: The development of gene therapy using genome editing tools recently became relevant. With the invention of programmable nucleases, it became possible to treat hereditary diseases due to introducing targeted double strand break in the genome followed by homology directed repair (HDR) or non-homologous end-joining (NHEJ) reparation. CRISPR-Cas9 is more efficient and easier to use in comparison with other programmable nucleases. To improve the efficiency and safety of this gene editing tool, various modifications CRISPR-Cas9 basis were created in recent years, such as prime editing – in this system, Cas9 nickase is fused with reverse transcriptase and guide RNA, which contains a desired correction. Prime editing demonstrates equal or higher correction efficiency as HDR-mediated editing and much less off-target effect due to inducing nick. There are several studies in which prime editing is used to correct mutations in which researchers reported little or no evidence of off-target effects. The system can also be used to functionally characterize disease variants. However, prime editing still has several limitations that could be further improved. The effectiveness of the method is not yet high enough to apply it in clinical trials. Delivery of prime editors is also a big challenge due to their size. In the present article, we observe the development of the platform, and discuss the candidate proteins for efficiency enhancing, main delivery methods and current applications of prime editing.

Recent advances in machine learning techniques have led to development of a number of protein design and engineering approaches. One of them, ProteinMPNN, predicts an amino acid sequence that would fold and match user-defined backbone structure. In this short report, we test whether ProteinMPNN can be used to reengineer a flavin-binding fluorescent protein, CagFbFP. We fixed the native backbone conformation and the identity of 20 amino acids interacting with the chromophore (flavin mononucleotide, FMN), while letting ProteinMPNN predict the rest of the sequence. The software package suggested replacing 36-48 out of the remaining 86 amino acids. The three designs that we tested experimentally displayed different expression levels, yet all were able to bind FMN and displayed fluorescence, thermal stability and other properties similar to those of CagFbFP. Our results demonstrate that ProteinMPNN can be used to generate diverging unnatural variants of fluorescent proteins, and, more generally, to reengineer proteins without losing their ligand-binding capabilities.

Prime editing is a promising approach for correcting pathogenic variants, but its efficiency remains variable across genomic contexts. Here, we systematically evaluated 12 modifications of the PEmax system for correcting the CFTR F508del pathogenic variant that caused cystic fibrosis in patient-derived airway basal cells. We chose EXO1 and FEN1 nucleases to improve the original system. While all tested variants showed comparatively low efficiency in this AT-rich genomic region, 4-FEN modification demonstrated significantly improved editing rates (up to 2.13 fold) compared to standard PEmax. Our results highlight two key findings: first, the persistent challenge of AT-rich target sequence correction even with optimized editors, and second, the performance of 4-FEN suggests its potential value for other genomic targets.

ATP synthases are large enzymes present in every living cell. They consist of a transmembrane and a soluble domain, each comprising multiple subunits. The transmembrane part contains an oligomeric rotor ring (c-ring), whose stoichiometry defines the ratio between the number of synthesized ATP molecules and the number of ions transported through the membrane. Currently, c-rings of F-Type ATP synthases consisting of 8 to 17 (except 16) subunits have been experimentally demonstrated. Here, we present an easy-to-use high-throughput computational approach based on AlphaFold that allows us to estimate the stoichiometry of all homooligomeric c-rings, whose sequences are present in genomic databases. We validate the approach on the available experimental data, obtaining the correlation as high as 0.94 for the reference data set, and use it to predict the existence of c-rings with stoichiometry varying from 8 to 27. We then conduct molecular dynamics simulations of two c-rings with stoichiometry above 17 to corroborate the machine learning-based predictions. Our work strongly suggests existence of rotor rings with previously undescribed high stoichiometry in natural organisms and highlights the utility of AlphaFold-based approaches for studying homooligomeric proteins.