, Université Côte d'Azur, Dominique Douguet and Frédéric Payan, All rights reserved.

Reference

Douguet D. and Payan F., SenSaaS: Shape-based Alignment by Registration of Colored Point-based Surfaces, Molecular Informatics, 2020, 8, 2000081. doi: 10.1002/minf.202000081

Bibtex format :

@article{10.1002/minf.202000081,
author 		= {Douguet, Dominique and Payan, Frédéric},
title 		= {sensaas: Shape-based Alignment by Registration of Colored Point-based Surfaces},
journal 	= {Molecular Informatics},
volume 		= {39},
number 		= {8},
pages 		= {2000081},
keywords 	= {Shape-based alignment, molecular surfaces, point clouds, registration, molecular similarity},
doi 		= {https://doi.org/10.1002/minf.202000081},
url 		= {https://onlinelibrary.wiley.com/doi/abs/10.1002/minf.202000081},
eprint 		= {https://onlinelibrary.wiley.com/doi/pdf/10.1002/minf.202000081},
year 		= {2020}
}