Category Archives: Conferences

CCP4 Study Weekend 2017: From Data to Structure

This year’s CCP4 study weekend focused on providing an overview of the process and pipelines available, to take crystallographic diffraction data from spot intensities right through to structure. Therefore sessions included; processing diffraction data, phasing through molecular replacement and experimental techniques, automated model building and refinement. As well as updates to CCP4 and where is crystallography going to take us in the future?

Surrounding the meeting there was also a session for Macromolecular (MX) crystallography users of Diamond Light Source (DLS), which gave an update on the beamlines, and scientific software, as well as examples of how fragment screening at DLS has been used. The VMXi (Versatile Macromolecular X-tallography in-situ) beamline is being developed to image crystals that are forming in situ crystallisation plates. This should allow for crystallography to be optimized, as crystallization conditions can be screened, and data collected on experiments as they crystallise, especially helpful in cases where crystallisation has routinely led to non-diffracting crystals. VXMm is a micro/nanofocus MX beamline, which is in development, with a focus to get crystallographic from very small crystals (~300nm to 10 micron diameters, with a bias to the smaller size), thereby allowing crystallography of targets that have previously been hard to get sufficient crystals. Other updates included how technology developed for fast solid state data collection on x-ray free electron lasers (XFEL) can be used on synchrotron beamlines.

A slightly more in-depth discussion of two tools presented that were developed for use alongside and within CCP4, which might be of interest more broadly:

ConKit: A python interface for contact prediction tools

Contact prediction for proteins, at its simplest, involves estimating which residues within a certain certain spatial proximity of each other, given the sequence of the protein, or proteins (for complexes and interfaces). Two major types of contact prediction exist:

  • Evolutionary Coupling
  • Supervised machine learning
    • Using ab initio structure prediction tools, without sequence homologues, to predict which contacts exist, but with a much lower accuracy than evolutionary coupling.

fullscreen

ConKit is a python interface (API) for contact prediction tools, consisting of three major modules:

  • Core: A module for constructing hierarchies, thereby storing necessary data such as sequences in a parsable format.
    • Providing common functionality through functions that for example declare a contact as a false positive.
  • Application: Python wrappers for common contact prediction and sequence alignment applications
  • I/O: I/O interface for file reading, writing and conversions.

Contact prediction can be used in the crystallographic structure determination field, during unconventional molecular replacement, using a tool such as AMPLE. Molecular replacement is a computational strategy to solve the phase problem. In the typical case, by using homologous structures to determine an estimate a model of the protein, which best fits the experimental diffraction intensities, and thus estimate the phase. AMPLE utilises ab initio modeling (using Rosetta) to generate a model for the protein, contact prediction can provide input to this ab initio modeling, thereby making it more feasible to generate an appropriate structure, from which to solve the phase problem. Contact prediction can also be used to analyse known and unknown structures, to identify potential functional sites.

For more information: Talk given at CCP4 study weekend (Felix Simkovic), ConKit documentation

ACEDRG: Generating Crystallographic Restraints for Ligands

Small molecule ligands are present in many crystallographic structures, especially in drug development campaigns. Proteins are formed (almost exclusively) from a sequence containing a selection of 20 amino acids, this means there are well known restraints (for example: bond lengths, bond angles, torsion angles and rotamer position) for model building or refinement of amino acids. As ligands can be built from a much wider selection of chemical moieties, they have not previously been restrained as well during MX refinement. Ligands found in PDB depositions can be used as models for the model building/ refinement of ligands in new structures, however there are a limited number of ligands available (~23,000). Furthermore, the resolution of the ligands is limited to the resolution of the macro-molecular structure from which they are extracted.

