1.) Initially, a partial amino acid sequence was received from a purified protein. The protein was purified through a combination of Molecular Sieve (Gel-Filtration), Ion-Exchange and Affinity chromatography from cultured HeLa cells.

2.) The partial amino acid sequence was then run through a query search using the BLAST tool in order to obtain a possible list of  proteins that the sequence could encode for.

3.) After assessing mainly the accession numbers, percent identity, databases, and PRI review date values of the top search results, the correct protein was identified.

4.) Following the protein identification, the gene encoding for the particular protein was identified through NCBI.

5.) With the name of the gene obtained, we searched through NCBI Refseq, Ensembl, and Aceview in order to identify the database with the most reviewed transcript variants to produce an accurate gene map. Particularly interesting transcript variants (2 and 5) were chosen for further analysis due to their functionality differences and each were mapped to identify key sequence differences. A table composed of the NCBI Refseq and Ensembl transcript variants was formed for possible identification of common sequence similarities between each of them.

6.) The next step after researching the gene was to identify the structure of the specific protein. The main database that was used to identify the structure was Uniprot, which  served to provide an immense amount of information on the secondary structures, domains, protein interactions, and much more areas. Databases such as NCBI, CDD,  RefSeq, along with several scientific journals helped to assess the structure as well.

7.) After researching on the protein structure, the following step was to get an understanding of the protein’s function and the biochemical pathways that the protein is involved with. UniProt and NCBI initially helped with finding lists of the different functions that the protein does. Scientific papers such as those published on NCBI and ScienceDirect were analyzed to get more in depth information on the diagrams. The GeneCards database was particularly heavily used in order to get information on protein expression, subcellular localization, and protein interactions.

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8.) Information on ACE2’s protein interactions and general structure aided in identification of possible mutations which could cause different diseases in humans. The databases Disgenet and ClinVar were used to look at data on gene/disease associations collected for observations of certain SNPs and other mutations that are likely to have negative effects on the human body.