Category Archives: Group Meetings

What we discuss during cake at our Tuesday afternoon group meetings

Journal club: Human enterovirus 71 protein interaction network prompts antiviral drug repositioning

Viruses are small infectious agents, which possess genetic code but have no independent metabolism. They propagate by infecting host cells and hijacking their machinery, often killing the cells in the process. One of the key challenges in developing effective antiviral therapies is the high mutation rate observed in viral genomes. A way to circumvent this issue is to target host proteins involved in virion assembly (also known as essential host factors, or EHFs), rather than the virion itself.

In their recent paper, Lu Han et al. [1] consider human virus protein-protein interactions in order to explore possible host drug targets, as well as drugs which could potentially be re-purposed as antivirals. Their study focuses on enterovirus 71 (EV71), one of the leading causes of hand, foot, and mouth disease.

Human virus protein-protein interactions and target identification

EHFs are typically detected by knocking out genes in the host organism and determining which of the knockouts result in virus control. Low repeat rates and high costs make this technique unsuitable for large scale studies. Instead, the authors use an extensive yeast two-hybrid screen to identify 37 unique protein-protein interactions between 7 of the 11 virus proteins and 29 human proteins. Pathway enrichment suggests that the human proteins interacting with EV71 are involved in a wide range of functions in the cell. Despite this range in functionality, as many as 17 are also associated with other viruses, either through known physical interactions, or as EHFs (Fig 1).

Fig. 1. Interactions between viral and human proteins (denoted as EIPs), and their connection to different viruses.

One of these is ATP6V0C, a subunit of vacuole ATP-ase. It interacts with the EV71 3A protein, and is a known essential host factor for five other viruses. The authors analyse the interaction further, and show that downregulating ATP6V0C gene expression inhibits EV71 propagation, while overexpressing it enhances virus propagation. Moreover, treating cells with bafilomycin A1, a selective inhibitor for vacuole ATP-ase, inhibits EV71 infection in a dose-dependent manner. The paper suggests that therefore ATP6V0C may be a suitable drug target, not only against EV71, but also perhaps even for a broad-spectrum antiviral. While this is encouraging, bafilomycin A1 is a toxic antibiotic used in research, but not suitable for human or drug use. Rather than exploring other compounds targeting ATP6V0C, the paper shifts focus to re-purposing known drugs as antivirals.

Drug prediction using CMap

A potential antiviral will ideally disturb most or all interactions between host cell and virion. One way to do this would be to inhibit the proteins known to interact with EV71. In order to check whether any known compounds already do so, the authors apply gene set enrichment analysis (GSEA) to data from the connectivity map (CMap). CMap is a database of gene expression profiles representing cellular response to a set of 1309 different compounds.  Enrichment analysis of the database reveals 27 potential EV71 drugs, of which the authors focus on the top ranking result, tanespimycin.

Tanespimycin is an orphan cancer drug, originally designed to target tumor cells by inhibiting HSP90. Its complex effects on the cell, however, may make it an effective antiviral. Following their CMap analysis, the authors show that tanespimycin reduces viral count and virus-induced cytopathic effects in a dose-dependent manner, without evidence of cytotoxicity.

Overall, the paper presents two different ways to think about target investigation and drug choice in antiviral therapeutics — by integrating different types of known host virus protein-protein interactions, and by analysing cell response to known compounds. While extensive further study is needed to determine whether the results are directly clinically relevant to the treatment of EV71, the paper shows how  interaction data analysis can be employed in drug discovery.

References:

[1] Han, Lu, et al. “Human enterovirus 71 protein interaction network prompts antiviral drug repositioning.” Scientific Reports 7 (2017).

 

Drawing Networks in LaTeX with tikz-network

While researching on protein interaction networks it is often important to illustrate networks. For this many different tools are available, for example, Python’s NetworkX and Matlab, that allow the export of figures as pixelated images or vector graphics. Usually, these figures are then incorporated in the papers, which are commonly written in LaTeX. In this post, I want to present `tikz-network’, which is a novel tool to code and illustrate networks directly in LaTeX.

