:
Understanding the interaction mechanism of proteins and nucleic acids is one of the most
fundamental problems for genome editing with engineered nucleases. Due to some limitations of
experimental investigations, computational methods have played an important role in obtaining the
knowledge of protein-nucleic acid interaction. Over the past few years, dozens of computational
tools have been used for identification of nucleic acid binding site for site-specific proteins and
design of site-specific nucleases because of their significant advantages in genome editing. Here,
we review existing widely-used computational tools for target prediction of site-specific proteins as
well as off-target prediction of site-specific nucleases. This article provides a list of on-line
prediction tools according to their features followed by the description of computational methods
used by these tools, which range from various sequence mapping algorithms (like Bowtie,
FetchGWI and BLAST) to different machine learning methods (such as Support Vector Machine,
hidden Markov models, Random Forest, elastic network and deep neural networks). We also make
suggestions on the further development in improving the accuracy of prediction methods. This
survey will provide a reference guide for computational biologists working in the field of genome
editing.
The RRM domain of human fused in sarcoma protein reveals a non-canonical nucleic acid binding site
