After having recently published a large scale Molecular Dynamics simulations project of TCRpMHC [1,2] interaction I have extended my research to another technique of spatial sampling. At this week’s group meeting I presented the first results of my first MOSAICS [3] project.

The MOSAICS package is a software that allows for so called hierarchical natural Monte Carlo moves. That means that the user can specify regions in the protein of interest. These regions are indented to reflect “natural” sets of atoms and are expected to move together. An example would be a stable alpha-helix. “Hierarchical” means that region can be grouped together to super-regions. For example a helix that is broken by a kink [4] in its middle could have a region for the helix parts on both sides of the kink as well as for the overall helix. An example for peptide/MHC is illustrated below.

MOSAICS uses Monte Carlo moves to rearrange these region with respect to each other. A stochastic chain closure algorithm ensures that no chain breaks occur. An example of such movements in comparison to classical all-atom Molecular Dynamics is shown below.

In this study we used MOSAICS to simulate the detachment of peptides from MHCs for experimentally known binder and non-binder. An example of such a detaching peptide is shown below

Our results show that experimentally known non-binding peptides detach significantly faster from MHC than experimentally known binding peptides (results to be reported soon).

As a first conclusion of this project:
After having worked with both MOSAICS and Molecular Dynamics simulations, I think that both techniques have their advantages and disadvantages. They are summarized below:

Which technique should be chosen for which project depends mainly on what the aims of these projects are. If large moves of well defined segments are expected then MOSAICS might be the method of choice. If the aim is to investigate fine changes and detailed dynamics Molecular Dynamics simulations might be the better choice.

1.    Knapp B, Demharter S, Esmaielbeiki R, Deane CM (2015) Current Status and Future Challenges in T-cell receptor / peptide / MHC Molecular Dynamics Simulations. Brief Bioinform accepted.
2.    Knapp B, Dunbar J, Deane CM (2014) Large Scale Characterization of the LC13 TCR and HLA-B8 Structural Landscape in Reaction to 172 Altered Peptide Ligands: A Molecular Dynamics Simulation Study. PLoS Comput Biol 10: e1003748.
3.    Sim AY, Levitt M, Minary P (2012) Modeling and design by hierarchical natural moves. Proc Natl Acad Sci U S A 109: 2890-2895.
4.    Wilman HR, Ebejer JP, Shi J, Deane CM, Knapp B (2014) Crowdsourcing yields a new standard for kinks in protein helices. J Chem Inf Model 54: 2585-2593.

Last week I discussed the recent paper by Lhota et al. proposing a network-based similarity search method, applied to the problem of protein fold prediction. Similarity search is the foundation of bioinformatics. It plays a key role in establishing structural, functional and evolutionary relationships between biological sequences. Although the power of the similarity search has increased steadily in recent years, a high percentage of sequences remain uncharacterized in the protein universe. Cumulative evidence suggests that the protein universe is continuous. As a result, conventional sequence homology search methods may be not able to detect novel structural, functional and evolutionary relationships between proteins from weak and noisy sequence signals. To overcome the limitations in existing similarity search methods, the authors propose a new algorithmic framework, Enrichment of Network Topological Similarity (ENTS). While the method is general in scope, in the paper, authors focus exclusively on the protein fold recognition problem.

Fig 1: ENTS pipeline for protein fold prediction.

To initialize ENTS for structure prediction, ENTS builds a structural similarity graph of protein domains (Fig 1). The structural similarity graph is a weighted graph with one node for each structural domain and an edge between two nodes only if their pairwise similarity exceeds a certain threshold. In this article, the structural similarity score is determined by TM align with a threshold of 0.4. Next, some or all the structural domains in the database are labeled with SCOP. Given a query domain sequence and the goal to predict its structure, ENTS first links the query to all nodes in the structural similarity graph. The weights of these new edges are based only on the sequence profile-profile similarity derived from HHSearch. Then random walk with restart (RWR) is applied to perform a probabilistic traversal of the instance graph across all paths leading away from the query, where the probability of choosing an edge will be proportional to its weight. The algorithm will output a ranked list of probabilities of reaching each node in the structural graph from the query, thus potentially uncovering relationships missed by pair-wise comparison methods. ENTS also uses an enrichment analysis step to assess the reliability of the detected relationships, by comparing the mean relationship strength of a SCOP cluster in the structural graph and the query, to that of random clusters.

