For this week’s journal club, I presented the paper by Goldman et al, “Mechanical force releases nascent chain-mediated ribosome arrest in vitro and in vivo”. The reason for choosing this paper is that it discussed an influence on protein folding/creation/translation that is not considered in any of today’s modelling efforts and I think it is massively important that every so often we, as a community, step-back and appreciate the complexity of the system we attempt to understand. This work focuses on the the SecM protein, which is known to regulate SecA (which is part of the translocon) which in turn regulates SecM. The bio-mechanical manner in which this regulation takes place is not fully understood. However, SecM contains within its sequence a peptide motiff that binds so strongly to the ribosome tunnel wall that translation is stopped. It is hypothesised that SecA regulates SecM by applying a force to the nascent chain to pull it past this stalling point and, hence, allow translation to continue.

To begin their study, Goldman wanted to confirm that one could advance past the stall point merely by the application of force. By attaching the nascent chain and the ribosome to nano-tweezers and a micro-pipette respectively they could do this. However, to confirm that the system was stalled before applying a (larger) force, they created a sequence which included CaM, a protein which periodically hops between a folded and unfolded state when pulled at 7pN, followed by the section of SecM which causes the stalling. The nano-tweezers were able to sense the slight variations in length at 7pn from the unfolding and refolding of CaM, though no continuing extension, which would indicate translation, was found. This indicated the system had truly stalled due to the SecM sequence. Once at this point, Goldman increased the applied force, at which point distance between the pipette and the optical tweezers slowly increased until detachment when the stop codon was reached. As well as confirming that force on the nascent chain could make the SecM system proceed past the stalling point, they also noted a force dependence to the speed with which it would overcome this barrier.

With this force dependence established, they pondered whether a domain folding upchain of the stall point could generate enough force that it could cause translation to continue. To investigate, Goldman created a protein that contained Top7 followed by a linker of variable length, followed by the SeqM stalling motif, which was in turn followed by GFP. Shown in the figure above, altering the length of the linker region defined the location of Top7 while it attempts to fold. A long linker allows Top7 to fold completely clear from the ribosome tunnel. A short linker means that a it can’t fold due to many of its residues being inside the ribosome tunnel. Between these extremes, however, the protein may only have a few residues within the tunnel and by stretching the nascent chain it may access them so as to be able to fold. In addition, Top7 was chosen specifically as it was known to fold even under light pressure. Hence, by newtons third, Top7 would fold even while its C terminus would be under strain into the ribosome, it in turn generates an equal and opposite force on the stalling peptide sequence within the heart of the ribosome tunnel, which should allow translation to proceed past the stall. Crucially, if Top7 folded too far away from the ribosome, this interaction would not occur and translation would not continue.

Goldman’s experiments showed that this is in fact the case; they found that only linkers of 15 to 22 amino acid would successfully complete translation. This confirms that a protein folding at the mouth of the ribosome tunnel can generate sizeable force (they calculate roughly 12pN in this instance). Now I find this whole system especially interesting as the I wonder how this may generalise to all translation, both in terms of interactions of the nascent chain with the side wall and the domain folding at the ribosome tunnel mouth. Should I consider these when I calculate translation speeds for example? Oh well, we need a reasonable model for translation while ignoring these special cases first before I really need to worry!

I recently had the pleasure of working for 11 weeks with the wonderful people in OPIG. I studied protein interaction networks and how we might discern the parts of the network that are important for disease (and otherwise). In the past, people have looked at differential gene expression or used community detection to this end, but both of these approaches have drawbacks. The former misses the fact that biological systems are rarely just binary systems or interactions. Community detection addresses this, but it in turn does not take into account the dynamic nature of proteins in the cell – how do their interactions change over time? What about interactions or proteins that are only present in some cells? Community detection tries to look at all proteins and ignores important context like this.

