Stefan Arold

Brief Biography

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 web server.