Biology subjects

Skelton, D. J.

Publications and source records attributed to Skelton, D. J..

3 recordsLinked to original sources

bioRxiv2020

Codon optimisation for maximising gene expression in multiple species and microbial consortia

MotivationCodon optimisation, the process of adapting the codon composition of a coding sequence, is often used in synthetic biology to increase expression of a heterologous protein. Recently, a number of synthetic biology approaches that allow synthetic constructs to be deployed in multiple organisms have been published. However, so far, design tools for codon optimisation have not been updated to reflect these new approaches. ApproachWe designed an evolutionary algorithm (EA) to design coding sequences (CDSs) that encode a target protein for one or more target organisms, based on the Chimera average repetitive substring (ARS) metric -- a correlate of gene expression. A parameter scan was then used to find optimal parameter sets. Using the optimal parameter sets, three heterologous proteins were repeatedly optimised Bacillus subtilis 168 and Escherichia coli MG1655. The ARS scores of the resulting sequences were compared to the ARS scores of coding sequences that had been optimised for each organism individually (using Chimera Map). ResultsWe demonstrate that an EA is a valid approach to optimising a coding sequence for multiple organisms at once; both crossover and mutation operators were shown to be necessary for the best performance. In some scenarios, the EA generated CDSs that had higher ARS scores than CDSs optimised for the individual organisms, suggesting that the EA exploits the CDS design space in a way that Chimera Map does not. Availability and implementationThe implementation of the EA, with instructions, is available on GitHub: https://github.com/intbio-ncl/chimera_evolve.

synthetic biology↗

bioRxiv2019

Functional Analysis of Enzyme Families Using Residue-Residue Coevolution Similarity Networks

MotivationResidue-residue coevolution has been used to elucidate structural information of enzymes. Networks of coevolution patterns have also been analyzed to discover residues important for the function of individual enzymes. In this work, we take advantage of the functional importance of coevolving residues to perform network-based clustering of subsets of enzyme families based on similarities of their coevolution patterns, or \"Coevolution Similarity Networks\". The power of these networks in the functional analysis of sets of enzymes is explored in detail, using Sequence Similarity Networks as a benchmark.\n\nResultsA novel method to produce protein-protein networks showing the similarity between proteins based on the matches in the patterns of their intra-residue residue coevolution is described. The properties of these co-evolution similarity networks (CSNs) was then explored, especially in comparison to widely used sequence similarity networks (SSNs). We focused on the predictive power of CSNs and SSNs for the annotation of enzyme substrate specificity in the form of Enzyme Commission (EC) numbers using a label propagation approach. A method for systematically defining the threshold necessary to produce the optimally predictive CSNs and SSNs is described. Our data shows that, for the two protein families we analyse, CSNs show higher predictive power for the reannotation of substrate specificity for previously annotated enzymes retrieved from Swissprot. A topological analysis of both CSNs and SSNs revealed core similarities in the structure, topology and annotation distribution but also reveals a subset of nodes and edges that are unique to each network type, highlighting their complementarity. Overall, we propose CSNs as a new method for analysing the function enzyme families that complements, and offers advantages to, other network based methods for protein family analysis.\n\nAvailabilitySource code available on request.

bioinformatics↗

bioRxiv2018

Capturing Multicellular System Designs Using the Synthetic Biology Open Language (SBOL)

Synthetic biology aims to improve the development of biological systems and in-crease their reproducibility through the use of engineering principles, such as stan-dardisation and modularisation. It is important that these systems can be represented and shared in a standard way to ensure they are easily understood, reproduced, and utilised by other researchers. The Synthetic Biology Open Language (SBOL) is a data standard for sharing biological designs and information about their implementation and characterisation. Thus far, this standard has been used to represent designs in homogeneous systems, where the same design is implemented in every cell. In recent years there has been increasing interest in multicellular systems, where biological designs are split across multiple cells to optimise the system behaviour and function. Here we show how the SBOL standard can be used to represent such multicellular systems, and hence how researchers can better share designs with the community.

synthetic biology↗