Phosphosites targeting interactions: A sample of phosphosites that we predicted to target particular protein-protein interfaces, and which were stesed using the yeast two-hybrid system.
Phosphosites targeting interactions: ROC curve showing performance of our interface phosphosite predictor on a benchmark of known sites
Phosphosites targeting interactions: An known example of a phosphosite targeting an interface (Ser-88 in Human Dyenin light chain)
Cancer severity from interaction mechanisms: Figure shows shorter survival across all cancers for tumors with mutations altering the P53-zinc interface (red) compared to others (green).
Cancer severity from interaction mechanisms: Figure shows distinct populations within Breast carcinoma according to different mutations at the PIK3CA/PIK3R1 interface.
Cancer severity from interaction mechanisms: Figure shows differences in TP53 mutation preference in the two cancer shown. For example, Arginine (R) 273 is a preferred site in Brain Glioma, and the majority of changes are to Cysteine (C).
Cancer severity from interaction mechanisms: Figure shows how particular interfaces are targetted in different cancers. The size of the circle indicates how many mutations are seen, and the colour indicates whether the mutations are mostly disabling (red) or enabling (green).
Modelling of FFV gag protein: Figure shows mutations affecting the Gag–Elp interface, central beta-sheet affect budding or Gag folding.
Ciliary landscape: Figure shows the effect knocking down components of proteins where mutations lead to 3M syndrome have on cilia in mpkCCD cells. We hypothesized that 3M Syndrome is a previously undescribed ciliary disease.
Ciliary landscape: Figure shows the effect of variants in patients suffereing sever ciliary diseases and how they affect specific sub-complexes and interaction within the intraflagellar transport complex B.
Ciliary landscape: Figure shows the architecture of the intraflagellar transport complex B deduced by our Socioaffinity metric.
Ciliary landscape: Figure shows an overview of the complexes and interaction within the human primary cilium uncovered by Interaction Proteomics.
Mechismo: Figure shows a graphical representation of mutations and their effects on biomolecular interactions within Pancreatic cancer, including whether mutations enhance (green) or diminish (red) particular interactions. Generated using the Mechismo server.
Mechismo: Figure shows an example of a mutation within RhoA (L69R) thought to enhance an interaction with ARHGAP1 within Burkitt's lymphoma. Generated using the Mechismo server.
Mechismo: Figure shows a comparison of automated modelling techniques (model) to a simple strategy of looking at an alignment to template structure (aln) in terms of the fraction of residue contacts reproduced (as a function of sequence identity).
siRNA to protein-interaction mechanism: Figure shows how interactions within the IL2 signalling pathway are reproduced by the HIPPIE approach to deduece protein-interaction directionality using siRNA screen data.
RhoA mutations in Burkitt's lymphoma: Figure shows the location of key mutations within Burkitt's lymphoma that lie and the interface with one of its modulators (ARHGEF12)
JAK3 mutations in T-cell prolymphocytic leukemia: Figure shows the location of key mutations in this cancer thought to affect JAK3 function in particular ways.
Structure of Argonaute proteins by DNA shuffling: Figure shows the location of functional regions in modesl of Ago2 and Ago3 deduced by DNA shuffling techniques.
Mutations in ID3/TCF4 associated with Burkitt's Lymphoma: Figure shows the location of mutations observed in patients suffering from from Burkitt's Lymphoma at the dimer-interface and DNA binding site of TCF3
Mutations within Medulloblastoma: Figure shows the location of mutations observed in patients suffering from Medulloblastoma within the DNA binding site of DDX3X
Correlated mutations within HIV Gag: Figure shows the locations of predicted correlated positions within HIV Gag
Defining negative protein-protein interact datasets: Figure shows how the rational selection of negatives improves the prediction performance for specific biological contexts
Modification in mycoplasma pneumonia: Figure shows the location of phosphorylation (red) and acetylation (yellow) events in mycoplasma that occur at the interface of a GroES oligomer
Predicting drug-protein interactions: Figure shows how our method for predicting protein-chemical interactions can link proteins and chemicals via a series of protein/chemical superimpositions.
Wikipedia diputes reflect geopolitical instability: Mercator projection with countries coloured according to a dispute-index heat map (blue: few disputes; red: many disputes).
PepSite: A schematic detailing our PepSite approach for finding peptide binding sites on protein surfaces.
PepSite: A demonstration of some of the peptide binding sites predicted correctly using our program PepSite.
Mycoplasma modelling: A figure showing modelling details for the Mycoplasma pneumonia RNA polymerase
Mycoplasma modelling: Figure showing how we fit our models from Mycoplasma pneumoniae into a Cryoelectron tomogram for the bacteria.
WD40-peptide interactions: Figure from our review on the subject showing the location of peptide binding sites on this very promiscuous protein family