To create an illustration you define the network’s nodes with their positions and edges between these nodes. An example of a simple network is

\begin{tikzpicture}
   \Vertex[color = blue]{A}
   \Vertex[x=3,y=1,color=red]{B}
   \Vertex[x=0,y=2,color=orange]{C}
   \Edge[lw=5pt](A)(B)
   \Edge[lw=3pt,bend=15,Direct](A)(C)
\end{tikzpicture}

The illustrations can be much more complex and allow dashed lines, opacity, and many other features. Importantly, the properties do not need to be specified in the LaTeX file itself but can also be saved in an external file and imported with the  \Vertices{data/vertices.csv}command. This allows the representation of more complex networks, for example the multilayer network below is created from the two files, the first representing the nodes

id, x, y ,size, color,opacity,label,layer 
A, 0, 0, .4 , green, .9 , a , 1
B, 1, .7, .6 , , .5 , b , 1
C, 2, 1, .8 ,orange, .3 , c , 1
D, 2, 0, .5 , red, .7 , d , 2
E,.2,1.5, .5 , gray, , e , 1
F,.1, .5, .7 , blue, .3 , f , 2
G, 2, 1, .4 , cyan, .7 , g , 2
H, 1, 1, .4 ,yellow, .7 , h , 2

and the second having the edge information:

u,v,label,lw,color ,opacity,bend,Direct
A,B, ab  ,.5,red   ,   1   ,  30,false
B,C, bc  ,.7,blue  ,   1   , -60,false
A,E, ae  , 1,green ,   1   ,  45,true
C,E, ce  , 2,orange,   1   ,   0,false
A,A, aa  ,.3,black ,  .5   ,  75,false
C,G, cg  , 1,blue  ,  .5   ,   0,false
E,H, eh  , 1,gray  ,  .5   ,   0,false
F,A, fa  ,.7,red   ,  .7   ,   0,true
D,F, df  ,.7,cyan  ,   1   ,   30,true
F,H, fh  ,.7,purple,   1   ,   60,false
D,G, dg  ,.7,blue  ,  .7   ,   60,false

For details, please see the extensive manual on the GitHub page of the project. It is a very new project and I only started using it but I like it so far for a couple of reasons:

  • it is easy to use, especially for small example graphs
  • the multilayer functionality is very convenient
  • included texts are automatically in the correct size and font with the rest of the LaTeX document
  • it can be combined with regular tikz commands to create more complex illustrations

Comparing naive and immunised antibody repertoire

Hi! This is my first post on Blopig as I joined OPIG in July 2017 for my second rotation project and DPhil.

During immune reactions to foreign molecules known as antigens, surface receptors of activated B-cells undergo somatic hypermutation to attain its high binding affinity and specificity to the target antigen. To discover how somatic hypermutation occurs to adapt the antibody from its germline conformation, we can compare the naive and antigen-experienced antibody repertoires. In this paper, the authors developed a protocol to carry out such comparison, detected, synthesised, expressed and validated the observed antibody genes against their target antigen.

What they have done:

  1. Mice immunisation: Naive (no antigens), CGG (a large protein), NP-CGG (hapten attached to a large protein).
  2. Sequencing: Total RNA was extracted from each spleen, cDNA was synthesised according to standard procedures, and amplified with the universal 5’-RACE primer (as oppose to the degenerate 5’-Vh primers) and the 3’-CH1 primer to distinguish between immunoglobulin-classes (IgG1, IgG2c and IgM). High throughput pyrosequencing was then used to recover the heavy chain sequences only.
  3. VDJ recombination analysis: V, D and J segments were assigned and the frequency of the VDJ combinations were plotted in a 3D graph.
  4. Commonality of the VDJ combination: For each VDJ combination, the “commonality” was counted from the average occurrence if n mice have the combination: if n=1, it’s the average occurrence if any 1 mouse has the combination; if n=5, the combination must be observed in all mice to generate a degree of commonality – otherwise it’s 0.
    • The effect of increasing n on commonality scores in IgG1 class: As we tighten the requirement for the commonality calculation, it becomes clear that IGHV9-3 is likely to target the CGG carrier, while IGHV1-72 is against the NP hapten.
    • IGHV9-3 can accommodate a wider range of D gene when targeting CGG alone. IGHV1-72 only uses IGHD1-1.
  5. Clustering V gene usage: Sequences were aligned to the longest sequence in the set (of VDJ combination), and the pairwise distance between sequences in the set were used to cluster the sequences using the UPGMA method.
    • A number of sequences were commonly found in different individuals. Among these sequences, one was randomly selected to proceed to the next step.
  6. Synthesis and validation of the detected antibody against the NP hapten: by comparing the antibody repertoires against the CGG and NP-CGG, the gene of the antibody against NP can be recovered. The authors in this paper chose to pair 3 different light chains to the chosen heavy chain, and assess the binding of the 3 antibodies.
    • NP-CGG bind well to both IGHV1-72 and IGHV9-3 antibodies; NP-BSA to IGHV1-72 only; and CGG to IGHV9-3 only.
    • The binding capabilities are affected by the light chain in the pair.