ACEDRG utilises the crystallorgraphy open database (COD), a library of (>300,000) small molecules usually with atomic resolution data (often at least 0.84 Angstrom), to generate a dictionary of restraints to be used in refining the ligand. To create these restraints ACEDRG utilises the RDkit chemoinformatics package, generating a detailed descriptor of each atom of the ligands in COD. The descriptor utilises properties of each atom including the element name, number of bonds, environment of nearest neighbours, third degree neighbours that are aromatic ring systems. The descriptor, is stored alongside the electron density values from the COD.  When a ACEDRG query is generated, for each atom in the ligand, the atom type is compared to those for which a COD structure is available, the nearest match is then used to generate a series of restraints for the atom.

ACEDRG can take a molecular description (SMILES, SDF MOL, SYBYL MOL2) of your ligand, and generate appropriate restraints for refinement, (atom types, bond lengths and angles, torsion angles, planes and chirality centers) as a mmCIF file. These restraints can be generated for a number of different probable conformations for the ligand, such that it can be refined in these alternate conformations, then the refinement program  can use local scoring criteria to select the ligand conformation that best fits the observed electron density. ACEDRG can accessed through the CCP4i2 interface, and as a command line interface.

Hopefully a useful insight to some of the tools presented at the CCP4 Study weekend. For anyone looking for further information on the CCP4 Study weekend: Agenda, Recording of Sessions, Proceedings from previous years.

Seventh Joint Sheffield Conference on Cheminformatics Part 1 (#ShefChem16)

In early July I attended the the Seventh Joint Sheffield Conference on Cheminformatics. There was a variety of talks with speakers at all stages of their career. I was lucky enough to be invited to speak at the conference, and gave my first conference talk! I have written two blog posts about the conference: part 1 briefly describes a talk that I found interesting and part 2 describes the work I spoke about at the conference.

One of the most interesting parts of the conference was the active twitter presence. #ShefChem16. All of the talks were live tweeted which provided a summary of each talk and also included links to software or references. It also allowed speakers to gain insight and feedback on their talk instantly.

One of the talks I found most interesting presented the Protein-Ligand Interaction Profiler (PLIP). It is a method for the detection of protein-ligand interactions. PLIP is open-source and has a web-based online tool and a command-line tool. Unlike PyMol which only calculates polar contacts, and not the type of interaction, PLIP calculates 8 different types of interactions: hydrogen bonding, hydrophobic, π-π stacking, π-cation interactions, salt bridges, water bridges, halogen bonds, metal complexes. For a given pdb file the interactions are calculated and shown in a publication quality figure shown here.

Screen Shot 2016-07-20 at 14.16.23

The display can also be downloaded as a PyMol session so the display can be modified. 

This tool is an extremely useful way to calculate protein-ligand interactions and can be used to find the types of interactions formed by the protein-ligand complex.

PLIP can be found here: https://projects.biotec.tu-dresden.de/plip-web/plip/

Comp Chem Kitchen

I recently started  “Comp Chem Kitchen” with Richard Cooper and Rob Paton in the Department of Chemistry here in Oxford. It’s a regular forum and seminar series  for molecular geeks and hackers, in the original, untarnished sense of the word: people using and developing computational methods to tackle problems in chemistry, biochemistry and drug discovery. Our hope is that we will share best practices, even code snippets and software tools, and avoid re-inventing wheels.

In addition to local researchers, we invite speakers from industry and non-profits from time to time, and occasionally organize software demos and tutorials.

We also provide refreshments including free beer. (We are grateful to Prof. Phil Biggin and the MRC Proximity to Discovery Fund for offering to support CCK.)

CCK-1

Our first meeting, CCK-1,  was held in the Abbot’s Kitchen on May 24, 2016, at 5 pm, and was a great success—standing room only, in fact! The Abbot’s Kitchen—originally a laboratory—is a beautiful stone building built in 1860 in the Victorian Gothic style, alongside the Natural History Museum, at a time when Chemistry was first recognized as a discipline.

