The use of Active Machine Learning for Protospacer-Adjacent Motif recovery in Class 2 CRISPR-Cas systems

The recognition of target DNA sequences during the interference phase of prokaryotic CRISPR-Cas immunity relies on Protospacer-Adjacent Motif (PAM) sequences, specific for each Cas effector. PAM identification is a laborious and time consuming process that requires multiple stages including in vitro and in vivo cleavage assays followed by Next Generation Sequencing of targets that withstood cleavage. Determining PAM is an essential step of characterisation of any novel Cas9 ortholog and determines the likelihood of its potential use. This study investigates the potential of machine learning to predict PAM sequences for a given Cas9 ortholog based on the results of cleavage experiments and employing an Active Learning process akin to Reinforcement Learning with Human Feedback. Machine learning-facilitated PAM identification would streamline and accelerate existing pipelines for describing novel Cas proteins. We demonstrate that simple models with a small amount of data are sufficient for confident PAM predictions when training is effectively orchestrated.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202