Research programme: peptide therapeutics

ProteinQure empowers protein drug discovery teams through the use of cutting-edge computational methods. They create novel therapeutics for challenging drug targets such as GPCRs. They achieve this by combining state-of-the-art structure-based algorithms, including molecular dynamics simulations and machine learning. Their design platform allows them to explore vast regions of sequence space and predict functional properties of therapeutics to rapidly deliver new insights to our partners in pharmaceutical R&D.

ProteinQure can obtain structures for protein therapeutics and drug targets (up to ~100 amino acids). Their integrative models can use external data (sequence, structure, and functional measurements) to increase the speed and accuracy of their predictions. This approach has shown strong agreement to experimental structures in blind protein folding challenges (CASP). ProteinQure is applying these methods to develop a novel class of peptide-mimetic polymers with SRI International. These molecules have shown high binding affinity to well-established drug targets involved in cell signalling (cytokines, nucleotide-binding proteins). By modelling the combinatorial space containing non-natural amino acids, they support rational drug design and optimization of these peptides for stability and binding affinity.

ProteinQure specialises in the design of novel protein scaffolds and libraries suitable for modern protein engineering display technologies. Combining high-accuracy biophysical models and high-throughput sequence search, they have generated massive libraries of protein binders enriched for desired structural and functional characteristics (sequence diversity of 109-1010). Upon identifying hits, the company provides computational models of protein-protein interactions suitable for optimising affinity and selectivity. They have validated structural properties for novel protein scaffolds through CROs. Their computationally-designed libraries enable high-throughput hit identification across a broad range of targets, and they’re working with an undisclosed industry partner on a large-scale validation of these methods.