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

使用动态网络模型定量预测由长距离氨基酸取代引起的结合亲和力/特异性的变化

• 批准号: 10502084
• 负责人: Sefika Banu Ozkan
• 金额: $ 42.41万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10502084
• 关键词: 3-Dimensional; Affinity; Algorithms; Amino Acid Substitution; Amino Acids; Automobile Driving; Binding; Binding Sites; Biological; Biophysics; Collaborations; Computer Models; Computers; Coupled; Coupling; Cyclic AMP Receptor Protein; DNA Binding; Data; Development; Disease; Distal; Equilibrium; Escherichia coli; Event; Evolution; Exhibits; Genetic Epistasis; Genetic Variation; Goals; Homologous Protein; Human Genetics; Impairment; Lactose; Ligand Binding; Ligands; Machine Learning; Measures; Medicine; Methods; Missense Mutation; Modeling; Molecular; Molecular Genetics; Motion; Mutation; Open Reading Frames; Outcome; Phenotype; Positioning Attribute; Property; Protein Dynamics; Protein Engineering; Proteins; Publishing; Recording of previous events; Repressor Proteins; Research; Rest; SARS-CoV-2 protease; SARS-CoV-2 spike protein; Sampling; Site; Specificity; System; Technology; Testing; Variant; Viral; Viral Proteins; base; combinatorial; computerized tools; delta protein; experimental study; flexibility; functional outcomes; genetic evolution; improved; molecular dynamics; molecular modeling; network models; novel; outcome prediction; personalized medicine; predictive modeling; predictive tools; protein function; response; structural biology; success

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.

概括 先进的测序技术提供了越来越多的有关人类遗传变异的数据 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，在区分生物医学相关变体和中性变体（很少或很少）方面造成了瓶颈 特别是，当错义突变改变氨基时，结果预测非常差。 远离蛋白质功能/结合位点的酸这些缺点也会损害蛋白质。 我们建议通过开发预测的定量计算模型来改善这些需求。 为此，我们开发了一种方法。 其中（1）首先通过分子动力学模拟揭示蛋白质的集体运动，然后（2）力 扰动用于破坏蛋白质的平衡，从而近似配体结合的效果。 在已发表的研究和初步数据中使用了这种方法来阐明动力学的传播 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的变化。 网络对蛋白质功能具有至关重要的影响，从而得出我们的中心假设：长期的影响 配体结合上的距离取代是蛋白质动态耦合变化的新兴特性， 当前提案的目标是扩展这种计算方法来开发。 模型预测：（目标 1）长距离调节引起的结合亲和力变化的幅度 取代；（目标 2）哪对非接触取代对结合亲和力具有非加性效应 （“上位性”）；和（目标 3）哪些长距离位置有助于配体特异性。 完善的合作使我们能够在计算预测和实验之间进行迭代 测试，从而能够开发具有计算精度的定量模型。 充分表征的大肠杆菌乳糖阻遏蛋白 (LacI)，实验结果验证了我们的研究 初步计算模型并为目标 1-3 提供具体假设。 用于展示我们方法的通用性，并将包括 LacI 同源物 PurR，即 cAMP 受体 蛋白质和病毒蛋白酶 SARS-Cov2-Mpro 结果将用于提供新的计算工具。 预测长距离替代的功能结果将促进研究。 通过推进蛋白质结构生物学、分子遗传学、进化论和医学的交叉 对远离功能位点的取代如何改变配体结合的机制理解。