Frustration and Direct-Coupling Analyses to Predict Formation and Function of Adeno-Associated Virus
Frustration and Direct-Coupling Analyses to Predict Formation and Function of Adeno-Associated Virus
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DOI：
10.1016/j.bpj.2020.12.018
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发表时间：
2021-02-02
影响因子：
3.4
通讯作者：
Suh，Junghae
中科院分区：
文献类型：
--
作者：
Thadani，Nicole N.;Zhou，Qin;Suh，Junghae
腺相关病毒（Adeno-associated virus， AAV）具有高效的基因传递和相对温和的免疫原性，是一种很有前景的基因治疗载体。为了提高递送目标特异性，研究人员采用组合和合理的文库设计策略来生成新的AAV衣壳变体。这些方法经常提出高比例的非成形或非感染性衣壳蛋白序列，从而降低了合成载体DNA文库的有效深度，从而提高了新载体的发现成本。我们评估了两种计算技术，以评估残基突变对AAV衣壳蛋白-蛋白相互作用的影响，从而预测载体适应度的变化，推断这些方法可能为设计功能丰富的AAV文库提供信息，并加速候选治疗药物的鉴定。挫折计计算的能量函数源自蛋白质折叠的能量景观理论。直接耦合分析（DCA）是一种捕获蛋白质内残基协同进化的统计框架。我们使用挫折计来选择预测有利于组装或拆卸衣壳状态的候选蛋白质残基，然后使用挫折计和DCA预测这些位点的突变效应。通过实验评估衣壳突变体在病毒形成、稳定性和转导能力方面的变化。基于挫折计的指标显示与病毒稳定性的反直觉相关性，而dca衍生的指标与研究的小群残基中的病毒转导能力高度相关。我们的研究结果表明，共同进化模型可能能够阐明病毒功能所必需的复杂衣壳残基-残基相互作用网络，但需要进一步研究以了解蛋白质能量模拟与病毒衣壳亚稳态之间的关系。
Adeno-associated virus (AAV) is a promising gene therapy vector because of its efficient gene delivery and relatively mild immunogenicity. To improve delivery target specificity, researchers use combinatorial and rational library design strategies to generate novel AAV capsid variants. These approaches frequently propose high proportions of nonforming or noninfective capsid protein sequences that reduce the effective depth of synthesized vector DNA libraries, thereby raising the discovery cost of novel vectors. We evaluated two computational techniques for their ability to estimate the impact of residue mutations on AAV capsid protein-protein interactions and thus predict changes in vector fitness, reasoning that these approaches might inform the design of functionally enriched AAV libraries and accelerate therapeutic candidate identification. The Frustratometer computes an energy function derived from the energy landscape theory of protein folding. Direct-coupling analysis (DCA) is a statistical framework that captures residue coevolution within proteins. We applied the Frustratometer to select candidate protein residues predicted to favor assembled or disassembled capsid states, then predicted mutation effects at these sites using the Frustratometer and DCA. Capsid mutants were experimentally assessed for changes in virus formation, stability, and transduction ability. The Frustratometer-based metric showed a counterintuitive correlation with viral stability, whereas a DCA-derived metric was highly correlated with virus transduction ability in the small population of residues studied. Our results suggest that coevolutionary models may be able to elucidate complex capsid residue-residue interaction networks essential for viral function, but further study is needed to understand the relationship between protein energy simulations and viral capsid metastability.