7.3 Ancestral sequence reconstruction

Ancestral sequence reconstruction is a powerful tool in molecular evolution. It lets scientists infer ancient DNA sequences from modern ones, shedding light on how genes and proteins evolved over time.

This method combines computational techniques with evolutionary models to reconstruct ancestral states. It helps researchers understand genetic adaptations, protein function changes, and the molecular basis of evolution in various organisms.

Ancestral Sequence Reconstruction

Principles and Applications

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• Ancestral sequence reconstruction (ASR) computationally infers the most likely ancestral sequences based on the analysis of extant (present-day) sequences
• Relies on the principle that the evolutionary process leaves a traceable imprint on the sequences of extant species, allowing the inference of ancestral states
• Utilizes phylogenetic trees, which depict the evolutionary relationships among sequences, and models of sequence evolution, which describe the probabilities of different types of mutations
• Enables studying the molecular basis of adaptation (evolution of antibiotic resistance in bacteria)
• Facilitates understanding the evolution of protein function (emergence of novel enzymatic activities)
• Allows reconstructing ancient genes or genomes (ancestral mammalian genome)
• Helps identify key residues responsible for functional changes (amino acid substitutions conferring thermostability)

Computational Inference of Ancestral Sequences

Maximum Parsimony and Maximum Likelihood Methods

• Maximum parsimony (MP) is a non-parametric method that infers ancestral states by minimizing the total number of character changes along the phylogenetic tree
• Maximum likelihood (ML) is a parametric method that estimates ancestral states by maximizing the probability of observing the extant sequences given a specific model of sequence evolution
• ML incorporates explicit models of sequence evolution (Jukes-Cantor, Kimura 2-parameter) and accounts for branch lengths in the phylogenetic tree

Bayesian Inference and Computational Tools

• Bayesian inference (BI) is a parametric method that incorporates prior knowledge and calculates the posterior probability distribution of ancestral states using Bayes' theorem
• BI allows for the integration of information from multiple sources (fossil records, molecular clocks) and quantifies the uncertainty in ancestral state estimates
• Computational tools for ASR include PAML (Phylogenetic Analysis by Maximum Likelihood), MEGA (Molecular Evolutionary Genetics Analysis), and HyPhy (Hypothesis Testing using Phylogenies)
• These tools implement various evolutionary models, tree-building algorithms, and statistical methods for inferring ancestral sequences

Accuracy and Limitations of Ancestral Sequence Reconstruction

Factors Affecting Accuracy

• The accuracy of ASR depends on the quality of the sequence alignment, which should be carefully curated to ensure homology and minimize gaps
• The accuracy of the phylogenetic tree is crucial, as incorrect tree topologies can lead to erroneous ancestral state inferences
• The choice of evolutionary model should be appropriate for the specific dataset, considering factors such as sequence divergence and compositional bias
• ASR becomes less reliable for highly divergent sequences or deep evolutionary timescales due to the accumulation of multiple substitutions at the same site (saturation)

Assessing Robustness and Limitations

• Model misspecification, such as assuming an incorrect evolutionary model or ignoring rate heterogeneity among sites, can lead to incorrect inferences of ancestral states
• The presence of insertions, deletions, and gaps in sequence alignments can reduce the accuracy of ASR, as these events are often difficult to model and reconstruct
• Assessing the statistical support for inferred ancestral states, such as through bootstrapping or posterior probabilities, is crucial for evaluating the robustness of ASR results
• Experimental validation of reconstructed ancestral sequences (synthesizing and characterizing ancestral proteins) can provide additional support for computational predictions

Ancestral Sequence Reconstruction for Evolutionary Studies

Studying Molecular Evolution and Adaptation

• ASR can identify amino acid substitutions associated with adaptive changes in protein function (evolution of novel substrate specificities in enzymes)
• Comparing the properties of reconstructed ancestral proteins with their extant counterparts reveals the molecular mechanisms of adaptation (increased thermostability, altered ligand binding)
• ASR helps identify evolutionary convergence, where similar functional changes have occurred independently in different lineages (parallel evolution of echolocation in bats and dolphins)

Integrating ASR with Structural and Experimental Approaches

• Reconstructed ancestral sequences can be synthesized and experimentally characterized to validate computational predictions and study the functional consequences of evolutionary changes
• ASR can be combined with structural modeling to map adaptive substitutions onto protein structures and elucidate the structural basis of functional changes
• Integrating ASR with structural data (X-ray crystallography, NMR spectroscopy) provides a comprehensive understanding of the evolution of protein structure and function
• Experimental resurrection of ancestral proteins allows for the direct measurement of their biochemical properties and the testing of evolutionary hypotheses (ancestral steroid receptors, ancestral visual pigments)