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

TL;DR

This paper introduces a derivative-free neural network to optimize scoring functions in profile alignment, significantly enhancing remote sequence alignment accuracy over traditional linear methods.

Contribution

It develops a novel neural network-based scoring function optimized via evolutionary strategies, improving profile alignment performance for remote homologs.

Findings

Significant improvement in alignment sensitivity and precision.

Enhanced ability to capture non-linear relationships in profile comparison.

Easy integration of the new scoring function into existing aligners.

Abstract

A profile comparison method with position-specific scoring matrix (PSSM) is one of the most accurate alignment methods. Currently, cosine similarity and correlation coefficient are used as scoring functions of dynamic programming to calculate similarity between PSSMs. However, it is unclear that these functions are optimal for profile alignment methods. At least, by definition, these functions cannot capture non-linear relationships between profiles. Therefore, in this study, we attempted to discover a novel scoring function, which was more suitable for the profile comparison method than the existing ones. Firstly we implemented a new derivative free neural network by combining the conventional neural network with evolutionary strategy optimization method. Next, using the framework, the scoring function was optimized for aligning remote sequence pairs. Nepal, the pairwise profile aligner with the novel scoring function significantly improved both alignment sensitivity and precision, compared to aligners with the existing functions. Nepal improved alignment quality because of adaptation to remote sequence alignment and increasing the expressive power of similarity score. The novel scoring function can be realized using a simple matrix operation and easily incorporated into other aligners. With our scoring function, the performance of homology detection and/or multiple sequence alignment for remote homologous sequences would be further improved.