Multilayer networks are a generalisation of network that may incorporate different types of interactions [1]. This could be different time points in temporal data, measurements in different individuals or under different experimental conditions. Currently many measures and methods from monolayer networks are extended to be applicabile to multilayer networks. Those include measures of centrality [2], or methods that enable to find mesoscale structure in networks [3,4].

Examples of such mesoscale structure detection methods are stochastic block models and community detection. Both try to find groups of nodes that behave structurally similar in a network. In its most simplistic way you might think of two groups that are densely connected internally but only sparsely between the groups. For example two classes in a high school, there are many friendships in each class but only a small number between the classes. Often we are interested in how such patterns evolve with time. Here, the usage of multilayer community detection methods is fruitful.

Mucha et al. analysed the voting pattern in the U.S. Senate [4]. They find that the communities are oriented as the political party organisation. However, the restructuring of the political landscape over time is observable in the multilayered community structure. For example, the 37th Congress during the beginning of the American Civil War brought a major change in the voting patterns. Modern politics is dominated by a strong partition into Democrats and Republicans with third minor group that can be identified as the ‘Southern Democrats’ that had distinguishable voting patterns during the 1960.

Such multilayer community detection methods can be insightful for networks from other disciplines. For example they have been adopted to describe the reconfiguration in the human brain during learning [5]. Hopefully they will be able to give us insight in the structure and function of protein interaction.

[1] De Domenico, Manlio; Solé-Ribalta, Albert; Cozzo, Emanuele; Kivelä, Mikko; Moreno, Yamir; Porter, Mason A.; Gómez, Sergio; and Arenas, Alex [2013]. Mathematical Formulation of Multilayer Networks, Physical Review X, Vol. 3, No. 4: 041022.

[2] Taylor, Dane; Myers, Sean A.; Clauset, Aaron; Porter, Mason A.; and Mucha, Peter J. [2016]. Eigenvector-based Centrality Measures for Temporal Networks

[3]  Tiago P. Peixoto; Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Phys. Rev. E 92, 042807

[4] Mucha, Peter J.; Richardson, Thomas; Macon, Kevin; Porter, Mason A.; and Onnela, Jukka-Pekka [2010]. Community Structure in Time-Dependent, Multiscale, and Multiplex Networks, Science, Vol. 328, No. 5980: 876-878.

[5] Bassett, Danielle S.; Wymbs, Nicholas F.; Porter, Mason A.; Mucha, Peter J.; Carlson, Jean M.; and Grafton, Scott T. [2011]. Dynamic Reconfiguration of Human Brain Networks During Learning, Proceedings of the National Academy of Sciences of the United States of America, Vol. 118, No. 18: 7641-7646.

 

At the group meeting on the 3rd of February I presented the results of the paper “A General Method for Insertion of Functional Proteins within Proteins via Combinatorial Selection of Permissive Junctions” by Peng et. al. This is interesting to our group, and especially to me, because this is a novel way of designing an antibody, although I suspect that the scope of their research is much more general, their use of antibodies being a proof of concept.

Their premise is that the structure of a protein is essentially secondary structures and tertiary structure interconnected through junctions. As such it should be possible to interconnect regions from different proteins through junctions, and these regions should take up their native secondary and tertiary structures, thus preserving their functionality. The question is what is a suitable junction? This is important because these junctions should be flexible enough to allow the proper folding of the different regions, but also not too flexible as to have a negative impact on stability. There has been previous work done on trying to design suitable junctions, however the workflow presented in this paper is based on trying a vast number of junctions and then identifying which of them work.

As I said above their proof concept is antibodies. They used an antibody scaffold (the host), out of which they removed the H3 loop and then fused to it, using  junctions, two different proteins: Leptin and FSH (the guests). To identify the correct junctions they generated a library of antibodies with random three residues sequences on either side of the inserted protein plus a generic linker (GGGGS) that can be repeated up to three times.

