Protein-protein association: investigation of factors influencing association rates by brownian dynamics simulations

Competitive Interaction of the SGFRKMAF Peptide with 3CLpro Dimerization Intermediates: A Brownian Dynamics Investigation

The SGFRKMAF peptide is known to inhibit the dimerization of 3CLpro monomers, which is essential for SARS-CoV-2 replication. The mechanism behind this, however, is largely unknown. In this work, we used Brownian dynamics simulations to compare and contrast 3CLpro monomer-monomer interactions and 3CLpro monomer-SGFRKMAF peptide interactions. We found that formation of the 3CLpro wild-type dimer could potentially involve formation of three intermediates that are primarily stabilized by G11-G124, S1-S301, and T118-G278 interactions. Analysis of 3CLpro monomer interaction with the SGFRKMAF peptide, however, revealed the presence of eight basins of interactions where the peptide assumes the highest local densities at the 3CLPro monomer surface. The second highest-density basin was found to coincide with the interface region of the wild-type 3CLpro dimer, thereby directly blocking the 3CLpro dimer-dimer interactions. The other basins, however, were found to lie far from the interface region. Notably, we found that only 6% of the BD trajectories end up directly into the basin at the interface region and ∼39% of the trajectories end up into those basins lying away from the interface region, indicating a greater role for peptide binding at sites away from the dimer interface region. Importantly, the locations of the basins lying away from the interface were found to coincide with the 3CLpro residues involved in stabilization of the 3CLpro monomer-monomer intermediates. Given that the rate constant of the peptide reaching the monomer surface was found to be almost an order of magnitude higher than the rate constant of monomer-monomer association, the SGFRKMAF peptide has the potential to inhibit dimerization of 3CLpro monomers not only through blocking the interface region but also through blocking the formation of the intermediates involved in the dimerization process. This could potentially open new avenues for 3CLpro dimerization inhibitors that transcend traditional X-ray-based discovery approaches.

Perspective on Integrative Simulations of Bioenergetic Domains

Bioenergetic processes in cells, such as photosynthesis or respiration, integrate many time and length scales, which makes the simulation of energy conversion with a mere single level of theory impossible. Just like the myriad of experimental techniques required to examine each level of organization, an array of overlapping computational techniques is necessary to model energy conversion. Here, a perspective is presented on recent efforts for modeling bioenergetic phenomena with a focus on molecular dynamics simulations and its variants as a primary method. An overview of the various classical, quantum mechanical, enhanced sampling, coarse-grained, Brownian dynamics, and Monte Carlo methods is presented. Example applications discussed include multiscale simulations of membrane-wide electron transport, rate kinetics of ATP turnover from electrochemical gradients, and finally, integrative modeling of the chromatophore, a photosynthetic pseudo-organelle.

Interpreting the effect of mutations to protein binding sites from large-scale genomic screens

Predicting the functionality of missense mutations is extremely difficult. Large-scale genomic screens are commonly performed to identify mutational correlates or drivers of disease and treatment resistance, but interpretation of how these mutations impact protein function is limited. One such consequence of mutations to a protein is to impact its ability to bind and interact with partners or small molecules such as ATP, thereby modulating its function. Multiple methods exist for predicting the impact of a single mutation on protein-protein binding energy, but it is difficult in the context of a genomic screen to understand if these mutations with large impacts on binding are more common than statistically expected. We present a methodology for taking mutational data from large-scale genomic screens and generating functional and statistical insights into their role in the binding of proteins both with each other and their small molecule ligands. This allows a quantitative and statistical analysis to determine whether mutations impacting protein binding or ligand interactions are occurring more or less frequently than expected by chance. We achieve this by calculating the potential impact of any possible mutation and comparing an expected distribution to the observed mutations. This method is applied to examples demonstrating its ability to interpret mutations involved in protein-protein binding, protein-DNA interactions, and the evolution of therapeutic resistance.

Protein-Protein Interaction-Gaussian Accelerated Molecular Dynamics (PPI-GaMD): Characterization of Protein Binding Thermodynamics and Kinetics

Protein-protein interactions (PPIs) play key roles in many fundamental biological processes such as cellular signaling and immune responses. However, it has proven challenging to simulate repetitive protein association and dissociation in order to calculate binding free energies and kinetics of PPIs due to long biological timescales and complex protein dynamics. To address this challenge, we have developed a new computational approach to all-atom simulations of PPIs based on a robust Gaussian accelerated molecular dynamics (GaMD) technique. The method, termed "PPI-GaMD", selectively boosts interaction potential energy between protein partners to facilitate their slow dissociation. Meanwhile, another boost potential is applied to the remaining potential energy of the entire system to effectively model the protein's flexibility and rebinding. PPI-GaMD has been demonstrated on a model system of the ribonuclease barnase interactions with its inhibitor barstar. Six independent 2 μs PPI-GaMD simulations have captured repetitive barstar dissociation and rebinding events, which enable calculations of the protein binding thermodynamics and kinetics simultaneously. The calculated binding free energies and kinetic rate constants agree well with the experimental data. Furthermore, PPI-GaMD simulations have provided mechanistic insights into barstar binding to barnase, which involves long-range electrostatic interactions and multiple binding pathways, being consistent with previous experimental and computational findings of this model system. In summary, PPI-GaMD provides a highly efficient and easy-to-use approach for binding free energy and kinetics calculations of PPIs.

Challenges and frontiers of computational modelling of biomolecular recognition

Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterise biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.

Multiscale Model for Quantitative Prediction of Insulin Aggregation Nucleation Kinetics

We combined kinetic, thermodynamic, and structural information from single-molecule (protein folding) and two-molecule (association) explicit-solvent simulations for determination of kinetic parameters in protein aggregation nucleation with insulin as the model protein. A structural bioinformatics approach was developed to account for heterogeneity of aggregation-prone species, with the transition complex theory found applicable in modeling association kinetics involving non-native species. Specifically, the kinetic pathway for formation of aggregation-prone oligomeric species was found to contain a structurally specific dominant binding mode, making the kinetic process similar to native protein association. The kinetic parameters thus obtained were used in a population balance model, and accurate predictions for aggregation nucleation time varying over 2 orders of magnitude with changes in either insulin concentration or an aggregation-inhibitor ligand concentration were obtained, while an empirical parameter set was not found to be transferable for prediction of ligand effects. Further, this physically determined kinetic parameter set provided several mechanistic insights, such as identification of the rate-limiting step in aggregation nucleation and a quantitative explanation for the switch from Arrhenius to non-Arrhenius aggregation kinetics around the melting temperature of insulin.

