Normal mode analysis (NMA) using elastic network models (ENM) is a reliable and cost-effective computational method to characterise protein flexibility and by extension, their dynamics. The conservation of the intrinsic dynamics of proteins emerges as we attempt to understand the relationship between sequence, structure and functional conservation.
Our work has been focusing on measuring the conservation of proteins flexibility, and has been supported by our activity in methods development for efficient and reliable comparison of flexibility in large structure datasets. We have early on evaluated the performances of NMA/ENM compared to principal component analysis from molecular dynamics simulations and shown that the slow dynamics was faithfully reproduced (Skjaerven, Proteins, 2011). More recently we have looked at different measures to compare protein flexibility (Fuglebakk, Bioinformatics, 2012), highlighted the impact of the choice of structural alignment in the comparison outcome (Fuglebakk, BBA General Subjects, 2015; Tiwari, Curr Op Struct Biol, 2018), and compared various ENM variants (Fuglebakk, JCTC, 2013).
Our work shows that protein flexibility characterised by its lowest frequency modes is intrinsic to protein structures and their fold (Tiwari, PLoS CB, 2016). We could show using computer-generated protein models that dynamics is highly conserved at the architecture level (Hollup, Protein Science, 2011). These models, produced by the group of Willie Taylor (Crick Institute, London) allowed us to explore a larger structure space than what is available in the Protein Data Bank.
Please check our publications on the topic for further information, in particular the three following review articles:Skjaerven, Theochem, 2009; Fuglebakk, BBA General Subjects, 2015;Tiwari, Curr Op Struct Biol, 2018.
We maintain the WEBnm@ online tool for normal mode analysis. It offers many useful analyses of normal modes, including comparative analyses of aligned proteins (Tiwari, BMC Bioinformatics, 2014). The third edition is now online in its beta version and available for testing here: WEBnma3.