Using multidimensional scaling to visualize protein sequences by functionality I have a multiple sequence alignment (MSA) of protein sequences, with which I have performed multidimensional scaling to visualize their clustering.  Using R, I used the seqinr package to create an identity matrix (using dist.alignment).  Is there a similar method that is more "generalizable"?  Specifically, I'm interested in classifying the amino acids by some functional characteristics, such as polarity, instead of having the alignment list the amino acids by their identity (A, G, I, etc.).  

Here's a very short set of fake input data, with columns 2-4 representing amino acid residue positions:

a &lt;- c("Strain 1", "Strain 2", "Strain 3", "Strain 4")
b &lt;- c("Polar_hydrox", "Nonpolar", "Nonpolar", "Polar_hydrox")
c &lt;- c("Nonpolar", "Nonpolar", "Nonpolar", "Nonpolar")
d &lt;- c("Nonpolar_cyclic", "Nonpolar_cyclic", "Polar_amide", "Polar_amide")
e &lt;- c("Polar_amide", "Acidic", "Polar_amide", "Acidic")
x &lt;- data.frame(a, b, c, d, e)


Is what I'm asking a feasible/correct use of MDS?  Or should I be looking into a different method entirely?  I attempted to use Gower's formula to create a dissimilarity matrix on the original dataset (i.e. using the actual amino acid identities instead of the functional groupings), but compared to what I got using dist.alignment, the MDS plots look different.

Edit: Gower's formula and the dist.alignment function are obviously calculating the distance matrices differently, but I don't know the details on the different methods.  Would using Gower's formula be a good approach for calculating distance matrices for the example dataset?

Edit: clarified functions used for calculating distance matrix.
Some of the first MSA analysis, eg of codon usage of bacteria were performed using matrix factorization (see old papers from Des Higgins). His group also published discriminative methods for finding functional residues using supervised correspondence analysis 


  Wallace IM1, Higgins DG BMC Bioinformatics. 2007 Apr 23;8:135.
  Supervised multivariate analysis of sequence groups to identify
  specificity determining residues.


The method is available in the R package ade4 and Bioconductor package made4

Also have a look at the phylogenetic R package adephylo and several related packages