Multiscale Approach for Computing Gated Ligand Binding from Molecular Dynamics and Brownian Dynamics Simulations

We develop an approach to characterize the effects of gating by a multiconformation protein consisting of macrostate conformations that are either accessible or inaccessible to ligand binding. We first construct a Markov state model of the apo-protein from atomistic molecular dynamics simulations from which we identify macrostates and their conformations, compute their relative macrostate populations and interchange kinetics, and structurally characterize them in terms of ligand accessibility. We insert the calculated first-order rate constants for conformational transitions into a multistate gating theory from which we derive a gating factor γ that quantifies the degree of conformational gating. Applied to HIV-1 protease, our approach yields a kinetic network of three accessible (semi-open, open, and wide-open) and two inaccessible (closed and a newly identified, "parted") macrostate conformations. The parted conformation sterically partitions the active site, suggesting a possible role in product release. We find that the binding kinetics of drugs and drug-like inhibitors to HIV-1 protease falls in the slow gating regime. However, because γ = 0.75, conformational gating only modestly slows ligand binding. Brownian dynamics simulations of the diffusional association of eight inhibitors to the protease─having a wide range of experimental association constants (∼10⁴-10¹⁰ M^-1 s^-1)─yields gated rate constants in the range of ∼0.5-5.7 × 10⁸ M^-1 s^-1. This indicates that, whereas the association rate of some inhibitors could be described by the model, for many inhibitors either subsequent conformational transitions or alternate binding mechanisms may be rate-limiting. For systems known to be modulated by conformational gating, the approach could be scaled computationally efficiently to screen association kinetics for a large number of ligands.

Influence of molecular rebinding on the reaction rate of complex formation

We simulated Brownian diffusion and reaction-diffusion processes to study the influence of molecular rebinding on the reaction rates of bimolecular reactions. We found that the number of rebinding events, N_reb, is proportional to the target's size and inversely proportional to the diffusion coefficient D and simulation time-step Δt. We found the proportionality constant close to π^-1/2. We confirmed that N_reb is defined as a ratio of the activation-limited rate constant k_a to the diffusion-limited rate constant, k_D. We provide the formula describing the reactivity coefficient κ, modelling the transient-native complex transition for the activation-controlled reaction rates. We show that κ is proportional to (D/Δt)^1/2. Finally, we apply our rebinding-including reaction rate model to the real reactions of photoacid dissociation and protein association. Based on literature data for both types of reactions, we found the Δt time-scale. We show that for the photodissociation of a proton, the Δt is equal to 171 ± 18 fs and the average number of rebinding events is approximately equal to 40. For proteins, Δt is of the order of 100 ps with around 20 rebinding events. In both cases the timescale is similar to the timescale of fluctuation of the solvent molecules surrounding the reactants; vibrations and bending in the case of photoacid dissociation and diffusional motion for proteins.

Enhancing the population of the encounter complex affects protein complex formation efficiency

Optimal charge distribution is considered to be important for efficient formation of protein complexes. Electrostatic interactions guide encounter complex formation that precedes the formation of an active protein complex. However, disturbing the optimized distribution by introduction of extra charged patches on cytochrome c peroxidase does not lead to a reduction in productive encounters with its partner cytochrome c. To test whether a complex with a high population of encounter complex is more easily affected by suboptimal charge distribution, the interactions of cytochrome c mutant R13A with wild‐type cytochrome c peroxidase and a variant with an additional negative patch were studied. The complex of the peroxidase and cytochrome c R13A was reported to have an encounter state population of 80%, compared to 30% for the wild‐type cytochrome c. NMR analysis confirms the dynamic nature of the interaction and demonstrates that the mutant cytochrome c samples the introduced negative patch. Kinetic experiments show that productive complex formation is fivefold to sevenfold slower at moderate and high ionic strength values for cytochrome c R13A but the association rate is not affected by the additional negative patch on cytochrome c peroxidase, showing that the total charge on the protein surface can compensate for less optimal charge distribution. At low ionic strength (44 mm), the association with the mutant cytochrome c reaches the same high rates as found for wild‐type cytochrome c, approaching the diffusion limit.

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.

Thermodynamics and Free Energy Landscape of BAR-Domain Dimerization from Molecular Simulations

Proteins with BAR domains function to bind to and remodel biological membranes, where the dimerization of BAR domains is a key step in this function. These domains can dimerize in solution or after localizing to the membrane surface. Here, we characterize the binding thermodynamics of homodimerization between the LSP1 BAR domain proteins in solution, using molecular dynamics (MD) simulations. By combining the MARTINI coarse-grained protein models with enhanced sampling through metadynamics, we construct a two-dimensional free energy surface quantifying the bound versus unbound ensembles as a function of two distance variables. With this methodology, our simulations can simultaneously characterize the structures and relative stabilities of a range of sampled dimers, portraying a heterogeneous and extraordinarily stable bound ensemble, where the proper crystal structure dimer is the most stable in a 100 mM NaCl solution. Nonspecific dimers that are sampled involve contacts that are consistent with experimental structures of higher-order oligomers formed by the LSP1 BAR domain. Because the BAR dimers and oligomers can assemble on membranes, we characterize the relative alignment of the known membrane binding patches, finding that only the specific dimer is aligned to form strong interactions with the membrane. Hence, we would predict a strong selection of the specific dimer in binding to or assembling when on the membrane. Establishing the pairwise stabilities of homodimer contacts is difficult experimentally when the proteins form stable oligomers, but through the method used here, we can isolate these contacts, providing a foundation to study the same interactions on the membrane.

Model Lipid Membranes Assembled from Natural Plant Thylakoids into 2D Microarray Patterns as a Platform to Assess the Organization and Photophysics of Light-Harvesting Proteins

Natural photosynthetic "thylakoid" membranes found in green plants contain a large network of light-harvesting (LH) protein complexes. Rearrangement of this photosynthetic machinery, laterally within stacked membranes called "grana", alters protein-protein interactions leading to changes in the energy balance within the system. Preparation of an experimentally accessible model system that allows the detailed investigation of these complex interactions can be achieved by interfacing thylakoid membranes and synthetic lipids into a template comprised of polymerized lipids in a 2D microarray pattern on glass surfaces. This paper uses this system to interrogate the behavior of LH proteins at the micro- and nanoscale and assesses the efficacy of this model. A combination of fluorescence lifetime imaging and atomic force microscopy reveals the differences in photophysical state and lateral organization between native thylakoid and hybrid membranes, the mechanism of LH protein incorporation into the developing hybrid membranes, and the nanoscale structure of the system. The resulting model system within each corral is a high-quality supported lipid bilayer that incorporates laterally mobile LH proteins. Photosynthetic activity is assessed in the hybrid membranes versus proteoliposomes, revealing that commonly used photochemical assays to test the electron transfer activity of photosystem II may actually produce false-positive results.

