scholarly journals Comprehensive computational design of mCreI homing endonuclease cleavage specificity for genome engineering

2011 ◽  
Vol 39 (10) ◽  
pp. 4330-4339 ◽  
Author(s):  
Umut Y. Ulge ◽  
David A. Baker ◽  
Raymond J. Monnat
Keyword(s):  
Genome Engineering ◽  
Homing Endonuclease ◽  
Computational Design ◽  
Cleavage Specificity

IDENTIFICATION OF CONSERVED FEATURES OF LAGLIDADG HOMING ENDONUCLEASES

2010 ◽  
Vol 08 (03) ◽  
pp. 453-469 ◽  
Author(s):  
ALEXANDER GRISHIN ◽  
INES FONFARA ◽  
ANDREI ALEXEEVSKI ◽  
SERGEI SPIRIN ◽  
OLGA ZANEGINA ◽  
...  
Keyword(s):  
Dna Binding ◽  
Genome Engineering ◽  
Homing Endonuclease ◽  
Structural Features ◽  
Homing Endonucleases ◽  
Dna Interactions ◽  
Protein Dna Interactions ◽  
The Family ◽  
Dimerization Interface ◽  
Further Development

LAGLIDADG family of homing endonucleases are rare-cutting enzymes which recognize long target sequences and are of great interest in genome engineering. Despite advances in homing endonuclease engineering, effective methods of broadening the range of cleaved sequences are still lacking. Here, we present a study of conserved structural features of LAGLIDADG homing endonucleases that might aid further development of such methods. The protein–DNA interface of LAGLIDADG homing endonucleases differs considerably with the particular nuclease, and the analysis of conserved protein–DNA interactions could not identify any residues crucial for DNA binding and common to most nucleases of the family. For the homing endonuclease PI-SceI, a comparison of structural and experimental data derived from literature helped to identify 23 residues that are likely to be important for DNA binding. Analysis of the LAGLIDADG domain dimerization interface allowed the choosing of six positions that contribute to dimerization specificity most, while comparison of 446 sequences of LAGLIDADG endonucleases revealed groups of residues in these positions that appear to be most favorable for dimerization.

scholarly journals Designing Minimal Genomes Using Whole-Cell Models

2018 ◽  
Author(s):  
Joshua Rees ◽  
Oliver Chalkley ◽  
Sophie Landon ◽  
Oliver Purcell ◽  
Lucia Marucci ◽  
...  
Keyword(s):  
In Silico ◽  
Genome Engineering ◽  
Cell Model ◽  
Mycoplasma Genitalium ◽  
Computational Design ◽  
Functional Assay ◽  
Proof Of Concept ◽  
Computational Tools ◽  
Whole Cell ◽  
Genome Design

AbstractIn the future, entire genomes tailored to specific functions and environments could be designed using computational tools. However, computational tools for genome design are currently scarce. Here we present algorithms that enable the use of design-simulate-test cycles for genome design, using genome minimisation as a proof-of-concept. Minimal genomes are ideal for this purpose as they have a simple functional assay, the cell either replicates or not. We used the first (and currently only published) whole-cell model, for the bacterium Mycoplasma genitalium. Our computational design-simulate-test cycles discovered novel in-silico minimal genomes smaller than JCVI-Syn3.0, a bacteria with, currently, the smallest genome that can be grown in pure culture. In the process, we identified 10 low essentiality genes, 18 high essentiality genes, and produced evidence for at least two Mycoplasma genitalium in-silico minimal genomes. This work brings combined computational and laboratory genome engineering a step closer.

scholarly journals Reprogramming homing endonuclease specificity through computational design and directed evolution

2013 ◽  
Vol 42 (4) ◽  
pp. 2564-2576 ◽  
Author(s):  
Summer B. Thyme ◽  
Sandrine J. S. Boissel ◽  
S. Arshiya Quadri ◽  
Tony Nolan ◽  
Dean A. Baker ◽  
...  
Keyword(s):  
Dna Cleavage ◽  
Homing Endonuclease ◽  
Genetic Diseases ◽  
Computational Design ◽  
Computational Approach ◽  
Selection Strategy ◽  
Homing Endonucleases ◽  
Deleterious Mutations ◽  
Genome Modification ◽  
Targeted Genome Modification

Abstract Homing endonucleases (HEs) can be used to induce targeted genome modification to reduce the fitness of pathogen vectors such as the malaria-transmitting Anopheles gambiae and to correct deleterious mutations in genetic diseases. We describe the creation of an extensive set of HE variants with novel DNA cleavage specificities using an integrated experimental and computational approach. Using computational modeling and an improved selection strategy, which optimizes specificity in addition to activity, we engineered an endonuclease to cleave in a gene associated with Anopheles sterility and another to cleave near a mutation that causes pyruvate kinase deficiency. In the course of this work we observed unanticipated context-dependence between bases which will need to be mechanistically understood for reprogramming of specificity to succeed more generally.

scholarly journals Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering

2019 ◽  
Vol 63 (2) ◽  
pp. 267-284 ◽  
Author(s):  
Sophie Landon ◽  
Joshua Rees-Garbutt ◽  
Lucia Marucci ◽  
Claire Grierson
Keyword(s):  
Computational Models ◽  
Genome Engineering ◽  
Computational Design ◽  
Biological Research ◽  
Cellular Processes ◽  
Recent Developments ◽  
Entire Cell ◽  
Design Algorithms ◽  
Genome Design

Abstract Producing ‘designer cells’ with specific functions is potentially feasible in the near future. Recent developments, including whole-cell models, genome design algorithms and gene editing tools, have advanced the possibility of combining biological research and mathematical modelling to further understand and better design cellular processes. In this review, we will explore computational and experimental approaches used for metabolic and genome design. We will highlight the relevance of modelling in this process, and challenges associated with the generation of quantitative predictions about cell behaviour as a whole: although many cellular processes are well understood at the subsystem level, it has proved a hugely complex task to integrate separate components together to model and study an entire cell. We explore these developments, highlighting where computational design algorithms compensate for missing cellular information and underlining where computational models can complement and reduce lab experimentation. We will examine issues and illuminate the next steps for genome engineering.

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