 Dear students, in this module, I'll be talking about the strategy employed in the ab initio modelling process. To give you a hint on the background, the ab initio methodology for structure prediction relies on the physics and chemistry of each atom and amino acid that is involved in creating the structure. The fundamental target of ab initio methods is to minimize the energy that is there embedded within the structure. So if you minimize the energy, then it means you have a stable structure. But there are cases where the stable most structure is biologically not plausible. So you have to validate them. Okay. So towards the strategy, the first step is that you have to start with a rough initial model. So what this means is that if you have this sequence, then you have to fold it in some unique way that is just a rough estimate. So once you have created a rough estimate of the structure, you can utilize this structure towards optimizing it. Okay. Next. You have to create an energy function that is you have to compute the energy of the structure that you have and minimize it. So you can take the example of energy function that is simply the number of hydrogen bonds within a structure. So let's say if you have 10 hydrogen bonds and each hydrogen bond gives out an energy of let's say minus one, then the overall energy of the structure will be minus 10. So you simply compute the energy of the structure by looking at the energy that is given out. So if you minimize this, then it means the structure that is created by giving out that much energy is the most viable one. Now you have multiple structures. Let's say these are your structures and each one has an energy corresponding to the number of hydrogen bonds that are there in each structure. Then you have to find the global minimum. So as you can see here, this structure has minus eight, which is I while this structure has minus 15, which is the smallest. So this is the structure that is having the lowest energy. So you select this structure and then check for its biological plausibility. So next you build an initial model again, but this time it is much more accurate because it includes the energy and forces as well. And then so this was your sequence and this was the structure that you predicted in the previous slide. So what you do is you allow this sequence to fold into this structure. So during the process of folding, you study the dynamics and you arrive at the qualitative evaluation for this structure. So next, once you study the dynamics, then the native structure will emerge steadily. By steadily, I mean that the structure may vary slightly during the process and eventually you will arrive at the proper structure. So given a sequence, now you have a protein structure that has been created by using ebonychial methods. So in conclusion, in ebonychial modeling strategies, we typically start with an energy function. So the energy function gives you how much energy is still contained or how much energy is given out of the structure. The more negative the energy value, the better structure or the energetically stable structure you have. Next, you fold the structures in order to obtain the most stable structure. So you allow your sequence to fold into that structure confirmation and see if there are any optimizations that may be necessary. And the underlying principle will help you to arrive at a structure that has the minimum energy.