Part I: General questions on 3D structure modeling. Go to http://predictioncenter.org/ What is CASP? When did CASP 14 finish? Go to (https://predictioncenter.org/casp13/index.cgi) and click on CASP13 in numbers (https://www.predictioncenter.org/casp13/numbers.cgi) How many targets were released in each category? How many groups participated? Go to http://www.cathdb.info/ What is CATH? Go to the search bar and enter “2YPI.” What enzyme is this? To what superfamily does it belong? Click on the superfamily. How many unique PDBs are in this superfamily? Go to http://scop.mrc-lmb.cam.ac.uk/scop/ What is SCOP and how is it different from CATH? Search SCOP for pdb 2YPI. What is the Fold for this protein? How many different superfamilies does this fold contain? Part III: AlphaFold2 For our exercises, we have been looking at CASP13. Now we will take a look at CASP14 to learn about a particularly important new result. (https://predictioncenter.org/casp14/zscores_final.cgi). A new method called AlphaFold2 shattered the competition by predicting the most accurate protein tertiary structures ever reported. Use the various links on https://predictioncenter.org/casp14/index.cgi to answer the following questions: Who came in second? What was their average z-score? How did this compare to Alpha Fold? Use https://predictioncenter.org/casp14/results.cgi?view=tb-sel to compare the runner-up to AlphaFold2. Select AlphaFold2 and the runner-up on the left, all targets on the right, and click show results. Click the little arrows in the RMSD column to sort by RMSD (smallest to largest). What is the smallest RMSD achieved by AlphaFold2 and what was the smallest RMSD for the runner-up? How many of the first entries sorted by RMSD belong to AlphaFold2? AlphaFold2 used a new artificial intelligence algorithm recently developed for learning human language. It is the first application of this type of AI to a bioinformatics problem that has demonstrated this extraordinary level of success. In terms of our ability to predict the structure of proteins, AlphaFold2 is indeed a breakthrough. Use the following papers as starting points to learn more about AlphaFold2 and answer the questions that follow. https://www.nature.com/articles/d41586-021-02265-4 https://www.nature.com/articles/s41586-021-03819-2 https://www.nature.com/articles/s41592-021-01365-3 AlphaFold2 invents a neural network block they call an evoformer. What type of neural network is the evoformer based on? What are some of the well-known applications for this network? What types of information are used to train AlphaFold2? AlphaFold2 has an average GDT_TS score of 92. Why is this so impressive? What are some of the key limitations of AlphaFold2? Some have said that AlphaFold2 represents such a tremendous breakthrough that “the protein folding problem is solved”. Is this true? Why or why not? How does WSME-Linker improve on AlphaFold2? Instructions: Need to answer all the questions above Word requirement – minimum 500 Plagiarism free Solutions generated from any AI platform is strictly Prohibited Solution to be formatted in APA and use appropriate references with in-text citations