Back to Search
Start Over
Structural and Functional Modeling of Artificial Bioactive Proteins
- Source :
- Information, Information; Volume 8; Issue 1; Pages: 29, Information, Vol 8, Iss 1, p 29 (2017)
- Publication Year :
- 2017
- Publisher :
- MDPI AG, 2017.
-
Abstract
- A total of 32 synthetic proteins designed by Michael Hecht and co-workers was investigated using standard bioinformatics tools for the structure and function modeling. The dataset consisted of 15 artificial α-proteins (Hecht_α) designed to fold into 102-residue four-helix bundles and 17 artificial six-stranded β-sheet proteins (Hecht_β). We compared the experimentally-determined properties of the sequences investigated with the results of computational methods for protein structure and bioactivity prediction. The conclusion reached is that the dataset of Michael Hecht and co- workers could be successfully used both to test current methods and to develop new ones for the characterization of artificially-designed molecules based on the specific binary patterns of amino acid polarity. The comparative investigations of the bioinformatics methods on the datasets of both de novo proteins and natural ones may lead to: (1) improvement of the existing tools for protein structure and function analysis ; (2) new algorithms for the construction of de novo protein subsets ; and (3) additional information on the complex natural sequence space and its relation to the individual subspaces of de novo sequences. Additional investigations on different and varied datasets are needed to confirm the general applicability of this concept.
- Subjects :
- 0301 basic medicine
Protein structure and function
Synthetic protein
Computer science
function prediction
synthetic protein
Computational biology
010402 general chemistry
Functional modeling
01 natural sciences
structure prediction
de novo design
03 medical and health sciences
Protein structure
Information and Communication Sciences
lcsh:T58.5-58.64
lcsh:Information technology
business.industry
Basic Medical Sciences
0104 chemical sciences
Structure and function
Natural sequence
030104 developmental biology
Artificial intelligence
business
Biotechnology
Information Systems
Subjects
Details
- ISSN :
- 20782489
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- Information
- Accession number :
- edsair.doi.dedup.....e24dbd10c38257e9e2729a5d260a4ce6
- Full Text :
- https://doi.org/10.3390/info8010029