Category Archives: Group Meetings

What we discuss during cake at our Tuesday afternoon group meetings

Journal Club: Quantification and functional analysis of modular protein evolution in a dense phylogenetic tree

For journal club this week I decided to look at a paper by Moore et al. on the modular evolution of proteins.

Modular evolution, or the rearrangement of the domain architecture of a protein, is one of the key drivers behind functional diversification in the protein universe. I used the example in my talk of the multi-domain protein Peptidase T, which contains a catalytic domain homologous to Carboxypeptidase A, a zinc dependent protease. The additional domain in Peptidase T induces the formation of a dimer, which restricts the space around the active site and so affects the specificity of the enzyme.

The multi-domain protein Peptidase T in a dimer (taken from Bashton and Chothia 2007). The active site is circled in green. Carboxypeptidase A is made up of a single domain homologous to the catalytic domain (in blue) of Peptidase T.

I took this case study from a really interesting paper, The generation of new protein functions by the combination of domains (Bashton and Chothia, 2007), which explores several other comparisons between the functions of multi-domain proteins and their single domain homologues.

What this paper does not address however is the directionality of such domain reorganisations. In all these examples, it is not clear whether the multi-domain organisation has evolved from the single domain enzyme or vice versa. Which brings me back to the paper I was presenting on, which attempts a reconstruction of domain arrangements followed by a categorisation of rearrangement events.

Essentially, given a phylogenetic tree of 20 closely related pancrustacean species, the paper takes the different domain arrangements on the genomes (step 1), assigns the presence or absence of each arrangement at interior nodes on the tree (step 2), and then assigns each gained arrangement to one of four possible rearrangement events (step 3).

1. Domain Annotation
The authors use different methods to annotate domains on the genomes. They conclude the most effective methodology is to use the clan level (where families with suspected homologies are joined together… similar to our beloved superfamily classification) of Pfam-A (high quality family alignments with a manually chosen seed sequence). Moreover, they collapse any consecutive stretches of identical domains into one “pseudo-domain”, eliminating the effect of the domain repeat number on an arrangement’s definition.

2. Ancestral State Reconstruction
The ancestral state reconstruction of each domain arrangement (it’s presence/absence at each internal node on the tree) is a result of a 2-pass sweep across the tree: the first from leaves to root, and the second from the root to the leaves. On the first pass the presence of an arrangement on a parent node is decided by majority rule on the state of its children. If the arrangement is present in one child node but absent in the other, the state at the parent node is defined as uncertain. Any uncertain child nodes have a neutral impact on their parent node’s state (i.e. if a parent has a child with the arrangement and a child with an uncertain state the arrangement will be annotated as present in the parent node). On the second pass (from root to leaves) uncertain nodes are decided by the state at their parent node. An uncertain arrangement at the root will be annotated as present. For more details and a clearer explanation see Box 1 in the figure below.

A schematic for the assignment of domain recombination events. Box 1 gives the algorithm for the ancestral state reconstruction. Figure S2 from Moore et al. 2013.

3. Rearrangement events
For each gained event on a particular branch, the authors then postulated one of four simple rearrangement events dependent on the arrangements on the parent’s predicted proteome.

i) Fusion: A gained domain arrangement (A,B,C) on a child’s proteome is the result of fusion if the parent’s proteome contains both the arrangements (A,B) AND (C) (as one example).
ii) Fission: A gained arrangement (A,B,C) is the result of fission if the parent contains the arrangement (A,B,C,D) AND the child also contains the arrangement (D).
iii) Terminal Loss: A gained arrangement (A,B,C) is the result of terminal loss if the parent contains the arrangement (A,B,C,D) AND the child does not contain the arrangement (D).
iv) Domain gain: A gained arrangement (A,B,C) is the result of domain gain if the parent contains (A,B) but not (C).

Any gained arrangement which cannot be explained by these cases (as a single-step solution) is annotated as having no solution.

Results

The authors find, roughly speaking, that the domain arrangements they identify fall into a bimodal distribution. The vast majority are those which are seen on only one genome, of which over 80% are multi-domain arrangements. There are also a sizeable number of arrangements seen on every single genome, the vast majority of which are made up of a single domain. I do wonder though, how much of this signal is due to the relative difficulty of identifying and assigning multiple different domains compared to just a single domain. While it seems unlikely that this would explain the entirety of this observation (on average, 75% of proteins per genome were assigned) it would be interesting to have seen how the authors address this possible bias.

Interestingly, the authors also find a slight inflation in fusion events over fission events across the tree (around 1 more per million years), although there are more fusion events nearer the root of the tree, with fission dominating nearer the leaves, and in particular, on the dense Drosophila subtree.

Finally, the authors performed a functional term enrichment analysis on the domain arrangements gained by fusion and fission events and showed that, in both cases, terms relating to signalling were significantly overrepresented in these populations, emphasising the potential importance that modular evolution may play in this area.

How many bins?

