Theoretical study of the Usutu virus helicase 3D structure, by means of computer-aided homology modelling

<p>Abstract</p> <p>Background</p> <p>Usutu virus belongs to the <it>Flaviviridae </it>viral family and constitutes an important pathogen. The viral helicase is an ideal target for inhibitor design, since this enzyme is essential for the survival, proliferation and transmission of the virus.</p> <p>Results</p> <p>Towards a drug-design approach, the 3D model of the Usutu virus helicase structure has been designed, using conventional homology modelling techniques and the known 3D-structure of the Murray Valley Encephalitis virus helicase, of the same viral family, as template. The model was then subjected to extended molecular dynamics simulations in a periodic box, filled with explicit water molecules for 10 nanoseconds. The reliability of the model was confirmed by obtaining acceptable scores from a variety of <it>in silico </it>scoring tools, including Procheck and Verify3D.</p> <p>Conlcusion</p> <p>The 3D model of the Usutu virus helicase exhibits <it>in silico </it>all known structural characteristics of the <it>Flaviviridae </it>viral family helicase enzymes and could provide the platform for further <it>de novo </it>structure-based design of novel anti-Usutu agents.</p

The Shape of Science

oai:ojs.jmolbiochem.com:article/2

Protein signatures using electrostatic molecular surfaces in harmonic space

We developed a novel method based on the Fourier analysis of protein molecular surfaces to speed up the analysis of the vast structural data generated in the post-genomic era. This method computes the power spectrum of surfaces of the molecular electrostatic potential, whose three-dimensional coordinates have been either experimentally or theoretically determined. Thus we achieve a reduction of the initial three-dimensional information on the molecular surface to the one-dimensional information on pairs of points at a fixed scale apart. Consequently, the similarity search in our method is computationally less demanding and significantly faster than shape comparison methods. As proof of principle, we applied our method to a training set of viral proteins that are involved in major diseases such as Hepatitis C, Dengue fever, Yellow fever, Bovine viral diarrhea and West Nile fever. The training set contains proteins of four different protein families, as well as a mammalian representative enzyme. We found that the power spectrum successfully assigns a unique signature to each protein included in our training set, thus providing a direct probe of functional similarity among proteins. The results agree with established biological data from conventional structural biochemistry analyses.Comment: 9 pages, 10 figures Published in PeerJ (2013), https://peerj.com/articles/185