Key takeaway:

This work presented a metric of defining the “commonality” between individuals’ antibody repertoire and validated the identified antibody against its target antigen by combining with different light chains.

Proteins evolve on the edge of supramolecular self-assembly

Inspired by Eoin’s interesting talks on prions and prion diseases, and Nick’s discussion of how Cyro-Electron microscopy is going to be the end of an era for Crystallography. I thought I’d look at a paper that discusses aggregation of protein complexes, with some cryo-electron microscopy thrown in for good measure.

Supramolecular assembbly

a, A molecule gaining a single self-interacting patch forms a finite dimer. A self-interacting patch repeated on opposite sides of a symmetric molecule can result in infinite assembly. b, A point mutation in a dihedral octamer creates a new self-interacting patch (red), triggering assembly into a fibre.

Supramolecular assemblies are folded protein complexes forming into much larger units. This formation can be triggered by a mutation on a copy of the constituent homomers of the complex, acting as a self-interacting patch. If this patch were to form in a non-symmetric complex, it would likely form a finite assemble with a limited number of copies of the complex. However, if the complex has dihedral symmetry such that a patch is accessible at multiple separated locations, then complex can potentially form near infinite supramolecular assemblies. Continue reading

Slowing the progress of prion diseases

At present, the jury is still out on how prion diseases affect the body let alone how to cure them. We don’t know if amyloid plaques cause neurodegeneration or if they’re the result of it. Due to highly variable glycophosphatidylinositol (GPI) anchors, we don’t know the structure of prions. Due to their incredible resistance to proteolysis, we don’t know a simple way to destroy prions even using in an autoclave. The current recommendation[0] by the World Health Organisation includes the not so subtle: “Immerse in a pan containing 1N sodium hydroxide and heat in a gravity displacement autoclave at 121°C”.

There are several species including Water Buffalo, Horses and Dogs which are immune to prion diseases. Until relatively recently it was thought that rabbits were immune too. “Despite rabbits no longer being able to be classified as resistant to TSEs, an outbreak of ‘mad rabbit disease’ is unlikely”.[1] That being said, other than the addition of some salt bridges and additional H-bonds, we don’t know if that’s why some animals are immune.

We do know at least two species of lichen (P. sulcata and L. plumonaria) have not only discovered a way to naturally break down prions, but they’ve evolved two completely independent pathways to do so. How they accomplish this? We’re still not sure in fact, it was only last year that it was discovered that lichens may be composed of three symbiotic partnerships and not two as previously thought.[3]

With all this uncertainty, one thing is known: PrPSc, the pathogenic form of the Prion converts PrPC, the cellular form. Just preventing the production of PrPC may not be a good idea, mainly because we don’t know what it’s there for in the first place. Previous studies using PrP-knockout have shown hints that:

  • Hematopoietic stem cells express PrP on their cell membrane. PrP-null stem cells exhibit increased sensitivity to cell depletion. [4]
  • In mice, cleavage of PrP proteins in peripheral nerves causes the activation of myelin repair in Schwann Cells. Lack of PrP proteins caused demyelination in those cells. [5]
  • Mice lacking genes for PrP show altered long-term potentiation in the hippocampus. [6]
  • Prions have been indicated to play an important role in cell-cell adhesion and intracellular signalling.[7]

However, an alternative approach which bypasses most of the unknowns above is if it were possible to make off with the substrate which PrPSc uses, the progress of the disease might be slowed. A study by R Diaz-Espinoza et al. was able to show that by infecting animals with a self-replicating non-pathogenic prion disease it was possible to slow the fatal 263K scrapie agent. From their paper [8], “results show that a prophylactic inoculation of prion-infected animals with an anti-prion delays the onset of the disease and in some animals completely prevents the development of clinical symptoms and brain damage.”