Abbot's Kitchen, Oxford, Watercolor, Detailed, 512x683We heard a fascinating talk from Jerome Wicker from the Department of Chemistry who spoke about “Machine learning for classification of solid form data extracted from CSD and ZINC”, and described a method that could successfully discriminate (~80%) whether a small molecule would crystallize or not. The software tools he discussed included RDKit, CSD, and scikit-learn. There were also two lightning talks, each 5 minutes long, one from OPIG member Hannah Patel,  from the Department of Statistics, on “Novelty Score: Prioritising compounds that potentially form novel protein-ligand interactions and novel scaffolds using an interaction centric approach”, who briefly described her Django-based web interface to her RDKit-based tool to analyse structures of ligands bound to proteins and help guide future medicinal chemistry to find novel compounds. We also had a talk from Dr Michael Charlton from InhibOx spoke about “Antibacterial Drug Discovery and Machine Learning”.

CCK-2

Our next Comp Chem Kitchen, CCK-2, will be held next Tuesday (June 14th, 2016), and you can register free for CCK-2 here.

We will have talks from:

 

Hope to see you there!  (Did I say free beer?)

RDKit User Group Meeting 2016

If you’re interested in small molecules and cheminformatics, you might like to know that registration for the 2016 RDKit User Group Meeting is now open. The meeting will be held from the 26th-28th of October in Basel, Switzerland. From their announcement:

This year’s RDKit User Group Meeting will take place from 26-28 October at the Novartis Campus in Basel, Switzerland and is co-organized by people from Roche and Novartis.

Registration for the RDKit UGM is free:

https://www.eventbrite.com/e/5th-rdkit-user-group-meeting-tickets-22539677783

The previous years’ format seemed to work pretty well and the feedback was positive, so we will stick to the same format this year:

Days 1 and 2: Talks, lightning talks, roundtable(s), discussion, and talktorials. For those who haven’t attended before, talktorials are somewhere between a talk and a tutorial, they cover something interesting done with the RDKit and include the code used to do the work. During the presentation you’ll give an overview of what you did and also show the pieces of the code that are central to the work. The idea is to mix the science up with the tutorial aspects.

Day 3 will be a sprint: those who choose to stay will spend an intense day working in small groups to produce useful artifacts: new bits of code, KNIME nodes, KNIME workflows, tutorials, documentation, IPython notebooks, etc. We will once again try to structure this a bit by collecting a bunch of ideas for things to work on in advance.

There will also be, of course, social activities.

Looks like fun!

ISMB wrap-up (it was coming, we insist…!)

So ISMB 2015 seems a bit far away from now (just under 2 months!), but Dublin was an adventure, filled with lots of Guinness, Guinness, and … Guinness. Oh and of course, science (credits to Eleanor for the science of Guinness video)! We definitely had lots of that, and you can see some of our pictures from the week too (credits to Sam; https://goo.gl/photos/2qm9CPbfHtoC3VfH9)

Here are some key pieces of work that got to each of us here at OPIG.

