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Biologists traditionally craft high-quality multiple sequence alignments manually, employing their expertise in protein sequence evolution. This article delves into the realm of automatic multiple sequence alignment, emphasizing probabilistic modeling, scoring functions, and the challenges of inferring evolutionarily correct alignments. Learn about multidimensional dynamic programming, progressive alignment methods, and iterative refinement techniques in the realm of multiple sequence alignment. Understand how profile alignment and Hidden Markov Models contribute to accurate multiple alignments. Explore the significance of core structural elements and key residues in achieving confident alignments.
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Ch6 multiple sequence alignment methods Biologists produce high quality multiple sequence alignment by hand using knowledge of protein sequence evolution. Automatic multiple sequence alignment? -in probabilistic modeling, scoring function is primary concern, not the alignment algorithm 1 1
In principle, there is always an unambiguously correct evolutionary alignment even if the structures diverge • In practice, an evolutionarily correct alignment can be more difficult to infer than structural alignment • Our ability to define a single ‘correct’ alignment will vary with the relatedness of the sequences being aligned • Usually, a small subset of key residues will be identifiable which can be aligned unambiguously almost regardless of the sequence divergence • We should focus on the subset of columns corresponding to key residues and core structural elements that can be aligned with confidence
