Prof. Stefan Arold is an associate professor in the Biological and Environmental Sciences and Engineering Division, and a member of the Computational Bioscience Research Center (CBRC). Before joining KAUST in May 2013, he worked at the University of Oxford, UK (1998–2001; post-doctoral scientist), at the French National Center for Scientific Research (CNRS) (2001–2009; assistant/associate professor) and the MD Anderson Cancer Center, Houston, Texas, USA (2009-2013, sabbatical/faculty appointment).
Prof. Arold's research is dedicated to elucidating the molecular basis of the function and (de)regulation of proteins central to cellular key signaling networks. His investigations are based on an integrated structural biology approach that combines functional and structural data from multiple sources (small angle x-ray scattering, x-ray crystallography, NMR, computational methods, biochemistry, high-throughput ligand binding assays and functional analyses). Results are translated into protein-nanomachines with beneficial properties. Prof. Arold is also implicated in drug design against protein-protein interactions, and has a patent on a protein-protein interaction inhibitor against the HIV-1 Nef protein.
Proteome-level assessment of prevalence and function of
Leucine-Aspartic Acid (LD) motifs
Leucine-aspartic acid (LD) motifs were discovered in 1996 as short
protein-protein interaction motifs present in the paxillin protein family.
These LD motifs were shown to link paxillin to a dozen proteins controlling
(cancer) cell adhesion and motility, including viral proteins. Their biological
importance, and the existence of at least six different protein folds that
recognise these LD motifs suggested that they also exist beyond the paxillin
family. However, to date, only three more proteins with LD motifs were found.
Indeed, LD motifs are notoriously difficult to detect because sequence pattern
searches lead to an excessively high number of false positives. To assess the
occurrence and function of LD motifs on a proteome-wide level, we here combined
machine learning with large-scale binding assays. Our analysis of the human
proteome reveals at least 6 new LD motif–containing proteins, all involved in
cell adhesion and migration. We also discovered an inverse (class II) LD motif
consensus. Analysis of stem eukaryotes suggested that LD motif signalling
evolved ~800 Myr again unicellular eukaryotes. Thus LD motifs appear to be ancient
interaction motifs that nonetheless have retained a strong functional focus.
The LD motif finder tool (LDMF) is available as a