Spacer2PAM: A computational framework for identification of functional PAM sequences for endogenous CRISPR systems

Abstract

RNA-guided nucleases from clustered regularly interspaced short palindromic repeats (CRISPR) systems expand opportunities for precise, targeted genome modification. Endogenous CRISPR systems in many bacteria and archaea are particularly attractive to circumvent expression, functionality, and unintended activity hurdles posed by heterologous CRISPR effectors. However, each CRISPR system recognizes a unique set of PAM sequences, which requires extensive screening of randomized DNA libraries. This challenge makes it difficult to develop endogenous CRISPR systems, especially in organisms that are slow-growing or have transformation idiosyncrasies. To address this limitation, we present Spacer2PAM, an easy-to-use, easy-to-interpret R package built to identify potential PAM sequences for any CRISPR system given its corresponding CRISPR array as input. Spacer2PAM can be used in "Quick" mode to generate a single PAM prediction that is likely to be functional or in "Comprehensive" mode to inform targeted, unpooled PAM libraries small enough to screen in difficult to transform organisms. We demonstrate Spacer2PAM by predicting PAM sequences for industrially relevant organisms and experimentally identifying seven PAM sequences that mediate interference from the Spacer2PAM-predicted PAM library for the type I-B CRISPR system from Clostridium autoethanogenum. We anticipate that Spacer2PAM will facilitate the use of endogenous CRISPR systems for industrial biotechnology and synthetic biology.