As it’s known in non-parametric kernel density estimation the effect of the bandwidth on the estimated density is large and it is usually the parameter who makes the tradeoff between bias and roughness of the estimation (Jones et.al 1996). An analogous problem for histograms is the choice of the bin length and in cases of equal bin lengths the problem can be seen as finding the number of bins to use. A data-base methodology for building equal bin-length histograms proposed by (Knuth 2013) based on the marginal of the joint posterior of the number of bins and heights of the bins. To build the histogram first the number of bins has to be selected as the the value ( $\hat{M}$ ) that maximises the following posterior distribution for the number of bins:
$P(M|d,I)\, \alpha \,(M/V)^N \frac{\Gamma(M/2) \prod_{k=1}^M \Gamma(n_k+1/2)}{\Gamma(1/2)^M \Gamma(N+M/2)}$

where $M$ is the number of bins, $d$ is the data, $I$ is prior knowledge about the problem, i.e. in particular the use of equal length bins and the range of data $V$ , which has the relation $V=Mw$ where $w$ is the width of bins, $N$ is the number of data points and $n_k$ is the number of observations that fall in the $k$ th bin.

Now, the height ( $h_k$ ) of the bins of the histogram is given by:
$h_k=\frac{M}{V} \frac{n_k+1/2}{N+M/2}$ .

In the case of a normal distribution the authors suggest a sample of 150 data points to “accurately and consistently estimate the shape of the distribution”.

The following figure shows the relative log-posterior of the number of bins (left) and the estimated histogram for a mixture of three normal samples and a uniform [0,50] (right).

Knuth, K. H. (2013). Optimal data-based binning for histograms. arXiv preprint physics/0605197. The first version of this paper was published on 2006.

Jones, M. C., Marron, J. S., and Sheather, S. J. (1996). A brief survey of bandwidth selection for density estimation. Journal of the American Statistical Association,91(433), 401–407.

Journal club: Half a century of Ramachandran plots

In last week’s journal club we delved into the history of Ramachandran plots (Half a century of Ramachandran plots; Carugo & Djinovic-Carugo, 2013).

Polypeptide backbone dihedral angles. Source: Wikimedia Commons, Bensaccount

50 years ago Gopalasamudram Narayana Ramachandran et al. predicted the theoretically possible conformations of a polypeptide backbone. The backbone confirmations can be described using three dihedral angles: ω, φ and ψ (shown to the right).

The first angle, ω, is restrained to either about 0° (cis) or about 180° (trans) due to the partial double bond character of the C-N bond. The φ and ψ angles are more interesting, and the Ramachandran plot of a protein is obtained by plotting φ/ψ angles of all residues in a scatter plot.

The original Ramachandran plot showed the allowed conformations of the model compound N-acetyl-L-alanine-methylamide using a hard-sphere atomic model to keep calculations simple. By using two different van der Waals radii for each element positions on the Ramachandran plot could be classified into either allowed regions, regions with moderate clashes and disallowed regions (see Figure 3 (a) in the paper).

The model compound does not take side chains into account, but it does assume that there is a side chain. The resulting Ramachandran plot therefore does not describe the possible φ/ψ angles for Glycine residues, where many more conformations are plausible. On the other end of the spectrum are Proline residues. These have a much more restricted range of possible φ/ψ angles. The φ/ψ distributions of GLY and PRO residues are therefore best described in their own Ramachandran plots (Figure 4 in the paper).

Over time the Ramachandran plot was improved in a number of ways. Instead of relying on theoretical calculations using a model compound, we can now rely on experimental observations by using high quality, hand picked data from the PDB. The way how the Ramachandran plot is calculated has also changed: It can now be seen as a two-dimensional, continuous probability distribution, and can be estimated using a full range of smoothing functions, kernel functions, Fourier series and other models.
The modern Ramachandran plot is much more resolved than the original plot. We now distinguish between a number of well-defined, different regions which correlate with secondary protein structure motifs.

Ramachandran plots are routinely used for structure validation. The inherent circular argument (A good structure does not violate the Ramachandran plot; The plot is obtained by looking at the dihedral angles of good structures) sounds more daring than it actually is. The plot has changed over time, so it is not as self-reinforcing as one might fear. The Ramachandran plot is also not the ultimate guideline. If a new structure is found that claims to violate the Ramachandran plot (which is based on a huge body of cumulative evidence), then this claim needs to be backed up by very good evidence. A low number of violations of the plot can usually be justified. The Ramachandran plot is a local measure. It therefore does not take into account that domains of a protein can exert a force on a few residues and just ‘crunch’ it into an unusual conformation.

The paper closes with a discussion of possible future applications and extensions, such as the distribution of a protein average φ/ψ and an appreciation of modern web-based software and databases that make use of or provide insightful analyses of Ramachandran plots.

