단백질공학특강
- 서울대학교
- 로마스 카츨라우스카스
페이스북
트위터
카카오톡
네이버밴드
링크복사
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- 주제분류
- 공학 >화공 >화학공학
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- 강의학기
- 2011년 1학기
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- 조회수
- 2,713
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Overview of current goals and methods to improve proteins for industrial applications, including rational design, directed evolution and combined methods. Students will critically evaluate research papers on enzyme engineering and identify unsolved technical and theoretical problems in the field
차시별 강의
| 1. | |
Green Chemistry | Why use biocatalysis? Green chemistry & Catalytic power of enzymes Why proteins need engineering: stability, optimization for new substrate, new metabolic pathways |  |
| 2. | |
Enzyme Stability | Measuring Enzyme Enantioselectivity - stereoisomers, enantiomers, kinetic resolution, asymmetric synthesis, pharmaceutical intermediates Measuring Enzyme Enantioselectivity and Rates - Michaelis-Menten Kinetics, kinetic resolution, asymmetric synthesis, other metrics to measure rate | |
| 3. | |
Enzyme Stability, Selectivity | Looking at protein structures - protein data bank, Pymol, lactate dehydrogenase Rational design of more stable enzymes - entropy, hydrophobic effect, helix capping, molten globule | |
| 4. | |
Enzyme Selectivity | Improving selectivity by rational design - intermolecular forces, changing substrate specificity, enantioselectivity mechanisms Designing faster enzymes - diffusion limit, transition state stabilization, perhydrolysis | |
| 5. | |
Enzyme Selectivity | Designing faster enzymes, pt 2 - chorimate mutase, triosphosphate isomerase, weak binding of substrate. Computer modeling: molecular mechanics - multiple properties, qualitative design, force fields, atom types | |
| 6. | |
Engineering Selectivity | Enzyme modeling using molecular mechanics - Amber, MM2, FoldX Modeling to predict protein improvement - stability, molecular dynamics, QM/MM methods | |
| 7. | |
Circular Permutation /Selectivity | Guest lecture (tentative) Modeling to predict protein improvement - enantioselectivity, bioinformatics, consensus sequence | |
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Circular Permutation /Selectivity | Guest lecture (tentative) Modeling to predict protein improvement - enantioselectivity, bioinformatics, consensus sequence | |
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| 8. | Exam 1 | Exam 1 | ||
| 9. | |
Design faster enzymes | 1. Overview of directed evolution: screening, random mutagenesis 2. Mutagenesis using error prone PCR - methods, polymerase fidelity 3. Mutagenesis using error prone PCR - limitations, codon redundancy | |
| 10. | |
Mutagenesis Single Substitution | 1. Mutagenesis using recombination methods - chimeras & DNA shuffling, 2. Mutagenesis using recombination methods - limitations, homology 3. Mutagenesis using recombination methods - alternative methods, non-homologous recombination, | |
| 11. | |
Multiple Substitutions | 1. Focused substitutions at a single site, saturation mutagenesis methods, degenerate codons 2. Focused substitutions at a single site, choosing locations for mutagenesis Combining substitutions at multiple sites, stepwise approaches | |
| 12. | |
Recombination Methods | Combining substitutions at multiple sites, simultaneous, reduced amino acid sets Other mutagenesis approaches insertions, deletions, circular permutations | |
| 13. | |
Screening | Neutral drift libraries - evolutionary justification Incorporating unnatural amino acids, stop codon translation, substitutions using by feeding auxotrophic strains | |
| 14. | in class presentations | in class presentations | ||
| 15. | Exam 2 | Exam 2 |
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