The Charge Distribution on a Protein Surface Determines Whether Productive or Futile Encounter Complexes Are Formed

Protein complex formation depends strongly on electrostatic interactions. The distribution of charges on the surface of redox proteins is often optimized by evolution to guide recognition and binding. To test the degree to which the electrostatic interactions between cytochrome c peroxidase (CcP) and cytochrome c (Cc) are optimized, we produced five CcP variants, each with a different charge distribution on the surface. Monte Carlo simulations show that the addition of negative charges attracts Cc to the new patches, and the neutralization of the charges in the regular, stereospecific binding site for Cc abolishes the electrostatic interactions in that region entirely. For CcP variants with the charges in the regular binding site intact, additional negative patches slightly enhance productive complex formation, despite disrupting the optimized charge distribution. Removal of the charges in the regular binding site results in a dramatic decrease in the complex formation rate, even in the presence of highly negative patches elsewhere on the surface. We conclude that additional charge patches can result in either productive or futile encounter complexes, depending on whether negative residues are located also in the regular binding site.

Membrane Binding of Antimicrobial Peptides Is Modulated by Lipid Charge Modification

Peptide interactions with lipid bilayers play a key role in a range of biological processes and depend on electrostatic interactions between charged amino acids and lipid headgroups. Antimicrobial peptides (AMPs) initiate the killing of bacteria by binding to and destabilizing their membranes. The multiple peptide resistance factor (MprF) provides a defense mechanism for bacteria against a broad range of AMPs. MprF reduces the negative charge of bacterial membranes through enzymatic conversion of the anionic lipid phosphatidyl glycerol (PG) to either zwitterionic alanyl-phosphatidyl glycerol (Ala-PG) or cationic lysyl-phosphatidyl glycerol (Lys-PG). The resulting change in the membrane charge is suggested to reduce the binding of AMPs to membranes, thus impeding downstream AMP activity. Using coarse-grained molecular dynamics to investigate the effects of these modified lipids on AMP binding to model membranes, we show that AMPs have substantially reduced affinity for model membranes containing Ala-PG or Lys-PG. More than 5000 simulations in total are used to define the relationship between lipid bilayer composition, peptide sequence (using five different membrane-active peptides), and peptide binding to membranes. The degree of interaction of a peptide with a membrane correlates with the membrane surface charge density. Free energy profile (potential of mean force) calculations reveal that the lipid modifications due to MprF alter the energy barrier to peptide helix penetration of the bilayer. These results will offer a guide to the design of novel peptides, which addresses the issue of resistance via MprF-mediated membrane modification.

An electron transfer competent structural ensemble of membrane-bound cytochrome P450 1A1 and cytochrome P450 oxidoreductase

Cytochrome P450 (CYP) heme monooxygenases require two electrons for their catalytic cycle. For mammalian microsomal CYPs, key enzymes for xenobiotic metabolism and steroidogenesis and important drug targets and biocatalysts, the electrons are transferred by NADPH-cytochrome P450 oxidoreductase (CPR). No structure of a mammalian CYP-CPR complex has been solved experimentally, hindering understanding of the determinants of electron transfer (ET), which is often rate-limiting for CYP reactions. Here, we investigated the interactions between membrane-bound CYP 1A1, an antitumor drug target, and CPR by a multiresolution computational approach. We find that upon binding to CPR, the CYP 1A1 catalytic domain becomes less embedded in the membrane and reorients, indicating that CPR may affect ligand passage to the CYP active site. Despite the constraints imposed by membrane binding, we identify several arrangements of CPR around CYP 1A1 that are compatible with ET. In the complexes, the interactions of the CPR FMN domain with the proximal side of CYP 1A1 are supplemented by more transient interactions of the CPR NADP domain with the distal side of CYP 1A1. Computed ET rates and pathways agree well with available experimental data and suggest why the CYP-CPR ET rates are low compared to those of soluble bacterial CYPs.

Efficient Encounter Complex Formation and Electron Transfer to Cytochrome c Peroxidase with an Additional, Distant Electrostatic Binding Site

Electrostatic interactions can strongly increase the efficiency of protein complex formation. The charge distribution in redox proteins is often optimized to steer a redox partner to the electron transfer active binding site. To test whether the optimized distribution is more important than the strength of the electrostatic interactions, an additional negative patch was introduced on the surface of cytochrome c peroxidase, away from the stereospecific binding site, and its effect on the encounter complex as well as the rate of complex formation was determined. Monte Carlo simulations and paramagnetic relaxation enhancement NMR experiments indicate that the partner, cytochrome c, interacts with the new patch. Unexpectedly, the rate of the active complex formation was not reduced, but rather slightly increased. The findings support the idea that for efficient protein complex formation the strength of the electrostatic interaction is more critical than an optimized charge distribution.

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.

Protein-Protein Binding as a Two-Step Mechanism: Preselection of Encounter Poses during the Binding of BPTI and Trypsin

Biomolecular recognition between proteins follows complex mechanisms, the understanding of which can substantially advance drug discovery efforts. Here, we track each step of the binding process in atomistic detail with molecular dynamics simulations using trypsin and its inhibitor bovine pancreatic trypsin inhibitor (BPTI) as a model system. We use umbrella sampling to cover a range of unbinding pathways. Starting from these simulations, we subsequently seed classical simulations at different stages of the process and combine them to a Markov state model. We clearly identify three kinetically separated states (an unbound state, an encounter state, and the final complex) and describe the mechanisms that dominate the binding process. From our model, we propose the following sequence of events. The initial formation of the encounter complex is driven by long-range interactions because opposite charges in trypsin and BPTI draw them together. The encounter complex features the prealigned binding partners with binding sites still partially surrounded by solvation shells. Further approaching leads to desolvation and increases the importance of van der Waals interactions. The native binding pose is adopted by maximizing short-range interactions. Thereby side-chain rearrangements ensure optimal shape complementarity. In particular, BPTI’s P1 residue adapts to the S1 pocket and prime site residues reorient to optimize interactions. After the paradigm of conformation selection, binding-competent conformations of BPTI and trypsin are already present in the apo ensembles and their probabilities increase during this proposed two-step association process. This detailed characterization of the molecular forces driving the binding process includes numerous aspects that have been discussed as central to the binding of trypsin and BPTI and protein complex formation in general. In this study, we combine all these aspects into one comprehensive model of protein recognition. We thereby contribute to enhance our general understanding of this fundamental mechanism, which is particularly critical as the development of biopharmaceuticals continuously gains significance.

