Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutions

摘要 先进的测序技术提供了越来越多的关于人类基因变异的数据 和病毒进化。然而，预测蛋白质编码区错义突变的结果仍然存在 一个挑战，在区分与生物医学相关的变体和中性变体方面造成了瓶颈(很少或 对表型没有影响)。特别是，当错义突变改变氨基酸时，结果预测非常差。 远离蛋白质功能/结合部位的酸。这些缺陷也会损害蛋白质。 设计。我们建议通过开发定量的、可预测的计算模型来改善这些需求 长距离取代对结合作用的影响。为此，我们制定了一种方法 其中(1)蛋白质的集体运动首先通过分子动力学模拟来揭示，然后(2)力 微扰被用来破坏蛋白质的平衡，从而近似于配体结合的影响。我们 在已发表的研究和初步数据中使用了这种方法来说明动力学的传播 通过蛋白质的各向异性相互作用网络发生变化。结果表明，这些动态变化 网络对蛋白质功能有至关重要的影响，从而引出了我们的中心假设：长时间网络的影响 配基结合上的距离取代是蛋白质动态偶联的变化的紧急性质， 各向异性网络。当前提案的目标是将这种计算方法扩展到开发 模型预测：(目标1)远距离调节引起的结合亲和力变化的幅度 取代；(目标2)哪些非接触性取代对结合亲和力有非相加影响 (“上位性”)；以及(目标3)哪些远距离位置有助于配体专一性。为此，我们有一个 建立良好的协作，使我们能够在计算预测和实验之间迭代 测试，使开发具有计算精度的定量模型成为可能。我们的初步研究使用了 大肠杆菌乳糖抑制蛋白(LacI)的特性，实验结果验证了我们的 初步计算模型，并为目标1-3提供具体假设。其他模型蛋白质将 用来显示我们方法的一般性，并将包括Laci同源呼噜，cAMP受体 蛋白和一种病毒蛋白酶SARS-Cov2-MPRO。结果将被用来提供新的计算工具 预测远距离替代的功能结果。这个项目的成功将促进研究 在蛋白质结构生物学、分子遗传学、进化和医学的交界处，通过推进 从机制上理解远离功能部位的取代如何改变配基结合。

Summary Advanced sequencing technologies provide ever-increasing quantities of data about human genetic variation and viral evolution. However, predicting the outcomes of missense mutations in protein coding regions remains a challenge, creating a bottleneck in discriminating biomedically-relevant variants from neutral ones (with little or no effect on phenotype). In particular, outcome predictions are very poor when a missense mutation alters amino acids that are located far from a protein’s functional/binding sites. These shortcomings also impair protein design. We propose to ameliorate these needs by developing quantitative, computational models that predict the effects of long-distance substitutions on binding interactions. To that end, we have developed an approach in which (1) a protein’s collective motions are first revealed by molecular dynamics simulations and then (2) force perturbation is used to disrupt the protein’s equilibrium, thereby approximating the effects of ligand binding. We have used this approach in published studies and preliminary data to illuminate the propagation of dynamical changes through a protein’s anisotropic network of interactions. Results suggest that changes in these dynamic networks have crucial effects on protein function, thereby leading to our central hypothesis: The effects of long- distance substitutions on ligand binding are emergent properties of changes in the protein’s dynamically-coupled, anisotropic network. The goal of the current proposal is to extend this computational approach to develop models that predict: (Aim 1) the magnitudes of binding affinity changes arising from long-distance, modulating substitutions; (Aim 2) which pairs of non-contact substitutions have non-additive effects on binding affinities (“epistasis”); and (Aim 3) which long-distance positions contribute to ligand specificity. To that end, we have a well-established collaboration that allows us to iterate between computational predictions and experimental testing, enabling development of quantitative models with computed accuracies. Our preliminary studies used the well-characterized E. coli lactose repressor protein (LacI), for which experimental results validate our preliminary computational models and provide specific hypotheses for Aims 1-3. Additional model proteins will be used to show the generality of our approach and will include the LacI homolog PurR, the cAMP receptor protein, and a viral protease SARS-Cov2-Mpro. Results will be used to provide novel computational tools for predicting functional outcomes of long-distance substitutions. The success of this project will catalyze research at the interface of protein structural biology, molecular genetics, evolution and medicine by advancing the mechanistic understanding of how substitutions distal from functional sites alter ligand binding.