In collaboration with the group of Thomas Arnesen (University of Bergen), we use bioinformatics and molecular modelling to investigate the structure-dynamics-function relationship in N-terminal acetyltransferases. In particular, using various sequence analyses methods, we study sequence-function relationships within the family of N-terminal acetyltransferases in attempt to classify these enzymes and predict novel ones.


Our computational work on the NAT family of enzymes is joined with the experimental investigation of the same family through the collaboration with the Arnesen lab.

N terminal acetylation, catalyzed by N-terminal acetyltransferases (NATs), is one of the most common protein modifications, with up to 90% of human proteins being susceptible to acetylation. This reaction entails tagging an N-terminus of a protein with the acetyl group, thus changing its physicochemical properties. Being this common, acetylation has manifold roles, ranging from proper protein folding to cell apoptosis. Up or down-regulation of N-terminal acetylation has been shown to affect, among other things, cell proliferation and, thus, plays a role in cancer development. Acetylation misregulation can also lead to the development of some syndromes, like the Ogden syndrome (Myklebust et al., Hum Mol Genet, 2015). The high percentage of the acetylated proteome, links to metabolic regulation and medical relevance put acetyltransferases in the spotlight of scientific research.

The NAT family contains at least eight members and all eight have been discovered experimentally. There are two major characteristics of members of this family: high structural conservation and high sequence divergence.

Because of the high structural conservation, it is not obvious how and why different NATs have different substrate specificities. We address this problem using molecular modeling and molecular dynamics to map the interactions between NATs and their substrates (Grauffel et al., PLoS ONE, 2012) and normal mode analyses of NAT proteins where we have shown that the differential flexibility of a single loop can lead to drastic changes in NAT specificity (unpublished work).

One of the remaining questions is whether there are more than eight NATs. There is currently no way to computationally predict more members of the family and high structural conservation and high sequence divergence are making this problem more difficult. We study the sequence-function relationships using different sequence analyses methods, among which are sequence similarity networks and various phylogenetic analyses in order to make predictions of novel NATs possible. Combining sequence and structure analyses and already available data on NATs, we aim to create a detailed classification of N-terminal acetyltransferases and make reliable predictions about the existence of yet uncovered families.

Additionally, since one NAT enzyme has been shown to interact with the Golgi membrane, the group has proposed the membrane-binding model using computational approaches (Aksnes et al, JBC, 2017). More recently, we showed that the flexibility of the a1-a2 and b6-b7 loops is likely to be involved in substrate specificity by regulating the size of the entrance of the ligand to the catalytic site (Abboud et al., Comput Struct Biotechnol J, 2020). Using sequence similarity network we proposed a classification of eukaryotic GNAT acetyltransferases together with its phylogeny, and show how it can be used to annotate new acetyltransferases sequences (Kretnic et al, BioRXiv, 2020).



NATs project: Thomas Arnesen and al (Protein N-terminal acetylation group)


Bioinformatics, homology and threading modeling, molecular dynamics simulations, normal mode analysis