Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, United States of America.
PLoS One. 2012;7(3):e32630. doi: 10.1371/journal.pone.0032630. Epub 2012 Mar 5.
Minimotifs are short contiguous peptide sequences in proteins that have known functions. At its simplest level, the minimotif sequence is present in a source protein and has an activity relationship with a target, most of which are proteins. While many scientists routinely investigate new minimotif functions in proteins, the major web-based discovery tools have a high rate of false-positive prediction. Any new approach that reduces false-positives will be of great help to biologists.
We have built three filters that use genetic interactions to reduce false-positive minimotif predictions. The basic filter identifies those minimotifs where the source/target protein pairs have a known genetic interaction. The HomoloGene genetic interaction filter extends these predictions to predicted genetic interactions of orthologous proteins and the node-based filter identifies those minimotifs where proteins that have a genetic interaction with the source or target have a genetic interaction. Each filter was evaluated with a test data set containing thousands of true and false-positives. Based on sensitivity and selectivity performance metrics, the basic filter had the best discrimination for true positives, whereas the node-based filter had the highest sensitivity. We have implemented these genetic interaction filters on the Minimotif Miner 2.3 website. The genetic interaction filter is particularly useful for improving predictions of posttranslational modifications such as phosphorylation and proteolytic cleavage sites.
Genetic interaction data sets can be used to reduce false-positive minimotif predictions. Minimotif prediction in known genetic interactions can help to refine the mechanisms behind the functional connection between genes revealed by genetic experimentation and screens.
最小基序是蛋白质中具有已知功能的短连续肽序列。在最简单的层面上，最小基序序列存在于源蛋白中，并与目标蛋白（大多数为蛋白质）具有活性关系。虽然许多科学家经常研究蛋白质中新型最小基序的功能，但主要的基于网络的发现工具存在很高的假阳性预测率。任何减少假阳性预测的新方法都将对生物学家有很大帮助。
我们构建了三个过滤器，使用遗传相互作用来减少假阳性最小基序预测。基本过滤器识别源/靶蛋白对具有已知遗传相互作用的那些最小基序。同源基因遗传相互作用过滤器将这些预测扩展到预测的同源蛋白的遗传相互作用，基于节点的过滤器则识别那些与源蛋白或靶蛋白具有遗传相互作用的蛋白质也具有遗传相互作用的最小基序。每个过滤器都使用包含数千个真阳性和假阳性的测试数据集进行了评估。基于灵敏度和选择性性能指标，基本过滤器对真阳性的区分度最好，而基于节点的过滤器的灵敏度最高。我们已经在 Minimotif Miner 2.3 网站上实现了这些遗传相互作用过滤器。遗传相互作用过滤器对于提高磷酸化和蛋白水解切割位点等翻译后修饰的预测特别有用。
遗传相互作用数据集可用于减少假阳性最小基序预测。在已知遗传相互作用中进行最小基序预测可以帮助完善遗传实验和筛选揭示的基因之间功能联系背后的机制。
