40:52

Communicating Science 2023

13:11

Illustration of equipment used for video recordings

04:25

Lecture 13: Finishing up - recap of lecture themes

06:36

Lecture 13, concept 09: Computational design of new protein folds

00:27

Lecture 13: Study questions

04:28

Lecture 13, concept 08: A disaster in phase I trials for a new biological - TGN1412

05:34

Lecture 13, concept 07: Poly-unsaturated fatty acids (PUFAs) can rescue mutated voltage sensors

05:11

Lecture 13, concept 02: Full simulations of binding reveal kinetics & barriers

03:33

Lecture 13, concept 06: Allosteric modulation of igand-gated ion channels with different drugs

03:04

Lecture 13, concept 05: Liraglutide - a biological used to treat type-II diabetes

03:46

Lecture 13, concept 04: Strengths/weaknesses of protein/peptide drugs (biologicals)

04:36

Lecture 13, concept 03: Designing a helix to selectively bind a membrane protein

04:31

Lecture 13, concept 01: Free energy calculation provides good relative binding free energies

02:50

Lecture 12, concept 26: Docking quality depends on the input structure or homology model quality

00:47

Lecture 12: Study questions

03:16

Lecture 12, concept 29: Lead optimization for the HIV-1 protease inhibitor

02:35

Lecture 12, concept 28: Improving affinity - lead optimization

02:17

Lecture 12, concept 27: Validation with experimental co-crystal structure determination

01:26

Lecture 12, concept 25: Experimental vs. virtual (docking) high-throughput screening

01:48

Lecture 12, concept 24: Allowing ligand flexibility and producing a final ranked list

00:43

Lecture 12, concept 23: Knowledge-based (statistical) scoring on a grid is very fast

03:29

Lecture 12, concept 22: Docking scoring functions can be physical, empirical, or knowledge-based

01:45

Lecture 12, concept 21: Fragment-based drug design tries to build a drug into a pocket

05:32

Lecture 12, concept 20: Docking has limited accuracy, but achieves extreme throughput

03:35

Lecture 12, concept 19: Docking methods - find best ways to put two molecules together

04:03

Lecture 12, concept 18: Docking is virtual (computational) high-throughput screening

02:38

Lecture 12, concept 17: An example pharmacophore

01:53

Lecture 12, concept 16: A pharmacophore is a profile of a drug's average properties

04:29

Lecture 12, concept 15: Quantitative structure-activity relationship (QSAR) tries to predict drugs

03:34

Lecture 12, concept 14: Experimental high-throughput screening to test 500,000 molecules

03:26

Lecture 12, concept 13: Hit identification

02:09

Lecture 12, concept 12: The omeprazole drug was carefully optimised and made billions for Astra

06:37

Lecture 12, concept 11: From preclinical through phase I, II, & III trials to regulatory approval

06:04

Lecture 12, concept 10: Steps in the drug discovery process

03:06

Lecture 12, concept 09: Fantastic drugs and where to find them

07:22

Lecture 12, concept 08: Lipinski's rule(s) of 5

06:12

Lecture 12, concept 07: ADME / Tox

06:08

Lecture 12, concept 06: Affinity vs. efficacy & agonists vs. antagonists vs. inverse agonists

01:25

Lecture 12, concept 05: Membrane proteins are some of the most common drug targets

01:46

Lecture 12, concept 04: Ligand bind into pockets of receptors

02:03

Lecture 12, concept 03: From biology to target proteins to binding sites

07:19

Lecture 12, concept 02: Understand your drug target - SARS-CoV-2 as an example

03:10

Lecture 12, concept 01: Discover drugs by targeting proteins

07:45

Lecture 11, concept 26: Deep learning ab initio prediction of protein structure with Alphafold

01:32

Lecture 11: Study questions

04:45

Lecture 11, concept 25: Co-evolution (correlated mutations) improves ab initio prediction

06:03

Lecture 11, concept 24: Fragment-based ab initio prediction with ROSETTA

02:27

Lecture 11, concept 23: Homology modeling depends on good alignments and databases - use web servers

03:20

Lecture 11, concept 22: Homology modeling

03:03

Lecture 11, concept 21: Secondary structure prediction

03:23

Lecture 11, concept 20: Hard predictions are turning easy, impossible ones are becoming tractable

05:42

Lecture 11, Concept 19: Position-specific scoring matrices add more biological information

04:15

Lecture 11, concept 18: E-values indicate how statistically significant matches are

07:57

Lecture 11, concept 17: Protein structure RMSD vs. sequence identity

06:11

Lecture 11, concept 15: Phylogenetic trees

02:03

Lecture 11, concept 16: Sequencing will rapidly & cheaply tell us where structures mutate - variants

07:29

Lecture 11, concept 14: Protein sequence similarity: From mutation probabilities to scoring matrices

03:02

Lecture 11, concept 13: The extreme growth of biological sequence & structure databases

02:13

Lecture 11, concept 11: Detecting relationship from similarity - dot plots

03:53

Lecture 11, concept 10: Homologs, orthologs & paralogs