Hi,
It looks like nobody wants to take a stab at your question, so let me share my humble opinion.
First, it would be useful to understand your goal for this analysis: do you want to visualize the data and/or identify inherent structure in the data, or something else?
Second, I think that you may be expecting too much of the clustering algorithms. In my mind, basic clustering is primarily a visualization method to group items based on their similarities; different methods provide different ways to evaluate and display similarities. Hence, the different "look" of the dendrograms according to the different methods.
Third, if you are interested in identifying a classifier for your supplements based on amino acid composition, you may want to explore the Discriminant or the Principal Component platforms.
Of note, I don't think that the size of the dataset affects the quality of the clustering per se: the more data you have add the more complex "branching" you will get. At a minimum, you need 3 items to run a clustering analysis: it may not provide deep insight but technically that is all you need.
Best,
TS
Thierry R. Sornasse