Computer Simulation of Protein−Protein Interactions

Engineering Human Pancreatic RNase 1 as an Immunotherapeutic Agent for Cancer Therapy Through Computational and Experimental Studies

Most plant and bacterial toxins are highly immunogenic with non-specific toxic effects. Human ribonucleases are thought to provide a promising basis for reducing the toxic agent's immunogenic properties, which are candidates for cancer therapy. In the cell, the ribonuclease inhibitor (RI) protein binds to the ribonuclease enzyme and forms a tight complex. This study aimed to engineer and provide a gene construct encoding an improved version of Human Pancreatic RNase 1 (HP-RNase 1) to reduce connection to RI and modulate the immunogenic effects of immunotoxins. To further characterize the interaction complex of HP-RNase 1 and RI, we established various in silico and in vitro approaches. These methods allowed us to specifically monitor interactions within native and engineered HP-RNase 1/RI complexes. In silico research involved molecular dynamics (MD) simulations of native and mutant HP-RNase 1 in their free form and when bound to RI. For HP-RNase 1 engineering, we designed five mutations (K8A/N72A/N89A/R92D/E112/A) based on literature studies, as this combination proved effective for the intended investigation. Then, the cDNA encoding HP-RNase 1 was generated by RT-PCR from blood and cloned into the pSYN2 expression vector. Consequently, wild-type and the engineered HP-RNase 1 were over-expressed in E. coli TG1 and purified using an IMAC column directed against a poly-his tag. The protein products were detected by SDS-PAGE and Western blot analysis. HP-RNase 1 catalytic activity, in the presence of various concentrations of RI, demonstrated that the mutated version of the protein is able to escape the ribonuclease inhibitor and target the RNA substrate 2.5 folds more than that of the wild type. From these data, we tend to suggest the engineered recombinant HP-RNase 1 potentially as a new immunotherapeutic agent for application in human cancer therapy.

Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning

Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.

Application of molecular dynamic simulations in modeling the excited state behavior of confined molecules

Relative to isotropic organic solvent medium, the structure and conformation of a reactant molecule in an organized and confining medium are often different. In addition, because of the rigidity of the immediate environment, the reacting molecule have a little freedom to undergo large changes even upon gaining energy or modifications in the electronic structure. These alterations give rise to differences in the photochemistry of a molecular and supramolecular species. In this study, one such example is presented. α-Alkyl dibenzylketones upon excitation in isotropic solvents give products via Norrish type I and type II reactions that are independent of the chain length of the alkyl substituent. On the other hand, when these molecules are enclosed within an organic capsule of volume ~ 550 Å3, they give products that are strikingly dependent on the length of the α-alkyl substitution. These previously reported experimental observations are rationalized based on the structures generated by molecular modeling (docking and molecular dynamics (MD) simulations). It is shown that MD simulations that are utilized extensively in biologically important macromolecules can also be useful to understand the excited state behavior of reactive molecules that are part of supramolecular assemblies. These simulations can provide structural information of the reactant molecule and the surroundings complementing that with the one obtained from 1 and 2D NMR experiments. MD simulated structures of seven α-alkyl dibenzylketones encapsulated within the octa acid capsule provide a clear understanding of their unique behavior in this restricted medium. Because of the rigidity of the medium, these structures although generated in the ground state can rationalize the photochemical behavior of the molecules in the excited state.

In-Silico Selection of Aptamer Targeting SARS-CoV-2 Spike Protein

Aptamers are single-stranded, short DNA or RNA oligonucleotides that can specifically bind to various target molecules. To diagnose the infected cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in time, numerous conventional methods are applied for viral detection via the amplification and quantification of DNA or antibodies specific to antigens on the virus. Herein, we generated a large number of mutated aptamer sequences, derived from a known sequence of receptor-binding domain (RBD)-1C aptamer, specific to the RBD of SARS-CoV-2 spike protein (S protein). Structural similarity, molecular docking, and molecular dynamics (MD) were utilized to screen aptamers and characterize the detailed interactions between the selected aptamers and the S protein. We identified two mutated aptamers, namely, RBD-1CM1 and RBD-1CM2, which presented better docking results against the S protein compared with the RBD-1C aptamer. Through the MD simulation, we further confirmed that the RBD-1CM1 aptamer can form the most stable complex with the S protein based on the number of hydrogen bonds formed between the two biomolecules. Based on the experimental data of quartz crystal microbalance (QCM), the RBD-1CM1 aptamer could produce larger signals in mass change and exhibit an improved binding affinity to the S protein. Therefore, the RBD-1CM1 aptamer, which was selected from 1431 mutants, was the best potential candidate for the detection of SARS-CoV-2. The RBD-1CM1 aptamer can be an alternative biological element for the development of SARS-CoV-2 diagnostic testing.

