11:23pm Monday 11 December 2017

Movements of proteins can be predicted from their amino acid sequence

The research was published in Nature Communications, and makes it possible to study the movement patterns of proteins on a large genomic scale without knowing their structure. Such knowledge is in the first place essential to help identify the function of those proteins, and paves the road for improved understanding of the role of protein order and disorder as well as its evolution in protein families.
 
Until just before the turn of the century, it was generally assumed that the overall structure of functionally important proteins had to be very stable. This structure was the main determinant of their function, and only functionally important structural rearrangements were observed. In the last decennium it has become increasingly clear that many functionally crucial proteins do not have a stable overall structure, and that it is often precisely because of their movements that they can fulfil their function. This insight has only slowly come about because it is experimentally cumbersome to determine the movements of proteins; this information in only available for a small fraction of all known proteins.

In their recent article in Nature Communications the researchers, lead by Prof. Wim Vranken, show that it is possible to accurately predict the level of backbone rigidity for a given protein solely based on its amino acid sequence. This opens the door, within the limitations of the prediction, to get a picture of the backbone mobility of all known proteins. For example, the trend of these movements during the evolution of similar proteins in different organisms can be examined. The predictions also allow the determination, with good accuracy, of the rigid regions of proteins, the ones that are very flexible, and, crucially, the regions that can exhibit either behaviour. It is especially the regions in this dynamic and context-dependent ‘grey zone’ that are the most interesting for the function of a protein.

Involved researchers:
Elisa Cilia (Rome, 1981) is a postdoctoral researcher at the Machine Learning group (mlg.ulb.ac.be) of the Université Libre de Bruxelles (ULB). She is involved in projects related to understanding the information-processing capacities of protein structures. Her research interests are in computational biology and machine learning.

Rita Pancsa (Budapest, 1987) is a Ph.D. student at the Vrije Universiteit Brussel (VUB) and the VIB.  Her research covers the study of structure, function and evolution of intrinsically disordered proteins using bioinformatics approaches.

Peter Tompa (Budapest, 1959) is director of the VIB Department of Structural Biology and professor at the Structural Biology Brussels Lab (sbb.vub.ac.be) in the Vrije Universiteit Brussel. He is also professor at the Institute of Enzymology in Budapest and is a leading researcher in the field of Instrinsically Disordered Proteins.

Tom Lenaerts (Brasschaat, 1972) is professor at the Université Libre de Bruxelles (ULB) and the Vrije Universiteit Brussel (VUB). At the ULB he is co-heading the Machine Learning research group (mlg.ulb.ac.be), at the VUB he is part of the Artificial Intelligence research group (ai.vub.ac.be) of the informatics department. He is active in several interdisciplinary domains from computational biology to the analysis of social networks using game theory.

Wim Vranken (Leuven, 1970) is active in computational structure biology, with specific expertise in Nuclear Magnetic Resonance (NMR) related information. He is a researcher and professor in the Structural Biology Brussels Lab (sbb.vub.ac.be) of the Vrije Universiteit Brussel, part of the Department of Structural Biology of the VIB, and is deputy vice-director of the VUB/ULB Interuniversity Insitute of Bioinformatics in Brussels (ibsquare.be).
 
Literature
Cilia, E. , R. Pansca, P. Tompa, T. Lenaerts and W.F. Vranken. From protein sequence to dynamics and disorder with DynaMine. Nat. Commun. 4:2741 doi: 10.1038/ncomms3741 (2013).

 

Research


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