Ancestral sequence reconstruction (ASR) uses an alignment of extant protein sequences, a phylogeny describing the history of the protein family and a model of the molecular-evolutionary process to infer the sequences of ancient proteins, allowing researchers to directly investigate the impact of sequence evolution on protein structure and function. Like all statistical inferences, ASR can be sensitive to violations of its underlying assumptions. Previous studies have shown that, while phylogenetic uncertainty has only a very weak impact on ASR accuracy, uncertainty in the protein sequence alignment can more strongly affect inferred ancestral sequences. Here we show that errors in sequence alignment can produce errors in ASR across a range of realistic and simplified evolutionary scenarios. Importantly, sequence reconstruction errors can lead to errors in estimates of structural and functional properties of ancestral proteins, potentially undermining the reliability of analyses relying on ASR. We introduce an alignment-integrated ASR approach that combines information from many different sequence alignments. We show that integrating alignment uncertainty improves ASR accuracy and the accuracy of downstream structural and functional inferences, often performing as well as highly-accurate structure-guided alignment. Given the growing evidence that sequence alignment errors can impact the reliability of ASR studies, we recommend that future studies incorporate approaches to mitigate the impact of alignment uncertainty. Probabilistic modeling of insertion and deletion events has the potential to radically improve ASR accuracy when the model reflects the true underlying evolutionary history, but further studies are required to thoroughly evaluate the reliability of these approaches under realistic conditions.

中文翻译：

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祖先序列的比对整合重建可提高准确性。

祖先序列重建（ASR）使用现有蛋白质序列的比对，描述蛋白质家族历史的系统发育以及分子进化过程的模型来推断古代蛋白质的序列，从而使研究人员可以直接研究序列进化的影响关于蛋白质的结构和功能。像所有统计推断一样，ASR可能对违反其基本假设的情况很敏感。先前的研究表明，虽然系统发育不确定性对ASR准确性的影响非常微弱，但蛋白质序列比对中的不确定性会更强烈地影响推断的祖先序列。在这里，我们表明序列比对中的错误会在一系列现实和简化的进化场景中在ASR中产生错误。重要的，序列重建错误可能会导致祖先蛋白质的结构和功能特性估计错误，从而有可能破坏依赖ASR的分析的可靠性。我们引入了一个比对整合的ASR方法，该方法结合了来自许多不同序列比对的信息。我们表明，整合路线不确定性可以提高ASR准确性以及下游结构和功能推论的准确性，通常可以执行以及高度精确的结构导向路线。鉴于越来越多的证据表明序列比对错误会影响ASR研究的可靠性，因此我们建议未来的研究纳入减轻比对不确定性影响的方法。