From Protein Interface Recognition to Protein Complex Prediction

Similarly to ‘words’, which need to be “assembled into sentences, paragraphs, chapters and books” to tell a story, ‘protein structures’ need to be assembled into protein complexes to perform a specific task. To form complexes, proteins interact with other proteins, DNA, RNA and small molecules using their interface residues. All those types of interactions are under intense scrutiny by the research community, each of them defining a distinct field of research. During my PhD I focused on protein-protein interactions (PPIs) and prediction of their interfaces. Modifications in PPIs affect the events that take place within cells which may lead to critical diseases such as cancer. Therefore, knowledge about PPIs and their resulting 3D complexes can provide key information for drug design.

Docking is a popular computational method which predicts the possible structure of the complex produced by two proteins using the known 3D structure of the individual proteins. However, docking of two proteins can result in a large number of different conformational models whose majority is far from correct. This highlights one of the main limitations of docking. Therefore, scoring functions have been proposed which are used to re-score and re-rank docked conformations in order to detect near-native models. One way to distinguish native-like models from false docked poses is to use knowledge of protein interfaces. If one knows the possible location of interface residues on each individual protein, docked complexes which do not involve those interfaces can be rejected. Therefore, accurate prediction of protein interfaces can assist with detection of native-like conformations.

Various methods have been proposed for predicting protein interfaces as mentioned above. A high number of methods investigate protein sequential or structural features in order to characterise protein interfaces. Usage of 3D structural properties has improved the sequence-based predictions. Moreover, evolutionary conservation was shown to be an important property. Therefore, methods have integrated various structural features along with evolutionary information to increase performance.

The combination of different features using various techniques has been investigated by intrinsic-based predictors. However, it seems that these methods have reached their saturation, and combination of more properties does not improve their prediction performance. On the other hand, many studies have investigated the 3D structure of binding sites among protein families. They discovered that the binding site localisation and structure are conserved among homologous. These properties have improved the detection of functional residues and protein-ligand binding sites. Therefore, predictors took advantage of structurally conserved residues among homologous proteins to improve binding site predictions.

Although homologous template-based predictors improve the predictions, they are limited to those proteins whose homologous structure exists. Therefore, methods have extended their search for templates to structural neighbours, since interface conservation exists even among remote structural neighbours. In addition, with the increase in experimentally determined 3D complexes good quality templates can be found for many proteins. Therefore, usage of structural neighbours is the current focus of template-based protein interfaces predictors.

Although, template-based methods are currently the predictors under the main focus, one of their main limitations is their dependency to availability of the QP 3D structure. Also, these predictors have not investigated the contribution of interacting partners of structural neighbours in the prediction. In addition, since these methods perform structural comparisons their computational time is high which limits their application to high-throughput predictions.

One of my PhD contributions was toward developing, T-PIP (Template based Protein Interface Prediction), a novel PIP approach based on homologous structural neighbours’ information. T-PIP addresses the above mentioned limitations by quantifying, first, homology between QP and its structural neighbours and, second, the diversity between the ligands of the structural neighbours (here, ligands refers to the interacting partners of proteins). Finally, predictions can be performed for sequences of unknown structure if that of a homologous protein is available. T-PIP’s main contribution is the weighted score assigned to each residue of QP, which takes into account not only the degree of similarity between structural neighbours, but also the nature of their interacting partners.

In addition, we used T-PIP prediction to re-rank docking conformations which resulted in T-PioDock (Template based Protein Interface prediction and protein interface Overlap for Docking model scoring), a complete framework for prediction of a complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by T-PIP.

T-PioDock Pipeline

Exhaustive evaluation of interface predictors on standard benchmark datasets has confirmed the superiority of template base approaches and has showed that the T-PIP methodology performs best. Moreover, comparison between T-PioDock and other state-of-the-art scoring methods has revealed that the proposed approach outperforms all its competitors.

Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit to template based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly refer to pair-wise residue interactions which leaves ambiguity when assessing quality of complex conformations.

Network Analysis

Why networks?

Individual expression could be thought as a phenomenon regulated mostly by the individual, but in a second stand it is also modified by the interactions with the surroundings. Can the response of the individual be predicted by the group? (See the following video of an experiment conducted by Asch https://www.youtube.com/watch?v=FnT2FcuZaYI)

networkinside

Most common type of network analysis

Basic network summary statistics (description)
Clustering methods (Extract information)
Random graphs (Description, inference and to model network topology)
Learning machine methods (Prediction)

Random Graphs and the topology structure

Depending on the structure of a desired network different random models could be of use, for example, if the goal is to obtain a sparse and not highly connected network then an ER model could be of use (this model randomly assign the edges between nodes)

or if the goal is exactly the opposite (have a very highly connected network) a geometric graph could be of use (this model randomly assign positions in a n-dimensional space and then place edges between nodes closer than a given distance).

Is there already a random model?

According to our recent results we suspect there is no null model yet for PPIs, even though for some virus PPIs some of the random models seem to be very good models; however this virus PPIs are much smaller (around 300 nodes and up to 500 edges) than the networks of model organisms (usually with more than 2000 nodes and 5000 edges) such as yeast, human, fruit fly and Escherichia coli among others.

