Investigating Statistical Conditions of Coevolutionary Signals that Enable Algorithmic Predictions of Protein Partners.

ABSTRACT: This study examines the statistical conditions of coevolutionary signals that allow algorithmic predictions of protein partners based on amino acid sequences rather than 3D structures. It introduces a Markov stochastic model that predicts the number of correct protein partners based on coevolutionary information. The model defines state probabilities using a Poisson mixture of normal distributions, with key parameters including the total number of protein sequences M, the coevolutionary information gap α, and variance σ₀². The model suggests that algorithmic approaches that maximize coevolutionary information cannot effectively resolve partners in protein families with a large number of sequences M ≥ 100. The model shows that true-positive (TP) rates can

SUBMITTER: Fiorote J 

PROVIDER: S-EPMC12042258 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature