Modeling evolution of functional context in proteins

 描述（由申请人提供） 蛋白质的结构、功能和相互作用产生了印记在蛋白质序列上的进化模式。在这里，线粒体和核序列将用于研究进化过程，并了解蛋白质如何在结构，能量和功能限制的背景下进化。通过这种更深入的理解，将开发和告知蛋白质进化的改进模型，并评估它们在预测突变效应和结构特征方面的效用。它们也将被用来更好地预测adaptiv突发和水平的收敛和残基之间的共同进化，特别是在多基因家庭。本研究的动机是从以前的研究的见解。首先，预计从 在进化模拟中，蛋白质中各个位置的取代概率由于上位性（与相同或其他蛋白质中其他位点的取代的相互作用）而随时间波动。这些预期得到了强有力的证据的支持，即在真实的蛋白质中，取代过程确实会随时间而规律性地波动。然而，目前的蛋白质进化模型通常不允许取代过程随时间波动，蛋白质中氨基酸的收敛水平与这些模型的预期有很大的偏差。正因为如此，纳入这种波动是拟议模型的一个关键特征。第二，目前的方法，将结构纳入进化研究往往使用从头预测或伪能量潜力来预测取代的可接受性，但这些方法是不是特别准确的进化分析，其中包括序列已经偏离了已知的蛋白质结构的序列。为了解释这一点，而不是允许这样的预测独立，它们将根据预期的预测准确性和与具有已知结构的任何序列的距离而在不同程度上概率性地并入到古生物替代模型中。第三，最近开发了一种贝叶斯方法来构建复杂的进化模型，其目的是允许相对容易地计算在站点之间和随着时间的推移而波动的过程。这种使用所谓“替代历史部分抽样”的方法使所提出的方法可行。预计拟议的研究将在理解分子进化及其与结构和功能的关系方面取得重大进展。这项研究的一个预期结果将是更好地预测突变效应，这将提高在人类基因组和外显子组测序研究中识别致病突变的能力。人们进一步期望，当结构特征未知时，它们的预测将得到改进，研究人员将能够更好地了解蛋白质中的祖先功能变化是如何通过适应性序列变化而产生的。

 DESCRIPTION (provided by applicant) The structure, function, and interactions of proteins produce evolutionary patterns that are imprinted on protein sequences. Here, mitochondrial and nuclear sequences will be used to study evolutionary processes and develop understanding of how proteins evolve in the context of structural, energetic, and functional constraints. Improved models of protein evolution will be developed and informed by this deeper understanding, and their utility in predicting mutational effects and structural features will be evaluated. They will also be used to better predict adaptiv bursts and levels of convergence and coevolution among residues, particularly in multigene families. This research is motivated by insights from previous research. First, it is expected from evolutionary simulations that substitution probabilities at individual positions in a protein fluctuate in time due to epistasis (interactions with substitutions at other sites in the same or other proteins). These expectations are supported by strong evidence that substitution processes do regularly fluctuate with time in real proteins. However, current models of protein evolution do not usually allow substitution processes to fluctuate with time, and levels of amino acid convergence in proteins deviate substantially from expectations for such models. Because of this, incorporating such fluctuations is a key feature of the proposed models. Second, current approaches that incorporate structure into evolutionary studies tend to use de novo prediction or pseudo energy potentials to predict the acceptability of substitutions, but these methods are not especially accurate for evolutionary analysis, which includes sequences that have diverged substantially from the sequences of known protein structures. To account for this, rather than allowing such predictions to stand alone, they will be incorporated probabilistically into empirica substitution models to varying degrees depending on expected predictive accuracy and distance from any sequences with known structure. Third, a Bayesian approach to building complex evolutionary models was recently developed that is designed to allow relatively easy computation of processes that fluctuate among sites and over time. This approach using what is called "partial sampling of substitution histories" makes the proposed methodology feasible. It is expected that the proposed study will make significant improvements in understanding of molecular evolution and how it relates to structure and function. One expected result of this study will be better predictions of mutational effects, which will lead to an improved ability to identify disease-causing mutations in human genome and exome sequencing studies. It is further expected that predictions of structural features when they are unknown will be improved, and researchers will be able to better understand how ancestral functional changes in proteins have arisen through adaptive sequence change.