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A profile comparison method with position-specific scoring matrix (PSSM) isone of the most accurate alignment methods. Currently, cosine similarity andcorrelation coefficient are used as scoring functions of dynamic programming tocalculate similarity between PSSMs. However, it is unclear that these functionsare optimal for profile alignment methods. At least, by definition, thesefunctions cannot capture non-linear relationships between profiles. Therefore,in this study, we attempted to discover a novel scoring function, which wasmore suitable for the profile comparison method than the existing ones. Firstlywe implemented a new derivative free neural network by combining theconventional neural network with evolutionary strategy optimization method.Next, using the framework, the scoring function was optimized for aligningremote sequence pairs. Nepal, the pairwise profile aligner with the novelscoring function significantly improved both alignment sensitivity andprecision, compared to aligners with the existing functions. Nepal improvedalignment quality because of adaptation to remote sequence alignment andincreasing the expressive power of similarity score. The novel scoring functioncan be realized using a simple matrix operation and easily incorporated intoother aligners. With our scoring function, the performance of homologydetection and/or multiple sequence alignment for remote homologous sequenceswould be further improved.

Optimizing scoring function of dynamic programming of pairwise profile alignment using derivative free neural network