SCORING FUNCTIONS FOR COMPUTATIONAL ALGORITHMS APPLICABLE TO THE DESIGN OF SPIKED OLIGONUCLEOTIDES

Protein engineering by inserting stretches of random DNA sequences int o target genes in combination with adequate screening or selection met hods is a versatile technique to elucidate and improve protein functio ns. Established compounds for generating semi-random DNA sequences are spiked oligonucleotides which are synthesised by interspersing wild t ype (wt) nucleotides of the target sequence with certain amounts of ot her nucleotides. Directed spiking strategies reduce the complexity of a library to a manageable format compared with completely random libra ries. Computational algorithms render feasible the calculation of appr opriate nucleotide mixtures to encode specified amino acid subpopulati ons. The crucial element in the ranking of spiked codons generated dur ing an iterative algorithm is the scoring function. In this report thr ee scoring functions are analysed: the sum-of-square-differences funct ion s, a modified cubic function c,and a scoring function m derived fr om maximum likelihood considerations. The impact of these scoring func tions on calculated amino acid distributions is demonstrated by an exa mple of mutagenising a domain surrounding the active site serine of su btilisin-like proteases. At default weight settings of one for each am ino acid, the new scoring function m is superior to functions s and c in finding matches to a given amino acid population.