What is it about?
A meal is best described by listing its ingredients. Science has also used this ancient method very successfully to describe atoms, subatomic particles, and chemicals. Sooner or later, we also came to understand how these ingredients interact with each other through dynamic processes, allowing us to predict the properties and structures of atoms, atomic nuclei, and molecules. Our research illustrates that describing the ingredients of a protein in an appropriate way helps us understand how they interact with each other.
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Why is it important?
Protein interactions are often understood as structural complementarity between partners. We have uncovered a deeper and simpler reason why proteins recognize each other, which may lead to a more systematic understanding of how they function within organisms. Instead of developing complex artificial intelligence models, our focus was to design minimal models that can be understood and serve as systems describing biological matter.
Perspectives
The question of how the composition of proteins determines their properties remains. Strangely, this question is rarely raised when I present our results, even though it challenges the prevailing view of protein interactions. There is no mystery, though. The truth is that we have been inspired by the dynamic properties of protein crystals, which provide stunning insight into the formation of long-range structural patterns in proteins and their assemblies. However, this perspective seems to be more difficult for the scientific community to accept. This work serves as a Trojan horse, demonstrating the usefulness of an alternative viewpoint that will hopefully erode the ineffective paradigms holding back molecular biology. Our inspiration and explanation can be found in these publications: https://www.biorxiv.org/content/10.1101/2024.05.29.596429v1 https://www.nature.com/articles/s41598-019-55777-5 https://iopscience.iop.org/article/10.1088/2632-2153/ac022d/meta
Professor Gergely Katona
University of Gothenburg
Read the Original
This page is a summary of: Deciphering Peptide-Protein Interactions via Composition-Based Prediction: A Case Study with Survivin/BIRC5, Machine Learning Science and Technology, June 2024, Institute of Physics Publishing,
DOI: 10.1088/2632-2153/ad5784.
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