# Category Archives: Group Meetings

What we discuss during cake at our Tuesday afternoon group meetings

# Journal Club: Large-scale structure prediction by improved contact predictions and model quality assessment.

With the advent of statistical techniques to infer protein contacts from multiple sequence alignments (which you can read more about here), accurate protein structure prediction in the absence of a template has become possible. Taking advantage of this fact, there have been efforts to brave the sea of protein families for which no structure is known (about 8,500 – over 50% of known protein families) in an attempt to predict their topology. This is particularly exciting given that protein structure prediction has been an open problem in biology for over 50 years and, for the first time, the community is able to perform large-scale predictions and have confidence that at least some of those predictions are correct.

Based on these trends, last group meeting I presented a paper entitled “Large-scale structure prediction by improved contact predictions and model quality assessment”. This paper is the culmination of years of work, making use of a large number of computational tools developed by the Elofsson Lab at Stockholm University. With this blog post, I hope to offer some insights as to the innovative findings reported in their paper.

Let me begin by describing their structure prediction pipeline, PconsFold2. Their method for large-scale structure prediction can be broken down into three components: contact prediction, model generation and model quality assessment. As the very name of their article suggests, most of the innovation of the paper stems from improvements in contact prediction and the quality assessment protocols used, whereas for their model generation routine, they opted to sacrifice some quality in favour of speed. I will try and dissect each of these components over the next paragraphs.

Contact prediction relates to the process in which residues that share spatial proximity in a protein’s structure are inferred from multiple sequence alignments by co-evolution. I will not go into the details of how these protocols work, as they have been previously discussed in more detail here and here. The contact predictor used in PconsFold2 is PconsC3, which is another product of the Elofsson Lab. There was some weirdness with the referencing of PconsC3 on the PconsFold2 article, but after a quick google search, I was able to retrieve the article describing PconsC3 and it was worth a read. Other than showcasing PconsC3’s state-of-the-art contact prediction capabilities, the original PconsC3 paper also provides figures for the number of protein families for which accurate contact prediction is possible (over 5,000 of the ~8,500 protein families in Pfam without a member of known structure). I found the PconsC3 article feels like a prequel to the paper I presented. The bottom line here is that PconsC3 is a reliable tool for predicting contacts from multiple sequence alignments and is a sensible choice for the PconsFold2 pipeline.

Another aspect of contact prediction that the authors explore is the idea that the precision of contact prediction is dependent on the quality of the underlying multiple sequence alignment (MSA). They provide a comparison of the Positive Predicted Value (PPV) of PconsC3 using different MSAs on a test set of 626 protein domains from Pfam. To my knowledge, this is the first time I have encountered such a comparison and it serves to highlight the importance the MSA has on the quality of resulting contact predictions. In the PconsFold2 pipeline, the authors use consensus approach; they identify the consensus of four predicted contact maps each using a different alignment. Alignments were generated using Jackhmmer and HHBlits at E-Value cutoffs of 1 and 10^-4.

Now, moving on to the model generation routine. PconsFold2 makes use of CONFOLD to perform model generation. CONFOLD, in turn, uses the simulated annealing routine of the Crystallographic and NMR System (CNS) to produce models based on spatial and geometric constraints. To derive those constraints, predicted secondary structure and the top 2.5 L predicted contacts are given as input. The authors do note that the refinement stage of CONFOLD is omitted, which is a convenience I assume was adopted to save computational time. The article also acknowledges that models generated by CONFOLD are likely to be less accurate than the ones produced by Rosetta, yet a compromise was made in order to make the large-scale comparison feasible in terms of resources.

One particular issue that we often discuss when performing structure prediction is the number of models that should be produced for a particular target. The authors performed a test to assess how many decoys should be produced and, albeit simplistic in their formulation, their results suggest that 50 models per target should be sufficient. Increasing this number further did not lead to improvements in the average quality of the best models produced for their test set of 626 proteins.

After producing 50 models using CONFOLD, the final step in the PconsFold2 protocol is to select the best possible model from this ensemble. Here, they present a novel method, PcombC, for ranking models. PcombC combines the clustering-based method Pcons, the single-model deep learning method ProQ3D, and the proportion of predicted contacts that are present in the model. These three scores are combined linearly, and are given weights that were optimised via a parameter sweep. One of my reservations relating to this paper is that little detail is given regarding the data set that was used to perform this training. It is unclear from their methods section if the parameter sweep was trained on the test set with 626 proteins used throughout the manuscript. Given that no other data set (with known structures) is ever introduced, this scenario seems likely. Therefore, all the classification results obtained by PcombC, and all of the reported TM-score Top results should be interpreted with care since performance on validation set tends to be poorer than on a training set.

Recapitulating the PconsFold2 pipeline:

• Step 1: generate four multiple sequence alignments using HHBlits and Jackhmmer.
• Step 2: generate four predicted contact maps using PconsC3.
• Step 3: Use CONFOLD to produce 50 models using a consensus of the contact maps from step 2.
• Step 4: Use PCombC to rank the models based on a linear combination of the Pcons and ProQ3D scores and the proportion of predicted contacts that are present in the model.

