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Published October 31, 2014 | Published
Journal Article Open

Allosteric response and substrate sensitivity in peptide binding of the signal recognition particle

Abstract

We characterize the conformational dynamics and substrate selectivity of the signal recognition particle (SRP) using a thermodynamic free energy cycle approach and microsecond-timescale molecular dynamics simulations. The SRP is a central component of the co-translational protein targeting machinery that binds to the N-terminal signal peptide (SP) of nascent proteins. We determined the shift in relative conformational stability of the SRP upon substrate binding to quantify allosteric coupling between SRP domains. In particular, for dipeptidyl aminopeptidase, an SP that is recognized by the SRP for co-translational targeting, it is found that substrate binding induces substantial changes in the SRP towards configurations associated with targeting of the nascent protein, and it is found that the changes are modestly enhanced by a mutation that increases the hydrophobicity of the SP; however for alkaline phosphotase, an SP that is recognized for post-translational targeting, substrate binding induces the reverse change in the SRP conformational distribution away from targeting configurations. Microsecond-timescale trajectories reveal the intrinsic flexibility of the SRP conformational landscape and provide insight into recent single-molecule studies by illustrating that 10 nm lengthscale changes between FRET pairs occur via the rigid-body movement of SRP domains connected by the flexible linker region. In combination, these results provide direct evidence for the hypothesis that substrate-controlled conformational switching in the SRP provides a mechanism for discriminating between different SPs and for connecting substrate binding to downstream steps in the protein targeting pathway.

Additional Information

© 2014 The American Society for Biochemistry and Molecular Biology. Received May 27, 2014; Accepted September 18, 2014. First Published on September 18, 2014. C. W. acknowledges support from the National Science Foundation Graduate Research Fellowship Program under Award No. 1144469, and T.F.M. acknowledges support from a Camille and Henry Dreyfus Foundation Teacher-Scholar Award and an Alfred P. Sloan Foundation Research Fellowship. Computational resources were provided by the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Additionally, Anton computer time was provided by the National Resource for Biomedical Supercomputing (NRBSC), the Pittsburgh Supercomputing Center (PSC), and the BTRC for Multiscale Modeling of Biological Systems (MMBioS) through Grant P41GM103712-S1 from the National Institutes of Health. The Anton machine at NRBSC/PSC was generously made available by D.E. Shaw Research. The authors also thank Shu-ou Shan and David Akopian for helpful discussions.

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August 22, 2023
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