Alexandre Bonvin bio photo

Computational Structural Biology group focusing on dissecting, understanding and predicting biomolecular interactions at the molecular level.

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Proteins help to copy DNA, transport nutrients and perform many other important roles in cells. To perform these tasks, proteins often interact with other proteins and work together. These interactions can be very complex because each protein has a three-dimensional shape that may change when it binds to other proteins. Also, two proteins may form several connections with each other.

It is possible to calculate with experiments how likely it is that two proteins will physically interact with each other and how strong their connections will be. However, these measurement are time consuming and costly to do. Some researchers have developed computer models to help predict the interactions between proteins, but these models are often incorrect because they leave out some of the chemical or physical properties that influence the ability of proteins to interact.

With the aim of making a better model, we have examined 122 different combinations of proteins whose abilities to interact had been experimentally measured and found that the number of connections between each pair of proteins was a strong predictor of how tightly the proteins connect to each other. Particular features of the surface of the proteins—specifically, the region defined as non-interacting surface—can also influence how strong the interaction is.

This information was used to develop a new model that predicts how tightly proteins interact with each other based on the number of connections between the two proteins and the characteristics of the non-interacting surface. The model is simple, and more accurate than previous models. Defects in the interactions between proteins can lead to many diseases in humans, so this model may be useful for the development of new drugs to treat these conditions.

Read the full story in eLife:

  • A Vangone and A.M.J.J. Bonvin. Contacts-based prediction of binding affinity in protein-protein complexes] . eLife, 4 e07454 (2015).

  • Download the binding affinity scripts from our GitHub repository