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的进化模式印记在蛋白质序列上。在这里，线粒体和核序列将被用来研究进化过程，并发展对蛋白质如何在结构、能量和功能限制的背景下进化的理解。这种更深入的理解将开发和了解改进的蛋白质进化模型，并将评估它们在预测突变效应和结构特征方面的有效性。它们还将被用来更好地预测Adaptiv爆发以及残基之间的收敛和共同进化水平，特别是在多基因家族中。这项研究的动机是来自以前研究的洞察力。首先，预计从 由于上位性(与相同或其他蛋白质中其他位置的替换相互作用)，蛋白质中个别位置的替换概率随时间波动的进化模拟。这些预期得到了强有力的证据的支持，即在真实蛋白质中，替代过程确实会定期随时间波动。然而，目前的蛋白质进化模型通常不允许替代过程随时间波动，蛋白质中氨基酸收敛的水平与此类模型的预期有很大偏离。正因为如此，纳入这种波动是拟议模型的一个关键特征。其次，目前将结构纳入进化研究的方法倾向于使用从头预测或假能势来预测替换的可接受性，但这些方法对于进化分析并不是特别准确，因为进化分析包括与已知蛋白质结构的序列有很大差异的序列。为了解释这一点，不是允许这样的预测独立存在，而是根据预期的预测精度和与任何已知结构的序列的距离，它们将被不同程度地概率地合并到Empirica替换模型中。第三，最近开发了一种构建复杂进化模型的贝叶斯方法，旨在允许相对容易地计算在不同地点和时间上波动的过程。这种方法使用了所谓的“替代历史的部分抽样”，使所提出的方法可行。预计这项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影响，这将导致提高识别人类基因组和外显子组测序研究中致病突变的能力。人们还预计，当结构特征未知时，对它们的预测将得到改进，研究人员将能够更好地理解蛋白质的祖先功能变化是如何通过适应性序列变化而出现的。

 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.