Fragment-based drug discovery (FBDD) is based on the idea that using small (< 300 Da), highly soluble compounds to screen against a target will give higher hit rates and sample chemical space more efficiently compared to screens using larger, drug-like compounds.
The right tool for the job – The Joy of Excel
Excel’s pervasiveness has resulted in it being used (correctly or incorrectly) in just about every area of science.
Unfortunately, Excel has some traps for the new player and unless you’ve fallen for them before, they are not entirely obvious. They stem from the fact that Excel will try to help the user by reformatting data into what it thinks you mean.
Continue readingConstrained docking for bump and hole methodology
Selectivity is an important trait to consider when designing small molecule probes for chemical biology. If you wish to use a small molecule to study a particular protein, but that small molecule is fairly promiscuous in its binding habits, there are risks that any effects you observe may be due to it binding other proteins with similarly shaped binding pockets, instead of your protein of interest.
Continue readingIt’s been here all along: Analysis of the antibody DE loop
In my work, I mainly look at antigen-bound antibodies and this means a lot of analysing interfaces. Specifically, I spend a lot of my time examining the contributions of complementarity-determining regions (CDRs) to antigen binding, but what about antibodies where the framework (FW) region also contributes to binding? Such structures do exist, and these interactions are rarely trivial. As such, a recent preprint I came across where the authors examined the DE loops of antibodies was a great motivator to broaden my horizons!
Continue readingEpitope mapping with structural data for SARS-CoV-2 RBD and 10 known binders
In the past few months we have seen a lot of papers reporting antibodies that they found to bind to SARS-CoV-2 (a database can be found here: http://opig.stats.ox.ac.uk/webapps/covabdab/). Some of them were from the analysis of a patient’s immune system. Some of them come with crystal structures to show where they bind. Some don’t have structures, but they have the sequences and some competition assay data to show approximately where on the spike protein they bind. The main focus is around an area called the Receptor Binding Domain (RBD) which is where the spike protein engages the human ACE2 receptor and causes the downstream problems. In this paper, the authors ran a complete mutagenesis on the RBD of the SARS-CoV-2 spike protein.
Continue readingWhat can you do with the OPIG Antibody Suite? v2.0
Since my last blogpost on this topic back in 2018, OPIG has expanded its range of tools for antibody/BCR analysis. Here is an updated summary of the OPIG antibody databases and immunoinformatics tools.
Continue readingAdding paired BCR data to OAS
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 readingAntibody-protein binding and conformational changes
I 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 readingPyMOL: colouring proteins by property
We 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 readingRobust gene coexpression networks using signed distance correlation
Even 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.