The Transient Complex of Cytochrome c and Cytochrome c Peroxidase: Insights into the Encounter Complex from Multifrequency EPR and NMR Spectroscopy

We present a novel approach to study transient protein-protein complexes with standard, 9 GHz, and high-field, 95 GHz, electron paramagnetic resonance (EPR) and paramagnetic NMR at ambient temperatures and in solution. We apply it to the complex of yeast mitochondrial iso-1-cytochrome c (Cc) with cytochrome c peroxidase (CcP) with the spin label [1-oxyl-2,2,5,5-tetramethyl-Δ3-pyrroline-3-methyl)-methanethiosulfonate] attached at position 81 of Cc (SL-Cc). A dissociation constant KD of 20±4×10-6  M (EPR and NMR) and an equal amount of stereo-specific and encounter complex (NMR) are found. The EPR spectrum of the fully bound complex reveals that the encounter complex has a significant population (60 %) that shares important features, such as the Cc-interaction surface, with the stereo-specific complex.

Regulation of adenylyl cyclase 5 in striatal neurons confers the ability to detect coincident neuromodulatory signals

Long-term potentiation and depression of synaptic activity in response to stimuli is a key factor in reinforcement learning. Strengthening of the corticostriatal synapses depends on the second messenger cAMP, whose synthesis is catalysed by the enzyme adenylyl cyclase 5 (AC5), which is itself regulated by the stimulatory Gαolf and inhibitory Gαi proteins. AC isoforms have been suggested to act as coincidence detectors, promoting cellular responses only when convergent regulatory signals occur close in time. However, the mechanism for this is currently unclear, and seems to lie in their diverse regulation patterns. Despite attempts to isolate the ternary complex, it is not known if Gαolf and Gαi can bind to AC5 simultaneously, nor what activity the complex would have. Using protein structure-based molecular dynamics simulations, we show that this complex is stable and inactive. These simulations, along with Brownian dynamics simulations to estimate protein association rates constants, constrain a kinetic model that shows that the presence of this ternary inactive complex is crucial for AC5's ability to detect coincident signals, producing a synergistic increase in cAMP. These results reveal some of the prerequisites for corticostriatal synaptic plasticity, and explain recent experimental data on cAMP concentrations following receptor activation. Moreover, they provide insights into the regulatory mechanisms that control signal processing by different AC isoforms.

Modified Potential Functions Result in Enhanced Predictions of a Protein Complex by All-Atom Molecular Dynamics Simulations, Confirming a Stepwise Association Process for Native Protein-Protein Interactions

The relative prevalence of native protein-protein interactions (PPIs) are the cornerstone for understanding the structure, dynamics and mechanisms of function of protein complexes. In this study, we develop a scheme for scaling the protein-water interaction in the CHARMM36 force field, in order to better fit the solvation free energy of amino acids side-chain analogues. We find that the molecular dynamics simulation with the scaled force field, CHARMM36s, as well as a recently released version, CHARMM36m, effectively improve on the overly sticky association of proteins, such as ubiquitin. We investigate the formation of a heterodimer protein complex between the SAM domains of the EphA2 receptor and the SHIP2 enzyme by performing a combined total of 48 μs simulations with the different potential functions. While the native SAM heterodimer is only predicted at a low rate of 6.7% with the original CHARMM36 force field, the yield is increased to 16.7% with CHARMM36s, and to 18.3% with CHARMM36m. By analyzing the 25 native SAM complexes formed in the simulations, we find that their formation involves a preorientation guided by Coulomb interactions, consistent with an electrostatic steering mechanism. In 12 cases, the complex could directly transform to the native protein interaction surfaces with only small adjustments in domain orientation. In the other 13 cases, orientational and/or translational adjustments are needed to reach the native complex. Although the tendency for non-native complexes to dissociate has nearly doubled with the modified potential functions, a dissociation followed by a reassociation to the correct complex structure is still rare. Instead, the remaining non-native complexes undergo configurational changes/surface searching, which, however, rarely leads to native structures on a time scale of 250 ns. These observations provide a rich picture of the mechanisms of protein-protein complex formation and suggest that computational predictions of native complex PPIs could be improved further.

Modeling the Self-Assembly of Protein Complexes through a Rigid-Body Rotational Reaction-Diffusion Algorithm

The reaction-diffusion equations provide a powerful framework for modeling nonequilibrium, cell-scale dynamics over the long time scales that are inaccessible by traditional molecular modeling approaches. Single-particle reaction-diffusion offers the highest resolution technique for tracking such dynamics, but it has not been applied to the study of protein self-assembly due to its treatment of reactive species as single-point particles. Here, we develop a relatively simple but accurate approach for building rigid structure and rotation into single-particle reaction-diffusion methods, providing a rate-based method for studying protein self-assembly. Our simplifying assumption is that reactive collisions can be evaluated purely on the basis of the separations between the sites, and not their orientations. The challenge of evaluating reaction probabilities can then be performed using well-known equations based on translational diffusion in both 3D and 2D, by employing an effective diffusion constant we derive here. We show how our approach reproduces both the kinetics of association, which is altered by rotational diffusion, and the equilibrium of reversible association, which is not. Importantly, the macroscopic kinetics of association can be predicted on the basis of the microscopic parameters of our structurally resolved model, allowing for critical comparisons with theory and other rate-based simulations. We demonstrate this method for efficient, rate-based simulations of self-assembly of clathrin trimers, highlighting how formation of regular lattices impacts the kinetics of association.

MCMap-A Computational Tool for Mapping Energy Landscapes of Transient Protein-Protein Interactions

MCMap is a tool particularly well-suited for analyzing energy landscapes of transient macromolecular complexes. The program applies a Monte Carlo strategy, where the ligand moves randomly in the electrostatic field of the receptor. By applying importance sampling, the major interaction sites are mapped, resulting in a global distribution of ligand-receptor complexes. This approach displays the dynamic character of transiently interacting protein complexes where not a single complex but an ensemble of complexes better describes the protein interactions. The software provides a broad range of analysis options which allow for relating the simulations to experimental data and for interpreting them on a structural level. The application of MCMap is exemplified by the electron-transfer complex of cytochrome c peroxidase and cytochrome c from baker's yeast. The functionality of MCMap and the visualization of simulation data are in particular demonstrated by studying the dependence of the association on ionic strength and on the oxidation state of the binding partner. Furthermore, microscopically, a repulsion of a second ligand can be seen in the ternary complex upon the change of the oxidation state of the bound cytochrome c. The software is made available as open source software together with the example and can be downloaded free of charge from http://www.bisb.uni-bayreuth.de/index.php?page=downloads.

