![]() ![]() It seems therefore surprising that the experimental community has grown accustomed to interpret algorithmically derived optimal alignment solutions as biologically most relevant configuration of pairing similar proteins, although theory clearly states that such approximation may only be reasonable when comparing very similar sequences and not when dealing with distant homologs. ![]() However, analogous to the concept of point estimates and confidence intervals in statistical parameter inference , neglecting the goodness of fit for any application may result in unrealistic technical optima rather than focusing on quantifying the biological relevance (e.g., functional protein configuration) of reported alignment solutions. One could argue that handling only optimal alignments is the most parsimonious approach to dealing with complexities when scaling to millions or even billions of pairwise sequence comparisons when organizing a diverse sequence space according to their pairwise identities. ![]() While sufficient for many applications, including protein sequence similarity search, the reduction of comparison to one solution (even when optimal) can cause enormous information loss about biologically relevant, but suboptimal, alignment configurations, thereby systematically biasing the comparative method when applied at tree-of-life scale. When constructing a pairwise alignment, a combinatorial space of possible alignment configurations is explored and a single optimal alignment setting is selected (based on the predefined scoring-scheme) and reported to equip experimenters with one plausible solution rather than overwhelming them with a wide range of possible solutions. To compare two biological sequences according to a predefined scoring-scheme, pairwise alignment methodologies have proven useful for various practical applications . Building on this integrative foundation of sequence comparison and protein structural prediction, we explore how the sequence diversity across the tree of life can be compressed into alignment-robust sequence regions while minimizing the loss of protein structural information. Recent breakthroughs in protein structure prediction from primary sequence alone uncovered that integrating comparative and functional genomics into a predictive model can yield groundbreaking insights useful enough to guide mechanistic studies in molecular life sciences. In genomics, such attempts translate into comparing genetic sequences according to the similarity of their DNA or protein composition (comparative genomics) or mechanistic analysis of three-dimensional structural conformations of proteins (functional genomics). When exploring the diversity of life, we tend to either reduce observations according to similar principles and patterns shared across lineages (comparative method) or aim to deduce the individual mechanistic function with a cause-and-effect-revealing experimental design (functional assessment). ![]()
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