Claire:
Jianlin Cheng, from the University of Missouri, presented his research into model quality assessment methods – ways of choosing the model that is closest to the native structure from a set of decoys. Their method (MULTICOM) is a consensus method, which calcualtes an average rank from 14 different quality assessment methods. By combining this average rank with clustering and model combination to select five top models for a target, their method produces good results – in CASP11, the group were ranked third when considering the top-ranked models and second when considering the top five.
Alistair:
Accounting for the cell cycle in single cell RNA-seq
The current ubiquitous of RNA-seq throughout academia speaks volumes to the strength of the methodology.  It provides a transcript-wide measure of a cell’s expression at the point of cell lysis; from which one can investigate gene fusion, SNPs and changes in expression, to name only a few possibilities.  Traditionally, these measurement are made using a cell culture and as such the expression levels, and derived results, are based on averages taken over a number of cells. Recent advances have allowed the resolution to increase to the point where measurements can now instead be made on single isolated cells. With this increase in capability, it should now be possible to measure and identify subpopulations within a given culture. However, the inherent variability of expression, such as that caused by the cell cycle, often overshadows any change that could be attributed to these subpopulations. If one could characterise this variability, then this could be removed from the data and perhaps these subpopulations would then be elucidated.
Oliver Stegle gave a great presentation on doing exactly this for the cell cycle. They modeled the different phases as a set of latent variables such that they are inferred directly from the data (rather than merely observed). Via this model, they estimated that upwards of 30% of the inherent variability could be accounted for, and hence subtracted from the data. Applying such a technique to culture of T cells, they were able to identify the the different stages of differentiation of naive T cells into T helper 2 cells. Crucially, these would of been obscured had the cell cycle not been identified. Given this success with just accounting for the cell cycle, Stegle suggested that their technique can be expanded upon to elucidate other sources of gene expression heterogeneity while making it easier to identify these cellular subpopulations.
Jaro:
Dr. Julia Shifman from Hebrew University of Jerusalem studies protein-protein interactions. In her 3DSiG presentation she focused on the presence of “cold-spots” in protein sequence where in sillico mutations to several different amino acids improve the binding affinity. Such cold-spots are often observed at the periphery of the complex, where no interaction is observed.
Malte: 
Alex Cornish from Imperial College London presented his work on the structural difference between cell-type specific interaction networks. To generate these, he weighted protein-protein interaction network edges by cell-type specific gene expression data from the FANTOM5 project. Using these cell-type specific networks, he finds that it is possible to associate specific networks with specific diseases based on the distances between disease-associated genes in the networks. Furthermore, these disease – cell type associations can be used to assess the similarity between diseases.
Jin:
Barry Grant presented an overview of the research activity in his group — namely nucleotide switch proteins (e.g. GTPases, such as Ras and Kinesin). In the case of Kinesin, the group used simple statistical methods such as principal components analysis to draw inferences between conformation and functional states. The group then used correlated motions to construct a community network that describes how certain groups of residues behave in certain conformational states.
Sam:
Discovery of CREBBP Bromodomain Inhibitors by High-Throughput Docking and Hit Optimization Guided by Molecular Dynamics
Min Xu, Andrea Unzue, Jing Dong, Dimitrios Spiliotopoulos, Cristina Nevado, and Amedeo Caflisch Paper link
In this paper MD simulations were used to confirm the binding modes found by in silico docking and to guide the chemical synthesis of hit optimisation. In one example three binding modes were observed during MD, which helped to identify favourable electrostatic interactions  that improved selectivity. For another compound a favourable polar interaction in the binding site was observed during MD, which helped to increase its potency from micro- to nanomolar. Thus, given a robust in-silico screening and docking approach, it seems that MD simulations can be a useful addition to confirm binding modes and inspire derivatives that might have been overseen in static structures.
Eleanor:
Bumps and traffic lights along the translation of secretory proteins
Shelley Mahlab, Michal LinialIn her talk, Michal described how a theoretical measure of translation speed, tAI, shows the differences between the translation speeds of proteins targeted to different locations. Proteins with a signal peptide (either secreted or transmembrane) have significantly slower codons than proteins without, over approximately the first 30 codons, perhaps allowing more time for binding to the signal recognition particle. Transmembrane proteins had a lower predicted speed overall, which may be important for their insertion and correct folding.

3Dsig and ISMB 2014 (Boston)

This year we head to Boston for 3Dsig and ISMB 2014. The outcome was excellent with James Dunbar giving a talk at the 3Dsig on “Examining variable domain orientations in antigen receptors gives insight into TCR-like antibody design” and Alistair Martin oral poster presentation at ISMB on “Unearthing the structural information contained within mRNA ”. The Deane group received the most votes by different judges for the poster competition at 3Dsig with James Dunbar poster winning the best poster prize and Jinwoo Leem and Reyhaneh Esmaielbeiki posters receiving the honorary mentioned (all presented posters are Here).

Soooo Excited!

Soooo Excited!

This blog post contains a brief description of what we found most interesting at the conference.

