Discussion
CRISPR-Cpf1-based genome editing technology was invented as another
promising tool following the success of CRISPR-Cas9 and have been
applied for a broad spectrum of species including eukaryotic and
prokaryotic organisms. Cpf1 has several advantages over Cas9 such as
guided by a single and shorter RNA, requires a T-rich PAM and exhibits
better specificity, making it a complementary and alternative gene
editing tool.[37] However, predicting the activity of guide RNA is a
challenge due to the lack of the knowledge.
In this study, we made a large-scale library containing 12544 designed
guide RNAs .Take advantage of the library, we carry out screening
experiments. and presented an efficient and comprehensive computational
model for prediction of Cpf1 guide RNA activity trained by the results
of which. Consequently, we systematically studied the guide RNA activity
in bacteria for the first time, broadening the application of Cpf1 in
genome editing. We observed various activities of guide RNAs and tested
the results by independent validation experiments. Our trained model was
developed to select optimized guide RNAs better-performing than previous
approaches and analyzed the features contributed to guide RNA
activities. We propose that deepCpf1 and other models trained in
mammalian cells shows a higher false negative rate processing data from
in prokaryotic organisms on account of misattributing influences of NHEJ
repairs, chromatin structure, or lentivirus transduction to guide RNA
incapacity, hence our model may be applied to other bacteria or
unexplored host, potentially. Furthermore, our unbiased screening
methods may be applied in other developing CRISPR system utilization
scenario
However, there may be still several defects in our model that the
mechanism of Cpf1-RNA complex interacting with DNA target is unclear and
the generalization capacities are unproven. To further improve the
accuracy and efficiency of guide RNA design, we expect that more
datasets from well-designed experiments across species could be
generated, and more comprehensive algorithms such as machine and deep
learning (MDL) methods could be applied.[38-40]In addition,
improvement of the specificity of guide RNA to reduce off-target effects
is a clinical demand, thus our platform combining high-throughput
screening and analysis of computational methods may be used to address
this issue.
Currently, novel cpf1 orthologs and several other CRISPR nucleases were
harnessed for genome editing in mammalian and bacteria cells, while
relationship between guide RNA sequence and on-target activity was
underexplored. [41]Moreover, several Cpf1 variants of AsCpf1 were
described with increased activity and expanded protospacer-adjacent
motif (PAM) preferences.[42,43]The method described in this paper
could deal with such issues and make progress towards optimizing the
design of guide RNAs.