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Macromolecular Structure and Function

James MacDonald

James MacDonald

Contact

James MacDonald's Background and Interests

I studied undergraduate Physics at Imperial College London followed by an MSc in Biotechnology at the University of Essex. I then moved back to London to do a PhD at the School of Crystallography, Birkbeck College, University of London with Prof. Christine Slingsby and Prof. Julia Goodfellow. My PhD research was focussed simulating the unfolding of crystallins using molecular dynamics together with some wet-lab experiments to validate my theoretical predictions. This was followed by a Career Development Fellowship at the MRC National Institute for Medical Research (NIMR) in Mill Hill working in the lab of Willie Taylor. While at the NIMR I worked on developing software and methods for computational protein design as part of the DARPA PDP programme. In 2010 I returned to Imperial College and joined Paul Freemont's lab as a member of the Syntegron consortium. My current main research interests include developing cell-free systems for protein expression and biosynthetic pathways and computational protein design. My research involves both computational and experimental lab work. I also maintain this (MSF) website.

Cell-free protein expression and biosynthetic pathways

Cell-free protein expression systems allow the in vitro transcription and translation of genetic circuits by extracting the cellular machinery from cells and combining this with an energy solution containing various cofactors, amino acids and a secondary energy source. Cell-free technology enables the rapid prototyping of genetic circuits (See Chappell et al, 2013 and Sun et al, 2014) to help discover design rules and characterise parts. By decoupling transcription and translation from the cell's need to survive, cell-free has the potential as a platform for the production of proteins and molecules that would otherwise be difficult to produce in vivo. In collaboration with Dr. Richard Kelwick and other members of the Freemont lab, I am working on optimising cell-free systems.

Computational protein design

Natural enzymes can catalyse reactions at rates and specificities unmatched by artificial catalysts. Enzymes have precisely positioned chemical groups that stabilise the transition states of the desired chemical reactions. The ability to design proteins with atomic-level accuracy is therefore a prerequisite for the development of artificial enzymes.

In collaboration with Dr. James W. Murray I have designed and solved experimentally using X-ray crystallography artificial proteins with de novo designed loops. While previous computational protein design methods have mainly reused known protein scaffolds or backbone fragments, we have developed a new hierarchical computational design approach to rapidly sample backbone conformational space. We have used this method to design de novo loop embellishments on artificial extended scaffold proteins. This approach allows individual functional subunits to be designed and verified separately and then composed into a larger rigid scaffold with well defined spatial positions.

de novo loop design

De novo computationally designed protein loop. Example computationally designed loop where the cyan loop is the X-ray crystal structure and the pink loop is the computational design.

The computational protein design method uses a coarsed-grained potential energy function to explore backbone conformations in a sequence independent way (MacDonald et al, 2010 and MacDonald et al, 2013). Full backbones are then reconstructed using a highly accurate method we have recently published (Moore et al, 2013) and sequences designed using the Rosetta software package.

PD2 loop modelling

loop predictions

PD2 loop predictions. Example sub-Angstrom loop predictions where the cyan loops are the real structures and the pink loops are the predictions.

loop predictions

PD2 loop predictions. RMSD vs Energy plots for the sub-Angstrom loop predictions. Black dots are loops sampled using the full coarse-grained terms turned on while the purple dots are loops sampled using only the bond length and steric repulsion turned on.

I have developed software for modelling protein loops using a sequence independent coarse-grained potential energy function with often sub-Angstrom accuracy (MacDonald et al, 2013) . The latest C++ source code can be downloaded here.

PD2 backbone reconstruction

pd2_ca2main

PD2 backbone reconstruction. Ramachandran plot distributions for different residue types after reconstruction compared to real high resolution protein crystal structures (SCOP40) and our closest competitor (BBQ).

As part of the same PD2 software package we have also developed a new method for reconstructing protein backbones from alpha-carbon models (Moore et al, 2013) with an accompanying webserver. This new method was shown to be more accurate in terms of backbone RMSD, produce better Phaser LLG values when used for molecular replacement, and produce Ramachandran plot distributions closer to those observed in real proteins. The latest C++ source code can be downloaded here.

Other projects

DNA assembly - MODAL and R2oDNA Designer

MODAL

MODAL DNA assembly. Assembly using artifical linkers designer using R2oDNA Designer.

In collaboration with the labs of Dr. Tom Ellis and Dr. Geoff Baldwin I have worked on developing new methods for reliable multipart DNA assembly using synthetically designed artificial linker sequences. At the core of our approach is new a method for designing biologically-neutral linker sequences - R2oDNA Designer (Casini et al, 2014), which can be accessed as a webserver. These designed linkers have been used as part of the Modular Overlap-Directed Assembly with Linkers (MODAL) DNA assembly strategy (Casini et al, 2013) and the BASIC DNA assembly method being developed by the Baldwin lab.

Gene clusters in Arabidopsis - microarray expression analysis.

As part of the Syntegron consortium I am working with the group of Prof. Anne Osbourn at the John Innes Centre to identify putative specialised metabolic pathways organised in gene clusters.

Combinatorial protein fusion design (with Dr. Simon Moore)

Software development

PD2

Download the PD2 protein modelling software here.