N-terminal domains in two-domain proteins are biased to be shorter and predicted to fold faster than their C-terminal counterparts.
Authors: Jacob, Etai; Unger, Ron; Horovitz, Amnon

Chosen by: Alistair Martin

Is is not surprising that protein misfolding is selected against in the genome, with aggregation of misfolded proteins being associated to a an array of diseases. It is suggested that multi-domain proteins are more prone to misfolding and aggregation due to an effective higher local protein concentration. Jacob et al. investigated what mechanisms have developed to avoid this, focussing on ~3000 two domain proteins contained within Swiss-Prot.

They found that there are notable differences between the N- and C-terminal domains. Firstly, there exists a large propensity for the C-terminal domain to be longer than the N-terminal domain (1.6 times more likely). Secondly, the absolute contact order (ACO) is greater in the C-terminal domain when compared to the N-terminal domain. Both length and ACO inversely correlate to folding speed and thus they draw the conclusion that the first domain produced by the ribosome is under pressure to fold faster than the latter domain. These observations are enhanced in prokaryotes and lessened in eukaryotes, thereby suggesting a relationship to translational speed and cotranslational folding.

A novel tool to identify molecular interaction field similarities
Authors: Matthieu Chartier and Rafael Najmanovich

Chosen by: Claire Marks

One of the prize-winning talks at 3Dsig this year was “A novel tool to identify molecular interaction field similarities”, presented by Matthieu Chartier from the Université de Sherbrooke. The talk introduced IsoMIF – a new method for finding proteins that have similar binding pockets to one another. Unlike previous methods, IsoMIF does not require the superposition of the two proteins, and therefore the binding sites of proteins with very different folds can be compared.
IsoMIF works by placing probes at various positions on a grid in the binding pocket and calculating the interaction potential. Six probe types are used: hydrogen bond donor and acceptor, cation, anion, aromatic group, and hydrophobic group. A graph-matching algorithm then finds similar pockets to a query using the interaction potentials for each probe type at each grid point. On a dataset of nearly 3000 proteins, IsoMIF achieved good AUC values of 0.74-0.92.


The promise of evolutionary coupling for 3D biology
Presenter: Debora Marks


Chosen by: Reyhaneh Esmaielbeiki

I was impressed by the keynote talk given by Debora Marks at the 3Dsig. She gave an overall talk on how their group have worked on detecting evolutionary couplings (EC) between residues in proteins and how they use this information in predicting folds. In general, looking at the interacting residues in a 3D structure and comparing these position in a MSA displays co-evolving relationship. But the challenge is to solve the inverse, from sequence to structure, since not necessary all co-evolving residues are close in the 3D space (this relationship is shown in the figure below).

Evoulationary coupling in sequnce and structure

Evoulationary coupling in sequnce and structure

Debora showed that previous studies for detecting co-evolving residues used Mutual Information (MI). But, comparing the prediction out of MI with contacts maps shows that these methods perform poorly. This is because MI looks at the statistics of pair of residue at a time while residues in proteins are highly coupled and pairs are not independent from other pairs. Therefore, MI works good for RNA but not for proteins. Debora’s group have used mean field and pseudo likelihood maximization to overcome the limitation of MI and introduced the EVcoupling tool for predicting EC (Marks et al. PLoS One, 6(12), 2011). They have used the predicted EC as a distance restraint to predict the 3D structure of proteins using EVfold. Using EVfold they have managed to build structure with 2-5Å accuracy.

In a more recent work, they have built EVfold-membrane which is specific for membrane proteins (Hopf et al. Cell, 149(7), 1607-1621, 2012) and they tried modeling membrane proteins with unknown experimental structures. Recently close homologues to these structures were released and comparisons show that EVfold-membrane structures have accuracy of 3 to 4Å.

She also discussed the usage of detecting EC in identifying functional residues involved in ligand binding and conformational changes with an interesting example of two GPCRs, adrenergic beta-2 receptor and an opioid receptor (paper link).

