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L3.8 Protein modelling in drug discovery
s21
Lectures
L3.7 BIOINFORMATICS IN DRUG DISCOVERY: EXAMPLES AND PROSPECTS M Ovaska Orion Co.,Orion Pharma, POB 65, FIN-02101 Espoo Information is the raw material in the pharmaceutical industry. Bioinformatics is a discipline that encompasses storage, retrieval, distribution, analysis and interpretation of computer-stored information in biological research. The time elapse of a new drug from idea to market is typically lo-15 years, of which time the drug discovery process should not take more than three years. This puts extreme demands on information management during the discovery process. Two drug discovery projects from the research of Orion Pharma (Espoo) will be outlined: the discovery of a catechol 0-methyltransferase inhibitor (entacapone) for the treatment of Parkinson’s disease and the discovery of a calcium sensitizer (levosimendan) for the treatment of congestive heart failure. The emphasis is on the analysis of the information gained and on its use in the discovery process. In the 1980’s, when the two projects were started, basic biological knowledge about diseases was used to select the suitable macromolecular targets, catechol O-methyltransferase and troponin C. After this choice, recombinant protein production was set up, and pure proteins were produced for both screens and structural studies. Entacapone was discovered in 1987 and levosimendan in 1988. The X-ray structure of COMT was solved in 1992. Both entacapone and levosimendan are now in phase III trials. The discovery of additional compounds to the COMT and TnC targets was discontinued in 199596. At present the growth of bioinformation is exploding. The Human Genome Project will reveal - in principle - all possible targets for drug intervention. The number of known atomic structures of proteins is increasing rapidly. Much of the new information is available through the World Wide Web on all desktops. Better methods in computer-aided molecular design (CAMD), combinatorial technologies and high-throughput screening (I-ITS) speed up the discovery process. The emphasis of innovative research may move from drug discovery to target discovery.
L3.8 PROTEIN MODELLING IN DRUG M S Johnson - Dept. of Biochemistry Abe Akad. Univ., Tykistokatu 6, BioCity, Turku Centre for Biotechnology, BioCity, FIN-2052 1 Turku
DISCOVERY and Pharmacy, POB 66; and POB 123;
The research in our group has turned towards a new direction in which we are integrating computer-based methods and experimental approaches to provide details on the interactions between proteins of medicinal interest and inhibitors that may have roles as pharmaceutical agents. This work is highly collaborative but involves within our our group the techniques of molecular modelling, computational chemistry, and macromolecular crystallography. Knowledge-based protein modelling (1) is based on the fact that proteins that are homologous (related by a common ancestor) have similar 3D folds. In addition, since structural information tends to be much more conserved than the sequence itself even over long evolutionary tie spans, estimates of atomic structures can be made even for very distantly-related proteins. The process of modelling itself is conceptually simply, where details of the known 3D structure are extrapolating to the related sequence of unknown structure, according to an alignment which maps th’e one protein onto the other. In reality, for proteins of less than 35% sequence identity modelling can be quite difficult due to uncertainty in alignments and structural variations that do occur in related proteins (2,3). My focus in this talk will be on the utility of protein modelling as a means to further experimental research (4), and also how experimental methods - such as site directed mutagenesis - can help in confirming the details of the model. I will use examples from out current collaborations to illustrate what can be achieved by coupling experimental and theoretical approaches in order to maximize the potential of each technique. References: 1. Johnson MS, Srinivasan N., Sowdhamini R, Blundell TL. (1994) Crit. Rev. Biochem. Mol. Bio. 29, l-68. 2. Johnson MS. (1995) Molec. Med. Today 1, 188-194. 3. Johnson MS, May ACW, Rodionov MA, Overington JP. (1996) Meth. Enzym. 266, 57.5-598. 4. Johnson MS, Hoff& A-M, Cockcroft V. (1995) Kemia-Kemi 22,691-696.
Lectures
L3.7 BIOINFORMATICS IN DRUG DISCOVERY: EXAMPLES AND PROSPECTS M Ovaska Orion Co.,Orion Pharma, POB 65, FIN-02101 Espoo Information is the raw material in the pharmaceutical industry. Bioinformatics is a discipline that encompasses storage, retrieval, distribution, analysis and interpretation of computer-stored information in biological research. The time elapse of a new drug from idea to market is typically lo-15 years, of which time the drug discovery process should not take more than three years. This puts extreme demands on information management during the discovery process. Two drug discovery projects from the research of Orion Pharma (Espoo) will be outlined: the discovery of a catechol 0-methyltransferase inhibitor (entacapone) for the treatment of Parkinson’s disease and the discovery of a calcium sensitizer (levosimendan) for the treatment of congestive heart failure. The emphasis is on the analysis of the information gained and on its use in the discovery process. In the 1980’s, when the two projects were started, basic biological knowledge about diseases was used to select the suitable macromolecular targets, catechol O-methyltransferase and troponin C. After this choice, recombinant protein production was set up, and pure proteins were produced for both screens and structural studies. Entacapone was discovered in 1987 and levosimendan in 1988. The X-ray structure of COMT was solved in 1992. Both entacapone and levosimendan are now in phase III trials. The discovery of additional compounds to the COMT and TnC targets was discontinued in 199596. At present the growth of bioinformation is exploding. The Human Genome Project will reveal - in principle - all possible targets for drug intervention. The number of known atomic structures of proteins is increasing rapidly. Much of the new information is available through the World Wide Web on all desktops. Better methods in computer-aided molecular design (CAMD), combinatorial technologies and high-throughput screening (I-ITS) speed up the discovery process. The emphasis of innovative research may move from drug discovery to target discovery.
L3.8 PROTEIN MODELLING IN DRUG M S Johnson - Dept. of Biochemistry Abe Akad. Univ., Tykistokatu 6, BioCity, Turku Centre for Biotechnology, BioCity, FIN-2052 1 Turku
DISCOVERY and Pharmacy, POB 66; and POB 123;
The research in our group has turned towards a new direction in which we are integrating computer-based methods and experimental approaches to provide details on the interactions between proteins of medicinal interest and inhibitors that may have roles as pharmaceutical agents. This work is highly collaborative but involves within our our group the techniques of molecular modelling, computational chemistry, and macromolecular crystallography. Knowledge-based protein modelling (1) is based on the fact that proteins that are homologous (related by a common ancestor) have similar 3D folds. In addition, since structural information tends to be much more conserved than the sequence itself even over long evolutionary tie spans, estimates of atomic structures can be made even for very distantly-related proteins. The process of modelling itself is conceptually simply, where details of the known 3D structure are extrapolating to the related sequence of unknown structure, according to an alignment which maps th’e one protein onto the other. In reality, for proteins of less than 35% sequence identity modelling can be quite difficult due to uncertainty in alignments and structural variations that do occur in related proteins (2,3). My focus in this talk will be on the utility of protein modelling as a means to further experimental research (4), and also how experimental methods - such as site directed mutagenesis - can help in confirming the details of the model. I will use examples from out current collaborations to illustrate what can be achieved by coupling experimental and theoretical approaches in order to maximize the potential of each technique. References: 1. Johnson MS, Srinivasan N., Sowdhamini R, Blundell TL. (1994) Crit. Rev. Biochem. Mol. Bio. 29, l-68. 2. Johnson MS. (1995) Molec. Med. Today 1, 188-194. 3. Johnson MS, May ACW, Rodionov MA, Overington JP. (1996) Meth. Enzym. 266, 57.5-598. 4. Johnson MS, Hoff& A-M, Cockcroft V. (1995) Kemia-Kemi 22,691-696.