acdc-nn 0.0.5

A small example package

ACDC-NN

ACDC-NN is a novel antysymmetric neural network to predict proteins free energy changes upon point variations along the aminoacid sequence. The ACDC-NN model was built so that it can be used to make predictions in two different ways:

1. 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 ;

2. when only the wild-type structure is available, as usual, the inverse input 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 (LINK)

About this repository

Here you can find the istructions 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.

Installation

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

Usage

ACDC-NN expects the usage of pdb numbering, not the sequence one.

Examples

Single mutation:

> acdc-nn single Q104H ./profiles/2ocjA.prof ./pdb/2ocj.pdb A
0.15008962

Single mutation with the structure of the mutated protein

> acdc-nn inverse V51I ./profiles/1bsaA.prof ./pdb/1bsa.pdb A I51V ./profiles/1bniA.prof ./pdb/1bni.pdb A 
0.48577148
> acdc-nn inverse I51V ./profiles/1bniA.prof ./pdb/1bni.pdb A V51I ./profiles/1bsaA.prof ./pdb/1bsa.pdb A
-0.48577148

NB: In the above example we have specifically chosen two homologous proteins that have similar structure.

Cose ancora da fare:

• scegliere una licenza: GPL / MIT?

• spiegare come produrre i profili, magari mettere uno script

• sistemare il readme

• controllare che gli esempio coincidano con tests/test.sh