Computational design of multi-state proteins using molecular simulations and machine learning

The ability to computationally design de novo proteins is a key technology that underpins future progress in synthetic biology and biomaterials discovery. However current de novo design methods tend to produce rigid, hyperstable folds that lack functionality. To progress towards truly functional de novo protein design it is crucial to design proteins that adopt multiple conformational states. This would enable the design of protein as catalysts, or biosensors or bioswitches that respond to environmental changes such as a variation in pH or temperature.1

Such an endeavour is challenging for the energy functions in current protein-design software because it requires designing folds that adopt structurally distinct yet energetically similar states. Rigorous molecular dynamics simulation methods that describe protein dynamics and solvation effects at the atomistic level have shown potential for such 'dynamic design' problems.2 However current molecular dynamics methods are too slow and complex for routine application to protein design.

This PhD project will validate an interdisciplinary approach that leverages new classes of machine learning algorithms to substantially accelerate the speed of molecular dynamics simulations for de novo protein design problems. Throughout the project we will tackle a range of problems of fundamental importance in protein design, ranging from optimising secondary structure preferences of cyclic peptides,3 to tuning the preferred oligomerisation states of de novo-helical bundles.4 Such systems are well suited to a combined data-driven/physics-driven molecular modelling approach owing to a large amount of existing experimental data, and validated energy functions for atomistic modelling. The accurate modelling of such multi-state systems will open new routes for the design of versatile building blocks for synthetic biology applications, and for the discovery of new biomaterials. Experimental validation of the design method will be performed by designing switchable -helical bundles. Designs will be produced in the lab using either molecular biology or solid phase peptide synthesis and will be characterised in vitro using a range of biophysical techniques.