[0] https://www.cdc.gov/prions/cjd/infection-control.html
[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3323982/
[2] https://blogs.scientificamerican.com/artful-amoeba/httpblogsscientificamericancomartful-amoeba20110725lichens-vs-the-almighty-prion/
[3] http://science.sciencemag.org/content/353/6298/488
[4] “Prion protein is expressed on long-term repopulating hematopoietic stem cells and is important for their self-renewal”. PNAS. 103 (7): 2184–9. doi:10.1073/pnas.0510577103
[5] Abbott A (2010-01-24). “Healthy prions protect nerves”. Nature. doi:10.1038/news.2010.29
[6] Maglio LE, Perez MF, Martins VR, Brentani RR, Ramirez OA (Nov 2004). “Hippocampal synaptic plasticity in mice devoid of cellular prion protein”. Brain Research. Molecular Brain Research. 131 (1-2): 58–64. doi:10.1016/j.molbrainres.2004.08.004
[7] Málaga-Trillo E, Solis GP, et al. (Mar 2009). Weissmann C, ed. “Regulation of embryonic cell adhesion by the prion protein”. PLoS Biology. 7 (3): e55. doi:10.1371/journal.pbio.1000055
[8] http://www.nature.com/mp/journal/vaop/ncurrent/full/mp201784a.html

Journal Club: Statistical database analysis of the role of loop dynamics for protein-protein complex formation and allostery

As I’ve mentioned on this blog a few (ok, more than a few) times before, loops are often very important regions of a protein, allowing it to carry out its function effectively. In my own research, I develop methods for loop structure prediction (in particular for antibody CDR H3), and look at loop conformational changes and flexibility. So, when I came across a paper that has the words ‘loops’, ‘flexibility’ and ‘antibody’ in its abstract, it was the obvious choice to present at my most recent journal club!

In the paper, entitled “Statistical database analysis of the role of loop dynamics for protein-protein complex formation and allostery”, the authors focus on how loop dynamics change upon the formation of protein-protein complexes. To do this, they use an algorithm they previously published called ToeLoop – given a protein structure, this classifies the loop regions as static, slow, or fast, based on both sequential and structural features:

  • relative amino acid frequencies;
  • the frequency of loop secondary structure types as annotated by DSSP (bends, β-bridges etc.);
  • the average solvent accessible surface area;
  • the average hydrophobicity index for the loop residues;
  • loop length;
  • contacts between atoms of the loop and the rest of the protein.

Two scores are calculated using the properties listed above: one that distinguishes ‘static’ loops from ‘mobile’ loops (with a reported 81% accuracy), and another that further categorises the mobile loops into ‘slow’ and ‘fast’ (74% accuracy). Results from the original ToeLoop paper indicate that fast loops are shorter, have more negatively charged residues, larger solvent accessibilities, lower hydrophobicity, and fewer contacts.

Gu et al. use ToeLoop to investigate the dynamic behaviour of loops during protein-protein complex formation. For a set of 230 protein complexes, they classified the loops of the proteins in both their free and complexed forms (illustrated by the figure below).

The loops from 230 protein complexes, in both free and bound forms, were categorised as fast, slow, or static using the ToeLoop algorithm. The loops are coloured according to their predicted dynamics. Allosteric loops, defined as those whose mobility increases upon binding, are indicated using blue arrows.

In the uncomplexed form, the majority of loops were annotated as static (63.6%), followed by slow (26.2%) and finally fast (10.2%). This indicates that most loops are inflexible. After complex formation, the number of static loops increases and the number of mobile loops decreases (67.8%, 23.0%, and 9.2% for static, slow and fast respectively). Mobility, on the whole, is therefore reduced upon binding, which is as expected – the presence of a binding partner restricts the range of possible movement.

The authors then divided the loops into two groups, interface and non-interface, according to the average minimum distance of each loop residue to the binding partner (cutoff values from 4 to 8 Å were tested and each gave broadly similar results). The dynamics of non-interface loops changed less upon binding than those of the interface loops (again, this was as expected). However, an interesting result is that slow loops are more common at the interface than any other parts of the protein, with 37.2% of interface loops being annotated as slow compared to 24.8% of non-interface loops. It is suggested by the authors that this is due to protein promiscuity; i.e. slow loops allow proteins to bind to different partners.

