| Title |
Knowledge‐based protein modelling and design
|
|---|---|
| Published in |
European Journal of Biochemistry, March 2005
|
| DOI | 10.1111/j.1432-1033.1988.tb13917.x |
| Pubmed ID | |
| Authors |
Tom BLUNDELL, Devon CARNEY, Stephen GARDNER, Fiona HAYES, Brendan HOWLIN, Tim HUBBARD, John OVERINGTON, Diljeet Athwal SINGH, Bancinyane Lynn SIBANDA, Michael SUTCLIFFE |
| Abstract |
A systematic technique for protein modelling that is applicable to the design of drugs, peptide vaccines and novel proteins is described. Our approach is knowledge-based, depending on the structures of homologous or analogous proteins and more generally on a relational data base of protein three-dimensional structures. The procedure simultaneously aligns the known tertiary structures, selects fragments from the structurally conserved regions on the basis of sequence homology, aligns these with the 'average structure' or 'framework', builds on the loops selected from homologous proteins or a wider database, substitutes sidechains and energy minimises the resultant model. Applications to modelling an homologous structure, tissue plasminogen activator on the basis of another serine proteinase, and to modelling an analogous protein, HIV viral proteinase on the basis of aspartic proteinases, are described. The converse problem of ab initio design is also addressed: this involves the selection of an amino acid sequence to give a particular tertiary structure, in this case a symmetrical domain of two Greek-key motifs. |
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Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Unknown | 71 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 24% |
| Student > Master | 12 | 17% |
| Student > Bachelor | 8 | 11% |
| Researcher | 5 | 7% |
| Professor | 4 | 6% |
| Other | 9 | 13% |
| Unknown | 17 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 29% |
| Biochemistry, Genetics and Molecular Biology | 12 | 17% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 8% |
| Computer Science | 6 | 8% |
| Chemistry | 4 | 6% |
| Other | 6 | 8% |
| Unknown | 17 | 24% |