The human interactome consists of approximately 200,000 protein-protein interactions and we only understand a few thousand of them to date
Using computational methods, CNIO researchers have discovered that the study of the evolution of thousands of bacterial proteins allows deciphering many interactions between human proteins
Cells operate like an incredibly well-synchronized orchestra of molecular interactions among proteins. Understanding this molecular network is essential not only to understand how an organism works but also to determine the molecular mechanisms responsible for a multitude of diseases. In fact, it has been observed that protein interacting regions are preferentially mutated in tumours. The investigation of many of these interactions is challenging. However, a study coordinated by Simone Marsili and David Juan, from Alfonso Valencia’s team at the CNIO, will advance our knowledge on thousands of them. The work, published in the journal Proceedings of the National Academy of Sciences (PNAS), demonstrates that it is possible to understand a significant number of interactions among human proteins from the evolution of their counterparts in simpler cells, such as bacteria cells.
According to Juan Rodríguez, from the Structural Computational Biology Group at the CNIO and first author of the paper, “the complexity of human beings does not only result from the number of proteins that we have, but primarily from how they interact with each other. However, out of 200,000 protein-protein interactions estimated, only a few thousand have been characterised at the molecular level”. It is very difficult to study the molecular properties of many important interactions without reliable structural information. It is this “twilight zone” that, for the first time, CNIO researchers have managed to explore.
FROM BACTERIA TO HUMANS TO UNDERSTAND DISEASES
Although more than 3,000 million years of evolution separate bacteria and humans, the CNIO team has utilized the information accumulated over thousands of bacterial sequences to predict interactions between proteins in humans. “We have used the protein coevolution phenomenon: proteins that interact tend to experience coordinated evolutionary changes that maintain the interaction despite the accumulation of mutations over time,” says David Juan. “We have demonstrated that we can use this phenomenon to detect molecular details of interactions in humans that we share with very distant species. What is most interesting is that this allows us to transfer information from bacteria in order to study interactions in humans that we knew almost nothing about,” adds Simone Marsili.
These new results may lead to important implications for future research. “A deeper understanding of these interactions opens the door to the modeling of three-dimensional structures that may help us to design drugs targeting important interactions in various types of cancer,” explains David Juan. “This knowledge can also improve our predictions of the effects of various mutations linked to tumour development,” says Rodríguez.
The laboratory of Alfonso Valencia, head of the Structural Biology and Biocomputing Programme, has been working in the field of protein coevolution since the 1990s. This field has significantly advanced in recent years. “Thanks to the amount of biological data that is being generated today, we can use new computational methods that take into account a greater number of factors,” explains Valencia. According to the researchers, the pace of innovation in massive experimental techniques is providing additional data, making it possible to design more complex statistical models that provide an ever more complete view of the biological systems, “something particularly important in multifactorial diseases, such as cancer.”
This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund.
Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone. Juan Rodriguez-Rivas, Simone Marsili, David Juan and Alfonso Valencia (PNAS 2016). DOI: http://dx.doi.org/10.1073/pnas.1611861114