The 4600 loops analysed in the study were split into two groups based on their proximity to the interface. As expected, interface loops are affected more by binding than non-interface loops. Slow loops are more prevalent at the interface than elsewhere on the protein.

Binding-induced dynamic changes were then investigated in more detail, by dividing the loops into 9 categories based on the transition (i.e. static-static, slow-static, slow-fast etc.). The dynamic behaviour of most loops (4120 out of 4600) does not change, and those loops whose mobility decreased upon binding were found close to the interface (average distance of ~12 Å). A small subset of the loops (termed allosteric by the authors) demonstrated an increase in flexibility upon complex formation (142 out of 4600); these tended to be located further away from the interface (average distance of ~30 Å).

One of these allosteric loops was investigated further as part of a case study. The complex in question was an antibody-antigen complex, in which one loop distant from the binding site transitioned from static to slow upon binding. The loops directly involved in binding (the CDRs) either displayed reduced flexibility or remained static. The presence of an allosteric loop was supported by experimental data – the loop is shown to change conformation upon binding (RMSD of 3.6 Å between bound and unbound crystal structures from the PDB), and the average B-factor for the loop atoms increased on complex formation from around 26 Å2 to approximately 140 Å2. The authors also carried out MD simulations of the unbound antibody and antigen as well as the complex, and showed that the loop moved more in the complex than in the free antibody. The authors propose that the increased flexibility of the loop offsets the entropy loss that occurs due to binding, thereby increasing the strength of binding. ToeLoop could, therefore, be a useful tool in the development of antibody therapies (or other protein drugs) – it could be used in tandem with an antibody modelling protocol, allowing the dynamic behaviour of loop regions to be monitored and possibly designed to increase affinities.

Finally, the authors explored the link between loop dynamics and binding affinity. Again, they used ToeLoop to predict the flexibility of loops, but this time the complexes were from a set of 170 with known affinity. They demonstrated that affinity is correlated with the number of static loop residues present at the interface – ‘strong’ binders (those with picomolar affinity) tend to contain more static residues than more weakly binding pairs of proteins. This is in accordance with the theory that the rigidification of flexible loops upon binding leads to lower affinities, due to the loss of entropy.

When Does Chemical Elaboration Induce a Ligand To Change Its Binding Mode?

When Does Chemical Elaboration Induce a Ligand To Change Its Binding Mode?

For my journal club in June, I chose to present a Journal of Medicinal Chemistry article entitled “When Does Chemical Elaboration Induce a Ligand To Change Its Binding Mode?” by Malhotra and Karanicolas. This article uses a large scale collection of ligand pairs to investigate the circumstances in which elaborations of a ligand change the original binding mode.

One of the primary goals in medicinal chemistry is the optimisation of biological activity by chemical elaboration of a hit compound. This hit-to-lead optimisation often assumes that addition of functional groups to a given hit scaffold will not change the original binding mode.

In order to investigate the circumstances in which this assumption holds true and how often it holds true, they built up a large-scale collection of 297 related ligand pairs solved in complex with the same protein partner. Each pair consisted of a larger and smaller ligand; the larger ligand could have arisen from elaboration of the smaller ligand. They found that for 41 out of the 297 pairs (14%), the binding mode changed upon elaboration of the smaller ligand.

They investigated many physicochemical properties of the ligand, the protein-ligand complex and the protein binding pocket. They summarise the statistical significance and predictive power of the investigated properties with the table shown below.

They found that the property with the lowest p-value was the “rmsd after minimisation of the aligned complex” (RMAC). They developed this metric to probe whether the larger ligand could be accommodated in the protein without changing binding mode. They did so by aligning the shared substructure of the larger ligand onto the smaller ligand’s complex and then carrying out an energy minimisation. By monitoring the RMSD difference of the larger ligand relative to the initial pose (RMAC), they can gauge how compatible the larger ligand is with the protein. Larger RMAC values indicate greater incompatibility, hence a greater likelihood for the binding mode to not be preserved.

The authors generated receiver operating characteristic (ROC) plots to compare the predictive power of the properties considered. ROC curves are made by plotting the true positive rate (TPR) against the false positive rate (FPR). A random classifier would yield the dotted line from the bottom left to the top right, shown in the plots below. The best predictors would give a point in the top left corner of the plot. The properties that do well include RMAC, pocket volume, molecular weight, lipophilicity and potency.