My aim was to develop approaches that combined these elements. We used Pearson’s correlation coefficient on gene expression data and community detection on an interaction network. We showed that the distribution of the correlation of pairs of genes is weighted towards 1.0 for those that interact compared to those that do not, and for those in the same community compared to those that are not – see the figure above. We went on to assign a “score” to communities based on their correlation in each set of expression data. For example, one community might have a high score in expression data from cells undergoing amino acid starvation. We ended up with a list of communities which seemed to be important in certain environmental conditions. We made use of functional enrichment – drawing on the lovely Malte’s work – to try and verify these scores.

I had a great time with some lovely people and produced something that I thought was very interesting. I really hope I see this work pop up again and get taken to interesting places! So long, and thanks for all the cookies!

Click here for some more pretty plots and a code repository (by request only).

Overall, the authors conclude that, where possible, datasets should be collected at RT, as the derived models offer a more realistic description of the biologically-relevant conformational ensemble of the protein.

Yesterday’s group meeting got transformed into a mock interview for the final evaluation step of my ERC starting grant application. To be successful with an ERC starting grant application one has to pass three evaluation steps.

The first step consists of a short research proposal (max 5 pages), CV, successful previous grant writing, early achievements track record, and a publication list. If a panel of reviewers (usually around 4-6) decides that this is “excellent” (this word is the main evaluation criterion) then the application is transferred to step two.

In step two a full scientific proposal is evaluated. The unfair procedure is that if step one is not successful then the full proposal is not even read (although it had to be submitted together with step one).

Fortunately, my proposal passed step one and step two. The final hurdle will be a 10 minutes interview + 15 minutes questions in Brussels where the final decision will take place.

I already had one mock interview with some of the 2020 research fellows (thanks to Konrad, Remi, and Laurel), one with David Gavaghan, and the third one took place yesterday with our whole research group.

After those three mock interviews I hope to be properly prepared for the real interview!

Antibody modelling has come a long way in the past 5 years. The Antibody Modelling Assessment (AMA) competitions (effectively an antibody version of CASP) have shown that most antibody design methods are capable of modelling the antibody variable fragment (Fv) at ≤ 1.5Å. Despite this feat, AMA-II provided two important lessons:

1. We can still improve our modelling of the framework region and the canonical CDRs.

Stage two of the AMA-II competition showed that CDR-H3 modelling improves once the correct crystal structure was provided (bar the H3 loop, of course). In addition, some of the canonical CDRs (e.g. L1) were modelled poorly, and some of the framework loops had also been poorly modelled.

2. We can’t treat orientation as if it doesn’t exist.

Many pipelines are either vague about how they predict the orientation, or have no explicit explanation on how the orientation will be predicted for the model structure. Given how important the orientation can be toward the antibody’s binding mode (Fera et al., 2014), it’s clear that this part of the pipeline has to be re-visited more carefully.

In addition to these lessons, one question remains:

What do we do with these models?

No pipeline, as far as we are aware, have no comments on what we should do beyond creating the model from a pipeline. What are its implications? Can we even use it for experiments, and use it as a potential therapeutic in the long-term? In light of these lessons and this blaring question, we developed our own method.

Before we begin, how does modelling work?

In my mind, most, if not all, pipelines follow this generic paradigm:

Our method, ABodyBuilder, also follows this 4-step workflow;

1. We choose the template structure based on sequence identity; below a threshold, we predict the structure of the heavy and light chains separately
2. In the event that we use the structures from separate antibodies, we predict the orientation from the structure with the highest global sequence identity.
3. We model the loops using FREAD (Choi, Deane, 2011)
4. Graft the side chains in using SCWRL.

Following the modelling procedure, our method also annotates the accuracy of the model in a probabilistic context — i.e., an estimated probability that a particular region is modelled at a given RMSD threshold. Moreover, we also flag up any issues that an experimentalist can run into should they ever decide to model the antibody.

