 Dear students, in this module we are going to look at the side chain modeling as a very important step in the homology modeling pipeline. We have already looked at the template recognition and the alignment followed by the correction of the alignment and then backbone generation and loop modeling. So now you need to see the side chain modeling aspect of homology modeling. Remember we have already generated the backbone of the protein structure and placed those side chains that had the same amino acid for the target and the template. Plus for the gaps we have inserted the loops as and when PDB was guiding us. So now we are left with just one thing that is to place the side chains for those amino acids that are not the same between the target and the template. So everything else is done. So let's see how this can be performed. So as I just mentioned that the conserved residues that is the residues that work same between the template and the target have already been copied. So you now have a structure with the backbone with some side chains added while some side chains are missing. So the ones that are already added are because they had the same amino acid with the template. So this is your target structure at the moment. So now we are going to look at how we can insert these side chains. So the method is very simple. We insert these side chains by simply copying the torsion angles that is the alpha carbon and beta carbon angles to the target. So rotomers they tend to conserve these angles. So I will just show you an example how these rotomers conserve these angles in homologous proteins. So because of that there are certain preferences of amino acids and libraries have been built to show you which flanking residue that is the neighbor neighboring residue can be used to estimate the side chain position. So let's see. So here I have a backbone that is shown in blue. This is your backbone and this is one amino acid position. This is your alpha carbon and as you expect that at the alpha carbon position you have the R group attached or the side chain. So in this example you can see that this is tyrosine. So if you have tyrosine in the sequence then only two torsion angles are acceptable. So for tyrosine you only have two possibilities for the side chain. So the first one is in this conformation and the second one is in this conformation. So once again if you have tyrosine in the backbone of a protein structure that you are predicting and you want to attach the side chain then tyrosine only prefers two positions for the side chain. So this is the first one and this is the second one. So by looking at the preference of tyrosine you can set the torsion angle that is the angle between the backbone, carbon alphas and nitrogens. So these two angles are preferred and therefore you can attach the rotamer side chain like the preference. So now the next step is the optimization of this model. One would argue that we have properly constructed the backbone and then inserted the side chains as well as the loops and the turns followed by the insertion of the rotamers. So why do we need to optimize the model further? So the answer to that question is that once you insert the rotamers then the rotamers can create static hindrance or electrostatic interactions between them can cause the structure to become unstable. So therefore once you insert the rotamers and side chains you may want to evaluate your model for optimization. So as I just mentioned so the updated side chains can affect the backbone. So once the backbone gets affected for instance here if you have interaction between two rotamers then this needs to be optimized by shifting one rotamer to the other conformation and removing this one. So this is what is the optimization part. So you have to perform this entire process on the full backbone and that can only be done in a simulation where you consider the entire backbone along with all the rotamers and loops. The technology or the area that deals with such optimizations is called the molecular dynamics. So molecular dynamics or simply the MD simulations can help you to optimize the predicted protein structure. So in the MD simulations you place the protein in a force field that is you trap the protein in a region which is part of the simulation such that all the energy and the steric properties of the protein can be studied in isolation. So the model therefore is placed in a force field that I will just show you and all the molecules within the structure are followed in time to see how better they can suit to the predicted structure. So the large anomalies like bumps will be removed but sadly some errors will still exist. The golden principle in MD simulations is to minimize the overall energy of the protein. So if you minimize the energy of the protein it will mean that the rotamers have all settled into positions that are convenient and comfortable for them and therefore they are not creating instability to the protein structure which is a very good thing. Okay so here is your protein so this example is for Crambin so this is a cabbage protein and here in the box you can see we have trapped this protein which actually acts as an isolation chamber for this protein and we compute the possible conformations of the entire backbone as well as all the rotamers. So by doing that we come at a conformation of the protein that has the minimum energy. So the minimum energy protein is the protein that you will use as the result of your prediction paradigm. So in conclusion we have now optimized the protein structure as well and now this protein structure is your final prediction. So this will have some errors which will obviously be there because it is only a prediction but that you have taken care of the major sources of errors and minimize them. The next step is to validate this structure and that will conclude the homology modeling pipeline for you. Thank you. Thank you. Bye. Bye. Bye. Bye. Bye. Bye. Bye.