 So we started out a few weeks ago starting biomolecular structure and in particular the amino acids. Why they have their properties, they have what amino acids, how we classify them and how they give rise to the diversity and also the chirality that I thought upon today with D and L amino acids. From there we went on to looking at the molecular structure and in particular all the interactions, lenoargyls, electrostatics, torsions, everything. We then went back to physics because based on those interactions we needed tools that enabled to say what states are probable, what states are less probable. That then led to the hydrophobic effect where we particularly introduced entropy right and that I was able to go from there to talk about free energy instead of just enthalpy which is an exceptionally important concept that kept coming back through the class. We went on to instead of just studying pairs of states, studying the entire partition function, all possible states and from that we could start to talk about things in terms of stability of different states. Sure, I used water as the primary example here but this is equally through for protein folding as I showed you a few lectures ago and even things like ligand binding you can think of as a very small local phase transition for that ligand. We used that to interpret protein structures in particular small parts of the structure in terms of free energy rather than just entropy that enabled us to explain roughly how fast they form and everything which is so in hand in task 2 that we can make surprisingly good predictions based just on paper and pen. Although if we combine that with a computer we're able to study much larger systems where we can have a realistic environment, water, tens of thousands of atoms and make actual predictions about actual systems that we can compare to experiments. Not only that we could use so-called free energy calculation methods to make specific predictions about how good should it be for a molecule to bind in a specific place in the molecule or in your case what should the probability of the binding energy for Xenon B in a particular site. We looked quite a lot about protein structure, first the fibrous and the water soluble proteins then the membrane proteins and also the lipids that surround the membrane proteins. We focused even more than on what are these structures, what do we have them, we grouped them in folds. We started to reason why the folds we see are structurally stable and why evolution has selected for those folds. We're going to come back to the evolution in a few lines here. We then went on to study not just the states, the folds we have but how they form that is the kinetics where we're moving from one state to another. Now we primarily used folding as the simple universal examples here but as I showed you today this was equally true for the ligand binding to some of those binding sites so don't think that this is a narrow example limited to research on protein folding, it's not. What you learned about the kinetics is universally true. Based on the importance of the evolution here though in lecture 11 we focused on bioinformatics which on the one hand it's a completely different approach just focusing on sequence and data instead of structure but the reason why that works so well is that four billion years of evolution has encoded all the physical interactions here in the protein structures we see and that means that I can compare their sequence to effectively compare the structures. It's really cool that it works and the only reason why that does work is evolution. Last lecture we then combined both the physical knowledge, the protein structure, the bioinformatics and a bit of the simulation to come up with drug discovery in particularly using docking effectively as poor man's way of doing a simulation or studying the partition function. It is a remarkably powerful tool that is used throughout both academia and pharmaceutical companies today. And the last hour we have now finished up by trying to combine that with simulations and in particular how we can use protein design where we now even more explicitly try to include the amino acid properties and the Boltzmann distribution to go to the places of the map where evolution has not yet been. It's been a great pleasure for me to take you through this class. Lots of work preparing these recordings but it's very fun so let me thank you for watching it this far and in particular if you're in Stockholm I hope to get the chance to see in the future. Thanks!