We will soon be publishing our article with details about this.

Django for scientific applications

In my current work I am developing a cheminformatics tool using structural and activity data to investigate protein-ligand binding. I ~~have only ever properly used~~ love python and I listen to Saulo, so I decided to used Django to develop my application. I didn’t understand what it was and why it might be useful before I started using it but below I thought I’d discuss a few of the features that I think have been useful and might encourage others to use it.

Firstly I will outline how Django works. I wanted to download all the PDB structures for CDK2 and store the information in a data structure that is robust and easily used. We have a Target and a Protein. A Target is associated to a particular UniProt accession. Cyclin-dependent kinase 2 (CDK2) is a Target. A Protein is a set of 3D coordinates, so 1AQ1 is a Protein.

class Target(models.Model):
"""A Django model to define a given protein target"""
    UniProt = models.CharField(max_length=20,unique=True)
    InitDate = models.DateTimeField(auto_now_add=True)
    Title = models.CharField(max_length=10)

In the above Target model I have three different fields. The first field denotes the UniProt accession for the Target and is “unique”. This means that only one Target can have any given UniProt accession in my data structure. If I try to add another with the same value in the UniProt field it will throw an exception. The second field denotes the time and date that the model was created. This means I can check back to when the target was created. The third is the Title I would like to use for this, for example CDK2.

I can then make a new Target objects by:

new_target = Target()
new_target.Title = "CDK2"
new_target.UniProt = "P24941"

and save it to the database by:

new_target.save() # Django takes care of the required SQL

The next model is for the Protein molecules:

class Protein(models.Model):
    """A Django model to define a given protein"""
    Code = models.CharField(max_length=6,unique=True)
    InitDate = models.DateTimeField(auto_now_add=True)
    TargetID = models.ForeignKey(Target)
    Apo = models.BoolenField()
    PDBInfo = models.FileField(upload_to='pdb')

The model contains the PDB Code, e.g. 1AQ1, and the date it was added to the database. It also consists of a foreign key, relating it to its Target and a boolean indicating if the structure is apo or holo. Finally there is a file field relating this entry to the appropriate file path where the PDB information is stored.

Once the data has been added to the database, Django then deals with all SQL queries from the database:

my_prot = Protein.objects.get(Code="1aq1") # Gives me the Protein object "1aq1"
CDK2_prots = Protein.objects.filter(TargetID__Title="CDK2") # All PDB entries associated to CDK2, as a query set, behaving similarily to a list
CDK2_list = [x for x in CDK2_prots] # Now exactly like a list

The “__” in the above query allows one to span the foreign key relationship, so it is searching for the Title of the Target not the Title of the Protein. Finally I can then access the PDB files for each of these proteins.

my_prot = Protein.objects.get(Code="1aq1") # Gives me the Protein object "1aq1"
print my_prot.Code # prints "1aq1"
# my_prot.PDBInfo has the behaviour of a file handle
pdb_lines = my_prot.PDBInfo.readlines()# Reads the lines of the file

There, you’ve made a queryable database, where Django deals with all the hard stuff and everything is native to python. Obviously in this example it might not be so difficult to imagine alternative ways of creating the same thing using directory structures, but as the structure of your data becomes more complex, Django can be easily manipulated and as it grow it utilises the speed advantages of modern databases.

A very long introductory post about protein structure prediction

If you are a protein informatician, bioinformatician, biochemist, biologist or simply a person well informed about science, you probably heard about protein structure prediction. If that is the case, you might be wondering what all the fuss is about, right? If you never heard those terms before, don’t panic! You are about to find out what protein structure prediction is all about!

Based on my group meeting’s presentation last Wednesday, this blog entry will discuss why protein structure prediction is important and the potential limitations of existing methods. I will also discuss how the quality of input may be a potential source for lack of accuracy in existing software.

First, let us remember a little biology: our genetic code encrypts the inner-works of a complicated cellular machinery tightly regulated by other (macro)molecules such as proteins and RNAs. These two types of macromolecules are agents that perform the set of instructions codified by DNA. Basically, RNAs and proteins are involved in a series of processes that regulate cellular function and control how the genetic code is accessed and used.

For that reason, a huge chunk of genomic data can be ~~pretty useless~~ not that useful if considered on their own. Scientists around the globe have invested millions of moneys and a huge chunk of time in order to amass piles and piles of genome sequencing data. To be fair, this whole “gotta sequence ’em all” mania did not provide us with the fundamental answers everybody was hoping for. Cracking the genetic code was like watching an episode of Lost, in which we were left with more questions than answers. We got a very complicated map that we can’t really understand just yet.

