Characterizing the Effects of Protein and RNA Variability in Molecular Function and Interactions

建议书摘要 这个MIRA for ESI项目建议研究和表征人类基因组的功能变异 生物分子，它与分子进化的联系，以及在科学和技术上利用这些景观的能力 生物医学应用。它由两个科学目标组成，描绘了我们实验室的愿景。 我们的第一个目标是开发全球概率和计算模型，帮助我们回答 假设蛋白质可变性及其相互作用的格局可以被表征和量化。 我们将通过基于从以下位置获得的大量数据创建概率模型来构建我们的框架 测序和我们将对特定的影响做出详细的预测和实验证实 我们的方法论预测的突变。我们对功能突变的前景很感兴趣，这是 比起破坏性的突变空间，要难得多。对于我们的第二个目标，我们将扩大我们的 对包括核酸，特别是蛋白质-RNA相互作用在内的分子相互作用的假设。我们会 整合测序技术和计算方法来推断蛋白质的突变图景- RNA识别。我们将检验这一假设，即不仅天然的核酸基序可以选择性地 但也有从我们的量化模型中衍生出来的变体。我们将开发一个框架来编码 并根据推断的景观预测识别，并计划将我们的结果与实验相结合 有关RNA结合蛋白的技术可以帮助证实我们的假设。 在过去的几年里，我们的实验室已经能够推断蛋白质序列家族的全球模型并量化 来自这些模型的共同进化信号成功。这些全球模型在研究中产生了影响 蛋白质折叠、蛋白质动力学、蛋白质复合体预测及其在生物医学中的应用 可药物界面发现和药物-基因相互作用。生物分子的功能变体很难 澄清，因为破坏性突变空间占主导地位。等电点能够显示氨基酸信号 或者核酸协同进化也可以作为预测工具来探索和编码功能 生物分子的突变空间。这一想法代表了一种范式转变，在这种转变中，对进化的量化 信号可以用作发现机制。该项目的总体愿景旨在量化和揭示 进化过程塑造的功能性生物分子可变性的光谱。这将有助于我们的工作 与生物医学有关的发展，如推断疾病突变的影响，抗生素耐药性， 生物分子传感器设计以及序列组成如何影响相互作用网络。

Proposal Summary This MIRA for ESI project proposes to investigate and characterize functional mutational variability in biomolecules, its connections to molecular evolution and the ability to use these landscapes in scientific and biomedical applications. It is composed of two scientific objectives that delineate the vision of our laboratory. Our first goal is to develop global probabilistic and computational models that will help us answer the hypothesis that the landscape of protein variability and their interactions can be characterized and quantified. We will build our framework by creating probabilistic models based on large quantities of data obtained from sequencing and we will make detailed predictions and experimental confirmation on the effect of specific mutations predicted by our methodology. We are interested in the landscape of functional mutations, which is much harder to characterize than the disruptive mutational space. For our second goal, we will expand our hypothesis to molecular interactions that include nucleic acids, particularly protein-RNA interactions. We will integrate sequencing technology and computational approaches to infer mutational landscapes of protein- RNA recognition. We will test the hypothesis that not only native nucleic acid motifs can be selectively recognized but also variants derived from our quantitative models. We will develop a framework to encode and predict recognition from inferred landscapes and plan to integrate our results with experimental technologies on RNA binding proteins that could help confirm our hypothesis. In the past few years our lab has been able to infer global models of families of protein sequences and quantify coevolutionary signals from these models successfully. These global models have had an impact in the study of protein folding, protein dynamics and the prediction of protein complexes as well as their applications in druggable interface discovery and drug-gene interactions. Functional variants of biomolecules are hard to elucidate, as the disruptive mutational space is dominant. The PI was able to show that signals of amino acid or nucleic acid coevolution can also be used as predictive tools to explore and encode the functional mutational space of biomolecules. This idea represents a paradigm shift where quantification of evolutionary signals can be used as a discovery mechanism. The overall vision of this project aims to quantify and uncover the spectrum of functional biomolecular variability sculpted by evolutionary processes. This will help us work on developments related to biomedicine, such as inferring the effects of mutations in disease, antibiotic resistance, biomolecular sensor design and how sequence composition has an impact on interaction networks.