Although engineered LAGLIDADG homing endonucleases (LHEs) are finding increasing applications in biotechnology, their generation remains a challenging, industrial-scale process. As new single-chain LAGLIDADG nuclease scaffolds are identified, however, an alternative paradigm is emerging: identification of an LHE scaffold whose native cleavage site is a close match to a desired target sequence, followed by small-scale engineering to modestly refine recognition specificity. The application of this paradigm could be accelerated if methods were available for fusing N- and C-terminal domains from newly identified LHEs into chimeric enzymes with hybrid cleavage sites. Here we have analyzed the structural requirements for fusion of domains extracted from six single-chain I-OnuI family LHEs, spanning 40-70% amino acid identity. Our analyses demonstrate that both the LAGLIDADG helical interface residues and the linker peptide composition have important effects on the stability and activity of chimeric enzymes. Using a simple domain fusion method in which linker peptide residues predicted to contact their respective domains are retained, and in which limited variation is introduced into the LAGLIDADG helix and nearby interface residues, catalytically active enzymes were recoverable for ≈ 70% of domain chimeras. This method will be useful for creating large numbers of chimeric LHEs for genome engineering applications.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3439895PMC
http://dx.doi.org/10.1093/nar/gks502DOI Listing

Publication Analysis

Top Keywords

domain fusion
8
i-onui family
8
laglidadg homing
8
homing endonucleases
8
chimeric enzymes
8
interface residues
8
linker peptide
8
laglidadg
5
engineering domain
4
fusion chimeras
4

Similar Publications

Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.

Neural Netw

December 2024

Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA; Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA. Electronic address:

Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting functional abnormalities in various brain disorders. However, existing studies often focus on a single domain or modality, neglecting the valuable complementary information offered by multiple domains from both fMRI and EEG, which is crucial for a comprehensive representation of disorder pathology. This limitation poses a challenge in effectively leveraging the synergistic information derived from these modalities.

View Article and Find Full Text PDF

Many conditions, such as pulmonary edema, bleeding, atelectasis or collapse, lung cancer, and shadow formation after radiotherapy or surgical changes, cause Lung Opacity. An unsupervised cross-domain Lung Opacity detection method is proposed to help surgeons quickly locate Lung Opacity without additional manual annotations. This study proposes a novel method based on adversarial learning to detect Lung Opacity on chest X-rays.

View Article and Find Full Text PDF

Unified Knowledge-Guided Molecular Graph Encoder with multimodal fusion and multi-task learning.

Neural Netw

December 2024

School of Computer Science, Wuhan University, Luojiashan Road, Wuchang District., Wuhan, 430072, Hubei Province, China; Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, No. 8, Yangqiaohu Avenue, Zanglong Island Development Zone, Jiangxia District, Wuhan, 2007, Hubei Province, China. Electronic address:

The remarkable success of Graph Neural Networks underscores their formidable capacity to assimilate multimodal inputs, markedly enhancing performance across a broad spectrum of domains. In the context of molecular modeling, considerable efforts have been made to enrich molecular representations by integrating data from diverse aspects. Nevertheless, current methodologies frequently compartmentalize geometric and semantic components, resulting in a fragmented approach that impairs the holistic integration of molecular attributes.

View Article and Find Full Text PDF

Identification of novel BCR::ABL1 kinase domain mutation in patients with chronic myeloid leukaemia and imatinib resistance.

Malays J Pathol

December 2024

National Institutes of Health, Institute for Medical Research, Cancer Research Centre, Haematology Unit, 40170 Shah Alam, Selangor, Malaysia.

Introduction: The emergence of mutations in the BCR::ABL1 kinase domain (KD) impairs imatinib mesylate (IM) binding capacity, thus contributing to IM resistance. Identification of these mutations is important for treatment decisions and precision medicine in chronic myeloid leukaemia (CML) patients. Our study aims to determine the frequency of BCR::ABL1 KD mutations in CML patients with IM resistance.

View Article and Find Full Text PDF

Background: Developing effective targeted treatment approaches to overcome drug resistance remains a crucial goal in cancer research. Immunotoxins have dual functionality in cancer detection and targeted therapy.

Objective: This study aimed to engineer a recombinant chimeric fusion protein by combining a nanobody-targeting domain with an exotoxin effector domain.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!