Brownian dynamics simulation of the competitive reactions: Binase dimerization and the association of binase and barstar

Ермакова, Елена

doi:10.1016/j.bpc.2007.06.012

Cited by 14 publications

(14 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The main residues of the active site identified in structural complexes from binase with nucleotide-type ligands (with bases: Glu59, Phe55, and with phosphates: Glu72, Arg86 and His101) [42] are available for substrate binding in both subunits. This assumption could be confirmed by the results obtained by the Brownian dynamics simulation method [43] . It was shown that three types of binase dimers could be formed.…”

Section: Discussionsupporting

confidence: 63%

New Insight into Secreted Ribonuclease Structure: Binase Is a Natural Dimer

et al. 2014

View full text Add to dashboard Cite

The biological effects of ribonucleases (RNases), such as the control of the blood vessels growth, the toxicity towards tumour cells and antiviral activity, require a detailed explanation. One of the most intriguing properties of RNases which can contribute to their biological effects is the ability to form dimers, which facilitates efficient RNA hydrolysis and the evasion of ribonuclease inhibitor. Dimeric forms of microbial RNase binase secreted by Bacillus pumilus (former B. intermedius) have only been found in crystals to date. Our study is the first report directly confirming the existence of binase dimers in solution and under natural conditions of enzyme biosynthesis and secretion by bacilli. Using different variants of gel electrophoresis, immunoblotting, size-exclusion chromatography and mass-spectrometry, we revealed that binase is a stable natural dimer with high catalytic activity.

show abstract

Section: Discussionsupporting

confidence: 63%

New Insight into Secreted Ribonuclease Structure: Binase Is a Natural Dimer

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Barstar changes its folding pattern with pH (31), forming a molten globule like state at pH values at~3. The pI of barstar is~5.0 (32,33). This provides a unique opportunity to test the charged colloid model.…”

Section: Introductionmentioning

confidence: 99%

On the Stability of the Soluble Amyloid Aggregates

Sahoo

Nag

Sengupta

et al. 2009

Biophysical Journal

View full text Add to dashboard Cite

Many amyloid proteins form metastable soluble aggregates (or protofibrils, or protein nanoparticles, with characteristic sizes from approximately 10 to a few hundred nm). These can coexist with protein monomers and amyloid precipitates. These soluble aggregates are key determinants of the toxicity of these proteins. It is therefore imperative to understand the physical basis underlying their stability. Simple nucleation theory, typically applied to explain the kinetics of amyloid precipitation, fails to predict such intermediate stable states. We examine stable nanoparticles formed by the Alzheimer's amyloid-beta peptide (40 and 42 residues), and by the protein barstar. These molecules have different hydrophobicities, and therefore have different short-range attractive interactions between the molecules. We also vary the pH and the ionic strength of the solution to tune the long-range electrostatic repulsion between them. In all the cases, we find that increased long-range repulsion results in smaller stable nanoparticles, whereas increased hydrophobicity produces the opposite result. Our results agree with a charged-colloid type of model for these particles, which asserts that growth-arrested colloid particles can result from a competition between short-range attraction and long-range repulsion. The nanoparticle size varies superlinearly with the ionic strength, possibly indicating a transition from an isotropic to a linear mode of growth. Our results provide a framework for understanding the stability and growth of toxic amyloid nanoparticles, and provide cues for designing effective destabilizing agents.

show abstract

“…For instance, machine learning techniques have been used to predict association rate constants based on the chemical or structural properties of proteins3738. Physics-based methods, such as Brownian dynamic (BD) simulation, are widely used to reproduce the association of two proteins39404142434445464748495051525354555657585960. These all atom-based methods are computationally expensive, as they have to take into account the large amount of freedom in both interacting proteins.…”

mentioning

confidence: 99%

Predicting Protein–protein Association Rates using Coarse-grained Simulation and Machine Learning

Xie

Chen

2017

Sci Rep

View full text Add to dashboard Cite

Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

show abstract

Brownian dynamics simulation of the competitive reactions: Binase dimerization and the association of binase and barstar

Cited by 14 publications

References 31 publications

New Insight into Secreted Ribonuclease Structure: Binase Is a Natural Dimer

New Insight into Secreted Ribonuclease Structure: Binase Is a Natural Dimer

On the Stability of the Soluble Amyloid Aggregates

Predicting Protein–protein Association Rates using Coarse-grained Simulation and Machine Learning

Contact Info

Product

Resources

About