scholarly journals Influence of Protein Scaffold on Side-Chain Transfer Free Energies

2017 ◽  
Vol 113 (3) ◽  
pp. 597-604 ◽  
Author(s):  
Dagen C. Marx ◽  
Karen G. Fleming
2019 ◽  
Vol 25 (1) ◽  
pp. 73-81 ◽  
Author(s):  
Daniel R Fuller ◽  
Christopher R Conant ◽  
Tarick J El-Baba ◽  
Zhichao Zhang ◽  
Kameron R Molloy ◽  
...  

Biomolecular degradation plays a key role in proteostasis. Typically, proteolytic enzymes degrade proteins into smaller peptides by breaking amino acid bonds between specific residues. Cleavage around proline residues is often missed and requires highly specific enzymes for peptide processing due to the cyclic proline side-chain. However, degradation can occur spontaneously (i.e. in the absence of enzymes). In this study, the influence of the first residue on the stability of a series of penultimate proline containing peptides, with the sequence Xaa–Pro–Gly–Gly (where Xaa is any amino acid), is investigated with mass spectrometry techniques. Peptides were incubated as mixtures at various solution temperatures (70℃ to 90℃) and were periodically sampled over the duration of the experiment. At elevated temperatures, we observe dissociation after the Xaa–Pro motif for all sequences, but at different rates. Transition state thermochemistry was obtained by studying the temperature-dependent kinetics and although all peptides show relatively small differences in the transition state free energies (∼95 kJ/mol), there is significant variability in the transition state entropy and enthalpy. This demonstrates that the side-chain of the first amino acid has a significant influence on the stability of the Xaa–Pro sequence. From these data, we demonstrate the ability to simultaneously measure the dissociation kinetics and relative transition state thermochemistries for a mixture of peptides, which vary only in the identity of the N-terminal amino acid.


2009 ◽  
Vol 113 (26) ◽  
pp. 8967-8974 ◽  
Author(s):  
Gerhard König ◽  
Stefan Boresch
Keyword(s):  

2017 ◽  
Author(s):  
Kyle A. Barlow ◽  
Shane O Conchúir ◽  
Samuel Thompson ◽  
Pooja Suresh ◽  
James E. Lucas ◽  
...  

AbstractComputationally modeling changes in binding free energies upon mutation (interface ΔΔG) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using “backrub” to generate an ensemble of models, then applying torsion minimization, side chain repacking and averaging across this ensemble to estimate interface ΔΔG values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting non-linear reweighting model improved agreement with experimentally determined interface DDG values, but also highlights the necessity of future energy function improvements.


2020 ◽  
Author(s):  
Dagan C. Marx ◽  
Karen G. Fleming

ABSTRACTThrough the insertion of nonpolar side chains into the bilayer, the hydrophobic effect has long been accepted as a driving force for membrane protein folding. However, how the changing chemical composition of the bilayer affects the magnitude side chain transfer free energies has historically not been well understood. A particularly challenging region for experimental interrogation is the bilayer interfacial region that is characterized by a steep polarity gradient. In this study we have determined the for nonpolar side chains as a function of bilayer position using a combination of experiment and simulation. We discovered an empirical correlation between the surface area of nonpolar side chain, the transfer free energies, and the local water concentration in the membrane that allows for to be accurately estimated at any location in the bilayer. Using these water-to-bilayer values, we calculated the interface-to-bilayer transfer free energy . We find that the are similar to the “biological”, translocon-based transfer free energies, indicating that the translocon energetically mimics the bilayer interface. Together these findings can be applied to increase the accuracy of computational workflows used to identify and design membrane proteins, as well as bring greater insight into our understanding of how disease-causing mutations affect membrane protein folding and function.


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