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

Water-soluble pillar[5]arene sulfo-derivatives self-assemble into biocompatible nanosystems to stabilize therapeutic proteins

Pillar[5]arenes containing sulfonate fragments have been shown to form supramolecular complexes with therapeutic proteins to facilitate targeted transport with an increased duration of action and enhanced bioavailability. Regioselective synthesis was used to obtain a water-soluble pillar[5]arene containing the fluorescent label FITC and nine sulfoethoxy fragments. The pillar[5]arene formed complexes with the therapeutic proteins binase, bleomycin, and lysozyme in a 1:2 ratio as demonstrated by UV-vis and fluorescence spectroscopy. The formation of stable spherical nanosized macrocycle/binase complexes with an average particle size of 200 nm was established by dynamic light scattering and transmission electron microscopy. Flow cytometry demonstrated the ability of macrocycle/binase complexes to penetrate into tumor cells where they exhibited significant cytotoxicity towards A549 cells at 10^-5-10^-6 M while maintaining the enzymatic activity of binase.

Classification of protein-protein association rates based on biophysical informatics

BACKGROUND

Proteins form various complexes to carry out their versatile functions in cells. The dynamic properties of protein complex formation are mainly characterized by the association rates which measures how fast these complexes can be formed. It was experimentally observed that the association rates span an extremely wide range with over ten orders of magnitudes. Identification of association rates within this spectrum for specific protein complexes is therefore essential for us to understand their functional roles.

RESULTS

To tackle this problem, we integrate physics-based coarse-grained simulations into a neural-network-based classification model to estimate the range of association rates for protein complexes in a large-scale benchmark set. The cross-validation results show that, when an optimal threshold was selected, we can reach the best performance with specificity, precision, sensitivity and overall accuracy all higher than 70%. The quality of our cross-validation data has also been testified by further statistical analysis. Additionally, given an independent testing set, we can successfully predict the group of association rates for eight protein complexes out of ten. Finally, the analysis of failed cases suggests the future implementation of conformational dynamics into simulation can further improve model.

CONCLUSIONS

In summary, this study demonstrated that a new modeling framework that combines biophysical simulations with bioinformatics approaches is able to identify protein-protein interactions with low association rates from those with higher association rates. This method thereby can serve as a useful addition to a collection of existing experimental approaches that measure biomolecular recognition.

Understanding the Impacts of Conformational Dynamics on the Regulation of Protein-Protein Association by a Multiscale Simulation Method

Complexes formed among diverse proteins carry out versatile functions in nearly all physiological processes. Association rates which measure how fast proteins form various complexes are of fundamental importance to characterize their functions. The association rates are not only determined by the energetic features at binding interfaces of a protein complex but also influenced by the intrinsic conformational dynamics of each protein in the complex. Unfortunately, how this conformational effect regulates protein association has never been calibrated on a systematic level. To tackle this problem, we developed a multiscale strategy to incorporate the information on protein conformational variations from Langevin dynamic simulations into a kinetic Monte Carlo algorithm of protein-protein association. By systematically testing this approach against a large-scale benchmark set, we found the association of a protein complex with a relatively rigid structure tends to be reduced by its conformational fluctuations. With specific examples, we further show that higher degrees of structural flexibility in various protein complexes can facilitate the searching and formation of intermolecular interactions and thereby accelerate their associations. In general, the integration of conformational dynamics can improve the correlation between experimentally measured association rates and computationally derived association probabilities. Finally, we analyzed the statistical distributions of different secondary structural types on protein-protein binding interfaces and their preference to the change of association rates. Our study, to the best of our knowledge, is the first computational method that systematically estimates the impacts of protein conformational dynamics on protein-protein association. It throws lights on the molecular mechanisms of how protein-protein recognition is kinetically modulated.

Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein-Protein Association

The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.

Supramolecular Organization As a Factor of Ribonuclease Cytotoxicity

One of the approaches used to eliminate tumor cells is directed destruction/modification of their RNA molecules. In this regard, ribonucleases (RNases) possess a therapeutic potential that remains largely unexplored. It is believed that the biological effects of secreted RNases, namely their antitumor and antiviral properties, derive from their catalytic activity. However, a number of recent studies have challenged the notion that the activity of RNases in the manifestation of selective cytotoxicity towards cancer cells is exclusively an enzymatic one. In this review, we have analyzed available data on the cytotoxic effects of secreted RNases, which are not associated with their catalytic activity, and we have provided evidence that the most important factor in the selective apoptosis-inducing action of RNases is the structural organization of these enzymes, which determines how they interact with cell components. The new idea on the preponderant role of non-catalytic interactions between RNases and cancer cells in the manifestation of selective cytotoxicity will contribute to the development of antitumor RNase-based drugs.

