Computational studies of protein dynamics and dynamic similarity

At the time of writing this thesis, the complete genomes of more than 180 organisms have been sequenced and more than 80000 biological macromolecular structures are available in the Protein Data Bank (PDB). While the number of sequenced genomes and solved three-dimensional structures are rapidly increasing, the functional annotation of protein sequences and structures is a much slower process, mostly because the experimental de-termination of protein function is expensive and time-consuming. A major class of in silico methods used for protein function prediction aim to transfer annotations between proteins based on sequence or structural similarities. These approaches rely on the assumption that homologous proteins of similar primary sequences and three-dimensional structures also have similar functions. While in most cases this assumption appears to be valid, an increasing number of examples show that proteins of highly similar sequences and/or structures can have different biochemical functions. Thus the relationship between the divergence of protein sequence, structure and function is more complex than previously anticipated.
On the other hand, there is mounting evidence suggesting that minor changes of the sequences and structures of proteins can cause large differences in their conformational dynamics. As the intrinsic fluctuations of many proteins are key to their biochemical functions, the fact that very similar (almost identical) sequences or structures can have entirely different dynamics might be important for understanding the link between sequence, structure and function. In other words, the dynamic similarity of proteins could often serve as a better indicator of functional similarity than the similarity of their sequences or structures alone. Currently, little is known about how proteins are distributed in the 'dynamics space' and how protein motions depend on structure and sequence. These problems are relevant in the field of protein design, studying protein evolution and to better understand the functional differences of proteins. To address these questions, one needs a precise definition of dynamic similarity, which is not trivial given the complexity of protein motions.
This thesis is intended to explore the possibilities of describing the similarity of proteins in the 'dynamics space'. To this end, novel methods of characterizing and comparing protein motions based on molecular dynamics simulation data were introduced. The generally applicable approach was tested on the family of PDZ domains; these small protein-protein interaction domains play key roles in many signalling pathways. The methodology was successfully used to characterize the dynamic dissimilarities of PDZ domains and helped to explain differences of their functional properties (e.g. binding promiscuity) also relevant for drug design studies. The software tools developed to implement the analysis are also introduced in the thesis. Finally, a network analysis study is presented to reveal dynamics-mediated intramolecular signalling pathways in an allosteric PDZ domain.

Keywords: PDZ domains
flexibility
molecular dynamics simulations
protein dynamics
Advisor: Biggin, P
Hein, J