For testing the method, the authors first constructed a structural graph using 36,003 non-redundant protein domains from the PDB. The query benchmark set consisted of 885 SCOP domains, constructed by randomly selecting each domain from folds spanning at least two super-families. An additional step before prediction on the query set was to remove all domains from the structral graph which were in the same super-family as the query. The method was compared to existing methods such as CNFPred and HHSearch and it’s network approach and enrichment analysis step were found to contribute significantly to the accuracy of fold prediction. While the method seems to be an improvement on existing methods and is a novel use of network-based approaches to fold prediction, the false positive rate is still very high. One way of overcoming this, suggested by the authos is the use of energy-based scoring functions to further prune the list of potential hits returned by the method.

Let’s assume you have a child. Tom is 5 years old, he’s a piece of work. He loves running around the house and the only time you can get a bit of rest is when he’s drawing. Tom loves drawing, so you use that whenever you need a bit of time to sit down. Today is one of those days, you didn’t sleep well and there was trouble at work. However, when you pick up Tom from daycare, he’s excited and full of energy, so you suggest Tom draw a farm which you can put on the wall in your office at work. You sit down and Tom is busy for half an hour. After half an hour Tom proudly presents his work. There are pigs in sties, horses in stables and cows on the meadow. He looks up at you and asks: “Is this a good farm?”. Of course, you say it is an amazing farm.

Only, what if you didn’t know what a farm looked like? You could ask the neighbour’s kid, Emily, to draw a farm and see what the images have in common? You could do this at a whole different scale and start a drawing competition for all the 5-year-olds in the neighbourhood and look which 5-year-olds draw most accurately to see who you should believe what a farm looks like. Clearly we’re not talking about children drawing farms any more. But what if Tom were a functional similarity metric that has just evaluated the results of a community detection algorithm run on a protein interaction network to generate communities?

That was a bit of a jump there. Let me explain. I have spent the last 2 years looking into how protein interaction networks (pins) can be partitioned into functional biological modules. It is widely believed that functional modules are an important scale of organization in humans or any other organisms (c.f. Hartwell et al 1999). These modules consist of proteins which perform the same or similar biological functions via interacting with each other. Thus, there may be a module which contains proteins that interact to e.g. heal wounds.

Finding Functional Modules

We attempt to find these modules by looking at a network which contains data on which proteins interact with which other ones (pins) and then use algorithms to group proteins together based on these interactions. The resulting protein communities contain proteins that interact more with each other than with the rest of the network. So that’s that, right?

…No, sadly it isn’t. The issue is that there are probably tens, if not hundreds of algorithms to find these communities and they don’t agree with each other. Furthermore, the underlying network contains a lot of false interactions and is missing a lot of true interactions. This affects different community detection methods in different ways. So we need a way to evaluate the results these community detection algorithms are presenting. But how do we judge what a good community is when it is exactly this community that we are looking for? How do we know it is “a good farm”, when we don’t know what a farm looks like?

Evaluating Community Detection by Functional Similarity

Luckily we do have some idea of what is “good”. Most proteins have annotations which suggest what biological functions they are involved in. As we are looking for functional modules which group together proteins involved in the same or a similar function, this is exactly along our alleyway! Lucky us :). Right?

Again, you might have expected this one, the answer is “not entirely”. Not only are there a lot of ways to find communities in a network, there are also a huge number of ways to use the above-mentioned functional annotations (GO annotations, www.geneontology.org) to calculate how “functionally similar” two proteins are. Now you might think that all these functional similarity measures use the same functional annotation data, so they should generally agree on what is a “good” and what is a “bad” community. This was my first intuition, so I checked. Below are two graphs which show the results of this test. In both cases I evaluate exactly the same sets of communities which I get from partitioning a pin using Link Clustering (Ahn et al 2010) at different resolutions. The plots show the average functional homogeneity of communities in different community size bands as judged by the Pandey Method on the left (Pandey et al 2008), and the simGIC method on the right (Pesquita et al 2007).

