Prediction and Validation Tools for Novel Membrane Interaction Surfaces from Protein Structures

蛋白质结构新型膜相互作用表面的预测和验证工具

• 批准号: BB/H024697/1
• 负责人: Michael Overduin
• 金额: $ 15.37万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FH024697%2F1
• 关键词: Prediction; Validation; Tools; Novel; Membrane; Prediction; Validation; Tools; Novel; Membrane

It is generally thought that about one quarter of genomes encodes transmembrane proteins, many of which are important receptors and drug targets. Most of the remainder are typically assumed to be soluble proteins including signalling and metabolic enzymes. However an unknown number of these actually bind reversibly to membrane surfaces, and these interactions determine where these proteins are located inside cells, and regulate their enzymatic and signaling activities. In fact, it can be argued that a protein's location is just as important as its intrinsic activity, restricting its access to locally concentrated substrates, cofactors and ligands within the viscous and compartmentalized cell. Without an effective way to identify and test these membrane interaction surfaces, our knowledge of protein function will continue to be limited and our research progress in molecular biology and biochemistry will be restricted. Hence we are developing new computational and biophysical methods to accurately detect and validate protein-membrane interactions which localize proteins inside cells, providing new insights and tools for understanding cellular processes and disease mechanisms. A variety of protein modules including BAM, FYVE, PH and PX domains are known to bind membrane surfaces in response to changes cell stimulation, growth and differentiation. We propose that by analyzing the structural properties of such proteins, the principles of membrane binding can be elucidated and generalized, and entirely new classes of PMPs can be found. The interactions are diverse. Some proteins bind membranes specifically yet dynamically by reversible recognition of individual phospholipid headgroups, yet others bind tightly, being anchored to the bilayer where they help assemble molecular complexes and catalyze reactions. Yet our studies have revealed common themes including exposed hydrophobic loops, basic patches and polarized surfaces. These properties are integrated here by an algorithm that automatically identifies membrane binding sites from structures in seconds. Once suitably trained, this method will allow users to accurately predict the new types of PMPs, and experimental methods and lipid/micelle libraries will be available to allow researchers to efficiently validate such discoveries. The lack of fast and accurate tools to detect protein surfaces that interact with membranes has impeded progress in the fields of molecular and cellular biology, and has limited interactions between the fields of proteomics and lipidomics. The dearth of understanding about membrane protein interactions is compounded by technical difficulties of studying 'sticky' membrane interacting domains and delicate bilayer structures. Thus there is a real need for convenient and insightful computational and new experimental tools to analyze protein membrane recognition. Our solution aims to provide sufficient information to allow users to design and test how proteins are targeted to specific membrane domains, to predict their spatial orientations on membrane surfaces, and to reveal whether conformational changes could accompany binding events. Broad applicability is ensured by the fact that protein membrane interactions determine the organization and regulated activities of so many cellular organelles and molecular complexes. Some lipid binding domains influence cell proliferation, differentiation, survival, migration, adhesion and invasion. Others are involved in neurogenesis, angiogenesis, wound healing, immunity and developmental diseases. Our research will enable a deeper understanding of their interactions in sufficient detail to aid in the design of ligands and inhibitors, and may aid in the design of therapeutic agents where lipids normally bind. The tools will be developed and applied using selected human proteins to achieve high impact and disease relevancy, and will be standardized where possible to maximize applicability to any protei

人们普遍认为，大约四分之一的基因组编码跨膜蛋白，其中许多是重要的受体和药物靶标。通常假定其余大多数是可溶性蛋白，包括信号传导和代谢酶。然而，这些数量实际上实际上与膜表面可逆地结合，这些相互作用决定了这些蛋白质位于细胞内部的位置，并调节其酶促和信号活性。实际上，可以说，蛋白质的位置与其内在活性一样重要，从而限制了其在粘性和隔室化的细胞内访问局部浓缩的底物，辅因子和配体。如果没有有效的方法来识别和测试这些膜相互作用表面，我们对蛋白质功能的了解将继续受到限制，我们在分子生物学和生物化学方面的研究进展将受到限制。因此，我们正在开发新的计算和生物物理方法，以准确检测和验证细胞内部蛋白质的蛋白质 - 膜相互作用，从而提供了新的见解和工具，以了解细胞过程和疾病机制。已知多种蛋白质模块，包括BAM，FYVE，pH和PX结构域，它们会响应细胞刺激，生长和分化的变化而结合膜表面。我们建议，通过分析此类蛋白质的结构特性，可以阐明和概括膜结合的原理，并且可以找到全新的PMP类别。互动是多种多样的。一些蛋白质通过对单个磷脂头组的可逆识别来特异性地结合膜，而另一些蛋白质则紧密结合，固定在双层上，它们有助于聚集分子复合物和催化反应。然而，我们的研究揭示了常见的主题，包括暴露的疏水环，基本斑块和极化表面。这些属性是通过一种算法在此集成的，该算法会在几秒钟内自动识别结构的膜结合位点。一旦经过适当培训，此方法将允许用户准确预测新型的PMP，并且可以使用实验方法和脂质/胶束库，以允许研究人员有效验证此类发现。缺乏检测与膜相互作用的蛋白质表面的快速和准确的工具阻碍了分子和细胞生物学领域的进步，并且在蛋白质组学和脂质组学领域之间的相互作用有限。研究“粘性”膜相互作用域和精致的双层结构的技术困难使人们缺乏对膜蛋白相互作用的理解。因此，真正需要方便，有见地的计算和新的实验工具来分析蛋白质膜识别。我们的解决方案旨在提供足够的信息，以允许用户设计和测试蛋白质如何针对特定的膜域，以预测其在膜表面上的空间取向，并揭示构象变化是否可以伴随结合事件。蛋白质膜相互作用决定了许多细胞细胞器和分子复合物的组织和调节活性，从而确保了广泛的适用性。一些脂质结合结构域影响细胞的增殖，分化，存活，迁移，粘附和侵袭。其他人则参与神经发生，血管生成，伤口愈合，免疫和发育疾病。我们的研究将使他们的相互作用有足够的详细信息有助于设计配体和抑制剂的设计，并可以更深入地了解它们的相互作用，并可能有助于设计脂质通常结合的治疗剂。这些工具将使用选定的人蛋白开发和应用，以实现高影响和疾病相关性，并将在可能的情况下进行标准化以最大化任何蛋白质的适用性