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Conformational dynamics and energetics of viral RNA recognition by lab-evolved proteins.

ABSTRACT: The conserved and structured elements in viral RNA genomes interact with proteins to regulate various events in the viral life cycle and have become key targets for developing novel therapeutic approaches. We probe physical interactions between lab-evolved proteins and a viral RNA element from the HIV-1 genome. Specifically, we study the role of an arginine-rich loop in recognition of designed proteins by the viral RNA element. We report free energy calculations to quantitatively estimate the protein/RNA binding energetics, focusing on the mutations of arginine residues involved in recognition of the major groove of RNA by proteins.

SUBMITTER: Kumar A

PROVIDER: S-EPMC8579469 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Conformational dynamics and energetics of viral RNA recognition by lab-evolved proteins.

Kumar Amit A Vashisth Harish H

Physical chemistry chemical physics : PCCP 20211110 43

The conserved and structured elements in viral RNA genomes interact with proteins to regulate various events in the viral life cycle and have become key targets for developing novel therapeutic approaches. We probe physical interactions between lab-evolved proteins and a viral RNA element from the HIV-1 genome. Specifically, we study the role of an arginine-rich loop in recognition of designed proteins by the viral RNA element. We report free energy calculations to quantitatively estimate the pr ...[more]

PMID: 34714308

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Project description:All-atom molecular dynamics simulations are widely used to study the flexibility of protein conformations. However, enhanced sampling techniques are required for simulating protein dynamics that occur on the millisecond timescale. In this work, we show that torsional molecular dynamics simulations enhance protein conformational sampling by performing conformational search in the low-frequency torsional degrees of freedom. In this article, we use our recently developed torsional-dynamics method called Generalized Newton-Euler Inverse Mass Operator (GNEIMO) to study the conformational dynamics of four proteins. We investigate the use of the GNEIMO method in simulations of the conformationally flexible proteins fasciculin and calmodulin, as well as the less flexible crambin and bovine pancreatic trypsin inhibitor. For the latter two proteins, the GNEIMO simulations with an implicit-solvent model reproduced the average protein structural fluctuations and sample conformations similar to those from Cartesian simulations with explicit solvent. The application of GNEIMO with replica exchange to the study of fasciculin conformational dynamics produced sampling of two of this protein's experimentally established conformational substates. Conformational transition of calmodulin from the Ca(2+)-bound to the Ca(2+)-free conformation occurred readily with GNEIMO simulations. Moreover, the GNEIMO method generated an ensemble of conformations that satisfy about half of both short- and long-range interresidue distances obtained from NMR structures of holo to apo transitions in calmodulin. Although unconstrained all-atom Cartesian simulations have failed to sample transitions between the substates of fasciculin and calmodulin, GNEIMO simulations show the transitions in both systems. The relatively short simulation times required to capture these long-timescale conformational dynamics indicate that GNEIMO is a promising molecular-dynamics technique for studying domain motion in proteins.

Dataset Information

Conformational dynamics and energetics of viral RNA recognition by lab-evolved proteins.

Publications

Conformational dynamics and energetics of viral RNA recognition by lab-evolved proteins.

Similar Datasets

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