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.

Epitope-Based Potential Vaccine Candidate for Humoral and Cell-Mediated Immunity to Combat Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic

The emergence of severe acute respiratory syndrome from novel Coronavirus (SARS-CoV-2) has put an immense pressure worldwide where vaccination is believed to be an efficient way for developing hard immunity. Herein, we employ immunoinformatic tools to identify B-cell, T-cell epitopes associated with the spike protein of SARS-CoV-2, which is important for genome release. The results showed that the highly immunogenic epitopes located at the stalk part are mostly conserved compared to the receptor binding domain (RDB). Further, two vaccine candidates were computationally modeled from the linear B-cell, T-cell epitopes. Molecular docking reveals the crucial interactions of the vaccines with immune-receptors, and their stability is assessed by MD simulation studies. The chimeric vaccines showed remarkable binding affinity toward the immune cell receptors computed by the MM/PBSA method. van der Waals and electrostatic interactions are found to be the dominant factors for the stability of the complexes. The molecular-level interaction obtained from this study may provide deeper insight into the process of vaccine development against the pandemic of COVID-19.

Profiling selectivity of chagasin mutants towards cysteine proteases cruzain or cathepsin L through molecular dynamics simulations

Chagasin, an endogenous cysteine protease inhibitor from Trypanosoma cruzi, can control the activity of the parasitic cruzain and its homologous human cathepsin L. While chagasin inhibits both enzymes with similar potency, mutations have different effects on binding to these enzymes. Mutants T31A and T31A/T32A bind well to cathepsin L, but their affinity for cruzain drops ∼40 to 140-fold. On the other hand, the mutant W93A binds well to cruzain, but it loses potency against cathepsin L. Here, we employed molecular dynamics simulations to understand the selectivity in inhibition of cruzain or cathepsin L by chagasin mutants W93A, T31A, and T31A/T32A. Our results allowed profiling the nonbonded interactions in the interfaces of each mutant with these cysteine proteases. Additionally, we observed differences in the binding conformation of the chagasin loops L2 and L6 of the W93A mutant, favoring interactions with cruzain and reducing interactions with cathepsin L. These differences are associated with a partial dissociation of the W93A-cathepsin L complex, providing a likely cause for the selectivity of the mutant W93A towards cruzain.Communicated by Ramaswamy H. Sarma.

Boundary homogenization for trapping patchy particles

Many systems in chemical and biological physics involve diffusing particles being trapped by absorbing patches on otherwise reflecting surfaces. Such systems are commonly studied by boundary homogenization, in which the heterogenous boundary condition on the patchy surface is replaced by a uniform boundary condition involving a single parameter which encapsulates the effective trapping properties of the surface. In prior works on boundary homogenization, the surface is patchy and the diffusing particles are homogeneous. In this paper, we consider the opposite scenario in which a homogenous surface traps patchy particles, which could model proteins with localized binding sites, cells with membrane receptors, or patchy colloids or nanoparticles. We derive an explicit formula for the effective trapping rate which reveals a fundamental interplay between the translational and rotational diffusivities of the patchy particle, a phenomenon not typically seen in boundary homogenization. Motivated by receptors on the cell membrane, our analysis also allows for the possibility that the patches diffuse on the surface of the particle. We formulate the system in terms of a high-dimensional, time-dependent, anisotropic diffusion equation and employ matched asymptotic analysis to derive the effective trapping rate. We confirm our results by detailed numerical simulations.

A force consistent method for electrostatic energy calculation in fluctuating charge model

A practical approach to include the polarization effect in a molecular force field is the fluctuating charge method in which atomic charges vary as the configuration of the molecular system changes. However, the use of the Coulomb formula to evaluate energy in a fluctuating charge method is theoretically inconsistent with the forces given by the fluctuating method. In this work, we propose a force-consistent method to correctly calculate electrostatic energies of molecular systems using a fluctuating charge model (Effective Polarizable Bond or EPB). In this protocol, the electrostatic energy is obtained by numerical interaction of the atomic forces along the MD trajectory, rather than using the default Coulomb formula in the EPB model. Test study on the benchmark Barnase-Barstar protein-protein interaction system demonstrates that although the total electrostatic energy of the system shows little deviation due to the averaging effect, specific residue-residue electrostatic interaction energy is affected and the level of the effect depends on the charges of the interacting residues with charged residues showing pronounced differences in calculated energies between using the current protocol and the standard Coulomb formula. It is recommended that the proposed numerical interaction method should be preferred in the calculation of electrostatic energy in fluctuating charge models used in molecular dynamics simulations.

