Ayub Umair, Naveed Hammad
FAST School of Computing, National University of Computer and Emerging Sciences, Lahore, Pakistan.
Computational Biology Research Lab, Department of Computing, National University of Computer and Emerging Sciences, Islamabad, Pakistan.
Evol Bioinform Online. 2022 Jul 20;18:11769343221110658. doi: 10.1177/11769343221110658. eCollection 2022.
The advancement of high-throughput PPI profiling techniques results in generating a large amount of PPI data. The alignment of the PPI networks uncovers the relationship between the species that can help understand the biological systems. The comparative study reveals the conserved biological interactions of the proteins across the species. It can also help study the biological pathways and signal networks of the cells. Although several network alignment algorithms are developed to study and compare the PPI data, the development of the aligner that aligns the PPI networks with high biological similarity and coverage is still challenging.
This paper presents a novel global network alignment algorithm, BioAlign, that incorporates a significant amount of biological information. Existing studies use global sequence and/or 3D-structure similarity to align the PPI networks. In contrast, BioAlign uses the local sequence similarity, predicted secondary structure motifs, and remote homology in addition to global sequence and 3D-structure similarity. The extra sources of biological information help BioAlign to align the proteins with high biological similarity. BioAlign produces significantly better results in terms of AFS and Coverage (6-32 and 7-34 with respect to MF and BP, respectively) than the existing algorithms. BioAlign aligns a much larger number of proteins that have high biological similarities as compared to the existing aligners. BioAlign helps in studying the functionally similar protein pairs across the species.
高通量蛋白质-蛋白质相互作用（PPI）分析技术的进步导致产生了大量的PPI数据。PPI网络的比对揭示了物种之间的关系，这有助于理解生物系统。比较研究揭示了跨物种蛋白质保守的生物相互作用。它还可以帮助研究细胞的生物途径和信号网络。尽管已经开发了几种网络比对算法来研究和比较PPI数据，但开发能够以高生物相似性和覆盖率比对PPI网络的比对器仍然具有挑战性。
本文提出了一种新颖的全局网络比对算法BioAlign，该算法整合了大量生物信息。现有研究使用全局序列和/或三维结构相似性来比对PPI网络。相比之下，BioAlign除了使用全局序列和三维结构相似性之外，还使用局部序列相似性、预测的二级结构基序和远程同源性。这些额外的生物信息来源有助于BioAlign以高生物相似性比对蛋白质。在AFS和覆盖率方面（分别相对于MF和BP为6 - 32和7 - 34），BioAlign产生的结果比现有算法显著更好。与现有比对器相比，BioAlign能比对更多具有高生物相似性的蛋白质。BioAlign有助于研究跨物种功能相似的蛋白质对。