So, how well does PconsFold2 perform? The conclusion is that it depends on the quality of the contact predictions. For the protein families where abundant sequence information is available, PconsFold2 produces a correct model (TM-Score > 0.5) for 51% of the cases. This is great news. First, because we know which cases have abundant sequence information beforehand. Second, because this comprises a large number of protein families of unknown structure. As the number of effective sequence (a common way to assess the amount of information available on an MSA) decreases, the proportion of families for which a correct model has been generated also decreases, which restricts the applicability of their method to protein families with abundant sequence information. Nonetheless, given that protein sequence databases are growing exponentially, it is possible that over the next years, the number of cases where protein structure prediction achieves success is likely to increase.

One interesting detail that I was curious about was the length distribution of the cases where modelling was successful. Can we detect the cases for which good models were produced simply by looking at a combination of length and number of effective sequences? The authors never address this question, and I think it would provide some nice insights as to which protein features are correlated to modelling success.

We are still left with one final problem to solve: how do we separate the cases for which we have a correct model from the ones where modelling has failed? This is what the authors address with the last two subsections of their Results. In the first of these sections, the authors compare four ways of ranking decoys: PcombC, Pcons, ProQ3D, and the CNS contact score. They report that, for the test set of 626 proteins, PcombC obtains the highest Pearson’s Correlation Coefficient (PCC) between the predicted and observed TM-Score of the highest ranking models. As mentioned before, this measure could be overestimated if PcombC was, indeed, trained on this test set. Reported PCCs are as follows: PcombC = 0.79, Pcons = 0.73, ProQ3D = 0.67, and CNS-contact = -0.56.

In their final analysis, the authors compare the ability of each of the different Quality Assessment (QA) scores to discern between correct and incorrect models. To do this, they only consider the top-ranked model for each target according to different QA scores. They vary the false positive rate and note the number of true positives they are able to recall. At a 10% false positive rate, PcombC is able to recall about 50% of the correct models produced for the test set. This is another piece of good news. Bottomline is: if we have sufficient sequence information available, PconsFold2 can generate a correct model 51% of the time. Furthermore, it can detect 50% of these cases, meaning that for ~25% of the cases it produced something good and it knows the model is good. This opens the door for looking at these protein families with no known structure and trying to accurately predict their topology.

That is exactly what the authors did! On the most interesting section of the paper (in my opinion), the authors predict the topology of 114 protein families (at FPR of 1%) and 558 protein families (at FPR of 10%). Furthermore, the authors compare the overlap of their results with the ones reported by a similar study from the Baker group (previously presented at group meeting here) and find that, at least for some cases, the predictions agree. These large-scale efforts  force us to revisit the way we see template-free structure prediction, which can no longer be dismissed as a viable way of obtaining structural models when sufficient sequences are available. This is a remarkable achievement for the protein structure prediction community, with the potential to change the way we conduct structural biology research.

# Journal Club post: Interface between Ig-seq and antibody modelling

Hi everyone! In this blog post, I would like to review a couple of relatively recent papers about antibody modelling and immunoglobulin gene repertoire NGS, also known as Ig-seq. Previously I used to work as a phage display scientist and I initially struggled to understand all new terminology about computational modelling when I joined Charlotte’s group last January. Hence, the paramount aim of my blog post is to decipher commonly used jargon in the computational world into less complicated text.

The three-dimensional structure of an antibody dictates its function. Antibody sequences obtained from Ig-seq cannot be directly translated into antibody folding, aggregation and function. Several ways exist to interrogate antibody structure, including X-ray crystallography and NMR spectroscopy, expression, and computational modelling. These methods vary in throughput as well as precision. Here, I will concentrate my attention on computational modelling. First of all, the most commonly confused term is a decoy. In antibody structure prediction, a decoy is a modelled antibody structure that can be ranked and selected by a tool as the closest to the native antibody structure. A number of antibody modelling tools exist, each employing a different methodology and a number of generated decoys. Good reviews on the antibody structure prediction are here (1,2). I will try to draw a very gross summary about how all these unique modelling tools work. To do so, I assume that people are familiar with antibody sequence/structure relationship – if not please check (3). Antibody framework region are sequence invariant, hence their structure can be deduced from sequence identity with high confidence. PDB (4) act as the source of structures for antibody modelling. Canonical CDRs (all CDRs except for CDR-H3) can be put into a limited number of structures. Several definitions of canonical classes exist (5,6), but, in essence, the canonical CDR must contain residues that define a particular class. Next, antibody orientation is calculated or copied from PDB. CDR-H3 modelling is very challenging and different approaches have been devised (7–9). The structure space of CDR-H3 is very vast (10) and hence, this loop cannot be put into a canonical class. Once CDR-H3 is modelled, the resultant decoy is checked for clashes (like impossible orientation of side chains).