Quantum Molecular Dynamics, Topological, Group Theoretical and Graph Theoretical Studies of Protein-Protein Interactions

BACKGROUND

Protein-protein interactions (PPIs) are becoming increasingly important as PPIs form the basis of multiple aggregation-related diseases such as cancer, Creutzfeldt-Jakob, and Alzheimer's diseases. This mini-review presents hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies facilitate the discovery of some PPI inhibitors of therapeutic importance.

OBJECTIVE

The objective of this review is to present hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies enable the discovery of some PPI inhibitors of therapeutic importance.

METHODS

This article presents a detailed survey of hybrid quantum dynamics that combines classical and quantum MD for PPIs. The article also surveys various developments pertinent to topological, graph theoretical, group theoretical and docking studies of PPIs and highlight how the methods facilitate the discovery of some PPI inhibitors of therapeutic importance.

RESULTS

It is shown that it is important to include higher-level quantum chemical computations for accurate computations of free energies and electrostatics of PPIs and Drugs with PPIs, and thus techniques that combine classical MD tools with quantum MD are preferred choices. Topological, graph theoretical and group theoretical techniques are shown to be important in studying large network of PPIs comprised of over 100,000 proteins where quantum chemical and other techniques are not feasible. Hence, multiple techniques are needed for PPIs.

CONCLUSION

Drug discovery and our understanding of complex PPIs require multifaceted techniques that involve several disciplines such as quantum chemistry, topology, graph theory, knot theory and group theory, thus demonstrating a compelling need for a multi-disciplinary approach to the problem.

Unbiased Atomistic Insight into the Mechanisms and Solvent Role for Globular Protein Dimer Dissociation

Association and dissociation of proteins are fundamental processes in nature. Although simple to understand conceptually, the details of the underlying mechanisms and role of the solvent are poorly understood. Here, we investigate the dissociation of the hydrophilic β-lactoglobulin dimer by employing transition path sampling. Analysis of the sampled path ensembles reveals a variety of mechanisms: (1) a direct aligned dissociation (2) a hopping and rebinding transition followed by unbinding, and (3) a sliding transition before unbinding. Reaction coordinate and transition-state analysis predicts that, besides native contact and neighboring salt-bridge interactions, solvent degrees of freedom play an important role in the dissociation process. Bridging waters, hydrogen-bonded to both proteins, support contacts in the native state and nearby lying transition-state regions, whereas they exhibit faster dynamics in further lying transition-state regions, rendering the proteins more mobile and assisting in rebinding. Analysis of the structure and dynamics of the solvent molecules reveals that the dry native interface induces enhanced populations of both disordered hydration water near hydrophilic residues and tetrahedrally ordered hydration water nearby hydrophobic residues. Although not exhaustive, our sampling of rare unbiased reactive molecular dynamics trajectories enhances the understanding of protein dissociation via complex pathways including (multiple) rebinding events.

Computational-guided determination of the functional role of 447-52D long CDRH3

447-52D (447) is a human monoclonal antibody that recognizes a conserved epitope in the crown region of the third variable loop (V3) of HIV-1 gp120, and like many anti-HIV-1 antibodies with broad neutralization capabilities, it has a long heavy-chain complementarity determining region (CDRH3). Here, we use a combination of computational mutagenesis and modeling in tandem with fluorescence polarization assays to interrogate the molecular basis of 447 CDRH3 length and the individual contribution of selected CDRH3 residues to affinity. We observe that 447 CDRH3 length provides a large binding surface area and the best enthalpic contributions derived from hydrophobic packing, main-chain hydrogen bonds, electrostatic and van der Waals interactions. We also found out that CDRH3 residue Try100I is critical to 447 binding affinity.

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.