Functional homogeneity plots of a protein interaction network partitioned into communities by Link Clustering at different resolutions. The average functional homogeneity of communities is shown in different community size bands to give the coloured lines. Functional homogeneity is calculated as the average pairwise functional similarity between all protein pairs in a community. In Figure A) the Pandey method is used to calculate functional similarity between two proteins, while in Figure B) the simGIC method is used

You can clearly see that the two plots look different. Now it would be okay if they looked different and still agreed on the most important part: where is my pin partitioned into communities in the best way? To check this I look for maxima in the plots. Figure A) tells me that at a resolution of about 0.2 communities of size 2-35 are on average quite functionally homogeneous. At a slightly lower resolution (approx 0.1) communities of size 36+ look like they are partitioned decently. So depending on the community sizes I’m looking for, I can say which resolution I should go for. However, these peaks don’t show up in Figure B). We clearly need an evaluation of the evaluation metric. Which one should I believe?

Features that are common to both cases (the yellow peak around 0.4 and the magenta maximum around 0.3) might be “true”. But what is to say that the first set of peaks isn’t also important? In our earlier analogy, the neighbour’s daughter Emily has drawn a farm that looks quite different. You’re not about to say Tom’s wrong only because Emily drew a different picture, right? So right about now we need that drawing competition!

Evaluating Evaluation metrics: The drawing competition

Now we’ve stumbled twice already in how to evaluate our results. This time, we should make sure that we can evaluate the different results confidently. The plan is that we let the kids draw something else. Not a farm¸ because we don’t know how that looks like. We ask them to draw something related, like a house. We live in a house, so that should be easier to evaluate. And apparently houses and farms are not too dissimilar, so that the kids that do well in their house drawings, may be the ones that have the best farm drawings as well (or so we hope).

In terms of PINs, we have to create a network with community structure which looks a bit like a PIN. We can then use a community detection algorithm to find the communities and let our kids (functional similarity metrics) go away and evaluate the communities at different resolutions. This time we however know which maxima should show up, as we created the network and therefore know the community structure that should be found.

To create a PIN-similar network is not a mean feat and can be the topic of a whole PhD thesis, so I have focussed on a small, simple network, which is hopefully PIN-similar enough to be a meaningful evaluation for the functional similarity metrics. My network is generated on the basis of functional labels ordered in the ontology below.

A tree defining the relationship between labels 1-15. Nodes are annotated with these labels to generate a network. Labels which share more parent labels are closer related (i.e. 14 and 15 share the parent label 5).

Each node is assigned one or two labels between 6 and 15 randomly without replacement. Then the ontology is traced upwards to get the ancestral label sets associated with each node (i.e. label 15 has the ancestral labels 5 and 1). Edge probabilities are calculated between two nodes based on the number of common labels these nodes have in their ancestral label sets. Finally edges are added based on the edge probabilities to give a network with a density comparable to a PIN.

When this network is clustered into communities and functionally evaluated at different resolutions, the results look like this:

Average functional homogeneity of communities of size 3+ in a PIN-like random graph of 500 nodes. The top left graph (black) is based on label overlaps and is thus ground truth, while the other three are generated by the Pandey method (red), simGIC (blue), and simUI (green). The coloured graphs are generated by using the above-mentioned functional similarity metrics after 33% of the labels from 6-15 and 20% of the labels from 2-5 where “hidden” (reverted to the parent label).

The black graph shows how many labels nodes in the same community have in common at different resolutions. As these labels overlaps were used to generate the edge probabilities, this graph serves as a gold standard. The other three graphs were generated using the functional similarity metrics to be compared. To make the PIN-like network more realistic some labels were hidden, as we don’t always know the exact function of every protein when we evaluate PINs.

Now that we have the results, we just need to see how all the coloured graphs compare to the black ones, or in our analogy: how all the kid’s drawings compare to what our house looks like. But because we are doing science, one drawing competition sadly won’t do. What if one child draws our house very well, but happens to do a bit worse exactly in the parts where the house is similar to a farm. We’d think he’s the guy to listen to, and start thinking a farm looks like a shed?!

What we really need, is more drawing competitions. Lots of them. And this is where I am happy I’m at a computer where running one of these competitions takes maybe a minute. To get a bit more confidence in the results, I ran every simulation 100 times, for 27 different sets of parameters. And the results? Well, that’s the best bit. The results say Tom was drawing a great farm all along. Tom, the kid you’ve seen grow up and been bringing to daycare for years now, he was right. You weren’t lying at all when you said it was a great farm. Only in real life he’s not called Tom, he’s called the Pandey method ;).