crisprzip 1.1.0

A kinetic model of CRISPR-Cas target recognition.

CRISPRzip

Welcome to the codebase of CRISPRzip from the Depken Lab at TU Delft.

About the project

CRISPRzip is a physics-based model to study the target recognition dynamics of CRISPR-associated nucleases like Cas9 (Eslami-Mossalam, 2022). Their interactions with target DNA is represented as an energy landscape, with which you can simulate binding and cleavage kinetics. The parameters have been obtained by machine learning on high-throughput data. CRISPRzip makes quantitative predictions of on-target efficiency and off-target risks of different guide RNAs.

With CRISPRzip, we hope to contribute to assessing the risks that come with particular choices in CRISPR application, and as such contribute to the development of safe gene editing technology.

References

Eslami-Mossallam B et al. (2022) A kinetic model predicts SpCas9 activity, improves off-target classification, and reveals the physical basis of targeting fidelity. Nature Communications. 10.1038/s41467-022-28994-2

Installation

CRISPRzip is on PyPi and can be installed with pip.

pip install crisprzip

Usage

CRISPRzip makes predictions about cleavage and binding activity on on- and off-targets. First, you define the protospacer and target sequence, and then, you can predict the fraction cleaved or bound.

# 1. load parameter set
from crisprzip.kinetics import load_landscape
searcher = load_landscape("sequence_params")

# 2. define Cas9, gRNA and DNA target
searchertargetcomplex = searcher.probe_sequence(
    protospacer = "AGACGCATAAAGATGAGACGCTGG",
    target_seq  = "AGACCCATTAAGATGAGACGCGGG",  # A13T G17C
)

# 3. predict activity
f_clv = searchertargetcomplex.get_cleaved_fraction(
    time=600,  # 10 minutes
    on_rate=1E-1
)
f_bnd = searchertargetcomplex.get_bound_fraction(
    time=600,  # 10 minutes
    on_rate=1E-1
)

# 4. format output
print(f"After 10 minutes, the target (A13T G17C) is ...")
print(f"- cleaved for {100 * f_clv:.1f}% by Cas9")
print(f"    or  ")
print(f"- bound for {100 * f_bnd:.1f}% by dCas9")

Output:

After 10 minutes, the target (A13T G17C) is ...
- cleaved for 10.5% by Cas9
    or  
- bound for 94.2% by dCas9

See the tutorial or the docs for more examples how to explore sequence, time and concentration dependency.

Contributing

We encourage contributions in any form - reporting bugs, suggesting features, drafting code changes. Read our Contributing guidelines and our Code of Conduct.

Acknowledgements

Many thanks to Elviss Dvinskis, Raúl Ortiz and Aysun Urhan from the DCC team at TU Delft for their support to get this package released!

Waiver

Technische Universiteit Delft hereby disclaims all copyright interest in the program “CRISPRzip” (a physics-based CRISPR activity predictor) written by the Author(s). Paulien Herder, Dean of Applied Sciences

(c) 2024, Hidde Offerhaus, Delft, The Netherlands.