|13 Feb 2017|
Which proteins are most likely to interact with other proteins, and how might these interactions affect disease, and life itself? Results from data computed by World Community Grid are being used to help create a protein sociability index, which could help future advances in biology and medicine.
Dr. Alessandra Carbone, the principal investigator for the Help Cure Muscular Dystrophy project, recently co-authored a paper that addresses one of the fundamental questions about protein-protein interaction. Using data from the project, which ran in two phases on World Community Grid, researchers devised a method to determine which proteins are most likely to interact with other proteins. Dr. Carbone says, “The idea can be summarized as ‘tell me if a protein is sociable, and I will tell you who its friends are.’”
How different proteins interact with each other is important to understanding life and disease processes. The first steps of identifying protein interactions include computationally searching for complementary surfaces among the collection of proteins, because those are more likely to interact, as was done using World Community Grid for the Help Cure Muscular Dystrophy project.
However, some proteins tend to evaluate as sticking to many proteins, hiding their true interactions. Dr. Carbone and her co-investigators found an algorithm to improve the accuracy of identifying true protein interactions using a sociability factor to compensate for the apparent “stickiness” of some proteins.
The concept of sociability, together with the right predictions of protein binding sites and an appropriate energy scoring of the interaction, turns out to be a crucial component for discrimination of protein partners. Sociability is proved to be more discriminative than geometrical shape of interacting surfaces. This work brings to light new avenues of investigation on protein interaction principles which could become fundamental to solving this difficult computational problem of high interest for biology and medicine.
Thank you to all World Community Grid volunteers who contributed to the Help Cure Muscular Dystrophy project!
Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico-chemical and geometrical properties determine who interact with whom remains far from fully understood.
We show that characterizing how a protein behaves with many potential interactors in a complete cross-docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S-index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface-based (ranking) score to discriminate partners from non-interactors.
We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking.