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.