Access the PD2_ca2main backbone reconstruction server here.

R2oDNA Designer

Access the R2oDNA Designer synthetic DNA linker server here.

Read R2oDNA Designer FAQs.

Links

Google Scholar

LinkedIn profile

The Syntegron project

ResearchGate profile

Synthetic Biology Index of Tools and Software (SynBITS)

Journal covers

JCC cover

Journal of Computational Chemistry. PD2 backbone reconstruction paper cover page.

Publications


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17  —   original research
Synthetic beta-solenoid proteins with the fragment-free computational design of a beta-hairpin extension.
MacDonald JT, Kabasakal BV, Godding D, Kraatz S, Henderson L, Barber J, Freemont PS, Murray JW.
Proc Natl Acad Sci USA (2016) – [DOI]

16  —   original research
A protease-based biosensor for the detection of schistosome cercariae
Webb AJ, Kelwick R, Doenhoff MJ, Kylilis N, MacDonald J, Wen KY, Mckeown C, Baldwin G, Ellis T, Jensen K, Freemont PS
Scientific Reports 6:24725 (2016) – [Pubmed]

15  —   original research
Delineation of metabolic gene clusters in plant genomes by chromatin signatures
Yu N, N├╝tzmann HW, MacDonald JT, Moore B, Field B, Berriri S, Trick M, Rosser SJ, Kumar SV, Freemont PS, Osbourn A
Nucleic Acids Res 44:2255-2265 (2016) – [Pubmed]

14  —   review article
Developments in the tools and methodologies of synthetic biology
Kelwick R, MacDonald JT, Webb AJ, Freemont P
Front Bioeng Biotechnol 2:60 (2014) – [Pubmed]

13  —   original research
Structural and Mechanistic Insight into the Listeria monocytogenes Two-Enzyme Lipoteichoic Acid Synthesis System
Campeotto I, Percy MG, MacDonald JT, Forster A, Freemont PS, Grundling A
J Biol Chem epub: (2014) – [Pubmed]

12  —   original research
R2oDNA Designer: Computational design of biologically-neutral synthetic DNA sequences
Casini A, Christodoulou G, Freemont PS, Baldwin GS, Ellis T, MacDonald JT
ACS Synthetic Biology Epub: (2014) – [Pubmed]

11  —   original research
One-pot DNA construction for synthetic biology: the Modular Overlap-Directed Assembly with Linkers (MODAL) strategy
Casini A, MacDonald JT, De Jonghe J, Christodoulou G, Freemont PS, Baldwin GS, Ellis T
Nucleic Acids Res 42:e7 (2013) – [Pubmed]

10  —   original research
Validating a Coarse-Grained Potential Energy Function through Protein Loop Modelling
MacDonald JT, Kelley LA, Freemont PS
PLoS ONE 8:e65770 (2013) – [Pubmed]

9  —   original research
High-quality protein backbone reconstruction from alpha carbons using Gaussian mixture models
Moore BL, Kelley LA, Barber J, Murray JW, MacDonald JT
J Comput Chem 34:1881-9 (2013) – [Pubmed]

8  —   original research
Analytic markovian rates for generalized protein structure evolution
Coluzza I, MacDonald JT, Sadowski MI, Taylor WR, Goldstein RA
PLoS One 7:e34228 (2012) – [Pubmed]

7  —   original research
Structural basis for the recognition and cleavage of abasic DNA in Neisseria meningitidis
Lu D, Silhan J, MacDonald JT, Carpenter EP, Jensen K, Tang CM, Baldwin GS, Freemont PS
Proc Natl Acad Sci USA 109:16852-7 (2012) – [Pubmed]

6  —   review article
Computational design approaches and tools for synthetic biology
MacDonald JT, Barnes C, Kitney RI, Freemont PS, Stan GB
Integr Biol (Camb) 3:97-108 (2011) – [Pubmed]


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5  —   book chapter
Designing New Proteins
Sadowski MI, MacDonald JT
in Amino Acids, Peptides and Proteins in Organic Chemistry: Protection Reactions, Medicinal Chemistry, Combinatorial Synthesis, Volume 4 (edited by Hughes AB), John Wiley & Sons (2011) – [DOI]

4  —   original research
De novo backbone scaffolds for protein design
MacDonald JT, Maksimiak K, Sadowski MI, Taylor WR
Proteins 78:1311-25 (2010) – [Pubmed]

3  —   original research
Probing the "dark matter" of protein fold space
Taylor WR, Chelliah V, Hollup SM, MacDonald JT, Jonassen I
Structure 17:1244-52 (2009) – [Pubmed]

2  —   original research
Unfolding crystallins: the destabilizing role of a beta-hairpin cysteine in betaB2-crystallin by simulation and experiment
MacDonald JT, Purkiss AG, Smith MA, Evans P, Goodfellow JM, Slingsby C
Protein Sci 14:1282-92 (2005) – [Pubmed]


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1  —   original research
Comparison of Generalised Born/Surface Area with Periodic Boundary Simulations to Study Protein Unfolding
Purkiss AG, MacDonald JT, Goodfellow JM, Slingsby C
Molecular Simulation 30:333-340 (2004) – [DOI]