A0590
Title: Model-free inference for characterizing protein mutations through a coevolutionary lens
Authors: Zhao Ren - University of Pittsburgh (United States) [presenting]
Wen Zhou - New York University (United States)
Fan Yang - University of Pittsburgh (United States)
Abstract: Multiple sequence alignment (MSA) data play a crucial role in the study of protein mutations, with contact prediction being a notable application. Existing methods are often model-based or algorithmic and typically do not incorporate statistical inference to quantify the uncertainty of the prediction outcomes. A novel framework that transforms the task of contact prediction into a statistical testing problem is proposed. The approach is motivated by the partial correlation for continuous random variables. With one-hot encoding of MSA data, a partial correlation graph is constructed for multivariate categorical variables. In this framework, two connected nodes in the graph indicate that the corresponding positions on the protein form a contact. A new spectrum-based test statistic is introduced to test whether two positions are partially correlated. Moreover, the new framework enables the identification of amino acid combinations that contribute to the correlation within the identified contacts, an important but largely unexplored aspect of protein mutations. Numerical experiments demonstrate that the proposed method is valid in terms of controlling Type I errors and powerful in general. Real data applications on various protein families further validate the practical utility of the approach in coevolution and mutation analysis.
