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The scientific response to the COVID-19 pandemic has been astounding. Aside from efforts to generate vaccines, the genomic surveillance of the virus has been truly remarkable. For example, the nextstrain project has sequence many SARS-CoV-2 genomes. In fact, the rapid identification of multiple new strains and mutations by diverse groups of scientists has resulted in a nomenclature crisis.

One highly memorable strategy is to give pet names to SARS-CoV-2 mutants. The “UK variant” designated B.1.1.7 has an N501Y mutation. The first letter refers to the original amino acid, the number represents the position of the amino acid in the spike protein and the second letter is the amino acid at that position after the mutation. So the N501Y mutant becomes Nelly, E484K is called Eek, D614G is dubbed Doug and so on.

I really like this strategy. Obviously, there are shortcomings: Nelly could refer to N to Y changes in the spike other than at 501, while subsequent mutations also pose a problem. Nonetheless, pet names are very memorable. I wondered how plausible is this as a general naming strategy?

Can all possible mutations be described by English words?

Let’s write some R code and find out! Scroll down to see the result.

## The code

First we grab a list of English words. There is a handy text file available on GitHub for this purpose. It has almost half a million words. We also make a vector of all twenty amino acids. I checked that the spike protein actually has at least one of every amino acid, and it does. We then make a 20 x 20 matrix to hold the count of words in the dictionary that match all 380 possible mutations. Why 380? Well, there are 400 combinations but 20 of those are not mutations, A to A, C to C and so on.

I use grep to find matches, iterate using two for-loops and fill out the matrix with the matches. Feel free to tell me a more efficient way to do this. I used ggplot to show the results because it is complicated to show the colourscale in base R.

```library(ggplot2)
library(reshape2)
# character vector of 20 amino acids
aaList <- c("A","C","D","E","F","G","H","I","K","L","M","N","P","Q","R","S","T","V","W","Y")
# character vector of English words
allWords <- readLines("https://github.com/dwyl/english-words/raw/master/words_alpha.txt", warn = FALSE)
# empty matrix to hold results
mutMatrix <- matrix(nrow = length(aaList), ncol = length(aaList))

for(i in 1:length(aaList)) {
for(j in 1:length(aaList)) {
if(i == j) {
# no mutation is set as 0
mutMatrix[i,j] = 0
} else {
# make string for grepping
grepStr <- paste0("^",aaList[i],".*",aaList[j],"\$")
# list of words that match the grep expression
subList <- grep(grepStr, allWords, value = TRUE, ignore.case = TRUE)
# store number of words in matrix
mutMatrix[i,j] = length(subList)
}
}
}
# log transform for easier visualisation
mutMatrixLog <- log10(mutMatrix)

# ggplot version
mutDF <- melt(mutMatrixLog)
mutDF\$from <- aaList[mutDF\$Var1]
mutDF\$to <- aaList[mutDF\$Var2]
ggplot(data = mutDF, aes(x = from, y = to, fill = value)) +
geom_tile(colour = "white") +
scale_fill_distiller(palette = "YlGnBu", limits = c(0,4),
breaks=c(0,1,2,3,4),
labels=c("1","10","100","1000", "10000")) +
labs(x = "From", y = "To", fill = "Words") +
theme_minimal(base_size = 10) +
coord_equal()
```

## The result

Almost all the mutations have some words in the dictionary to describe them. For example the N501Y “Nelly” mutation could easily be Nabobery or Nuttily. There are 2407 matches for the “from N to Y” combination.

The grey tiles show where there are no matches at all. So D to Q, G to Q etc. have no matches and I guess that not surprising. There are only 29 words in the dictionary that end in Q. Many of those are abbreviations that would receive a stern frown in a Scrabble match.

```subList <- grep("Q\$", allWords, value = TRUE, ignore.case = TRUE)
length(subList)
 30
subList
 "aeq"      "antiq"    "aq"       "colloq"   "cq"       "eq"       "esq"      "faq"      "freq"     "hq"       "iq"
 "iraq"     "liq"      "loq"      "meq"      "nastaliq" "pdq"      "pontacq"  "pq"       "q"        "req"      "seq"
 "seqq"     "shoq"     "sq"       "sqq"      "suq"      "tareq"    "umiaq"    "zindiq"
```

The heat map shows that mutations that lead to valine (V), tryptophan (W) or phenylalanine (F) are, like Glutamine (Q), lower frequency. The resulting residue is a tighter constraint than the starting letter/amino acid. Here, the lowest frequencies were Q and Y.

The top three most bountiful mutations are: C to S, P to S and S to E with 7149, 6576 and 5422 words respectively.

## Conclusion

Although this naming strategy is fun and memorable, the answer is No: it cannot be used as a general naming system for mutations.

The post title comes from the eponymous debut album by Os Mutantes.