Accurate elucidation of genome wide protein–protein interactions is crucial for understanding the regulatory processes of the cell. High-throughput techniques, such as the yeast-2-hybrid (Y2H) assay, co-immunoprecipitation (co-IP), mass spectrometric (MS) protein complex identification, affinity purification (AP) etc., are generally relied upon to determine protein interactions. Unfortunately, each type of method is inherently subject to different types of noise and results in false positive interactions. On the other hand, precise understanding of proteins, especially knowledge of their functional associations is necessary for understanding how complex molecular machines function. To solve this problem, computational techniques are generally relied upon to precisely predict protein interactions. In this work, we present a novel method that combines structural and non-structural biological data to precisely predict protein interactions. The conceptual novelty of our approach lies in identifying and precisely associating biological information that provides substantial interaction clues. Our model combines structural and non-structural information using Bayesian statistics to calculate the likelihood of each interaction. The proposed model is tested on Saccharomyces cerevisiae's interactions extracted from the DIP and IntAct databases and provides substantial improvements in terms of accuracy, precision, recall and F1 score, as compared with the most widely used related state-of-the-art techniques.

中文翻译：

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通过整合结构和非结构生物学数据估算蛋白质相互作用的贝叶斯方法

准确阐明全基因组蛋白之间的相互作用对于理解细胞的调节过程至关重要。高通量技术，例如酵母2杂交（Y2H）分析，免疫共沉淀（co-IP），质谱（MS）蛋白复合物鉴定，亲和纯化（AP）等。通常依赖于确定蛋白相互作用。不幸的是，每种类型的方法都固有地受到不同类型的噪声的影响，并导致假阳性相互作用。另一方面，对蛋白质的精确理解，尤其是对它们的功能关联的了解，对于理解复杂的分子机器如何发挥作用是必要的。为了解决该问题，通常依赖于计算技术来精确预测蛋白质相互作用。在这项工作中，我们提出了一种结合结构和非结构生物学数据以精确预测蛋白质相互作用的新颖方法。我们方法的概念新颖之处在于识别并精确关联可提供大量交互线索的生物学信息。我们的模型使用贝叶斯统计信息将结构信息和非结构信息结合起来，以计算每次相互作用的可能性。所建议的模型已在上进行了测试从DIP和IntAct数据库中提取的酿酒酵母的相互作用，与最广泛使用的相关最新技术相比，在准确性，准确性，召回率和F1得分方面都得到了实质性的提高。