bioRxivBiophysicsPreprint
Characterizing the Conformational Dynamics of an Intrinsically Disordered Localization Sequence
Matthew Brownd, Parth ChaturvediiD, Ashkan FakharzadehiD, Mahmoud MoradiiD
Abstract
Mitochondrial localization peptides (MLPs) play a critical role in directing proteins to mitochondria, yet how subtle sequence variations influence their conformational behavior remains poorly understood. Here, we investigate the conformational dynamics of a 15-residue MLP derived from the androgen receptor, together with a comprehensive panel of single-residue variants generated by systematic substitution at the second position. Across all variants, the peptide remains intrinsically disordered, exhibiting broad conformational heterogeneity and no stable folded state. Global measures of compactness show that single-residue substitutions induce only modest changes to overall peptide dimensions. In contrast, residue-level analysis reveals that the identity of the second residue subtly reshapes local structural preferences, particularly near the N-terminus. Small or hydrophobic substitutions enhance transient -helical sampling, whereas polar or charged substitutions favor increased disorder and {beta}- or polyprolinelike conformations. Comparison across variants further distinguishes mutations that preserve wild-type-like structural behavior from those that produce more pronounced deviations in the conformational ensemble. Enhanced sampling simulations highlight the complexity and ruggedness of the underlying free-energy landscape and demonstrate the challenges associated with achieving convergence for short intrinsically disordered peptides. Collectively, these results show that even minimal sequence changes can bias the dynamic structural ensemble of mitochondrial localization peptides, suggesting a potential mechanism by which targeting efficiency may be modulated. More broadly, this work underscores the importance of advanced sampling strategies for accurately characterizing intrinsically disordered localization signals and provides a framework for connecting sequence variation to functional targeting behavior.
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