Kinetics of Disordered Proteins and their Interactions

Change log

Disordered proteins and regions are highly prevalent in the human proteome, and are often implicated in disease. However, methods to study these systems in detail are lacking, and the potential for thermodynamic and kinetic characterisation using experimental methods is limited. Molecular simulations and associated analysis methods have advanced to the point where investigating disordered proteins and their interactions with other (bio-)molecules on an atomistic scale is now possible. Amyloid-β 42 (Aβ42) is an aggregation-prone biomolecule implicated in Alzheimer’s disease, and recent work has shown that small molecules can inhibit the aggregation by dynamically binding to the monomeric form of this disordered protein. In this work I performed long-timescale simulations of Aβ42 with and without the addition of small molecules, and analysed the kinetics of the system using a neural network and a probabilistic state definition. Without a small molecule, the system occupies several states and transitions occur on the range of microseconds. With the small molecule 10074-G5, the dominant disordered state increases in population, and transitions out of this state become slower. Additionally, the conformational entropy of the protein backbone is increased, with the small molecule forming nanosecond-lifetime π-stacking interactions with aromatic side chains. These findings are consistent with nuclear magnetic resonance experiments, and indicate the possibility of designing molecules with high specificity. Another approach to targeting aggregation prone proteins such as Aβ42 consists of using specially engineered single-domain antibodies (sdAbs) with a modified complementarity determining region (CDR). These complementarity determining regions (CDRs) are often disordered and their dynamics are poorly understood. I performed enhanced sampling simulations of both an sdAb designed using a sequence-matching method as well as one developed with a structural approach to better understand their conformational space and provide information to improve selectivity and specificity of designed antibodies. These results show that it is possible to provide a comprehensive characterisation of the kinetics and thermodynamics of disordered proteins in terms of kinetic ensembles, which are defined by the structures and corresponding populations in their different states together with the transition rates between these states.

Vendruscolo, Michele
molecular dynamics, conformational entropy, disordered protein, metadynamics, markov model, neural network
Doctor of Philosophy (PhD)
Awarding Institution
University of Cambridge