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多目标领域中的蛋白质-蛋白质相互作用网络对齐器研究。

A protein-protein interaction network aligner study in the multi-objective domain.

机构信息

Escuela Politécnica, Universidad de Extremadura,(1) Campus Universitario s/n, 10003 Cáceres, Spain.

出版信息

Comput Methods Programs Biomed. 2024 Jun;250:108188. doi: 10.1016/j.cmpb.2024.108188. Epub 2024 Apr 21.

Abstract

BACKGROUND AND OBJECTIVE

The protein-protein interaction (PPI) network alignment has proven to be an efficient technique in the diagnosis and prevention of certain diseases. However, the difficulty in maximizing, at the same time, the two qualities that measure the goodness of alignments (topological and biological quality) has led aligners to produce very different alignments. Thus making a comparative study among alignments of such different qualities a big challenge. Multi-objective optimization is a computer method, which is very powerful in this kind of contexts because both conflicting qualities are considered together. Analysing the alignments of each PPI network aligner with multi-objective methodologies allows you to visualize a bigger picture of the alignments and their qualities, obtaining very interesting conclusions. This paper proposes a comprehensive PPI network aligner study in the multi-objective domain.

METHODS

Alignments from each aligner and all aligners together were studied and compared to each other via Pareto dominance methodologies. The best alignments produced by each aligner and all aligners together for five different alignment scenarios were displayed in Pareto front graphs. Later, the aligners were ranked according to the topological, biological, and combined quality of their alignments. Finally, the aligners were also ranked based on their average runtimes.

RESULTS

Regarding aligners constructing the best overall alignments, we found that SAlign, BEAMS, SANA, and HubAlign are the best options. Additionally, the alignments of best topological quality are produced by: SANA, SAlign, and HubAlign aligners. On the contrary, the aligners returning the alignments of best biological quality are: BEAMS, TAME, and WAVE. However, if there are time constraints, it is recommended to select SAlign to obtain high topological quality alignments and PISwap or SAlign aligners for high biological quality alignments.

CONCLUSIONS

The use of the SANA aligner is recommended for obtaining the best alignments of topological quality, BEAMS for alignments of the best biological quality, and SAlign for alignments of the best combined topological and biological quality. Simultaneously, SANA and BEAMS have above-average runtimes. Therefore, it is suggested, if necessary due to time restrictions, to choose other, faster aligners like SAlign or PISwap whose alignments are also of high quality.

摘要

背景与目的

蛋白质-蛋白质相互作用(PPI)网络比对已被证明是诊断和预防某些疾病的有效技术。然而,同时最大化衡量比对质量的两个标准(拓扑质量和生物学质量)的难度导致比对器产生非常不同的比对结果。因此,对具有不同质量的比对结果进行比较研究是一项具有挑战性的任务。多目标优化是一种计算机方法,在这种情况下非常强大,因为同时考虑了两个相互冲突的质量。使用多目标方法分析每个 PPI 网络比对器的比对结果,可以更全面地了解比对结果及其质量,并得出非常有趣的结论。本文提出了一种在多目标领域中全面的 PPI 网络比对器研究。

方法

研究并比较了每个比对器以及所有比对器的比对结果,并使用 Pareto 支配方法进行了比较。针对五个不同的比对场景,展示了每个比对器和所有比对器共同生成的最佳比对结果在 Pareto 前沿图上的分布。然后,根据比对器的拓扑质量、生物学质量和综合质量对其进行排名。最后,还根据平均运行时间对比对器进行了排名。

结果

关于构建整体最佳比对结果的比对器,我们发现 SAlign、BEAMS、SANA 和 HubAlign 是最佳选择。此外,SANA、SAlign 和 HubAlign 比对器生成的比对结果具有最佳的拓扑质量。相反,BEAMS、TAME 和 WAVE 比对器生成的比对结果具有最佳的生物学质量。但是,如果有时间限制,建议选择 SAlign 获得高拓扑质量的比对结果,选择 PISwap 或 SAlign 获得高生物学质量的比对结果。

结论

建议使用 SANA 比对器获得最佳的拓扑质量比对结果,使用 BEAMS 获得最佳的生物学质量比对结果,使用 SAlign 获得最佳的拓扑和生物学质量综合比对结果。同时,SANA 和 BEAMS 的运行时间较长。因此,如果由于时间限制需要选择其他更快的比对器,如 SAlign 或 PISwap,它们的比对结果也具有较高的质量。

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