She concluded her talk by talking about her recent work EVcomplex (paperlink). The aim is to use the detected EC between two different protein chains and use this information in the docking software as a distance restraint. Although, this method has provided models of the E.coli ATP synthase but there are currently several limitation (all mentioned in the Discussion of the paper) for using this work in large scale.

in general, EC was a popular topic at the conference with interesting posters from the UCL Bioinformatics group.

Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization
Authors: Rudi Agius, Mieczyslaw Torchala, Iain H. Moal, Juan Fernández-Recio, Paul A. Bates

Chosen by:Jinwoo Leem

This was a paper by Agius et al. published in 2013 in PLOS Comp Biol. Essentially, the work was centralised around finding a set of novel descriptors to characterise mutant proteins and ultimately predict the koff (dissociation rate constant) of mutants. Their approach is quite unique; they perform two rounds of alanine-scanning mutagenesis, one on the wild-type (WT) and one on the mutant protein. They identify ‘hotspot’ residues as those that have a change of G of 2kcal/mol (or more) from alanine scanning, and the descriptor is formed from the summation of the energy changes of the hotspots.

The results were very encouraging, and from their random forest-trained model with hotspot descriptors, they see correlations to koff up to 0.79. However, the authors show that traditional ‘molecular’ descriptors (e.g. statistical potentials) perform just as well, with a correlation of 0.77. The exact contribution of their ‘hotspot’ descriptors to the prediction of koff seems unclear, especially considering how well molecular descriptors perform. Having said this, the paper shows a very unique way to approach the issue of predicting the effects of mutations on proteins, and on a larger dataset with a more diverse range of proteins (not necessarily mutants, but different proteins altogether!) these ‘hotspot’-specific methods may prove to be much more predictive.

On the Origin and Completeness of Ligand Binding Pockets with applications to drug discovery
Authors: Mu Gao & Jeffrey Skolnick.
Presented by Jeffrey Skolnick

Chosen by:Nicholas Pearce

The prevalence of ligand-binding pockets in proteins enables a wide range of biochemical reactions to be catalysed in the cell. Jeffrey Skolnick presented research which proposes that ligand-binding pockets are inherent in proteins. One mechanism that he hypothesised for the creation of these pockets is the mis-stacking of secondary structure elements, leading to imperfections in their surfaces – pockets. Using their method for calculating pocket similarity – APoc – Gao & Skolnick characterised the space of ligand-binding pockets, using artificial structures and structures from the PDB, and find it to be ~400-1000 pockets in size.

They suggest that the relatively small size of pocket-space could be one of the reasons that such a large amount of off-target promiscuity is seen in drug design attempts.

From this result, Skolnick went on to discuss several interesting possibilities for the evolutionary history of ligand-binding pockets. One of the interesting hypotheses is that many, if not all, proteins could have inherent low-level catalytic ability across a wide range of biochemical reactions. Motivated by the small size of pocket-space, it is conceivable that one protein would be able to catalyse many different reactions – this could give an insight into the evolutionary history of protein catalysis.

If primordial proteins could catalyse many different reactions, albeit inefficiently, this would give a possibility for how the first lifeforms developed. Nature need only then work on increasing specificity and efficiency from a background of weak catalytic ability. However, even through the course of evolution to produce more specific proteins, this background of activity could remain, and explain the background of ‘biochemical noise’ that is seen in biological systems.

Drug promiscuity and inherent reactivity may not only be present due to the small size of pocket-space – they may be a direct consequence of evolution and the fundamental properties of proteins.

Alistair Martin & codon!

Alistair Martin & codon!

James Dunbar presenting good stuff about antibodies

James Dunbar presenting good stuff about Antibodies

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.

group_photo

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:

jin_eq

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).

jin_figure

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.

brandenburg_gate

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

Antimicrobial Drug Discovery Conference (Madrid)

I am a big fan of taking something, either a poster or a talk, to a conference, and getting something back – other than a €6 box of airport chocolates.  This blog post is in that spirit.