Synthesis meets theory: Past, present and future of rational chemistry

Chemical synthesis has its roots in the empirical approach of alchemy. Nonetheless, the birth of the scientific method, the technical and technological advances (exploiting revolutionary discoveries in physics) and the improved management and sharing of growing databases greatly contributed to the evolution of chemistry from an esoteric ground into a mature scientific discipline during these last 400 years. Furthermore, thanks to the evolution of computational resources, platforms and media in the last 40 years, theoretical chemistry has added to the puzzle the final missing tile in the process of “rationalizing” chemistry. The use of mathematical models of chemical properties, behaviors and reactivities is nowadays ubiquitous in literature. Theoretical chemistry has been successful in the difficult task of complementing and explaining synthetic results and providing rigorous insights when these are otherwise unattainable by experiment. The first part of this review walks the reader through a concise historical overview on the evolution of the “model” in chemistry. Salient milestones have been highlighted and briefly discussed. The second part focuses more on the general description of recent state-of-the-art computational techniques currently used worldwide by chemists to produce synergistic models between theory and experiment. Each section is complemented by key-examples taken from the literature that illustrate the application of the technique discussed therein.

Multiscale Simulation of Receptor-Drug Association Kinetics: Application to Neuraminidase Inhibitors

A detailed understanding of the drug-receptor association process is of fundamental importance for drug design. Due to the long time scales of typical binding kinetics, the atomistic simulation of the ligand traveling from bulk solution into the binding site is still computationally challenging. In this work, we apply a multiscale approach of combined Molecular Dynamics (MD) and Brownian Dynamics (BD) simulations to investigate association pathway ensembles for the two prominent H1N1 neuraminidase inhibitors oseltamivir and zanamivir. Including knowledge of the approximate binding site location allows for the selective confinement of detailed but expensive MD simulations and application of less demanding BD simulations for the diffusion controlled part of the association pathway. We evaluate a binding criterion based on the residence time of the inhibitor in the binding pocket and compare it to geometric criteria that require prior knowledge about the binding mechanism. The method ranks the association rates of both inhibitors in qualitative agreement with experiment and yields reasonable absolute values depending, however, on the reaction criteria. The simulated association pathway ensembles reveal that, first, ligands are oriented in the electrostatic field of the receptor. Subsequently, a salt bridge is formed between the inhibitor's carboxyl group and neuraminidase residue Arg368, followed by adopting the native binding mode. Unexpectedly, despite oseltamivir's higher overall association rate, the rate into the intermediate salt-bridge state was found to be higher for zanamivir. The present methodology is intrinsically parallelizable and, although computationally demanding, allows systematic binding rate calculation on selected sets of potential drug molecules.

Protein-Imprinted Polymers: The Shape of Things to Come?

The potential to develop materials with antibody-like molecular recognition properties has helped sustain interest in protein-imprinted polymers over the past several decades. Unfortunately, despite persistent research, the field of noncovalent protein imprinting has seen limited success in terms of achieving materials with high selectivity and high affinity. In this Perspective, important yet sometimes overlooked aspects of the imprinting and binding processes are reviewed to help understand why there has been limited success. In particular, the imprinting and binding processes are viewed through the scope of free radical polymerization and hydrogel swelling theories to underscore the complexity of the synthesis and behavior of protein-imprinted polymers. Additionally, we review the metrics of success commonly used in protein imprinting literature (i.e., adsorption capacity, imprinting factor, and selectivity factor) and consider the relevance of each to the characterization of an imprinted polymer's recognition characteristics. Throughout, common shortcomings are highlighted, and experiments that could help verify or disprove the efficacy of noncovalent protein imprinting are discussed.

Crowding in Cellular Environments at an Atomistic Level from Computer Simulations

The effects of crowding in biological environments on biomolecular structure, dynamics, and function remain not well understood. Computer simulations of atomistic models of concentrated peptide and protein systems at different levels of complexity are beginning to provide new insights. Crowding, weak interactions with other macromolecules and metabolites, and altered solvent properties within cellular environments appear to remodel the energy landscape of peptides and proteins in significant ways including the possibility of native state destabilization. Crowding is also seen to affect dynamic properties, both conformational dynamics and diffusional properties of macromolecules. Recent simulations that address these questions are reviewed here and discussed in the context of relevant experiments.

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.

PB-AM: An open-source, fully analytical linear poisson-boltzmann solver

We present the open source distributed software package Poisson-Boltzmann Analytical Method (PB-AM), a fully analytical solution to the linearized PB equation, for molecules represented as non-overlapping spherical cavities. The PB-AM software package includes the generation of outputs files appropriate for visualization using visual molecular dynamics, a Brownian dynamics scheme that uses periodic boundary conditions to simulate dynamics, the ability to specify docking criteria, and offers two different kinetics schemes to evaluate biomolecular association rate constants. Given that PB-AM defines mutual polarization completely and accurately, it can be refactored as a many-body expansion to explore 2- and 3-body polarization. Additionally, the software has been integrated into the Adaptive Poisson-Boltzmann Solver (APBS) software package to make it more accessible to a larger group of scientists, educators, and students that are more familiar with the APBS framework. © 2016 Wiley Periodicals, Inc.

Calmodulin Gates Aquaporin 0 Permeability through a Positively Charged Cytoplasmic Loop

Aquaporin 0 (AQP0), the major intrinsic protein of the eye lens, plays a vital role in maintaining lens clarity by facilitating the transport of water across lens fiber cell membranes. AQP0 reduces its osmotic water permeability constant (P_f) in response to increases in the external calcium concentration, an effect that is mediated by an interaction with the calcium-binding messenger protein, calmodulin (CaM), and phosphorylation of the CaM-binding site abolishes calcium sensitivity. Despite recent structural characterization of the AQP0-CaM complex, the mechanism by which CaM modulates AQP0 remains poorly understood. By combining atomistic molecular dynamics simulations and oocyte permeability assays, we conclude that serine phosphorylation of AQP0 does not inhibit CaM binding to the whole AQP0 protein. Instead, AQP0 phosphorylation alters calcium sensitivity by modifying the AQP0-CaM interaction interface, particularly at an arginine-rich loop that connects the fourth and fifth transmembrane helices. This previously unexplored loop, which sits outside of the canonical CaM-binding site on the AQP0 cytosolic face, mechanically couples CaM to the pore-gating residues of the second constriction site. We show that this allosteric loop is vital for CaM regulation of the channels, facilitating cooperativity between adjacent subunits and regulating factors such as serine phosphorylation. Similar allosteric interactions may also mediate CaM modulation of the properties of other CaM-regulated proteins.