They also combined properties to enhance predictive power and conclude that RMAC and molecular weight together offers good predictivity.Finally, the authors look at the pairs that have low RMAC values (i.e. the elaboration should be compatible with the protein pocket), yet show a change in binding mode. For these cases, a specific substitution may enable formation of a new, stronger interaction or for pseudosymmetric ligands, the alternate pose can mimic many of the interactions of the original pose.

Antibody Developability: Experimental Screening Assays

[This blog post is centered around the paper “Biophysical properties of the clinical-stage antibody landscape” (http://www.pnas.org/content/114/5/944.abstract) by Tushar Jain and coworkers. It is designed as a very basic intro for computational scientists into the world of experimental biophysical assays.]

A major concern in the development of antibody therapies is being able to predict “developability issues” at the screening stage, to avoid costly Phase II/Phase III clinical trial failures. Examples of such issues include an antibody being difficult to manufacture, possessing unsuitable pharmacodynamic or pharmokinetic profiles, having a propensity to aggregate (both in storage and in vivo) and being highly immunogenic.

This post is designed to give a clear and concise summary of the principles behind some of the most common biophysical experimental assays used to assess antibody candidates for future developability issues.

1. Ease of manufacture

HEK Titre (HEKt): This assay tests the expression level of the antibody (the higher the better). The heavy and light chain sequences are subcloned into vectors (such as pcDNA 3.4+, ThermoFisher) and these vectors are subsequently transfected into a suspension of Human embryonic kidney (HEK293) cells. After a set number of days the supernatant is harvested to assess the degree of expression.

2. Stability of 3D structure

Melting temperature using Differential Scanning Fluorimetry (Tm with DSF) Assay: This assay tests the thermal stability of the antibody. The higher the thermal stability, the less likely the protein will spontaneously unfold and become immunogenic. The antibody is mixed with a dye that fluoresces when in contact with hydrophobic regions, such as SPYRO orange. The mixture is then taken through a range of temperatures (eg. 40°C -> 95°C at a rate of 0.5°C/2min). As the protein begins to unfold, buried hydrophobic residues will become exposed and the level of fluorescence will suddenly increase. The value of T when the increase in fluorescence intensity is greatest gives us a Tm value.

(Further reading: http://www.beta-sheet.org/resources/T22-Niesen-fingerprinting_Oxford.pdf)

3. Stickiness assays (Aggregation propensity/Low solubility/High viscosity)

Affinity-capture Self-interaction Nanoparticle Spectroscopy (AC-SINS) Assay: This assay tests how likely an antibody is to interact with itself. It uses gold nanoparticles that are coated with anti-Fc antibodies. When a dilute solution of antibodies is added, they rapidly become immobilised on the gold beads. If these antibodies subsequently attract one another, it leads to shorter interatomic distances and longer absorption wavelengths that can be detected by spectroscopy.

(Further reading: https://www.ncbi.nlm.nih.gov/pubmed/24492294)

Clone Self-interaction by Bio-layer Interferometry (CSI-BLI) Assay: A more high-throughput method that uses a label-free technology to measure self-interaction. Antibodies are loaded onto the biosensor tip and white light is shone down the instrument to yield an internal reflection interference pattern. Then the tip is inserted into a solution of the same antibody, and if self-interaction occurs, then the interference pattern shifts by an amount proportional to the change in thickness of the biological layer. Images from: http://www.fortebio.com/bli-technology.html

(Further Reading: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896597/)

Hydrophobic Interaction Chromatography (HIC) Assay: Antibodies are mixed into a polar mobile phase and then washed over a hydrophobic column. UV-absorbance or other techniques can then be used to determine the degree of adhesion.

(Further Reading: https://www.ncbi.nlm.nih.gov/pubmed/4094424)

Standup Monolayer Chromatography (SMAC) Assay: Antibodies are injected onto a pre-packed Zenix HPLC column and their retention times are calculated. The longer the retention time, the lower their colloidal stability and the more prone they are to aggregate.

