Optimizing amino acid substitution matrices with a local alignment kernel

Background: Detecting remote homologies by direct comparison of protein sequences remains a challenging task. We had previously developed a similarity score between sequences, called a local alignment kernel, that exhibits good performance for this task in combination with a support vector machine. The local alignment kernel depends on an amino acid substitution matrix. Since commonly used BLOSUM or PAM matrices for scoring amino acid matches have been optimized to be used in combination with the Smith-Waterman algorithm, the matrices optimal for the local alignment kernel can be different. Results: Contrary to the local alignment score computed by the Smith-Waterman algorithm, the local alignment kernel is differentiable with respect to the amino acid substitution and its derivative can be computed efficiently by dynamic programming. We optimized the substitution matrix by classical gradient descent by setting an objective function that measures how well the local alignment kernel di...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Hiroto Saigo, Jean-Philippe Vert, Tatsuya Akutsu
• Keywords: Amino Acid, BMCBI 2006, Local Alignment Kernel, Smith-Waterman Algorithm