Hello,
Today is the day for my final blog post before I enter a thesis writing mode. Using this given opportunity, I would like to present to you our recent update to the Observed Antibody Space (OAS) resource where we included paired antibody data (http://opig.stats.ox.ac.uk/webapps/oas).
Continue readingI came across a recent paper on the antibody-protein binding and conformational changes. As I work mainly on the binding site/Fv regions of antibodies, I am intrigued to see the role of the constant domains in the overall antibody function.
Continue readingWe all love pretty, colourful pictures of proteins. There is quite a variety of programs to produce publication-quality images of proteins, some of the most popular being VMD, PyMOL and Chimera. Each has advantages and disadvantages — for example, VMD is particularly good to deal with molecular dynamics simulations (perhaps that’s why it is called “Visual Molecular Dynamics”?), and Chimera is able to produce breathtaking graphics with very little user input. In my work, however, I tend to peruse PyMOL: a Python interface is incredibly helpful to produce quick analyses.
Continue readingEven within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes/proteins, using a network of gene coexpression data that includes functional annotations. However, the lack of trustworthy functional annotations can impede the validation of such networks. Hence, there is a need for a principled method to construct gene coexpression networks that capture biological information and are structurally stable even in the absence of functional information.
In my latest paper, we introduce the concept of signed distance correlation as a measure of dependency between two variables and apply it to generate gene coexpression networks. Distance correlation offers a more intuitive approach to network construction than commonly used methods such as Pearson correlation. We propose a framework to generate self-consistent networks using signed distance correlation purely from gene expression data, with no additional information. We analyse data from three different organisms to illustrate how networks generated with our method are more stable and capture more biological information compared to networks obtained from Pearson or Spearman correlations.
Jack recently made a blog post in which he provided a script which can transfer your files between your personal computer and a given remote machine via temporarily hosting a file on file.io (blog post here); where you have some sensitive data that you do not want to risk hosting online, you can also fairly easily use SCP to keep business strictly between your local and remote machine.
What I am referring to is described here. This blog post refers to the case where you want to SCP from local host to a remote machine which is only accessible via a gate server (this isn’t necessarily true for the Stats computers as we can use the VPN to directly access our remote machine of choice by the way). I won’t effectively plagiarise the blog post I linked to as the explanation is clear enough in itself, but you just use port forwarding and the localhost address of your local machine!
Best wishes,
Eve
This blog post finally combines the two great passions of my life: antibodies and dogs. Therapeutic antibody development is a huge area and is certainly not limited to humans. In the process of developing antibodies, we often use mouse or rat antibodies, obtained by injecting the animal with the antigen of choice and then collecting the resulting antibodies. The first monoclonal antibodies (mAbs) were produced in this way, by fusing spleen B cells from an immunised mouse or rabbit with immortalised myeloma cells to form antibody-expressing hybridoma cells. However, using antibodies to treat disease in animals lags behind humans.
Continue readingI have an unconscious habit of personification, and I always see the antibody light chain as lazy for not contributing more residues to binding interfaces (obviously a generalisation – e.g. insertions in CDRL4 in anti-HIV bNAbs [1]). Perhaps this is why I have a personal preference for the more diverse [2] heavy chain with its specificity-determining [3] CDR3. Having written this down, I realised it’s actually pretty weird to consider an antibody chain as a person and I ought to re-educate myself about the role that light chains play.
Continue readingAt group meeting a few weeks ago I presented this paper, “Landscape of Non-canonical Cysteines in Human VH Repertoire Revealed by Immunogenetic Analysis“, from Prabakaran and Chowdhury. The paper is an investigation of the frequency, location and patterns of cysteines contained in human antibody sequences. Cysteines are important amino acids found in proteins, including antibodies, which can form disulphide bonds with other cysteines due to the presence of their reactive sulfhydryl group in the side chain.
Continue readingLast Tuesday marked two exciting milestones for me in OPIG! Not only had I been looking forward to group socials since the beginning of lockdown, but I’d never met anyone other than Charlotte in person since starting in the group in April. As such, the annual cycling pub trip was an apt introduction to several OPIG members (who are now exempt from the game I play by myself during weekly Zoom group meetings: “Guess how tall this person is in real life!”) and a chance to interact with people other than my housemates!
Continue readingWhile entropy is a major driving force in many chemical changes and is a key component of the free energy of a molecule, it can be challenging to calculate with standard quantum thermochemical methods. With proper consideration in flexible molecules, we can break down the total entropy into different components, including vibrational, translational, rotational and conformational entropy. The calculation of conformational entropy is the most time-consuming as we have to sample all thermally-accessible conformers. Here, we attempt to understand the components that contribute to the conformational entropy of a molecule, and develop a physically-motivated statistical model to rapidly predict the conformational entropies of small molecules.
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