Abstract：
Mutations are the cause of several diseases as well as the underlying force of evolution. A thorough understanding of their biophysical consequences is essential. We present a computational framework for evaluating different levels of mutual information (MI) and its dependence on mutation. We used molecular dynamics trajectories of the third PDZ domain and its different mutations. Nonlinear MI between all residue pairs are calculated by tensor Hermite polynomials up to the fifth order and compared with results from multivariate Gaussian distribution of joint probabilities. We show that MI is written as the sum of a Gaussian and a nonlinear component. Results for the PDZ domain show that the Gaussian term gives a sufficiently accurate representation of MI when compared with nonlinear terms up to the fifth order. Changes in MI between residue pairs show the characteristic patterns resulting from specific mutations. Emergence of new peaks in the MI versus residue index plots of mutated PDZ shows how mutation may change allosteric pathways. Triple correlations are characterized by evaluating MI between triplets of residues. We observed that certain triplets are strongly affected by mutation. Susceptibility of residues to perturbation is obtained by MI and discussed in terms of linear response theory.
摘要：
突变是几种疾病的原因，也是进化的潜在力量。彻底了解其生物物理后果至关重要。我们提出了一个计算框架，用于评估不同级别的互信息（MI）及其对突变的依赖性。我们使用第三PDZ结构域及其不同突变的分子动力学轨迹。所有残差对之间的非线性MI通过张量Hermite多项式计算到五阶，并与联合概率的多元高斯分布的结果进行比较。我们证明MI被写为高斯和非线性分量的总和。PDZ域的结果表明，与高达五阶的非线性项相比，高斯项给出了MI的足够准确的表示。残基对之间的MI变化显示了由特定突变产生的特征性模式。突变PDZ的MI相对于残基指数图中新峰的出现表明突变可能如何改变变构途径。通过评估残基三联体之间的MI来表征三重相关性。我们观察到某些三胞胎受到突变的强烈影响。通过MI获得残基对扰动的敏感性，并根据线性响应理论进行了讨论。
