A biological function is rarely accomplished by a single gene. More often, proteins come together in complexes, and it is their collaboration within a complex that enables the associated biological function. However, the current map of the interactome is incomplete, meaning we have not observed all the interactions occurring in the cell yet. Gold standard experimental methods for the determination of all the protein-protein interactions (PPIs) in human interactome are time-consuming, expensive and may not even be feasible considering the vast number of protein pairs that need to be tested.
For decades, scientists and engineers dedicated their efforts to forecasting protein interactions, predominantly relying on network topology methods. However, the emergence of AlphaFold2 intelligence has redefined the computational biology field by harnessing 3D molecular structural data to predict interacting protein in complexes, offering a promising alternative to traditional laboratory experiments.
It is in this context that we introduce an innovative concept known as Network Shape Intelligence (NSI). It is the intelligence displayed by any topological network automata to perform valid connectivity predictions without training, but only processing the input knowledge associated to the local topological network organization. NSI transcends conventional link prediction methods by weaving together principles inspired by brain network science. It achieves this by minimizing external links within local communities, a strategy founded on local topology and plasticity principles initially developed for brain networks but subsequently extended to diverse complex networks.
In addition to the incompleteness of the PPI network, the question of the reliability of the existing wealth of information through observed physical links also arises.
Therefore, to evaluate the performance of a predictor we must make sure that the tested positive and negative interactions are reliable. We introduce the Bona Fide Evaluation Methodology (BFEM). The rigor of protein interaction predictions is ensured through a balanced classification scenario, meticulously constructed using the well-studied yeast protein interactome. Our methodology focuses on creating a golden standard set of true and false interactions, enhancing the reliability of our evaluations.
We show that by using only local network information and without the need for training, these network automata designed for modelling and predicting network connectivity can outperform AlphaFold2 intelligence in vanilla protein interactions prediction. We find that the set of interactions mispredicted by AlphaFold2 predominantly consists of proteins whose amino acids exhibit higher probability of being associated with intrinsically disordered regions. Finally, we suggest that the future advancements in AlphaFold intelligence could integrate principles of NSI to further enhance the modelling and structural prediction of protein interactions.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89529 |
| Date | 06 February 2024 |
| Creators | Abdelhamid, Ilyes |
| Contributors | Schroeder, Michael, Guzzi, Pietro Hiram, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.1101/2023.08.10.552825 |
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