The accuracy estimation is a data-driven estimation of model quality. Many pipelines end up giving you just a model – but there’s no way of determining model accuracy until the native structure is determined. This is particularly problematic for CDRH3 where RMSDs can reach up to >4.0A between models and native structures, and it would be incredibly useful to have an a priori, expected estimation of model accuracy.

Furthermore, by commenting on motifs that can conflict with antibody development, we aim to offer a convenient solution for users when they are considering in vitro experiments with their target antibody. Ultimately, ABodyBuilder is designed with the user in mind, making an easy-to-use, informative software that facilitates antibody modelling for novel applications.

Many methods are available for prediction of topology of transmembrane helices, this being one of the success stories of protein structure prediction with accuracies over 90%. However, there are still areas where there is disagreement in some areas about the partitioning between the states of dissolved in water and positioned across a lipid bilayer. Complications arise because there are so many methods of measuring the thermodynamics of this transition – experimental and theoretical, in vivo and in vitro. It is uncertain what difference the translocon makes to the energetics of insertion – is the topology and conformation of a membrane protein the global thermodynamic minimum or just a kinetic product?

This paper uses three approaches to measure partitioning to test the agreement between different methods. The authors aim to reconcile differences calculated so far for insertion of an arginine residue into the membrane (ranging from +2 to +15 kcal/mol). This is an important question, because many transmembrane helices are only marginally hydrophobic and it is not known how and when they insert in the folding process. Arginine is chosen here because the pKa of 12.5 of the side chain is very high so it will not deprotonate in the centre of a bilayer and complications of protonation and deprotonation do not need to be considered. The same peptide is used for each method, of the form L_nRL_n, and the ratio between the interface and transmembrane states is used to calculate estimates of ΔG. In order to make sure that there were helices with a ΔG close to zero for accurate estimates, they used a range of values of n from 5-8.

The first method was an insertion assay using reconstituted microsomes, where this helix was inserted into the luminal domain of LepB. A glycosylation site was added at each end of the helix, but glycosylation takes place only on sites inside microsomes. Helices inserted into the membrane are only glycosylated once, whereas secreted helices are glycosylated twice and those which did not go through the translocon are not glycosylated. SDS-PAGE can separate these states by mass, and the ratio between single and double glycosylation gives the partitioning between inserted and interface helices out of those which entered the translocon. As expected, the trend is for longer helices with more leucine to favour the transmembrane state.

Adapted from Figure 4a: The helix, H, either passes through the translocon into the lumen (“S”) resulting in two glycosylations (green pentagons), or is inserted (TM) resulting in one glycosylation.

The second method was also experimental: oriented synchrotron radiation circular dichroism (ORSCD). Here they used just the peptide with one glycine at each end, as this would be able to equilibrate between the two states quickly. Theoretical spectra can be calculated for a helix , and therefore the ratio in which they must be combined to give the measured spectrum for a given peptide gives the ratio of transmembrane and interface states present.

Figure 2b: TM and IP are the theoretical spectra for the transmembrane and interface states, and the peptides fall somewhere in between.

Finally, the authors present 4 μs molecular dynamics simulations of the same peptides at 140°C, so that equilibration between the two states would be fast. The extended peptide at the start of the simulation quickly associates with the membrane and adopts a helical conformation. An important observation to note is that the transmembrane state is in fact at around 30° to the membrane normal, to allow the charged guanidinium group of the arginine to “snorkel” up to interact with charged phosphate groups of the lipids. Therefore this state is defined as transmembrane, in contrast to the OSRCD experiments where the theoretical TM spectrum was calculated for a perpendicular helix. This may be a source of some inaccuracy in the propensities calculated from OSRCD.

Figure 2c: Equilibration in the simulation for the L₇RL₇ peptide. Transmembrane and interface states are seen in the partitioning and equilibration phases after the helix has formed.

Figure 3c: As the simulations run, the proportion of helices in the transmembrane state (P_TM) converges to a different value for each peptide.

