Monthly Archives: September 2015

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;

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

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.
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.
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.
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.
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.
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.
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.

ERC starting grant mock interview

Yesterday’s group meeting got transformed into a mock interview for the final evaluation step of my ERC starting grant application. To be successful with an ERC starting grant application one has to pass three evaluation steps.

The first step consists of a short research proposal (max 5 pages), CV, successful previous grant writing, early achievements track record, and a publication list. If a panel of reviewers (usually around 4-6) decides that this is “excellent” (this word is the main evaluation criterion) then the application is transferred to step two.

In step two a full scientific proposal is evaluated. The unfair procedure is that if step one is not successful then the full proposal is not even read (although it had to be submitted together with step one).

Fortunately, my proposal passed step one and step two. The final hurdle will be a 10 minutes interview + 15 minutes questions in Brussels where the final decision will take place.

I already had one mock interview with some of the 2020 research fellows (thanks to Konrad, Remi, and Laurel), one with David Gavaghan, and the third one took place yesterday with our whole research group.

After those three mock interviews I hope to be properly prepared for the real interview!

Molecular Dynamics of Antibody CDRs

Efficient design of antibodies as therapeutic agents requires understanding of their structure and behavior in solution. I have recently performed molecular dynamics simulations to investigate the flexibility and solution dynamics of complementarity determining regions (CDRs). Eight structures of the Fv region of antibody SPE7 were found in the Protein Data Bank with identical sequences. Twenty-five replicas of 100 ns simulations were performed on the Fvregion of one of these structures to investigate whether the CDRs adopted the conformation of one of the other X-Ray structures. The simulations showed the H3 and L3 loops start from one conformation and adopt another experimentally determined conformation.

This confirms the potential of molecular dynamics to be used to investigate antibody binding and flexibility. Further investigation would involve simulating different systems, for example using solution NMR resolved structures, and comparing the conformations deduced here to the canonical forms of CDR loops. Looking forward it is hoped molecular dynamics could be used to predict the bound conformation of an antibody from the unbound structure.

Click here for simulation videos.