Analysis of Ligand-Receptor Association and Intermediate Transfer Rates in Multienzyme Nanostructures with All-Atom Brownian Dynamics Simulations

We present the second-generation GeomBD Brownian dynamics software for determining interenzyme intermediate transfer rates and substrate association rates in biomolecular complexes. Substrate and intermediate association rates for a series of enzymes or biomolecules can be compared between the freely diffusing disorganized configuration and various colocalized or complexed arrangements for kinetic investigation of enhanced intermediate transfer. In addition, enzyme engineering techniques, such as synthetic protein conjugation, can be computationally modeled and analyzed to better understand changes in substrate association relative to native enzymes. Tools are provided to determine nonspecific ligand-receptor association residence times, and to visualize common sites of nonspecific association of substrates on receptor surfaces. To demonstrate features of the software, interenzyme intermediate substrate transfer rate constants are calculated and compared for all-atom models of DNA origami scaffold-bound bienzyme systems of glucose oxidase and horseradish peroxidase. Also, a DNA conjugated horseradish peroxidase enzyme was analyzed for its propensity to increase substrate association rates and substrate local residence times relative to the unmodified enzyme. We also demonstrate the rapid determination and visualization of common sites of nonspecific ligand-receptor association by using HIV-1 protease and an inhibitor, XK263. GeomBD2 accelerates simulations by precomputing van der Waals potential energy grids and electrostatic potential grid maps, and has a flexible and extensible support for all-atom and coarse-grained force fields. Simulation software is written in C++ and utilizes modern parallelization techniques for potential grid preparation and Brownian dynamics simulation processes. Analysis scripts, written in the Python scripting language, are provided for quantitative simulation analysis. GeomBD2 is applicable to the fields of biophysics, bioengineering, and enzymology in both predictive and explanatory roles.

Protofold II: Enhanced Model and Implementation for Kinetostatic Protein Folding1

A reliable prediction of three-dimensional (3D) protein structures from sequence data remains a big challenge due to both theoretical and computational difficulties. We have previously shown that our kinetostatic compliance method (KCM) implemented into the Protofold package can overcome some of the key difficulties faced by other de novo structure prediction methods, such as the very small time steps required by the molecular dynamics (MD) approaches or the very large number of samples needed by the Monte Carlo (MC) sampling techniques. In this paper, we improve the free energy formulation used in Protofold by including the typically underrated entropic effects, imparted due to differences in hydrophobicity of the chemical groups, which dominate the folding of most water-soluble proteins. In addition to the model enhancement, we revisit the numerical implementation by redesigning the algorithms and introducing efficient data structures that reduce the expected complexity from quadratic to linear. Moreover, we develop and optimize parallel implementations of the algorithms on both central and graphics processing units (CPU/GPU) achieving speed-ups up to two orders of magnitude on the GPU. Our simulations are consistent with the general behavior observed in the folding process in aqueous solvent, confirming the effectiveness of model improvements. We report on the folding process at multiple levels, namely, the formation of secondary structural elements and tertiary interactions between secondary elements or across larger domains. We also observe significant enhancements in running times that make the folding simulation tractable for large molecules.

Effect of Copper and Zinc on the Single Molecule Self-Affinity of Alzheimer's Amyloid-β Peptides

The presence of trace concentrations of metallic ions, such as copper and zinc, has previously been shown to drastically increase the aggregation rate and neurotoxicity of amyloid-β (Aβ), the peptide implicated in Alzheimer’s disease (AD). The mechanism of why copper and zinc accelerate Aβ aggregation is poorly understood. In this work, we use single molecule force spectroscopy (SMFS) to probe the kinetic and thermodynamic parameters (dissociation constant, K_d, kinetic dissociation rate, k_off, and free energy, ΔG) of the dissociation of an Aβ dimer, the amyloid species which initiates the amyloid cascade. Our results show that nanomolar concentrations of copper do not change the single molecule affinity of Aβ to another Aβ peptide in a statistically significant way, while nanomolar concentrations of zinc decrease the affinity of Aβ-Aβ by an order of magnitude. This suggests that the binding of zinc ion to Aβ may interfere with the binding of Aβ-Aβ, leading to a lower self-affinity.

Recognition Dynamics of p53 and MDM2: Implications for Peptide Design

Peptides that inhibit MDM2 and attenuate MDM2-p53 interactions, thus activating p53, are currently being pursued as anticancer drug leads for tumors harboring wild type p53. The thermodynamic determinants of peptide-MDM2 interactions have been extensively studied. However, a detailed understanding of the dynamics that underlie these interactions is largely missing. In this study, we explore the kinetics of the binding of a set of peptides using Brownian dynamics simulations. We systematically investigate the effect of peptide C-terminal substitutions (Ser, Ala, Asn, Pro) of a Q16ETFSDLWKLLP27 p53-based peptide and a M1PRFMDYWEGLN12 12/1 phage-derived peptide on their interaction dynamics with MDM2. The substitutions modulate peptide residence times around the MDM2 protein. In particular, the highest affinity peptide, Q16ETFSDLWKLLS27, has the longest residence time (t ∼ 25 μs) around MDM2, suggesting its potentially important contribution to binding affinity. The binding of the p53-based peptides appears to be kinetically driven while that of the phage-derived series appears to be thermodynamically driven. The phage-derived peptides were found to adopt distinctly different modes of interaction with the MDM2 protein compared to their p53-based counterparts. The p53-based peptides approach the N-terminal region of the MDM2 protein with the peptide C-terminal end oriented toward the protein, while the M1PRFMDYWEGLN12-based peptides adopt the reverse orientation. To probe the determinants of this switch in orientation, a designed mutant of the phage-derived peptide, R3E (M1PEFMDYWEGLN12), was simulated and found to adopt the orientation adopted by the p53-based peptides and also to result in almost a 5-fold increase in the peptide residence time (∼120 μs) relative to the p53-based peptides. On this basis, we suggest that the R3E mutant phage-derived peptide has a higher affinity for MDM2 than the p53-based peptides and would therefore, competitively inhibit MDM2-p53. The study, therefore, provides a novel computational framework for kinetics-based lead optimization for anticancer drug development strategies.

The neuronal porosome complex in health and disease

Cup-shaped secretory portals at the cell plasma membrane called porosomes mediate the precision release of intravesicular material from cells. Membrane-bound secretory vesicles transiently dock and fuse at the base of porosomes facing the cytosol to expel pressurized intravesicular contents from the cell during secretion. The structure, isolation, composition, and functional reconstitution of the neuronal porosome complex have greatly progressed, providing a molecular understanding of its function in health and disease. Neuronal porosomes are 15 nm cup-shaped lipoprotein structures composed of nearly 40 proteins, compared to the 120 nm nuclear pore complex composed of >500 protein molecules. Membrane proteins compose the porosome complex, making it practically impossible to solve its atomic structure. However, atomic force microscopy and small-angle X-ray solution scattering studies have provided three-dimensional structural details of the native neuronal porosome at sub-nanometer resolution, providing insights into the molecular mechanism of its function. The participation of several porosome proteins previously implicated in neurotransmission and neurological disorders, further attest to the crosstalk between porosome proteins and their coordinated involvement in release of neurotransmitter at the synapse.