For that reason, I feel obliged to justify myself: protein structures ARE useful. If we know a protein structure, we can formulate a very educated guess about that protein’s function. Combine that with empirical data (e.g. where and when the protein is expressed) and it can help us unveil a lot of info about the protein’s role in cellular processes. Basically, it can answer some of the questions about the (genomic) map. ~~If only we could do that with Lost…~~

There is also evidence that knowing a protein’s structure can help us design specific drugs to target and inhibit that protein. Although the evidence of such biomedical application is sparse, I believe that with development of the field, there is a trend for protein structures to become more and more important in drug discovery protocols.

Still, if we look at the number of known genome sequences and known protein structures and at the growth of those figures over the past decade, we look at a drastic scenario:

There is a tendency for the gap between the number of protein sequences and protein structures to increase. Hence, we are getting more and more questions and little to no answers. Observe how the green line (the protein sequences associated with a known or predicted function) is very close to the red line (the number of known protein structures). However, there is a growing gap between the red and the blue line (the number of protein sequences). Source: http://gorbi.irb.hr/en/method/growth-of-sequence-databases/

Well, gathering protein structure data is just as important, if not more important, than gathering sequence data. This motivated the creation of Structural Genomics Consortiums (SGC), facilities that specialize in solving protein structures.

I am sorry to tell you that this is all old news. We have known this for years. Nonetheless, the graph above hasn’t changed. Why? The cost limitations and the experimental difficulties associated with protein structure determination are holding us back. Solving protein structures in the lab is hard and time consuming and we are far from being as efficient at structure determination as we are at genome sequencing.

There is a possible solution to the problem: you start with a protein sequence (a sequential aminoacid list) and you try to predict its structure. This is known as protein structure prediction or protein structure modelling. Well, we have a limited number of building blocks (20) and a good understanding of their physicochemical properties, it shouldn’t be that hard right?

Unfortunately, modelling protein structure is not as simple as calculating how fast a block slides on an inclined plane. Predicting protein structure from sequence is a very hard problem indeed! It has troubled a plethora of minds throughout the past decades, making people lose many nights of sleep (I can vouch for that).

We can attribute that to two major limitations:

1- There are so many possible ways one can combine 20 “blocks” in a sequence of hundreds of aminoacids. Each aminoacid can also assume a limited range of conformations. We are looking at a massive combinatorial problem. The conformational space (the space of valid conformations a protein with a given sequence can assume) is so large that if you could check a single conformation every nanosecond, it would still take longer than the age of the universe to probe all possible conformations.

2- Our physics (and our statistics) are inaccurate. We perform so many approximations in order to make the calculations feasible with current computers that we end up with very inaccurate models.

Ok! So now you should know what protein structure prediction is, why it is important and, more importantly, why it is such a hard problem to solve. I am going to finish off by giving you a brief overview of the two most commons approaches to perform protein structure prediction: template-based modelling (also known as homology modelling) and de novo structure prediction.

There is a general understanding that if two proteins have very similar sequences (namely, if they are homologs), than they will have similar structures. So, we can use known structures of homologs as templates to predict other structures. This is known as homology modelling.

One can do a lot of fancy talk to justify why this works. There is the evolutionary argument: “selective pressure acts on the phenotype level (which can encompass a protein structure) rather than the genotype level. Hence protein structures tend to be more conserved than sequence. For that reason and considering that sequence alone is enough to determine structure, similar sequences will have even more similar structures.”

One can also formulate some sort of physics argument: “a similar aminoacid composition will lead to a similar behaviour of the interacting forces that keep the protein structure packed together. Furthermore, the energy minimum where a certain protein structure sits is so stable that it would take quite a lot of changes in the sequence to disturb that minimum energy conformation drastically.”

Probably the best argument in favour of homology modelling is that it works somewhat well. Of course, the accuracy of the models has a strong dependency on the sequence similarity, but for proteins with more than 40% identity, we can use this method in order to obtain good results.

This raises another issue: what if we can’t find a homolog with known structure? How can we model our templateless protein sequence then? Well, turns out that if we group proteins together into families based on their sequence similarity, more than half of the families would not have a member with known structure. [This data was obtained by looking at the representativeness of the Pfam (a protein family database) on the PDB (a protein structure database).]

Ergo, for a majority of cases we have to perform predictions from scratch (known as free modelling or de novo modelling).

Well, not necessarily from scratch. There is a specific approach to free modelling where we can build our models using existing knowledge. We can use chunks of protein, contiguous fragments extracted from known structures, to generate models. This is known as a fragment-based approach to de novo protein structure prediction. And that is one big name!

One can think of this as a small scale homology modelling, where both the physics and evolutionary arguments should still hold true to some degree. And how do we do? Can we generate good models? We perform appallingly! Accuracies are too low to generate any useful knowledge in a majority of cases. The problem with the rare cases when you get it right is that you have no means to know if you actually got the right answer.

The poor quality of the results can be justified by the 2 biggest limitations discussed above. Yet something else might be in play. In homology modelling, if you use a bad template, you will most certainly get a bad model. In a similar way, using a bad set of fragments will lead you to a very poor final model.