They say that the theoretical size of the library is 10^9 (however I would say it is 9*20^6), and the actually achieved diversity of their library was of size 2.88*10^7 for Leptin and 1.09*10^7. Next step is to identify which junctions have allowed the guest protein to fold properly. For this they devised an autocrine-based selection method using engineered cells that have beta lactamase receptors which have either Leptin or FSH as agonists. A fluoroprobe in the cell responds to the presence of beta lactamase producing a blue color, instead of green and therefore this allows the cells with the active antibody-guest  designed protein (clone) to be identified using FRET-based fluorescence-activated cell sorting.

They managed to identify 6 clones that worked for Leptin and 3 that worked for FSH with the linkers being listed in the below table:

There does not seem to be a pattern emerging from those linker sequences, although one of them repeats itself. For my research it would have been interesting if a pattern did emerge, and then that could be used as a generic linker for future designers. However, this is still another prime example of how well an antibody scaffold can be used a starting point for protein engineering.

As a bonus they also tested in vivo how their designs work and they discovered that the antibody-leptin design (IgG-Leptin) has a longer lifetime. This is probably due to the fact that being a larger protein this is not filtered out by the kidneys.

At the meeting on February 10 I covered the article by Sormanni et al. describing a methodology for computationally designing antibodies against intrinsically disordered regions of proteins.

Antibodies are proteins that are a natural part of our immune system. For over 50 years lab-made antibodies have been used in a wide variety of therapeutic and diagnostic applications. Nowadays, we can design antibodies with high specificity and affinity for almost any target. Nevertheless, engineering antibodies against intrinsically disordered proteins remains costly and unreliable. Since as many as about 33.0% of all eukaryotic proteins could be intrinsically disordered, and the disordered proteins are often implicated in various ailments and diseases such methodology could prove invaluable.

The initial step in the protocol involves searching the PDB for protein sequences that interact in a beta strand with segments of the target sequence. Next, such peptides are joined together using a so-called “cascade method”. The cascade method starts with the longest found peptide and grows it to the length of the target sequence by joining it with other, partially overlapping peptides coming from beta strands of the same type (parallel, antiparallel). In the cascade method, all fragments used must form the same hydrogen bond pattern. The resulting complementary peptide is expected to “freeze” part of the discorded protein by forcing it to locally form a beta sheet. After the complementary peptide is designed, it is grafted on a single-domain antibody scaffold. This decision has been made as antibodies have a longer half-life and lower immunogenicity.

To test their method the authors initially assessed the robustness of their design protocol. First, they run the cascade method on three targets – a-synuclein, Aβ42 and IAPP. They found that more than 95% of the residue position in the three proteins could be targeted by their method. In addition, the mean number of available fragments per position was 570. They also estimated their coverage on a larger scale, using 1690 disordered protein sequences obtained from DisProt database and from measured NMR chemical shifts. About 90% of residue positions from DisProt and 85% positions from the chemical shift could be covered by at least one designed peptide. The positions that were hard to target usually contained Proline, in agreement with the known result that Prolines tend to disrupt secondary structure formation.

To test the quality of their designs the authors created complementary peptides for a-synuclein, Aβ42 and IAPP and grafted them on the CDR3 region of a human single domain antibody scaffold. All designs were highly stable and bound their targets with high specificity. Following the encouraging result the authors measured the affinity of one of their designs (one of the anti-a-synuclein antibodies). The K­_d was found to lie in the range 11-27 μM. Such affinity is too low for pharmaceutical purposes, but it is enough to prevent aggregation of the target protein.

As the last step in the project the authors attempted a two-peptide design, where a second peptide was grafted in the CDR2 region of the single-domain scaffold. Both peptides were designed to bind the same epitope. The two peptide design managed to reach the affinity required for pharmaceutical viability (affinity smaller than 185 nM with 95% confidence). Nevertheless, the two loop design became very unstable rendering it not viable for pharmaceutical purposes.

Overall, this study presents a very exciting step towards computationally designed antibodies targeting disordered epitopes and deepens out understanding of antibody functionality.