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
Documentation: Full documentation is available at https://github.com/SENSAAS/sensaas/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
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
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 -c conda-forge pymol-bundle
Retrieve and unzip SENSAAS repository in your desired folder. See below for running the program sensaas.py or meta-sensaas.py. The directory containing executables is called sensaas-main.
Install:
- Python3.7 and numpy
- 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:
- PyMOL (a molecular viewer; more information at https://pymolwiki.org)
Retrieve and unzip SENSAAS repository. The directory containing executables is called sensaas-main.
Not tested
NSC is used to efficiently generate point clouds of molecules and to calculate their surfaces. It is written in C and was developed by Frank Eisenhaber who kindly licensed its use in SENSAAS. Please be advised that the use of NSC is strictly tied to SENSAAS and its code is released under the following license. If the NSC license is an issue for your application or if you wish to use NSC independently of SENSAAS, please contact the author Frank Eisenhaber (email: frank.eisenhaber@gmail.com) who will amicably manage your request.
References :
- F. Eisenhaber, P. Lijnzaad, P. Argos, M. Scharf, The Double Cubic Lattice Method: Efficient Approaches to Numerical Integration of Surface Area and Volume and to Dot Surface Contouring of Molecular Assemblies, Journal of Computational Chemistry, 1995, 16, N3, pp.273-284.
- F. Eisenhaber, P. Argos, Improved Strategy in Analytic Surface Calculation for Molecular Systems: Handling of Singularities and Computational Efficiency, Journal of Computational Chemistry, 1993,14, N11, pp.1272-1280.
Executables nsc (for Linux) or ncs-win (for windows) are included in this repository.
In case they do not work on your system, you may have to compile it using the source file nsc-300.c in directory src/. Instructions for compilation:
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for Windows: The current executable nsc-win.exe was compiled by using http://www.codeblocks.org. Rename the executable as nsc-win.exe because 'nsc-win.exe' is used to set the variable nscexe in the Python script sensaas.py
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for Linux:
cc src/nsc-300.c -lmrename a.out as nsc because 'nsc' is used to set the variable nscexe in the Python script sensaas.py:
cp a.out nsc
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
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gfit score estimates the geometric matching of point-based surfaces - it ranges between 0 and 1
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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
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A gfit+hfit score close to 2.0 means a perfect superimposition.
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A gfit+hfit score > 1.0 means that similaries were identified.
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)
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:
python utils/ordered-catsensaas-cutoff.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 100
You may have to run the script as follows:
python meta-sensaas.py examples/VALSARTAN.sdf examples/tetrazole.sdf -r 100
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
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
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SENSAAS code is released under the 3-Clause BSD License
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NSC code is released under the following license
Copyright (c) 2018-2021, CNRS, Inserm, Université Côte d'Azur, Dominique Douguet and Frédéric Payan, All rights reserved.
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}
}