Here, I would like to mention several examples on how antibody modelling can help to accelerate drug discovery. Dekosky et al. (11) mapped two Ig-seq datasets to antibody structures to interrogate how an antibody paratope changes in response to antigenic stimulation. The knowledge of paired full length VH-VL is crucial for the best antibody structure prediction. In this study they employed paired chain Ig-seq (12). However, this technique cannot sequence full length VH/VL, hence the V gene sequence had to be approximated. Computational paratope identification was employed to examine paratope convergences. There were several drawbacks of this paper: only 2,000 models (~1% of Ig-seq data) were modelled in 570,000 CPU time, and antibody sequences with longer than 16 aa long CDR-H3 were not included into analysis. The generation of a reliable configuration of long CDR-H3 is considered a hard task at this moment. Recently, Laffy et al. (13) investigated antibody promiscuity by mapping sequence to structure and validating the results with ELISA. The cohort of 10 antibodies, all with long CDR-H3 >= 15 aa were interrogated. They used a homology modelling tool to devise CDR-H3 structures. However, the availability of the appropriate structural template can be questioned, since CDR-H3 loops deposited in the PDB are predominantly shorter due to crystallographic constraints. As mentioned before, the paired VH/VL data is crucial for structure determination. Here, they used Dekosky et al. (11) data to devise the pairing. The approach can be streamlined once more paired data become available.

In conclusion, antibody modelling enables researchers to circumvent the cost and time associated with experimental approaches of antibody characterizations. The field of antibody modelling still needs improvements for faster and better structure prediction to achieve tasks such as modelling the entirety of Ig-seq data or long CDR-H3 loops. Currently, the fastest tool of antibody modelling is ABodyBuilder (8). It generates a model in 30 sec and its version is available online (http://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/Modelling.php). The availability of more structural information as well as algorithm improvements will facilitate more confident antibody modelling.

1. Kuroda D, Shirai H, Jacobson MP, Nakamura H. Computer-aided antibody design. Protein Eng Des Sel (2012) 25:507–521. doi:10.1093/protein/gzs024
2. Krawczyk K, Dunbar J, Deane CM. “Computational Tools for Aiding Rational Antibody Design,” in Methods in molecular biology (Clifton, N.J.), 399–416. doi:10.1007/978-1-4939-6637-0_21
3. Georgiou G, Ippolito GC, Beausang J, Busse CE, Wardemann H, Quake SR. The promise and challenge of high-throughput sequencing of the antibody repertoire. Nat Biotech (2014) 32:158–168. doi:10.1038/nbt.2782
4. Berman H, Henrick K, Nakamura H, Markley JL. The worldwide Protein Data Bank (wwPDB): Ensuring a single, uniform archive of PDB data. Nucleic Acids Res (2007) 35: doi:10.1093/nar/gkl971
5. Nowak J, Baker T, Georges G, Kelm S, Klostermann S, Shi J, Sridharan S, Deane CM. Length-independent structural similarities enrich the antibody CDR canonical class model. MAbs (2016) 8:751–760. doi:10.1080/19420862.2016.1158370
6. North B, Lehmann A, Dunbrack RL. A new clustering of antibody CDR loop conformations. J Mol Biol (2011) 406:228–256. doi:10.1016/j.jmb.2010.10.030
7. Weitzner BD, Jeliazkov JR, Lyskov S, Marze N, Kuroda D, Frick R, Adolf-Bryfogle J, Biswas N, Dunbrack RL, Gray JJ. Modeling and docking of antibody structures with Rosetta. Nat Protoc (2017) 12:401–416. doi:10.1038/nprot.2016.180
8. Leem J, Dunbar J, Georges G, Shi J, Deane CM. ABodyBuilder: Automated antibody structure prediction with data–driven accuracy estimation. MAbs (2016) 8:1259–1268. doi:10.1080/19420862.2016.1205773
9. Marks C, Nowak J, Klostermann S, Georges G, Dunbar J, Shi J, Kelm S, Deane CM. Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction. Bioinformatics (2017) 33:1346–1353. doi:10.1093/bioinformatics/btw823
10. Regep C, Georges G, Shi J, Popovic B, Deane CM. The H3 loop of antibodies shows unique structural characteristics. Proteins Struct Funct Bioinforma (2017) 85:1311–1318. doi:10.1002/prot.25291
11. DeKosky BJ, Lungu OI, Park D, Johnson EL, Charab W, Chrysostomou C, Kuroda D, Ellington AD, Ippolito GC, Gray JJ, et al. Large-scale sequence and structural comparisons of human naive and antigen-experienced antibody repertoires. Proc Natl Acad Sci U S A (2016)1525510113-. doi:10.1073/pnas.1525510113
12. Dekosky BJ, Kojima T, Rodin A, Charab W, Ippolito GC, Ellington AD, Georgiou G. In-depth determination and analysis of the human paired heavy- and light-chain antibody repertoire. Nat Med (2014) 21:1–8. doi:10.1038/nm.3743
13. Laffy JMJ, Dodev T, Macpherson JA, Townsend C, Lu HC, Dunn-Walters D, Fraternali F. Promiscuous antibodies characterised by their physico-chemical properties: From sequence to structure and back. Prog Biophys Mol Biol (2016) doi:10.1016/j.pbiomolbio.2016.09.002

# 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.

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.

### 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.

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

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.

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.

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.