Sequence Display-enabled deep learning helps improve SlugCas9, a compact CRISPR tool, to edit challenging DNA sites in APOC3 and PCSK9, two genes linked to coronary heart disease risk.
Image Credit: Rice University/Linqi Cheng and Han Xiao.
Scientific Frontline: Extended "At a Glance" Summary: Evolved SlugCas9 for Heart Disease
The Core Concept: Researchers have utilized artificial intelligence and Sequence Display technology to evolve compact CRISPR/Cas9 enzymes capable of targeting previously inaccessible DNA sequences. This advancement optimizes a compact enzyme known as SlugCas9 to edit genes associated with coronary heart disease.
Key Distinction/Mechanism: Cas9 enzymes must attach to a protospacer adjacent motif (PAM) to edit DNA, but traditional enzymes often fail to efficiently bind to slippery or elusive PAMs. The newly evolved SlugCas9 overcomes this limitation by securely binding to challenging PAM sites, thereby expanding the precise editing range of the CRISPR system.
Origin/History: Published in Nature Communications in November 2026, the research was conducted by Han Xiao and Linqi Cheng at Rice University, who successfully identified promising new Cas9 variants within a single week.
Major Frameworks/Components:
- The CRISPR/Cas9 system, utilizing the compact SlugCas9 enzyme for gene editing.
- Protospacer adjacent motifs (PAMs), which act as crucial docking stations for the enzymes.
- Sequence Display technology combined with deep learning artificial intelligence, which rapidly generate and evaluate large datasets to identify optimal protein sequences.
- The APOC3 and PCSK9 genes, which serve as the target sites and are linked to coronary heart disease risk.
Branches of Science: Molecular Genetics, Synthetic Biology, Biomedical Engineering, Computational Biology, and Structural Biology.
Future Application: These evolved enzymes can be deployed to introduce protective genetic alterations, correct inherited anomalies, and lower the risk of coronary heart disease in clinical settings. Furthermore, the underlying Sequence Display methodology can be repurposed to optimize other vital proteins for complex biomedical engineering tasks.
Why It Matters: This breakthrough circumvents a major technical barrier in CRISPR technology by enabling precise modifications at challenging genomic locations, thereby accelerating the development of highly specific gene therapies.
Rice University’s Han Xiao has used his AI-supported Sequence Display technology to expand the targeting abilities of CRISPR/Cas9. In a paper recently published in Nature Communications, Xiao evolved several variants of a compact Cas9 that could efficiently target stretches of DNA previously inaccessible for editing. The research included a proof-of-concept study in which the evolved Cas9s were successfully used to edit two genes implicated in coronary heart disease.
“My team used Sequence Display technology to evolve a compact Cas9 that can efficiently target priority areas of DNA,” said Xiao, a professor of chemistry and the corresponding author of this paper. “This shows the power of our approach, which rapidly generates large datasets that AI can use to identify optimal protein sequences for a given function. In this case, it allowed us to evolve several new Cas9 variants with enhanced activities.”
Cas9, a CRISPR-associated enzyme, works by attaching to a protospacer adjacent motif, or PAM, in DNA. This short stretch of DNA, which can be between two and six base pairs long, serves as the requisite binding site for the enzyme complex. Once the Cas9 enzyme recognizes a suitable PAM, it can edit the DNA as dictated by a guide, ultimately modifying an adjacent target DNA sequence that matches the guide RNA.
Edits made by CRISPR, therefore, must occur within a certain distance, measured in base pairs, of a PAM. While many Cas enzymes recognize multiple PAMs, most edit DNA more efficiently on specific PAMs. Consequently, genes containing only low-affinity PAMs are difficult to edit with CRISPR, especially if the work requires a specific type of Cas enzyme.
“We cannot easily or precisely change the PAMs in a given gene,” said Linqi Cheng, a graduate student and the first author of the paper. “But with Sequence Display technology, we could quickly evolve a Cas9 enzyme that would better recognize the PAMs already present in the gene.”
For this experiment, the researchers used a compact and popular Cas9 enzyme called SlugCas9. Although SlugCas9 could identify and bind to many PAMs, there were four of particular interest to the researchers that the enzyme struggled with. SlugCas9 recognized these four but only weakly, often dissociating prematurely or failing to bind them entirely.
Other researchers had attempted to evolve alternative Cas9 variants that could dock on these challenging PAMs, but with little success. It was difficult to direct the evolution in a way that increased the enzyme’s binding abilities so specifically. With Sequence Display, however, Xiao’s team was able to identify promising candidates within a week. They then used the evolved SlugCas9 to edit coronary heart disease genes that were previously difficult to access.
“The specific sites we wanted to edit in APOC3 and PCSK9 had PAMs that prior versions of SlugCas9 could not reliably bind to,” Cheng said. “With our evolved SlugCas9, we were able to easily and precisely edit these sites in cultured cells, introducing potentially protective genetic changes linked to a lower risk of coronary heart disease, and unlocking the power and possibility of CRISPR.”
“CRISPR/Cas9 is one of the most promising technologies of the modern age in both research and medicine,” said Xiao, who is the director of the SynthX Center. “It relies on Cas9 enzymes that are optimized and evolved for each application. This finding shows that Sequence Display can be key to rapidly evolving Cas9, and other proteins, to address complex, pressing technical requirements essential for successful biomedical engineering.”
Funding: This work was funded by a SynthX Seed Award (SYN-IN-2024-002), the National Institutes of Health (R35-GM133706, R01-CA277838, and R01-AI165079), the Robert A. Welch Foundation (C-1970), the US Department of Defense (W81XWH-21-1-0789, HT9425-23-1-0494, and HT9425-25-1-0021), the 2024 Rice Synthetic Biology Institute Seed Grant, and a Medical Research Award from the Robert J. Kleberg Jr. and Helen C. Kleberg Foundation.
Published in journal: Nature Communications
Authors: Linqi Cheng, Xinzhe Zheng, Haoxue Ding, Shiyu Jason Jiang, Yu Hu, Chenhang Wang, Chen-Long Li, Zuotong Tian, Rain Jay Leeuwon, Jinyan Rui, Haoxin Ye, Teng Yuan, Yijie Liu, Kaiqiang Yang, Boyang Zhou, Xiongyi Huang, and Han Xiao
Source/Credit: Rice University | Rachel Leeson
Edited by: Scientific Frontline
Reference Number: gen110426_01