Fragment-based quantum mechanical calculation of protein-protein binding affinities

The electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method has been successfully utilized for efficient linear-scaling quantum mechanical (QM) calculation of protein energies. In this work, we applied the EE-GMFCC method for calculation of binding affinity of Endonuclease colicin-immunity protein complex. The binding free energy changes between the wild-type and mutants of the complex calculated by EE-GMFCC are in good agreement with experimental results. The correlation coefficient (R) between the predicted binding energy changes and experimental values is 0.906 at the B3LYP/6-31G*-D level, based on the snapshot whose binding affinity is closest to the average result from the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculation. The inclusion of the QM effects is important for accurate prediction of protein-protein binding affinities. Moreover, the self-consistent calculation of PB solvation energy is required for accurate calculations of protein-protein binding free energies. This study demonstrates that the EE-GMFCC method is capable of providing reliable prediction of relative binding affinities for protein-protein complexes. © 2018 Wiley Periodicals, Inc.

AESOP: A Python Library for Investigating Electrostatics in Protein Interactions

Electric fields often play a role in guiding the association of protein complexes. Such interactions can be further engineered to accelerate complex association, resulting in protein systems with increased productivity. This is especially true for enzymes where reaction rates are typically diffusion limited. To facilitate quantitative comparisons of electrostatics in protein families and to describe electrostatic contributions of individual amino acids, we previously developed a computational framework called AESOP. We now implement this computational tool in Python with increased usability and the capability of performing calculations in parallel. AESOP utilizes PDB2PQR and Adaptive Poisson-Boltzmann Solver to generate grid-based electrostatic potential files for protein structures provided by the end user. There are methods within AESOP for quantitatively comparing sets of grid-based electrostatic potentials in terms of similarity or generating ensembles of electrostatic potential files for a library of mutants to quantify the effects of perturbations in protein structure and protein-protein association.

Protein-Protein Docking

Protein-protein docking algorithms are powerful computational tools, capable of analyzing the protein-protein interactions at the atomic-level. In this chapter, we will review the theoretical concepts behind different protein-protein docking algorithms, highlighting their strengths as well as their limitations and pointing to important case studies for each method. The methods we intend to cover in this chapter include various search strategies and scoring techniques. This includes exhaustive global search, fast Fourier transform search, spherical Fourier transform-based search, direct search in Cartesian space, local shape feature matching, geometric hashing, genetic algorithm, randomized search, and Monte Carlo search. We will also discuss the different ways that have been used to incorporate protein flexibility within the docking procedure and some other future directions in this field, suggesting possible ways to improve the different methods.

Dynamics theory for molecular liquids based on an interaction site model

Dynamics theories for molecular liquids based on an interaction site model have been developed over the past few decades and proved to be powerful tools to investigate various dynamical phenomena.

Effects of the dielectric properties of the ceramic-solvent interface on the binding of proteins to oxide ceramics: a non-local electrostatic approach

The rapid development of nanoscience and nanotechnology has raised many fundamental questions that significantly impede progress in these fields. In particular, understanding the physicochemical processes at the interface in aqueous solvents requires the development and application of efficient and accurate methods. In the present work we evaluate the electrostatic contribution to the energy of model protein-ceramic complex formation in an aqueous solvent. We apply a non-local (NL) electrostatic approach that accounts for the effects of the short-range structure of the solvent on the electrostatic interactions of the interfacial systems. In this approach the aqueous solvent is considered as a non-ionic liquid, with the rigid and strongly correlated dipoles of the water molecules. We have found that an ordered interfacial aqueous solvent layer at the protein- and ceramic-solvent interfaces reduces the charging energy of both the ceramic and the protein in the solvent, and significantly increases the electrostatic contribution to their association into a complex. This contribution in the presented NL approach was found to be significantly shifted with respect to the classical model at any dielectric constant value of the ceramics. This implies a significant increase of the adsorption energy in the protein-ceramic complex formation for any ceramic material. We show that for several biocompatible ceramics (for example HfO2, ZrO2, and Ta2O5) the above effect predicts electrostatically induced protein-ceramic complex formation. However, in the framework of the classical continuum electrostatic model (the aqueous solvent as a uniform dielectric medium with a high dielectric constant ∼80) the above ceramics cannot be considered as suitable for electrostatically induced complex formation. Our results also show that the protein-ceramic electrostatic interactions can be strong enough to compensate for the unfavorable desolvation effect in the process of protein-ceramic complex formation.