Biological Activities of Secretory RNases: Focus on Their Oligomerization to Design Antitumor Drugs

Ribonucleases (RNases) are a large number of enzymes gathered into different bacterial or eukaryotic superfamilies. Bovine pancreatic RNase A, bovine seminal BS-RNase, human pancreatic RNase 1, angiogenin (RNase 5), and amphibian onconase belong to the pancreatic type superfamily, while binase and barnase are in the bacterial RNase N1/T1 family. In physiological conditions, most RNases secreted in the extracellular space counteract the undesired effects of extracellular RNAs and become protective against infections. Instead, if they enter the cell, RNases can digest intracellular RNAs, becoming cytotoxic and having advantageous effects against malignant cells. Their biological activities have been investigated either in vitro, toward a number of different cancer cell lines, or in some cases in vivo to test their potential therapeutic use. However, immunogenicity or other undesired effects have sometimes been associated with their action. Nevertheless, the use of RNases in therapy remains an appealing strategy against some still incurable tumors, such as mesothelioma, melanoma, or pancreatic cancer. The RNase inhibitor (RI) present inside almost all cells is the most efficacious sentry to counteract the ribonucleolytic action against intracellular RNAs because it forms a tight, irreversible and enzymatically inactive complex with many monomeric RNases. Therefore, dimerization or multimerization could represent a useful strategy for RNases to exert a remarkable cytotoxic activity by evading the interaction with RI by steric hindrance. Indeed, the majority of the mentioned RNases can hetero-dimerize with antibody derivatives, or even homo-dimerize or multimerize, spontaneously or artificially. This can occur through weak interactions or upon introducing covalent bonds. Immuno-RNases, in particular, are fusion proteins representing promising drugs by combining high target specificity with easy delivery in tumors. The results concerning the biological features of many RNases reported in the literature are described and discussed in this review. Furthermore, the activities displayed by some RNases forming oligomeric complexes, the mechanisms driving toward these supramolecular structures, and the biological rebounds connected are analyzed. These aspects are offered with the perspective to suggest possible efficacious therapeutic applications for RNases oligomeric derivatives that could contemporarily lack, or strongly reduce, immunogenicity and other undesired side-effects.

The Native Monomer of Bacillus Pumilus Ribonuclease Does Not Exist Extracellularly

Supported by crystallography studies, secreted ribonuclease of Bacillus pumilus (binase) has long been considered to be monomeric in form. Recent evidence obtained using native polyacrylamide gel electrophoresis and size-exclusion chromatography suggests that binase is in fact dimeric. To eliminate ambiguity and contradictions in the data we have measured conformational changes, hypochromic effect, and hydrodynamic radius of binase. The immutability of binase secondary structure upon transition from low to high protein concentration was registered, suggesting the binase dimerization immediately after translocation through the cell membrane and leading to detection of binase dimers only in the culture fluid regardless of ribonuclease concentration. Our results made it necessary to take a fresh look at the binase stability and cytotoxicity towards virus-infected or tumor cells.

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

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.

New insight into secreted ribonuclease structure: binase is a natural dimer

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.

pH-dependent association of proteins. The test case of monoclonal antibody HyHEL-5 and its antigen hen egg white lysozyme

We describe a method for determining diffusion-controlled rate constants for protein-protein association that explicitly includes the solution pH. The method combines the transient-complex theory for computing electrostatically enhanced association rates with an approach based on a rigorous thermodynamic cycle and partition functions for energy levels characterizing protonation states of associating proteins and their complexes. To test our method, we determine the pH-dependent kinetics of association of the HyHEL-5 antibody with its antigen hen egg white lysozyme. It was shown experimentally that their association rate constant depends on pH, increasing linearly in the pH range 6-8 and saturating or even exhibiting a flat maximum in the pH range 8-10. The presented methodology leads to a qualitative agreement with the experimental data. Our approach allows one to study diffusion-controlled protein-protein association under different pH conditions by taking into account the ensembles of protonation states rather than just the most probable protonation state of each protein.

On the stability of the soluble amyloid aggregates

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.