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

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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 be enhanced by disregarding mismatches among similar sequences. This approach allows a distinction, in terms of { α , σ₀² } , between optimized solutions with trivial errors and other degenerate solutions. Our findings enable the a priori classification of protein families where partners can be reliably predicted by ignoring trivial errors between similar sequences, advancing the understanding of coevolutionary models for large protein datasets.