(Further Reading: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4622974/)

Size-exclusion Chromatography (SEC) Assay: Antibodies are flowed through a column consisting of spherical beads with miniscule pores. Non-aggregated antibodies are small enough to get trapped in the pores, whereas aggregated antibodies will flow through the column more rapidly. Percentage aggregation can be worked out from the concentrations of the different fractions.

4. Degree of specificity

Cross-Interaction Chromatography (CIC) Assay: This assay measures an antibody’s retention time as it flows across a column conjugated with polyclonal human serum antibodies. If an antibody takes longer to exit the column, it indicates that its surface is likely to interact with several different in vivo targets.

(Further Reading: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896597/)

Enzyme-linked Immunosorbent Assay (ELISA) – with common antigens or Baculovirus Particles (BVPs): Common antigens or BVPs are fixed onto a solid surface and then a solution containing the antibody of interest linked to an enzyme (such as horseradish peroxidase, HRP) is washed over them. Incubation lasts for about an hour before any unreacted antibodies are washed off. When the appropriate enzyme substrate is then added, it triggers emission of a visible, fluorescent or luminescent nature, which can be detected. The intensity is proportional to the amount of antibody stuck to the surface.

(Further Reading: https://www.thermofisher.com/uk/en/home/life-science/protein-biology/protein-biology-learning-center/protein-biology-resource-library/pierce-protein-methods/overview-elisa.html)

Poly-Specificity Reagent (PSR) Binding Assay: A more high-throughput method that uses fluorescence-activated cell sorting (FACS), a type of flow cytometry. A PSR is generated by biotinylating soluble membrane proteins (from Chinese hamster ovary (CHO) cells, for example) and then is incubated with IgG-presenting yeast. After washing a secondary labeling mix is added, and flow cytometry is used to determine a median fluorescence intensity – the higher the median intensity, the greater the chance of non-specific binding.

(Further Reading: https://www.ncbi.nlm.nih.gov/pubmed/24046438)

Le Tour de Farce v5.0

Every summer the OPIGlets go on a cycle ride across the scorched earth of Oxford in search of life-giving beer. Now in its fifth iteration, the annual Tour de Farce took place on us on Tuesday the 13th of June.

Establishments frequented included The Victoria, The Plough, Jacobs Inn (where we had dinner and didn’t get licked by their goats, certainly not), The Perch and finally The Punter. Whilst there were plans to go to The One for their inimitable “lucky 13s” by 11PM we were alas too late, so doubled down in The Punter.

Highlights of this years trip included certain members of the group almost immediately giving up when trying to ride a fixie and subsequently being shown up by our unicycling brethren.

Conformational diversity analysis reveals three functional mechanisms in proteins

Conformational diversity analysis reveals three functional mechanisms in proteins

This paper was published recently in Plos Comp Bio and looks at the conformational diversity (flexibility) of protein structures by comparing solved structures of identical sequences.

The premise of the work is that different crystal structures of the same protein represent instances of the conformational space of the protein. These different instances are identical in amino acid sequence but often differ in other ways they could come from different crystal forms or the protein could have different co-factors bound or have undergone post translational modifications.

The data set used in the paper came from CoDNaS (conformational diversity of the native state) Database URL:http://ufq.unq.edu.ar/codnas.

Only structures solved using X-ray crystallography to a resolution better than 2.5A were used and only proteins for which at least 5 conformers were available (average of 15.53 conformers per protein). Just under 5000 different protein chains made up the set. In order to describe the protein chains the measure used was maximum conformational diversity (the maximum RMSD between any of the conformers of a given protein chain).

The authors describe a relationship between this maximum conformational diversity and the presence absence of intrinsically disordered regions (IDRs). An IDR was defined as a segment of at least 5 contiguous residues with missing electron density (the first and last 20 residues of the chain were not included).

The proteins were divided into three groups.

Rigid

  • No IDRS

Partially disordered

  • IDRs in at least one conformer
  • IDR in the maximum RMSD pair of conformational diversity

Malleable

  • IDRs in at least one conformer
  • No IDR in the maximum RMSD pair of conformational diversity

Rigid proteins have in general lower conformational diversity than partially disordered than Malleable. The authors describe how these differences are not due to crystallographic conditions, protein length, number of crystal contacts or number of conformers.

The authors then go on to compare other properties based on these three types of protein chains including amino acid composition, loop RMSD and cavities and tunnels.

They summarise their findings with the figure below.