Considering we already have the other two big issues (size of conformational space and accuracy of current potentials) to worry about, we should aim to use the best fragment library we possibly can. This has been the recent focus of my work. An attempt to make a small contribution to solve such a hard problem.

I would love to detail my work on finding better fragments here, but I believe this post is already far too long for anyone to actually endure it and read it until the end. So, congratulations if you made it through!

ISMB/ECCB Conference 2013 (Berlin)

It’s that time of the year again… when an intrepid group of OPIGlets trundle back tired but happy from another successful conference (this time it was ISMB/ECCB and its satellite conference 3Dsig in Berlin) armed with their favourite titbits from the presented work. This blog post is a mashup of some of our highlights as presented at the last group meeting.

Post-schnitzel and out and about in Berlin!

Definitely one of the best things for me was getting the chance to hear Sir Tom Blundell (our very own academic grandfather… Charlotte’s supervisor) give the keynote at 3Dsig, talking about everything from the structure of insulin to his deep, underlying love of jazz. Here are some more of our favourite things…

Empirical contact potentials derived from binding free energy changes upon mutation
(poster presentation by Iain H. Moal and Juan Fernández Racio)

Chosen by Jinwoo Leem

I was impressed by Moal (et al.)’s poster on predicting protein-protein binding affinities (in fact, it won the poster prize at 3D-Sig!). The poster describes a statistical potential that considers the number of mutations in a protein, and the type of interatomic contacts. Two variants of the potential were made; one for considering all atoms (atomic potential), and one considering residue side chains, represented as a centroid atom (residue potential). Ultimately, the energy change is represented as:

where N is the matrix of interatomic contacts between atoms i,j and P is a vector of contact types. Using weighted least-squares to minimise the residuals, r, the equation was used to predict affinity (ΔG) and affinity changes following mutations (ΔΔG).

As we can see in the top two graphs, the model shows decent performance for predicting ΔΔG of enzyme-inhibitor interactions, i.e. the model can indicate how a mutation affects binding affinities. Having said this, the ΔΔG predictions for Ab-Ag interactions were poor (Pearson’s correlation = 0.5-0.6).

Moreover, when the same potential was used to predict ΔG (bottom two graphs), the correlations were even worse. In fact, for flexible protein pairs, i.e. receptor-ligand pairs whose interface RMSD > 1.0Å, the correlation has gone to as low as 0.02.

Although the results are disappointing with respect to ΔG prediction, the model raises two interesting points. First, this is one of the few scoring functions that are specifically designed to predict affinity, rather than giving an arbitrary score for low RMSD. In addition, this model re-iterates the challenges in predicting Ab-Ag interactions. The solution for the latter point is not yet clear, but it may be of interest to re-train the model specifically with Ab-Ag complexes, and see if the model’s performance improves!

Predicting protein contact map using evolutionary and physical constraints by integer programming
(paper presentation by Zhiyong Wang and Jinbo Xu)

Chosen by Saulo de Oliveira

Last week, I decided to present a quick overview of a Paper Presentation I attended during the ISMB 2013.

The title of the presentation was “Predicting protein contact map using evolutionary and physical constraints by integer programming.” based on a paper by the same name.

Contact prediction (or evolutionary constraint prediction, a term I am much more fond of) was a trendy topic both at the 3DSig (2013) and at the ISMB (2013), with several presentations and posters on the subject.

In this particular presentation, Zhiyong Wang and Jinbo Xu described a new method to identify evolutionary constraints. The big differential of their talk and their work was approaching the problem in a different angle: their aim was to predict contacts when you have a low number of sequences in the multiple sequence alignment (refer to previous posts in the blog for an introduction to contact prediction).

They proposed a combination of machine learning and integer programming (similar to linear programming, again a topic we discussed previously here) to perform their predictions.

The features of the machine learning did not present any innovation. They were quite standard in the field such as mutation rates on PSIBLAST profiles and the Mutual Information (MI). The results of the Random Forest algorithm was employed to formulate constraints in a linear problem. These constraints were used to enforce physical properties of proteins, based mostly on our understanding of secondary structure.

Results seemed positive in both a random test set (CASP10) and 2 other test sets. By positive, I mean there was an improvement on the current state-of-the-art, especially for proteins with 10-1000 sequences in the MSA. Still, their precision was around 30, 40% for the top L/10 predictions (where L is the protein length). Further improvements are still necessary before we can apply these evolutionary constraints to improve protein structure prediction.

Evolution of drug resistance: structural models
(presentation by Maria Safi)

Chosen by Hannah Edwards

I found this talk by Maria Safi (which won the prize for best non-keynote presentation at 3Dsig) to be a really interesting method, despite my complete lack of background knowledge in the area (what are conferences for but to expose you to new ideas, right?).

Their idea was to produce a computationally viable method for identifying drug resistance in a protein’s mutant space. Drugs work by binding to their target protein in such a way as to inhibit its native function. If the protein mutates so as to maintain its native function but impair its binding to the drug it acquires resistance. The problem is, even within a reasonable distance of the native sequence, a proteins’ mutant space is huge, and it’s by no means trivial to test for maintenance of function and binding energy.

