ACDC-NN is a novel antisymmetric neural network to predict proteins free energy changes upon point variations along the amino acid sequence. The ACDC-NN model was built so that it can be used to make predictions in two different ways:
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when both the wild-type and variant structure are available, these are respectively used as direct and inverse inputs so that the network can provide a prediction that, by construction, is perfectly antisymmetric;
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when only the wild-type structure is available, as usual, the input for the inverse substitution is created starting from the direct one by inverting the variation encoding but preserving the same structure.
For further information about the ACDC-NN architecture and properties, please see the related paper https://doi.org/10.1088/1361-6463/abedfb
ACDC-NN Seq is a sequence-based version of ACDC-NN that does not require the structure of the protein, further information is available in the paper: https://doi.org/10.3390/genes12060911
Here you can find the instructions to easily install ACDC-NN on your computer using pip (see commands below). In this version, ACDC-NN was trained using all datasets available in the literature without correcting for sequence similarity. In case you want to replicate our paper results you will find a jupyter notebook inside the 'results_replication' folder. There ACDC-NN was trained using a 10-fold cross-validation taking into account sequence similarity to avoid overfitting.
We recommend using pip:
pip install acdc-nn
Requirements:
| Requirement | Minimum tested version |
|---|---|
| python | 3.6 |
| tensorflow | 2.3.1 |
| Biopython | 1.78 |
| numpy | 1.19.5 |
| pandas | 1.1.5 |
| silence_tensorflow | 1.1.1 |
To predict the change of the folding free energy (DDG) due to a point substitution in a protein sequence, ACDC-NN needs both evolutionary and structural information about the protein itself. The structural information is from a PDB file. The evolutionary information is from a profile file, simple tab-separated table of the frequencies of each residue in each position in homologous proteins. Positive DDG values are stabilizing.
When no structural information is available, the sequence-based ACDC-NN Seq network must be used:
acdc-nn seq SUB PROFILE
When information is available only for the wild-type protein, the predictor can be run as:
acdc-nn struct SUB PROFILE PDB CHAIN
where SUB is the point substitution, PROFILE and PDB are the paths to the profile and PDB files, and CHAIN is the PDB chain where the substitution occurs. SUB is in the form XNY where X is the wild-type residue, N is the position of the substitution, and Y is the mutated residue. X and Y are given as a one-letter amino acid code and N is 1-based and referred to the PDB numbering of the relevant chain, and not the position in the sequence. Both PDB and profile files are automatically decompressed when they have a ".gz" extension.
When information is available also for the mutated protein, a better prediction can be got as:
acdc-nn istruct SUB WT-PROFILE WT-PDB WT-CHAIN INV-SUB MT-PROFILE MT-PDB MT-CHAIN
To predict more than a few substitutions, we provide a batch mode:
acdc-nn batch SUBS
where SUBS is the path to a tab-separated table with a row for each substitution to be predicted. For substitutuion where no structural information is available the row format is:
SUB PROFILE
For substitutions where only the wild-type protein data is available, the row format is:
SUB PROFILE PDB CHAIN
For substitutions where also the mutated protein data is available, the row format is:
SUB WT-PROFILE WT-PDB WT-CHAIN INV-SUB MT-PROFILE MT-PDB MT-CHAIN
The three formats can be mixed arbitrarily in the same file.
These examples use the data in the tests directory of the github repository. No structure available:
> acdc-nn seq Q104H tests/profiles/2ocjA.prof.gz
0.06451824
Single substitution:
> acdc-nn struct Q104H tests/profiles/2ocjA.prof.gz tests/structures/2ocj.pdb.gz A
0.15008962
Single substitution with the structure of the mutated protein
> acdc-nn istruct V51I tests/profiles/1bsaA.prof.gz tests/structures/1bsa.pdb.gz A I51V tests/profiles/1bniA.prof.gz tests/structures/1bni.pdb.gz A
0.48577148
> acdc-nn istruct I51V tests/profiles/1bniA.prof.gz tests/structures/1bni.pdb.gz A V51I tests/profiles/1bsaA.prof.gz tests/structures/1bsa.pdb.gz A
-0.48577148
NB: In the above example we have specifically chosen two homologous proteins that have similar structure.
Profile files needed by ACDC-NN can be created using HH-suite, an open-source software package for sensitive protein sequence searching, freely available at https://github.com/soedinglab/hh-suite. Prepackaged sequence databases are also available from the same repository. Once you download the suite and one database, you can generate the profile in two steps. First, a multiple alignment file is generated running the following command:
./hhsuite/bin/hhblits -d UniRef30_2020_06_hhsuite/UniRef30_2020_06 -i PROT.fasta -cpu THREADS -n 2 -opsi
where PROT.fasta is your protein sequence, hhblits is the executable command from the HH-Suite and UniRef30_2020_06_hhsuite/UniRef30_2020_06 is one of the available sequence databases. This should generate a ".msa.psi" file, which contains all the alignments to similiar protein sequences in the database.
These alignments needs to be converted to a profile (a table of frequences) that is readable by ACDC-NN. For this second step, you can use a small python package named ddgun. If you installed ACDC-NN via pip, it should already be installed. If not you can get it with pip (pip install ddgun). The conversion command is the following:
ddgun mkprof FILE.msa.psi
which should yield the profile file needed by ACDC-NN.