A new computational strategy for the modelling of self-assembly biomolecules

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Metal ions play a vital role in many biological and chemical processes. Besides being of fundamental interest in chemistry, ions play a critical role in the assembly and function of biomolecules. Examples include protein cages,1, 2 multi-enzyme complexes,3 and RNA. Moreover, in combination with organic molecules, they also serve as building blocks for the assembly of synthetic systems metallocages. Metal-mediated self-assembly has emerged as a promising approach to develop complex biomimetic systems from simpler building blocks.4, 5 However, even for naturally occurring self-assemblies, understanding the fundamental rules driving self-organisation, structure and evolution remains a challenge, which substantially limits our ability to re-purpose the properties of these systems. The overall aim of this project is to . This aim will be met by addressing the following aspects: 1. Development of accurate force fields for metal centres (1-9 months). 2. Implementation of efficient modelling techniques to explore of self-assembly pathways (8-18 months). 3. Study of protein self-assembly in protein cages (18-36 months). 1. Development of accurate force fields for metal centres. While a plethora of automated force-field (FF) topology builders exist for organic molecules, systematic extensions to model transition metals (TMs) remain challenging. Here, we will utilise machine learning (ML) to develop potentials describing the behaviour of metal centres in aqueous solvent. Specifically, we will use the Gaussian Approximation Potentials (GAP) framework,6 in combination with ab initio and density functional theory calculations, to develop ML force field potentials that can describe metal centres in aqueous phase. To test the accuracy of these models, we will evaluate the properties of aquo-complexes for which experimental data are available. The metals initially considered will include: Mg (II), Fe (II), Fe (III), Cu(II), Co(II), Zn(II), and Ni(II). 2. Implementation of efficient modelling techniques to explore of self-assembly pathway. The student will be introduced to Markov state models (MSM) to explore the dynamics and kinetic of metal-driven self-assembly employing small peptide systems. NMR and calorimetry experimental data will be use to validate our results. The software package PyEMMA will be initially tested for this purpose.7 3. Study of protein self-assembly in protein cages. In a final stage, we aim to apply the force field models and the sampling techniques mentioned above to study the self-assembly in members of the ferritin superfamily. This knowledge will facilitate efforts towards controlled assembly/disassembly for new industrially relevant applications. We aim to address open questions regarding their assembly pathways, formation and stability of metastable species, metal-dependence and the potential for re-engineering. To make tangible steps towards this goal we will first study lower order species, which are predicted to be metastable states along the assembly process before moving into higher order aggregates or atomic resolution. This will be done in collaboration with Dr David Clarke (Edinburgh) who has carried out characterisation of this system and has the appropriate protocols for testing computationally designed constructs.Area: Theoretical Chemistry