Overall, the ΔG calculated experimental and molecular dynamics (MD) simulations agree very well. In fact, they agree better than those from previous studies of a similar format looking at polyleucine helices, where there was a consistent offset of 2 kcal/mol between the experiment and simulation derived values. The authors are unable to explain why the agreement for this study is better, but they indicate that it is unlikely to be related to any stabilisation by dimerisation in the experimental results, as a 4 μs MD simulation of two helices did not show them forming stable interactions. The calculated difference in insertion energy (ΔΔG) on replacing a leucine with argnine is therefore calculated to be +2.4-4.3 kcal/mol by experiment and +5.4-6.8 by simulation, depending on the length of the peptide (it is a more costly substitution for longer peptides as the charge is buried deeper). The difference between the experimental and simulation results is accounted for by their disagreement in the polyleucine study.

We thought this paper was a great example of experimental design, where the system was carefully chosen so that different experimental and theoretical approaches would be directly comparable. The outcome is good agreement between the methods, demonstrating that the vastly different values recorded previously seem to be because very different questions were being asked.

Computational protein design methods often use a known molecule with a well-characterised structure as a template or scaffold. The chosen scaffold is modified so that its function (e.g. what it binds) is repurposed. Ideally, one wants to be confident that the expressed protein’s structure is going to be the same as the designed conformation. Therefore, successful designed proteins tend to be rigid, formed of collections of regular secondary structure (e.g. α-helices and β-sheets) and have active site shapes that do not perturb far from the scaffold’s backbone conformation (see this review).

A recent paper (Lapidoth et al 2015) from the Fleishman group proposes a new protocol to incorporate backbone variation (read loop conformations) into computational protein design (Figure 1). Using an antibody as the chosen scaffold, their approach aims to design a molecule that binds a specific patch (epitope) on a target molecule (antigen).

Figure 1 from Lapidoth et al 2015 shows an overview of the AbDesign protocol

Protein design works in the opposite direction to structure prediction. i.e. given a structure tell me what sequence will allow me to achieve that shape and to bind a particular patch in the way I have chosen. To do this one first needs to select a shape that could feasibly be achieved in vivo. We would hope that if a backbone conformation has previously been seen in the Protein Data Bank that it is one of such a set of feasible shapes.

Lapidoth et al sample conformations by constructing a backbone torsion angle database derived from known antibody structures from the PDB. From the work of North et al and others we also know that certain loop shapes can be achieved with multiple different sequences (see KK’s recent post). The authors therefore reduce the number of possible backbone conformations by clustering them by structural similarity. Each conformational cluster is represented by a representative and a position specific substitution matrix (PSSM). The PSSM represents how the sequence can vary whilst maintaining the shape.

The Rosetta design pipeline that follows uses the pre-computed torsion database to make a scaffold antibody structure (1x9q) adopt different backbone conformations. Proposed sequence mutations are sampled from the corresponding PSSM for the conformation. Shapes and the sequences that can adopt them, are ranked with respect to a docked pose with the antigen using several structure-based filters and Rosetta energy scores. A trade off is made between predicted binding and stability energies using a ‘fuzzy logic’ scheme.

After several rounds of optimisation the pipeline produces a predicted structure and sequence that should bind the chosen epitope patch and fold to form a stable protein when expressed. The benchmark results show promise in terms of structural similarity to known molecules that bind the same site (polar interactions, buried surface area). Sequence similarity between the predicted and known binders is perhaps lower than expected. However, as different natural antibody molecules can bind the same antigen, convergence between a ‘correct’ design and the known binder may not be guaranteed anyway.

In conclusion, my take home message from this paper is that to sensibly sample backbone conformations for protein design use the variation seen in known structures. The method presented demonstrates a way of predicting more structurally diverse designs and sampling the sequences that will allow the protein to adopt these shapes.  Although, as the authors highlight, it is difficult to assess the performance of the protocol without experimental validation, important lessons can be learned for computational design of both antibodies and general proteins.