LOAD MINIMIZATION OF THE GENETIC-CODE - HISTORY DOES NOT EXPLAIN THE PATTERN

The average effect of errors acting on a genetic code (the change in a mino-acid meaning resulting from point mutation and mistranslation) ma y be quantified as its 'load'. The natural genetic code shows a clear property of minimizing this load when compared against randomly genera ted variant codes. Two hypotheses may be considered to explain this pr operty. First, it is possible that the natural code is the result of s election to minimize this load. Second, it is possible that the proper ty is an historical artefact. It has previously been reported that ami no acids that have been assigned to codons starting with the same base come from the same biosynthetic pathway. This probably reflects the m anner in which the code evolved from a simpler code, and says more abo ut the physicochemical mechanisms of code assembly than about selectio n. The apparent load minimization of the code may therefore follow as a consequence of the fact that the code could not have evolved any oth er way than to allow biochemically related amino acids to have related codons. Here then, we ask whether this 'historical' force alone can e xplain the efficiency of the natural code in minimizing the effects of error. We therefore compare the error-minimizing ability of the natur al code with that of alternative codes which, rather than being a rand om selection, are restricted such that amino acids from the same bioch emical pathway all share the same first base. We find that although on average the restricted set of codes show a slightly higher efficiency than random ones, the real code remains extremely efficient relative to this subset p=0.0003. This indicates that for the most part histori cal features do not explain the load-minimization property of the natu ral code. The importance of selection is further supported by the find ing that the natural code's efficiency improves relative to that of hi storically related codes after allowance is made for realistic mutatio nal and mistranslational biases. Once mistranslational biases have bee n considered, fewer than four per 100 000 alternative codes are better than the natural code.