On the plane to the “Antimicrobial Drug Discovery” conference in Madrid I was reading the Cassandra Project (a novel on smallpox, how apt) instead of the stack overflow of scientific papers I planned to read.  Classic JP.

The conference had a mix of experienced, invited speakers and early stage researchers.  It was very “biological” for a computational scientist, so quite removed from what I normally do – but an opportunity to learn nonetheless.

The keynote lecture was by Julian Davies, a fantastic speaker who gave a general overview of antibiotics and antibiotic resistance.  Antibiotic resistance is a real concern (even those politicians in the G8 noticed a few hours ago!) and there is a fear we might return to pre-antibiotics era when you could not cure common diseases like bacterial pneumonia.  Pharmaceutical companies all got out of antibiotic research years ago, and there have been no new antibiotic scaffolds for more than a decade.  I found this surprising as you would think that there was a truckload of money to be made from finding the new penicillin.  Apparently, there is little return in anti-infectives because of rapid mutation of the pathogen and its short-term use (curing the infection, as opposed to having to take your medication for life, such as beta-blockers for hyperventilation).  Bacteria should not only be considered at an individual cell level but also as a population with complex signalling between the individuals (which may offer a way to stop bacterial infection).  In order to combat infections and increasing resistance sick patients are now supplied with combinations of drugs – this is still dangerous due to the possible (toxic) drug-drug interactions.

Natural products, e.g. some toxins, are good antibiotics but it is very hard to optimize such compounds to improve their drug profile (chemical synthesis of natural products is difficult).  Also a lot of people at the conference were talking of how antimicrobial peptides will save the day.  The attendees with drug discovery experience raised an eyebrow about this, knowing how hard it will be to make a 30 residue peptide into a drug.

Some antibiotics work by having a hydrophilic part (e.g. carboxyl) and a hydrophobic part (e.g. an alkane chain).  This hydrophobic part sits in the membrane wall disrupting it, which creates a “leak” from the bacteria which eventually kills the pathogen.  There are other mechanisms of action such as blocking transporter or signalling channels.

There was a brilliant, energetic talk by Bruno Gonzalez-Zorn with the audience paying rapt attention.  He showed how bacteria have these multiple, small plasmids offering antibiotic resistance.  He discovered there was a common two-part theme to antibiotic resistance, where a particular gene is always present.

Paul Finn gave a much needed talk on why drug discovery is hard (e.g. target selection, difficulty to get drugs in therapeutic area, potency, toxicity, have to optimize for different variables, etc.).  Unknowingly proving this point, there was this earlier talk of a whole optimization series which got a small molecule inhibitor of a viral infection from 150uM down to 1uM (IC50) – a great result in itself, and when the investigators tested this ligand in vivo rather than in vitro it simply did not have any affect on the virus.

Cele Abad Zapatero, one of the main investigators of AltasCBS, made the point that, today, we do not know where we are in drug discovery.  He argued we need to move to chemical-biology space instead of simply chemical space and recommended the use of ligand efficiency indices (e.g. BEI, SEI).

Having fun in Madrid

Madrid was way too much fun.  Zidane (and a few thousand others) kissed this Champions League Cup in exactly the same place. Talking about microbes.  (click to enlarge)

And what did I take to the conference?  I took a poster, the design of which is based on Dunbar’s stylish template.  Marta, Ana and myself won a “highly commendable” poster prize with the best poster going to Laura (Synthetic inhibitors of bacterial cell division targeting the GTP binding site of FtsZ, since you asked).  There were 24 posters in all, and mine was the only computational study in a room otherwise filled with phages, bacteria and plasmids (literally as well as metaphorically).  There is a sinister heart-warming joy in winning a bottle of wine, instead of a cheque or a certificate.  James deserves a sip or two.

 

Poster Prize Presentation

Cheekily asking for a corkscrew during the poster prize award