Haoyang Cheng | Search PubMed

PubMed2026

Prime assembly with linear DNA donors enables large genomic insertions.

Targeted insertion of large DNA fragments has promising applications for genome engineering and gene therapy1,2. Twin prime-editing guide RNAs have enabled relatively large insertions, but the efficiency remains low for insertions greater than 400 base pairs3-6. Here we describe a prime assembly (PA) approach for the insertion of large DNA donor fragments, of which the ends are designed to overlap with the flaps generated by twin prime editing (twinPE). We used PA to insert one or multiple overlapping DNA fragments, with total insertion sizes ranging from 0.1 kb to 11 kb. An inhibitor of non-homologous end joining enhanced both the efficiency and precision of insertions. PA relies on DNA templates that are easily produced, does not require co-delivery of exogenous DNA-dependent DNA polymerases and proceeds in non-cycling cells, suggesting independence from canonical homology-directed repair pathways. Our study demonstrates that PA can initiate Gibson-like assembly in cells to generate gene insertions without double-stranded DNA breaks, recombinases or homology-directed repair.

Animals↗

PubMed2026

pKAKA: a protein language model for prioritizing kinase-disrupting variants in diseases.

Protein kinases are pivotal regulators of cellular signaling, and their genetic variations are frequently implicated in diseases. Although numerous kinase mutations have been identified as drivers of altered activity, with a few successfully targeted therapeutically, the functional impact of most variants remains uncharacterized. To bridge this gap, we curate a comprehensive dataset that contains 2553 experimentally validated kinase activity-related key alterations (KAKAs) from the literature. While many mutations outside canonical functional regions are known to affect kinase activity, systematic methods to predict their functional consequences are lacking. Consequently, we develop a computational method to predict potential KAKAs, leveraging transfer learning on the pre-trained protein language model ProtBert. Our model, termed pKAKA, achieves an impressive AUC score of 0.9593 and outperforms the AlphaMissense benchmark in comparative testing. Systematic analysis of kinase missense mutations underscores the critical role of KAKAs in pathogenesis, with highlights including JAK2 V617F in atherosclerotic cardiovascular disease, LRRK2 G2385R in Parkinson's disease, EGFR L858R in lung adenocarcinoma, and EGFR G598V in glioma. Overall, this study significantly advances our understanding of how mutations that influence kinase activity contribute to disease mechanisms.

Humans↗