Two major trains of thought have emerged in how one can define the translation speed, one uses the cognate tRNA concentrations and the other the codon bias within the genome. The former is a natural measure, the amount of cognate tRNA available to the ribosome for a given codon has been shown to affect the translation. In the extreme case, when no cognate tRNA is available, the ribosome is even found to detach from the transcript after a period of waiting. The latter, the codon bias, is the relative quantities of codons found within a synonymous group. The codons found the most are assumed to be optimal as it is hypothesised that the genome will be optimised to produce proteins in the fastest most efficient manner. Lastly, there is a new third school of thought were one has to balance both the supply and the usage of any given codon. Namely if a codon is overused it will actually have a lower tRNA concentration than would be suggested by its tRNA gene copy numbers (an approximation of the tRNA’s concentration). Each of these three descriptions have been used in their own respective computational studies to show the association of the speed, represented as each measure, to the protein structure.

A simplified schematic of ribosome profiling. Ribosome profiling begins with separating a cell’s polysomes (mRNA with ribosomes attached) from its lysate. Erosion by nuclease digestion removes all mRNA not shielded by a ribosome while also cleaving ribosomes attached to the same mRNA strand. Subsequent removal of the ribosomes leaves behind only the mRNA fragments which were undergoing translation at the point of cell lysis. Mapping these fragments back to the genome gives a codon-level resolution transcriptome-wide overview of the translation occurring within the cell. From this we can infer the translation speed associated with any given codon from any given gene.

However, while these definitions have been in existence for the past few decades, there has been no objective way, till now, to test how accurate they actually are in measuring the translation speed. Essentially, we have based years of research on the extrapolation of a few coarse experiments, or in some cases purely theoretical models, to all translation. There now exists an experimental measure of the translation occurring in-vivo. Ribosome profiling, outlined in above, measures the translation occurring within a cell, mapping the position of the ribosome on the genome at the points of cell lysing. Averaging over many cells gives an accurate measure of the expected translation occurring on any given transcript at any time.

Comparing the log transformed ribosome profile data to the translation speed as defined by each of the algorithms for B. Subtilis. We show the mean ribosome occupancy against the mean translation speed when stratified by codon, finding that the assigned values for each algorithm fails to capture the variation of the ribosome profiling data.

As an initial comparison shown above, we compared some of the most popular speed measures based on the above descriptions to the ribosome profiling data. None of the measures were found to recreate the ribosome profiling data adequately. In fact, while some association is found, it is opposite to what we would expect! The faster the codon according to the algorithm the more a ribosome is likely to occupy it!We thought that this may be due to treating all the codons together instead of with respect to the genes they are from. Essentially, is a given codon actually fast if it is just within a gene that is in general fast? To test for this, we created a set of models which account for a shift in ribosome data profile depending on the source gene. However, these showed even less association to the speed algorithms!

These findings suggest that the algorithms that the scientific community have based there work on for the past decades may in fact be poor representations of the translations speed. This leads to a conundrum, however, as these measures have been made use of in experimental studies, namely the paper by Sander et al (see journal club entry here). In addition, codon bias matching has been used extensively in increasing expression of non-native proteins in bacteria. Clearly these algorithms are a measure of something and, as such, this contradiction needs to be resolved in the near future.