Integrated in silico prediction of protein interaction motifs using interactome networks and high-resolution 3-dimensional structures

使用相互作用组网络和高分辨率 3 维结构对蛋白质相互作用基序进行计算机集成预测

• 批准号: BB/I006230/1
• 负责人: Richard Edwards
• 金额: $ 36.04万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI006230%2F1
• 关键词: Integrated; silico; prediction; protein; interaction

This project aims to identify sites on proteins that are critical for their interactions with other proteins. Many protein-protein interactions are mediated by Short Linear Motifs (SLiMs): short stretches of proteins (5-15 amino acids long), of which only a few positions are critical to function. These motifs are vital for biological processes of fundamental importance, such as signalling pathways and targeting proteins to the correct part of a cell. The primary objective of this project is to integrate a number of leading computational techniques to predict novel SLiMs and, in so doing, add crucial detail to protein-protein interaction networks. This will generate a valuable resource of potential SLiMs, including defined occurrences and interactions. Given the great promise SLiM-mediated interactions hold as future therapeutic targets, this resource could be a potential gold-mine for the pharma industry and future drug design. A crucial part of any predictive bioinformatics analysis is the rediscovery of previously known results. The Eukaryotic Linear Motif (ELM) database provides a rich resource of known eukaryotic motifs and these will form the basis of annotating known SLiMs that are (re)discovered during the course of the investigation. Data generated during this project will, in turn, be fed back into the ELM resource to improve existing ELM annotation and, potentially, provide new annotated occurrences of known motifs. As well as being useful for further investigation of specific protein-protein interactions, these predictions/annotations will be of interest to the wider scientific community who are interested in understanding the fundamental principles of how proteins interact with each other. To generate the SLiM predictions, this project will put together cutting-edge tools for two distinct but related activities: (1) predicting regions of proteins involved in SLiM-mediated interactions from 3D structures of proteins; (2) identifying over-represented recurring sequence patterns from proteins that all share a common interaction partner. First, structural features will be used to identify candidate regions in specific proteins. SLiMs typically interact with larger, globular domains in their partner protein, and structural signatures of such 'domain-motif interactions' can be used to highlight possible motif regions. These regions will then be compared to other proteins known to interact with the same protein as the candidate. Currently, the most successful approaches for this explicitly use a model of convergent evolution for detection. Under this model, the common motif identified must be shared by sequences that have no other detectable sequence similarity. Previously, we developed the most successful of these tools on benchmarking data, SLiMFinder, which accounts for both the evolutionary relationships found between input proteins and the total motif space being searched to estimate the statistical significance of over-represented motifs. For this project, an extension of SLiMFinder will be used that takes advantage of the fact that the 3D methods will have identified a specific short region on one of the proteins. This extra information makes the method much more sensitive. These results will be of great interest to anyone trying to understand the molecular basis of protein-protein interactions and signalling pathways. During the course of the project, the methods used will be further developed and validated, providing useful tools for future investigations. Open source software and webserver implementations will be made available to facilitate further application. This will make these methods available to bench scientists studying specific proteins and interactions.

该项目旨在确定蛋白质上与其他蛋白质相互作用的关键位点。许多蛋白质-蛋白质相互作用由短线性基序（SLiM）介导：短的蛋白质片段（5-15个氨基酸长），其中只有少数位置对功能至关重要。这些基序对于具有根本重要性的生物过程至关重要，例如信号传导途径和将蛋白质靶向细胞的正确部分。该项目的主要目标是整合一些领先的计算技术来预测新型SLiM，并在此过程中为蛋白质-蛋白质相互作用网络添加关键细节。这将生成潜在SLiM的宝贵资源，包括定义的事件和相互作用。鉴于SLiM介导的相互作用作为未来治疗靶点的巨大前景，这种资源可能是制药行业和未来药物设计的潜在金矿。任何预测性生物信息学分析的关键部分都是重新发现先前已知的结果。真核线性基序（ELM）数据库提供了丰富的资源，已知的真核生物基序，这些将形成的基础上注释已知的SLiM是（重新）发现的过程中的调查。在这个项目中产生的数据将反过来被反馈到ELM资源中，以改进现有的ELM注释，并可能提供已知图案的新注释。除了对进一步研究特定的蛋白质-蛋白质相互作用有用之外，这些预测/注释将对更广泛的科学界感兴趣，他们有兴趣了解蛋白质如何相互作用的基本原理。为了生成SLiM预测，该项目将为两个不同但相关的活动整合尖端工具：（1）从蛋白质的3D结构预测参与SLiM介导的相互作用的蛋白质区域;（2）从共享共同相互作用伙伴的蛋白质中识别过度代表的重复序列模式。首先，结构特征将用于识别特定蛋白质中的候选区域。SLiM通常与其伴侣蛋白中较大的球状结构域相互作用，这种“结构域-基序相互作用”的结构特征可用于突出可能的基序区域。然后将这些区域与已知与候选蛋白质相同的蛋白质相互作用的其他蛋白质进行比较。目前，最成功的方法是明确使用收敛进化模型进行检测。在这种模式下，共同的基序必须确定共享的序列，没有其他可检测的序列相似性。此前，我们在基准数据上开发了这些工具中最成功的工具SLiMFinder，它解释了输入蛋白质之间发现的进化关系和正在搜索的总基序空间，以估计过度代表基序的统计显著性。对于该项目，将使用SLiMSTK的扩展，该扩展利用了3D方法将识别其中一种蛋白质上的特定短区域的事实。这些额外的信息使该方法更加敏感。这些结果对于任何试图了解蛋白质-蛋白质相互作用和信号通路的分子基础的人来说都是非常有趣的。在项目执行过程中，将进一步发展和验证所使用的方法，为今后的调查提供有用的工具。将提供开放源码软件和网络服务器，以便利进一步的应用。这将使这些方法可用于研究特定蛋白质和相互作用的实验室科学家。

项目成果

SLiMScape 3.x: a Cytoscape 3 app for discovery of Short Linear Motifs in protein interaction networks.

• DOI: 10.12688/f1000research.6773.1
• 发表时间: 2015
• 期刊: F1000Research
• 作者: Olorin E;O'Brien KT;Palopoli N;Pérez-Bercoff Å;Shields DC;Edwards RJ
• 通讯作者: Edwards RJ

Referee report. For: SLiMScape 3.x: a Cytoscape 3 app for discovery of Short Linear Motifs in protein interaction networks [v1; approved 1, http://f1000r.es/5m5]

裁判报告。

• DOI: 10.5256/f1000research.7277.r9827
• 发表时间: 2015
• 作者: Stelzl U

Interactome-wide prediction of short, disordered protein interaction motifs in humans

• DOI: 10.1039/c1mb05212h
• 发表时间: 2012-01-01
• 期刊: MOLECULAR BIOSYSTEMS
• 作者: Edwards, Richard J.;Davey, Norman E.;Shields, Denis C.
• 通讯作者: Shields, Denis C.

SLiMPrints: conservation-based discovery of functional motif fingerprints in intrinsically disordered protein regions.

• DOI: 10.1093/nar/gks854
• 发表时间: 2012-11
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Davey NE;Cowan JL;Shields DC;Gibson TJ;Coldwell MJ;Edwards RJ
• 通讯作者: Edwards RJ

QSLiMFinder: improved short linear motif prediction using specific query protein data.

• DOI: 10.1093/bioinformatics/btv155
• 发表时间: 2015-07-15
• 期刊: Bioinformatics (Oxford, England)
• 作者: Palopoli N;Lythgow KT;Edwards RJ
• 通讯作者: Edwards RJ