Accounting for conformational entropy in predicting binding free energies of protein-protein interactions

2010 ◽  
Vol 79 (2) ◽  
pp. 444-462 ◽  
Author(s):  
Hetunandan Kamisetty ◽  
Arvind Ramanathan ◽  
Chris Bailey-Kellogg ◽  
Christopher James Langmead
2016 ◽  
Vol 18 (32) ◽  
pp. 22129-22139 ◽  
Author(s):  
Fu Chen ◽  
Hui Liu ◽  
Huiyong Sun ◽  
Peichen Pan ◽  
Youyong Li ◽  
...  

Understanding protein–protein interactions (PPIs) is quite important to elucidate crucial biological processes and even design compounds that interfere with PPIs with pharmaceutical significance.


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):  
Jinan Wang ◽  
Yinglong Miao

AbstractPeptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play an important role in cellular signaling. However, it is challenging to simulate both binding and unbinding of peptides and calculate peptide binding free energies through conventional molecular dynamics, due to long biological timescales and extremely high flexibility of the peptides. Based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique, we have developed a new computational method “Pep-GaMD”, which selectively boosts essential potential energy of the peptide in order to effectively model its high flexibility. In addition, another boost potential is applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Independent 1 μs dual-boost Pep-GaMD simulations have captured repetitive peptide dissociation and binding events, which enable us to calculate peptide binding thermodynamics and kinetics. The calculated binding free energies and kinetic rate constants agreed very well with available experimental data. Furthermore, the all-atom Pep-GaMD simulations have provided important insights into the mechanism of peptide binding to proteins that involves long-range electrostatic interactions and mainly conformational selection. In summary, Pep-GaMD provides a highly efficient, easy-to-use approach for unconstrained enhanced sampling and calculations of peptide binding free energies and kinetics.Significance StatementWe have developed a new computational method “Pep-GaMD” for enhanced sampling of peptide-protein interactions based on the Gaussian accelerated molecular dynamics (GaMD) technique. Pep-GaMD works by selectively boosting the essential potential energy of the peptide to effectively model its high flexibility. In addition, another boost potential can be applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Dual-boost Pep-GaMD has captured repetitive peptide dissociation and binding events within significantly shorter simulation time (microsecond) than conventional molecular dynamics. Compared with previous enhanced sampling methods, Pep-GaMD is easier to use and more efficient for unconstrained enhanced sampling of peptide binding and unbinding, which provides a novel physics-based approach to calculating peptide binding free energies and kinetics.


2011 ◽  
Vol 49 (08) ◽  
Author(s):  
LC König ◽  
M Meinhard ◽  
C Sandig ◽  
MH Bender ◽  
A Lovas ◽  
...  

1974 ◽  
Vol 31 (03) ◽  
pp. 403-414 ◽  
Author(s):  
Terence Cartwright

SummaryA method is described for the extraction with buffers of near physiological pH of a plasminogen activator from porcine salivary glands. Substantial purification of the activator was achieved although this was to some extent complicated by concomitant extraction of nucleic acid from the glands. Preliminary characterization experiments using specific inhibitors suggested that the activator functioned by a similar mechanism to that proposed for urokinase, but with some important kinetic differences in two-stage assay systems. The lack of reactivity of the pig gland enzyme in these systems might be related to the tendency to protein-protein interactions observed with this material.


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