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 Ų 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?

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.

For my journal club last week, I chose to look at a recent paper entitled “Addressing the Role of Conformational Diversity in Protein Structure Prediction”, by Palopoli et al [1]. In the study of proteins, structures are incredibly useful tools, offering information about how they carry out their function, and allowing informed decisions to be made in many areas (e.g. drug design). Since the experimental determination is difficult, however, the computational prediction of protein structures has become very important (and a number of us here at OPIG work on this!).

A problem, however, in both experimental structure determination and computational structure prediction, is that proteins are generally treated as static – the output of an X-ray crystallography experiment is a single structure, and in the majority of cases the goal of structure prediction is to produce one model that closely resembles the native structure. The accuracy of structure prediction algorithms is also normally measured by comparing the resulting model to a single, known experimentally-determined structure. The issue here is that proteins are not static – they are constantly moving and may adopt a number of different conformations; the structure observed experimentally is just a snapshot of that motion. The dynamics of a protein may even play an important role in its function; an example is haemoglobin, which after binding to oxygen changes conformation to increase affinity for further binding. It may be more appropriate, then, to represent a protein as an ensemble of structures, and not just one.

Conformational diversity helps the protein haemoglobin carry out its function (the transportation of oxygen in the blood). Haemoglobin has four subunits, each containing a haem group, shown in red. When oxygen binds to this group (blue), a histidine residue moves, shifting the position of an alpha helix. This movement is propagated throughout the entire structure, and increases the affinity for oxygen of the other subunits – binding therefore becomes increasingly easy (this is known as co-operative binding). Gif shown is from the PDB-101 Molecule of the Month series: S. Dutta and D. Goodsell, doi:10.2210/rcsb_pdb/mom_2003_5

How, though, could this be incorporated into protein structure prediction? This is the question being considered by the authors of this paper. They consider conformational diversity by looking at different conformers of the same protein – there are many proteins whose structures have been solved experimentally multiple times, and as such have a number of structures available in the PDB. Information about this is stored in a useful database called CoDNaS [2], which was developed by some of the authors of the paper under discussion. In some cases, there are model (or decoy) structures available for these proteins, generated by various structure prediction algorithms – for example, all models submitted for the CASP experiments [3], where the current accuracy of structure prediction is monitored through blind prediction, are freely available for download. The authors curated a collection of decoy sets for 91 different proteins for which multiple experimental structures are present in the PDB.

As mentioned previously, the accuracy of a model is normally evaluated by measuring its structural similarity to one known (or reference) structure – only one conformer of the protein is considered. The authors show that the model rankings achieved by this are highly dependent on the chosen reference structure. If the possible choices (i.e. the observed conformers) are quite similar the effect is small, but if there is a large difference, then two completely different decoys could be designated as the most accurate depending on which reference structure is used.

The key figure from this paper, in my opinion, is the one shown below. For the two most dissimilar experimentally-observed conformers for each protein in the set, the RMSD of the best decoy in relation to one conformer is plotted against the RMSD of the best decoy when measured against the other:

The straight line on this graph indicates what would be observed if there are decoys in the set that equally represent the two conformers; for example, if the best decoy with reference to conformer 1 has an RMSD of 1 Å, then there is also a decoy that is 1 Å away from conformer 2. Most points are on or near this line – this means that the sets of decoy structures are not biased towards one of the conformers. Therefore, structure prediction algorithms seem to be able to generate models for multiple conformations of proteins, and so the production of an ensemble of models is not an impossible dream. Several obstacles remain, however – although of equal distance to both conformers, the decoys could still be of poor quality; and decoy selection is often inaccurate, and so finding these multiple conformations amongst all others is a challenge.

[1] – Palopoli, N., Monzon, A. M., Parisi, G., and Fornasari, M. S. (2016). Addressing the Role of Conformational Diversity in Protein Structure Prediction. PLoS One, 11, e0154923.

[2] – Monzon, A. M., Juritz, E., Fornasari, S., and Parisi, G. (2013). CoDNaS: a database of conformational diversity in the native state of proteins. Bioinformatics, 29, 2512–2514.

[3] – Moult, J., Pedersen, J. T., Judson, R., and Fidelis, K. (1995). A Large-Scale Experiment to Assess Protein Structure Prediction Methods. Proteins, 23, ii–iv.