Statistical Bioinformatics Webinar: Wang -- Machine Learning Based Prediction and Analysis of Anti-CRISPR Proteins

Presented by Dr. Jiawei Wang, Monash University

Anti-CRISPR (Acr) proteins are widespread amongst phage and promote phage infection by
inactivating the bacterial host’s CRISPR-Cas defence system.  Except for their
universally short sequences, Acrs have little in common with each other.  With very low
sequence and structural similarity, at least 50 distinct Acr families have been
identified across both bacterial and archaeal domains of life where they each use
different molecular mechanisms to inhibit CRISPR-Cas systems.  Outside the confined
environment of a microbial cell, Acrs have inspired a number of downstream applications,
from gene editing technologies and protein engineering to phage therapy, applications
that are only limited by the relatively small number of known anti-CRISPR systems
compared to the thousands hidden in sequenced genomes.  In this talk, I will introduce
our work in design and implementation of an all-in-one solution to better assist
biologists to predict and analyze Acrs.  This includes development of a novel machine
learning based anti-CRISPR predictor (PaCRISPR) and a subsequent platform (AcrHub) to
annotate known Acrs, predict novel Acrs and visualize the relationship between known and
potential Acrs.  These tools can either work independently or within the platform
pipeline to facilitate prediction and downstream analysis of Acrs and thereby shorten
the gap between prediction, functional characterisation, and eventual experimental
validation.