TemStaPro: protein thermostability prediction using sequence representations from protein language models.

Motivation: Reliable prediction of protein thermostability from its sequence is valuable for both academic and industrial research. This prediction problem can be tackled using machine learning and by taking advantage of the recent blossoming of deep learning methods for sequence analysis. These methods can facilitate training on more data and, possibly, enable the development of more versatile thermostability predictors for multiple ranges of temperatures.

A new family of CRISPR-type V nucleases with C-rich PAM recognition.

Most CRISPR-type V nucleases are stimulated to cleave double-stranded (ds) DNA targets by a T-rich PAM, which restricts their targeting range. Here, we identify and characterize a new family of type V RNA-guided nuclease, Cas12l, that exclusively recognizes a C-rich (5'-CCY-3') PAM. The organization of genes within its CRISPR locus is similar to type II-B CRISPR-Cas9 systems, but both sequence analysis and functional studies establish it as a new family of type V effector.

A catalogue of biochemically diverse CRISPR-Cas9 orthologs.

Bacterial Cas9 nucleases from type II CRISPR-Cas antiviral defence systems have been repurposed as genome editing tools. Although these proteins are found in many microbes, only a handful of variants are used for these applications. Here, we use bioinformatic and biochemical analyses to explore this largely uncharacterized diversity.