Abstract
In allosteric proteins, the binding of a ligand modifies function at a distant active site. Such allosteric pathways can be used as target for drug design, generating considerable interest in inferring them from sequence alignment data. Currently, different methods lead to conflicting results, in particular on the existence of long-range evolutionary couplings between distant amino-acids mediating allostery. Here we propose a resolution of this conundrum, by studying epistasis and its inference in models where an allosteric material is evolved in silico to perform a mechanical task. We find in our model the four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range and have a simple mechanical interpretation. We perform a Direct Coupling Analysis (DCA) and find that DCA predicts well the cost of point mutations but is a rather poor generative model. Strikingly, it can predict short-range epistasis but fails to capture long-range epistasis, in consistence with empirical findings. We propose that such failure is generic when function requires subparts to work in concert. We illustrate this idea with a simple model, which suggests that other methods may be better suited to capture long-range effects.
Highlights
Allosteric regulation in proteins allows for the control of functional activity by ligand binding at a distal allosteric site [1] and its detection could guide drug design [2, 3]
To inform protocols of de novo drug design, it is fundamental to understand the impact of mutations on allosteric regulation and whether it can be predicted from evolutionary correlations
We show that Direct Coupling Analysis predicts quantitatively point mutation costs but underestimates strong long-range epistasis
Summary
Allosteric regulation in proteins allows for the control of functional activity by ligand binding at a distal allosteric site [1] and its detection could guide drug design [2, 3]. It was argued that the allosteric pathway was encoded in spatially extended and connected sectors, groups of strongly co-evolving amino-acids, supporting that long-range information on the allosteric pathway is contained in the MSA Another approach, Direct Couplings Analysis (DCA) [16], aims at inferring evolutionary couplihngs between amino-acids. We perform DCA and find that it predicts well the cost of point mutations but is a rather poor generative model It can predict short-range epistasis but fails to capture long-range effects, in agreement with empirical findings [25]. We test this result for one allosteric protein, the PDZ domain, where epistasis was experimentally measured in [12] along with the inference of DCA energetic couplings, showing support for our prediction. We illustrate why it may be so via a simple model, which suggests that neural networks may be better suited than DCA to capture long-range effects
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