Highly Efficient Computation of the Basal kon using Direct Simulation of Protein-Protein Association with Flexible Molecular Models

An essential baseline for determining the extent to which electrostatic interactions enhance the kinetics of protein–protein association is the “basal” k_on, which is the rate constant for association in the absence of electrostatic interactions. However, since such association events are beyond the milliseconds time scale, it has not been practical to compute the basal k_on by directly simulating the association with flexible models. Here, we computed the basal k_on for barnase and barstar, two of the most rapidly associating proteins, using highly efficient, flexible molecular simulations. These simulations involved (a) pseudoatomic protein models that reproduce the molecular shapes, electrostatic, and diffusion properties of all-atom models, and (b) application of the weighted ensemble path sampling strategy, which enhanced the efficiency of generating association events by >130-fold. We also examined the extent to which the computed basal k_on is affected by inclusion of intermolecular hydrodynamic interactions in the simulations.

Energetics of Hydrophilic Protein-Protein Association and the Role of Water

Hydrophilic protein-protein interfaces constitute a major part of all protein-protein interfaces and are thus of great importance. However, the quantitative characterization of their association is still an ongoing challenge and the driving force behind their association remains poorly characterized. Here, we have addressed the association of hydrophilic proteins and the role of water by means of extensive molecular dynamics simulations in explicit water using three well studied protein complexes; Barnase-Barstar, cytochrome c-cytochrome c peroxidase, and the N-terminal domain of enzyme I-histidine-containing phosphocarrier. The one-dimensional free energy profiles obtained from umbrella sampling simulations are downhill or, in other words, barrierless. Using these one-dimensional free energy profiles, the computed standard free energies of binding are -12.7 ± 1.1 kcal/mol, -9.4 ± 0.7 kcal/mol, and -8.4 ± 1.9 kcal/mol that are in reasonable to very good agreement with the experimental values of -19.6 kcal/mol, -8.8 kcal/mol, and -7.8 kcal/mol. As expected, analysis of the confined water between the hydrophilic complex partners shows that the density and the orientational order parameter deviate noticeably from the bulk values, especially at close separations of the confining proteins.

Rapid Estimation of Solvation Energy for Simulations of Protein-Protein Association

We have formulated the Energy by Linear Superposition of Corrections Approximation (ELSCA) for estimating the electrostatic and apolar solvation energy of bringing two proteins into close proximity or into contact as defined by the linearized Poisson-Boltzmann model and a linear function of the solvent-accessible surface area. ELSCA utilizes potentials of mean force between atom types found in the AMBER ff99 force field, a uniform distance-dependent dielectric, and a potential that mimics the change in solvent accessible surface area for bringing two solvated spheres into contact. ELSCA was trained by a linear least-squares fit on more than 39 000 putative complexes, each formed from pairs of nonhomologous proteins with a range of shapes, sizes, and charges. The training set was also designed to capture various stages of complex formation. ELSCA was tested against over 8000 non-native complexes of 45 enzyme/inhibitor, antibody/antigen, and other systems that are known to form complexes and gives an overall correlation of 0.962 with PBSA-derived energies for these complexes. The predictions have a slope of 0.89 on the actual values with a bias of 11.1 kcal/mol. When applied to native complexes of these 45 protein systems, ELSCA reproduces PBSA results with a correlation of 0.787, a slope of 1.13, and a bias of 13.0 kcal/mol. We report parameters for ELSCA in the context of the AMBER ff99 parameter set. Our model is most useful in macromolecular docking and protein association simulations, where large portions of each molecule may be considered rigid.

An Analytical Electrostatic Model for Salt Screened Interactions between Multiple Proteins

We present a new general analytical solution for computing the screened electrostatic interaction between multiple macromolecules of arbitrarily complex charge distributions, assuming they are well described by spherical low dielectric cavities in a higher dielectric medium in the presence of a Debye-Hückel treatment of salt. The benefits to this approach are 3-fold. First, by exploiting multipole expansion theory for the screened Coulomb potential, we can describe direct charge-charge interactions and all significant higher-order cavity polarization effects between low dielectric spherical cavities containing their charges, while treating these higher order terms correctly at all separation distances. Second, our analytical solution is general to arbitrary numbers of macromolecules, is efficient to compute, and can therefore simultaneously provide on-the-fly updates to changes in charge distributions due to protein conformational changes. Third, we can change spatial resolutions of charge description as a function of separation distance without compromising the desired accuracy. While the current formulation describes solutions based on simple spherical geometries, it appears possible to reformulate these electrostatic expressions to smoothly increase spatial resolution back to greater molecular detail of the dielectric boundaries.

"Bind and Crawl" Association Mechanism of Leishmania major Peroxidase and Cytochrome c Revealed by Brownian and Molecular Dynamics Simulations

Leishmania major, the parasitic causative agent of leishmaniasis, produces a heme peroxidase (LmP), which catalyzes the peroxidation of mitochondrial cytochrome c (LmCytc) for protection from reactive oxygen species produced by the host. The association of LmP and LmCytc, which is known from kinetics measurements to be very fast (∼10(8) M(-1) s(-1)), does not involve major conformational changes and has been suggested to be dominated by electrostatic interactions. We used Brownian dynamics simulations to investigate the mechanism of formation of the LmP-LmCytc complex. Our simulations confirm the importance of electrostatic interactions involving the negatively charged D211 residue at the LmP active site, and reveal a previously unrecognized role in complex formation for negatively charged residues in helix A of LmP. The crystal structure of the D211N mutant of LmP reported herein is essentially identical to that of wild-type LmP, reinforcing the notion that it is the loss of charge at the active site, and not a change in structure, that reduces the association rate of the D211N variant of LmP. The Brownian dynamics simulations further show that complex formation occurs via a "bind and crawl" mechanism, in which LmCytc first docks to a location on helix A that is far from the active site, forming an initial encounter complex, and then moves along helix A to the active site. An atomistic molecular dynamics simulation confirms the helix A binding site, and steady state activity assays and stopped-flow kinetics measurements confirm the role of helix A charges in the association mechanism.

