Hello friends of OPIG,
From my last blopig blog post [link: https://www.blopig.com/blog/2019/10/comparative-analysis-of-the-cdr-loops-of-antigen-receptors/], I summarised our findings that TCR CDRs are more flexible than their antibody counterparts. Because of this observation, we believe that it is more appropriate to represent TCR binding sites using an ensemble of conformations.
Continue readingSeb gave a talk at the Oxford Structural Genomics Consortium on Wednesday 9 Jan 2013. The talk mentioned the work of several other OPIG members. Below is the gist of it.
Homology modelling pipeline with several membrane-protein-specific steps. Input is the target protein’s sequence, output is the finished 3D model.
Given an incomplete model of a protein, as well as the current electron density map, we apply our loop modelling method FREAD to fill in a gap with many decoy structures. These decoys are then scored using electron density quality measures computed by EDSTATS. This process can be iterated to arrive at a complete model.
Over the past five years the Oxford Protein Informatics Group has produced several pieces of software to model various aspects of membrane protein structure. iMembrane predicts how a given protein structure sits in the lipid bilayer. MP-T aligns a target protein’s sequence to an iMembrane-annotated template structure. MEDELLER produces an accurate core model of the target, based on this target-template alignment. FREAD then fills in the remaining gaps through fragment-based loop modelling. We have assembled all these pieces of software into a single pipeline, which will be released to the public shortly. In the future, further refinements will be added to account for errors in the core model, such as helix kinks and twists.
X-ray crystallography is the most prevalent way to obtain a protein’s 3D structure. In difficult cases, such as membrane proteins, often only low resolution data can be obtained from such experiments, making the subsequent computational steps to arrive at a complete 3D model that much harder. This usually involves tedious manual building of individual residues and much trial and error. In addition, some regions of the protein (such as disordered loops) simply are not represented by the electron density at all and it is difficult to distinguish these from areas that simply require a lot of work to build. To alleviate some of these problems, we are developing a scoring scheme to attach an absolute quality measure to each residue being built by our loop modelling method FREAD, with a view towards automating protein structure solution at low resolution. This work is being carried out in collaboration with Frank von Delft’s Protein Crystallography group at the Oxford Structural Genomics Consortium.