ACIDES: on-line monitoring of forward genetic screens for protein engineering
Forward genetic screens of mutated variants are a versatile strategy for protein engineering and investigation, which has been successfully applied to various studies like directed evolution (DE) and deep mutational scanning (DMS). While next-generation sequencing can track millions of variants during the screening rounds, the vast and noisy nature of the sequencing data impedes the estimation of the performance of individual variants.
ACIDES combines statistical inference and in-silico simulations to improve performance estimation in the library selection process by attributing accurate statistical scores to individual variants. ACIDES allows experimentalists to reliably estimate variant performance on the fly and can aid protein engineering and research pipelines in a range of applications, including gene therapy.
We tested ACIDES first on a random-peptide-insertion experiment and then on multiple public datasets from DE and DMS studies. Please refer to the article for more details.
2. From NGS count data, ACIDES estimates the score (selectivity) of each variant with 95%-confidence intervals (CIs)
ACIDES is written in Python, tested with Python version 3.8.5, numpy 1.21.2, scipy 1.7.3, pandas 1.4.1, matplotlib 3.5.1, sklearn 1.1.0, and notebook 6.5.3. (Additionary, constrained-linear-regression (0.0.4) has to be installed: https://pypi.org/project/constrained-linear-regression/).
In conda, this environment can be created by
conda create -n ACIDES_env python=3.8.5 numpy=1.21.2 scipy=1.7.3 pandas=1.4.1 matplotlib=3.5.1 notebook=7.0.4 pip scikit-learn=1.1.0
followed by the installation of constrained-linear-regression as
"path to anaconda3"/envs/ACIDES_env/bin/pip install constrained-linear-regression
The tutorial of ACIDES in Jupyter Notebook is available from here.
Takahiro Nemoto, Tommaso Ocari, Arthur Planul, Muge Tekinsoy, Emilia A. Zin, Deniz Dalkara, Ulisse Ferrari, ACIDES: on-line monitoring of forward genetic screens for protein engineering, Nature Communications 14, 8504 (2023).