A spatiotemporal characterization of the effect of p53 phosphorylation on its interaction with MDM2

The interaction of p53 and MDM2 is modulated by the phosphorylation of p53. This mechanism is key to activating p53, yet its molecular determinants are not fully understood. To study the spatiotemporal characteristics of this molecular process we carried out Brownian dynamics simulations of the interactions of the MDM2 protein with a p53 peptide in its wild type state and when phosphorylated at Thr18 (pThr18) and Ser20 (pSer20). We found that p53 phosphorylation results in concerted changes in the topology of the interaction landscape in the diffusively bound encounter complex domain. These changes hinder phosphorylated p53 peptides from binding to MDM2 well before reaching the binding site. The underlying mechanism appears to involve shift of the peptide away from the vicinity of the MDM2 protein, peptide reorientation, and reduction in peptide residence time relative to wild-type p53 peptide. pThr18 and pSr20 p53 peptides experience reduction in residence times by factors of 13.6 and 37.5 respectively relative to the wild-type p53 peptide, indicating a greater role for Ser20 phosphorylation in abrogating p53 MDM2 interactions. These detailed insights into the effect of phosphorylation on molecular interactions are not available from conventional experimental and theoretical approaches and open up new avenues that incorporate molecular interaction dynamics, for stabilizing p53 against MDM2, which is a major focus of anticancer drug lead development.

Rates and Routes of Electron Transfer of [NiFe]-Hydrogenase in an Enzymatic Fuel Cell

Hydrogenase enzymes are being used in enzymatic fuel cells immobilized on a graphite or carbon electrode surface, for example. The enzyme is used for the anodic oxidation of molecular hydrogen (H2) to produce protons and electrons. The association and orientation of the enzyme at the anode electrode for a direct electron transfer is not completely resolved. The distal FeS-cluster in [NiFe]-hydrogenases contains a histidine residue which is known to play a critical role in the intermolecular electron transfer between the enzyme and the electrode surface. The [NiFe]-hydrogenase graphite electrode association was investigated using Brownian Dynamics simulations. Residues that were shown to be in proximity to the electrode surface were identified (His184, Ser196, Glu461, Glu464), and electron transfer routes connecting the distal FeS-cluster with the surface residues were investigated. Several possible pathways for electron transfer between the distal FeS-cluster and the terminal amino acid residues were probed in terms of their rates of electron transfer using DFT methods. The reorganization energies λ of the distal iron-sulfur cluster and coronene as a molecular model for graphite were calculated. The reorganization energy of the distal (His)(Cys)3 cluster was found to be not very different from that of a standard cubane clusters with a (Cys)4 coordination. Electronic coupling matrix elements and rates of electron transfer for the different pathways were calculated according to the Marcus equation. The rates for glutamate-mediated electrode binding were found to be incompatible with experimental data. A direct electron transfer from the histidine ligand of the distal FeS-cluster to the electrode yielded rates of electron transfer in excellent agreement with experiment. A second pathway, however, from the distal FeS-cluster to the Ser196 residue was found to be equally efficient and feasible.

SDA 7: A modular and parallel implementation of the simulation of diffusional association software

The simulation of diffusional association (SDA) Brownian dynamics software package has been widely used in the study of biomacromolecular association. Initially developed to calculate bimolecular protein-protein association rate constants, it has since been extended to study electron transfer rates, to predict the structures of biomacromolecular complexes, to investigate the adsorption of proteins to inorganic surfaces, and to simulate the dynamics of large systems containing many biomacromolecular solutes, allowing the study of concentration-dependent effects. These extensions have led to a number of divergent versions of the software. In this article, we report the development of the latest version of the software (SDA 7). This release was developed to consolidate the existing codes into a single framework, while improving the parallelization of the code to better exploit modern multicore shared memory computer architectures. It is built using a modular object-oriented programming scheme, to allow for easy maintenance and extension of the software, and includes new features, such as adding flexible solute representations. We discuss a number of application examples, which describe some of the methods available in the release, and provide benchmarking data to demonstrate the parallel performance.

Binding of Yeast Cytochrome c to Forty-Four Charge-Reversal Mutants of Yeast Cytochrome c Peroxidase: Isothermal Titration Calorimetry

Previously, we constructed, expressed, and purified 46 charge-reversal mutants of yeast cytochrome c peroxidase (CcP) and determined their electronic absorption spectra, their reaction with H2O2, and their steady-state catalytic properties [ Pearl , N. M. et al. (2008) Biochemistry 47 , 2766 - 2775 ]. Forty-four of the mutants involve the conversion of either an aspartate or glutamate residue to a lysine residue, while two are positive-to-negative mutations, R31E and K149D. In this paper, we report on a calorimetric study of the interaction of each charge-reversal mutant (excluding the internal mutants D76K and D235K) with recombinant yeast iso-1 ferricytochrome c(C102T) (yCc) under conditions where only one-to-one yCc/CcP complex formation is observed. Thirteen of the 44 surface-site charge-reversal mutants decrease the binding affinity for yCc by a factor of 2 or more. Eight of the 13 mutations (E32K, D33K, D34K, E35K, E118K, E201K, E290K, E291K) occur within, or on the immediate periphery, of the crystallographically defined yCc binding site [ Pelletier , H. and Kraut , J. (1992) Science 258 , 1748 - 1755 ], three of the mutations (D37K, E98K, E209K) are slightly removed from the crystallographic site, and two of the mutations (D165K, D241K) occur on the "back-side" of CcP. The current study is consistent with a model for yCc binding to CcP in which yCc binds predominantly near the region defined by crystallographic structure of the 1:1 yCc-CcP complex, whether as a stable electron-transfer active complex or as part of a dynamic encounter complex.

Multi-haem cytochromes in Shewanella oneidensis MR-1: structures, functions and opportunities

Multi-haem cytochromes are employed by a range of microorganisms to transport electrons over distances of up to tens of nanometres. Perhaps the most spectacular utilization of these proteins is in the reduction of extracellular solid substrates, including electrodes and insoluble mineral oxides of Fe(III) and Mn(III/IV), by species of Shewanella and Geobacter. However, multi-haem cytochromes are found in numerous and phylogenetically diverse prokaryotes where they participate in electron transfer and redox catalysis that contributes to biogeochemical cycling of N, S and Fe on the global scale. These properties of multi-haem cytochromes have attracted much interest and contributed to advances in bioenergy applications and bioremediation of contaminated soils. Looking forward, there are opportunities to engage multi-haem cytochromes for biological photovoltaic cells, microbial electrosynthesis and developing bespoke molecular devices. As a consequence, it is timely to review our present understanding of these proteins and we do this here with a focus on the multitude of functionally diverse multi-haem cytochromes in Shewanella oneidensis MR-1. We draw on findings from experimental and computational approaches which ideally complement each other in the study of these systems: computational methods can interpret experimentally determined properties in terms of molecular structure to cast light on the relation between structure and function. We show how this synergy has contributed to our understanding of multi-haem cytochromes and can be expected to continue to do so for greater insight into natural processes and their informed exploitation in biotechnologies.