The groups’ solution was to recognise that the vast majority of mutant space would not be of interest. As such they send their candidate mutants through a 2-pass search: the first, a quick and easy algorithm to swiftly eliminate the dead end mutants… those that either are not resistant to drug binding or do not maintain their native function, and the second, a more biochemically accurate yet computationally expensive algorithm to be applied to the shortlist identified during the first pass.

The first algorithm is based on restricted dead-end elimination which aims to minimise a simple energy potential based on the protein’s structural stability and it’s binding energy to the drug. The algorithm keeps the backbone structure constant but by differing the side chain conformations, the mutants result in different energy potentials. A mutation at residue r can then be eliminated if an alternative mutation at r will always result in a lower energy potential.

The second algorithm is based on the more sophisticated methodology of MM-PBSA, combining molecular mechanics with the Poisson-Boltzman Surface Area calculations to estimate the free energy of the compound. This final run identifies the candidate mutants.

A significant strength of their method is that it requires only the crystal structures of the drug and target protein. As a purely structural model it eliminates the need for large amounts of training data, which, for newly emerging diseases and drugs, is often impossible to have access to.

The main focus of Maria’s talk however was using these energy potentials to predict evolutionary pathways from a wild-type protein to a resistant candidate. By treating evolution as a random walk through mutant space, weighted by the energy potentials, and assuming selection pressure of resistance, they were able to computationally simulate evolutionary scenarios.

For example, Maria focussed on the ritonavir-HIV protease complex to illustrate this method. The majority of the mutants with resistance to ritonavir which have been observed in nature were predicted by the method. For the candidates that were predicted but have not been seen, further elucidation could be found from the simulated evolutionary pathways: around 60% of these candidates were not accessible under the evolutionary model.

Sequence comes to the Structural Rescue: Identifying Relevant Protein Interfaces in Crystal Structures
(presentation by Jose M. Duarte)

Chosen by Henry Wilman

Jose Duarte presented a tool, EPPIC, which identifies and classifies protein interfaces from pdb structures. The talk was titled ‘Sequence comes to the Structural Rescue: Identifying Relevant Protein Interfaces in Crystal Structures’, and follows from their publication Protein interface classification by evolutionary analysis, Duarte JM, Srebniak A, Schärer MA, Capitani G. BMC Bioinformatics. 2012 Dec 22..

As the title suggests, this uses both structural and sequence information to classify protein contacts as biological or crystal. There is a webserver, and a downloadable version. There are a number of methods that exist to classify interfaces, and this differs in a few ways.

The previous methods typically rely on the area of the interface. As you see in the paper, even the benchmark sets used to test the other methods are biased such that biological contacts have much greater areas than crystal contacts. When the authors constructed a set where the contact area was similar, they found the previous methods performed generally poorly. However, there are a number of ways that you can define the interface or contact area, and specifically what people call ‘core residues’ of the interface. They found one study performed much better on their training set than the others. This defined core residues as ones that lost the majority of their solvent accessible surface area on binding to the interface. A simple cut off of >= 6 core residues at an interface produced a good classification.

In addition to this, they used sequence information. We know that interfaces are important, and often mutations at interface residues are bad. So, for a biological interface, we would expect residues to be better conserved than non-interacting surface residues. The authors used sequence entropy as a measure of the conservation. They calculated this by collecting homologous sequences with PSI-Blast and aligned them using Clustal-Omega. For each position in the alignment, if x is the occupancy frequency for a given amino acid, the sequence entropy is given by the sum over all amino acids of xlog(x). (They actually use a reduced alphabet for this, to avoid penalising mutations to similar amino acids). They then compare the entropy of the ‘core’ residues in the interface to those on the surface of the protein, and those on the periphery of the interface. If the core residues have lower entropy, then the contact is classed as biological. There are simple thresholds for both of these comparisons.

They have three metrics – one structural (number of core residues), and two sequence (entropy of core residues vs. peripheral residues, and entropy of core residues vs. surface residues). They classify based on a majority vote of the three methods. If there are an insufficient number of homologous sequences (i.e. fewer than 8), then they ignore the sequence scores, and classify using the structure only.

So why do we care about protein interfaces? Lots of us work with experimental protein structures. Many of these come from X-ray crystallography experiments. This means that when the structural information is captured, the protein is not isolated – instead it is packed against many other copies of itself. A bit like a brick in a wall – a wall many rows of bricks deep. So our protein is in contact with many others. Some of these contacts occur within the natural environment of the cell, others are a result of the crystal packing structure.
Now, protein interfaces are important. ‘Why?’, I hear you ask. Interfaces are how proteins interact with each other, and with other molecules. Some interfaces are large, some small, some are involved in transient interactions, others in permanent ones. Many diseases occur due to amino acid mutations at these interfaces. These change how the protein functions, which can cause bad things to happen. Similarly, if we can learn how to interact with an interface, we are (theoretically) able to alter the function with some sort of drug, and cause good things (whilst avoiding bad things).

