PhD Scientific Days 2022

Budapest, 6-7 July 2022

Molecular Sciences II.

Identification of Network Topology Motif Characteristic of Disordered Proteins

Klára Schulc
Molekuláris Biológiai Tanszék

Text of the abstract

Introduction:
Network analysis is an increasingly common tool in systems biology research. Topological analysis of these networks allows the role of individual molecules to be investigated at the system level. Examples of this are intrinsically disordered proteins whose unique properties have long been discussed in the literature, but there are only a few examples of their systems-based research. A good tool for this is to study three-nodal signaling motifs in which each node is related to the other two within the motif. Among these, special motifs such as unbalanced triangles and cycles occupy a particularly important regulatory role in signaling networks.
Aims:
Our aim was to get to know the characteristics of intrinsically disordered proteins related to network motifs and to explore possible regularities.
Methods:
The directed edges of four different signaling networks were extracted, and motifs were identified by the FANMOD command-line program. Then, intrinsically disordered proteins were identified using the DisProt database. Statistical analyses were conducted with the chi-square method.
Result:
In this work, we showed that intrinsically disordered proteins were significantly more frequently present in three-node motifs in the Human Cancer Signaling Network, SignaLink and SIGNOR than the average. Within the motifs, they are significantly more frequent than average in rare motifs such as unbalanced triangles and cycles, and often form common motifs with known oncotherapeutical targets. Certain connection types among disordered proteins and targets are associated with the predictive biomarker properties of the protein.
Conclusion:
Our results imply that further investigation into these motifs may help to identify new biomarkers among disordered proteins, the importance of which can be further investigated in a tumor-type-specific method using different tissue gene expression data. Thus, changes in different biological processes such as carcinogenesis and the development of therapy resistance can also be analyzed on the motif level.
Funding:
Thematic Excellence Programme (2020-4.1.1.-TKP2020) of the Ministry for Innovation and Technology in Hungary, from the National Research, Development and Innovation Fund (TKP2021-EGA) as well as by the grant K-131458.
Kiegészítő Kutatási Kiválósági Ösztöndíj (EFOP-3.6.3-VEKOP-16-2017-00009)