So, this raises a few questions for people who study protein crystal structures. Things like, which bits of my protein interact? Do these interactions happen in a cell? Is my structure somehow distorted by the crystal packing? This EPPIC tool gives us one way to answer these.

Congratulations on reaching the end of this blog post… as a reward, the epic Brandenburg gate (taken by Jin)

Every Protein needs a Friend – Community Detection in Protein Interaction Networks

To make the OPIG soup, that has tasted of antibodies a lot lately, a little more diverse, I will try to spice things up with a dash of protein interaction networks, a pinch of community detection and a shot of functional similarity evaluation. I hope it remains edible!

In the 10 weeks I have spent at OPIG, my main focus has been on protein interaction networks, or more specifically, on this network:

View of the largest connected component of the HINT binary physical interaction network. Nodes represent proteins and edges are protein interactions.

Viewing this image, a popular German phrase comes to mind, which badly translated means: “As you see, you see nothing”. However, trying to “see” something in this, is what I’ve been trying to do. And as it turns out, I’m not the only person.

If we had a data set which says exactly which protein interacts with which other ones, then surely all biological pathway information must be incorporated in this data, and we should be able to cluster it into smaller modules or communities, which represent a biological function. This Gedankenexperiment is the theory which underlies my approach to these networks.

In reality, however, we don’t have this perfect data set. Protein interaction networks are very noisy with high estimated false positive and false negative rates for interactions, yet community detection algorithms have still been shown to be successful in outputting meaningful partitions of the network into communities. In this context “meaningful” refers to communities which group proteins together that have a similar biological function.

This brings us to a whole new problem. What is a “similar biological function” and how do you measure it? This question cannot be perfectly answered, but it seems the Gene Ontology annotations for biological process are a good place to start. In this framework, proteins are annotated with terms which describe the biological process they participate in. Of course there is not always a consensus about what term is to be assigned to a protein, and it is questionable how precisely a protein’s function within a process can be determined, but it wouldn’t be called work, if it was easy.

In my 10 weeks here, I’ve only scraped the tip of what is detection of functional communities in protein interaction networks, but it looks promising that the communities obtained may have some significance regarding biological modules. It is my hope that I can use data sets such as gene expression studies to further investigate this significance in the future, and maybe, if I’m very lucky, work towards helping people classify macrophage phenotypes or identify cancer in the distant future. The best place to do this, would definitely be in the friendly atmosphere that is OPIG!

[Database] SAbDab – the Structural Antibody Database

An increasing proportion of our research at OPIG is about the structure and function of antibodies. Compared to other types of proteins, there is a large number of antibody structures publicly available in the PDB (approximately 1.8% of structures contain an antibody chain). For those of us working in the fields of antibody structure prediction, antibody-antigen docking and structure-based methods for therapeutic antibody design, this is great news!

However, we find that these data are not in a standard format with respect to antibody nomenclature. For instance, which chains are “heavy” chains and which are “light“? Which heavy and light chains pair? Is there an antigen present? If so, to which H-L pair does it bind to? Which numbering system is used … etc.

To address this problem, w e have developed SAbDab: the Structural Antibody Database. Its primary aim is for easy creation of antibody structure and antibody-antigen complex datasets for further analysis by researchers such as ourselves. These sets can be selected using a number of criteria (e.g. experimental method, species, presence of constant domains…) and redundancy filters can be applied over the sequences of both the antibody and antigen. Thanks to Jin, SAbDab now also includes associated curated affinity (Kd) values for around 190 antibody-antigen complexes. We hope this will serve as a benchmarking tool for antibody-antigen docking prediction algorithms.

Alternatively, the database can be used to inspect and compare properties of individual structures. For instance, we have recently published a method to characterise the orientation between the two antibody variable domains, VH and VL. Using the ABangle tool, users can select structures with a particular VH-VL orientation, visualise and quantify conformational changes (e.g. between bound and unbound forms) and inspect the pose of structures with certain amino acids at specific positions. Similarly, the CDR (complimentary determining region) search and clustering tools, allow for the antibody hyper-variable loops to be selected by length, type and canonical class and their structures visualised or downloaded.

SAbDab also contains features such as the template search. This allows a user to submit the sequence of either an antibody heavy or light chain (or both) and to find structures in the database that may offer good templates to use in a homology modelling protocol. Specific regions of the antibody can be isolated so that structures with a high sequence identity over, for example, the CDR H3 loop can be found. SAbDab’s weekly automatic updates ensures that it contains the latest available data. Using each method of selection, the structure, a standardised and re-numbered version of the structure, and a summary file containing information about the antibody, can be downloaded both individually or en-masse as a dataset. SAbDab will continue to develop with new tools and features and is freely available at: opig.stats.ox.ac.uk/webapps/sabdab.

Oxford Protein Informatics Group

or "OPIG" to friends