Refined Parameterization of Nonbonded Interactions Improves Conformational Sampling and Kinetics of Protein Folding Simulations

Recent advances in computational technology have enabled brute-force molecular dynamics (MD) simulations of protein folding using physics-based molecular force fields. The extensive sampling of protein conformations afforded by such simulations revealed, however, considerable compaction of the protein conformations in the unfolded state, which is inconsistent with experiment. Here, we show that a set of surgical corrections to nonbonded interactions between amine nitrogen-carboxylate oxygen and aliphatic carbon-carbon atom pairs can considerably improve the realism of protein folding simulations. Specifically, we show that employing our corrections in ∼500 μs all-atom replica-exchange MD simulations of the WW domain and villin head piece proteins increases the size of the denatured proteins' conformations and does not destabilize the native conformations of the proteins. In addition to making the folded conformations a global minimum of the respective free energy landscapes at room temperature, our corrections also make the free energy landscape smoother, considerably accelerating the folding kinetics and, hence, reducing the computational expense of a protein folding simulation.

Theoretical modeling of multiprotein complexes by iSPOT: Integration of small-angle X-ray scattering, hydroxyl radical footprinting, and computational docking

Structural determination of protein-protein complexes such as multidomain nuclear receptors has been challenging for high-resolution structural techniques. Here, we present a combined use of multiple biophysical methods, termed iSPOT, an integration of shape information from small-angle X-ray scattering (SAXS), protection factors probed by hydroxyl radical footprinting, and a large series of computationally docked conformations from rigid-body or molecular dynamics (MD) simulations. Specifically tested on two model systems, the power of iSPOT is demonstrated to accurately predict the structures of a large protein-protein complex (TGFβ-FKBP12) and a multidomain nuclear receptor homodimer (HNF-4α), based on the structures of individual components of the complexes. Although neither SAXS nor footprinting alone can yield an unambiguous picture for each complex, the combination of both, seamlessly integrated in iSPOT, narrows down the best-fit structures that are about 3.2Å and 4.2Å in RMSD from their corresponding crystal structures, respectively. Furthermore, this proof-of-principle study based on the data synthetically derived from available crystal structures shows that the iSPOT-using either rigid-body or MD-based flexible docking-is capable of overcoming the shortcomings of standalone computational methods, especially for HNF-4α. By taking advantage of the integration of SAXS-based shape information and footprinting-based protection/accessibility as well as computational docking, this iSPOT platform is set to be a powerful approach towards accurate integrated modeling of many challenging multiprotein complexes.

Electrostatic Steering Accelerates C3d:CR2 Association

Electrostatic effects are ubiquitous in protein interactions and are found to be pervasive in the complement system as well. The interaction between complement fragment C3d and complement receptor 2 (CR2) has evolved to become a link between innate and adaptive immunity. Electrostatic interactions have been suggested to be the driving factor for the association of the C3d:CR2 complex. In this study, we investigate the effects of ionic strength and mutagenesis on the association of C3d:CR2 through Brownian dynamics simulations. We demonstrate that the formation of the C3d:CR2 complex is ionic strength-dependent, suggesting the presence of long-range electrostatic steering that accelerates the complex formation. Electrostatic steering occurs through the interaction of an acidic surface patch in C3d and the positively charged CR2 and is supported by the effects of mutations within the acidic patch of C3d that slow or diminish association. Our data are in agreement with previous experimental mutagenesis and binding studies and computational studies. Although the C3d acidic patch may be locally destabilizing because of unfavorable Coulombic interactions of like charges, it contributes to the acceleration of association. Therefore, acceleration of function through electrostatic steering takes precedence to stability. The site of interaction between C3d and CR2 has been the target for delivery of CR2-bound nanoparticle, antibody, and small molecule biomarkers, as well as potential therapeutics. A detailed knowledge of the physicochemical basis of C3d:CR2 association may be necessary to accelerate biomarker and drug discovery efforts.

Bridging scales through multiscale modeling: a case study on protein kinase A

The goal of multiscale modeling in biology is to use structurally based physico-chemical models to integrate across temporal and spatial scales of biology and thereby improve mechanistic understanding of, for example, how a single mutation can alter organism-scale phenotypes. This approach may also inform therapeutic strategies or identify candidate drug targets that might otherwise have been overlooked. However, in many cases, it remains unclear how best to synthesize information obtained from various scales and analysis approaches, such as atomistic molecular models, Markov state models (MSM), subcellular network models, and whole cell models. In this paper, we use protein kinase A (PKA) activation as a case study to explore how computational methods that model different physical scales can complement each other and integrate into an improved multiscale representation of the biological mechanisms. Using measured crystal structures, we show how molecular dynamics (MD) simulations coupled with atomic-scale MSMs can provide conformations for Brownian dynamics (BD) simulations to feed transitional states and kinetic parameters into protein-scale MSMs. We discuss how milestoning can give reaction probabilities and forward-rate constants of cAMP association events by seamlessly integrating MD and BD simulation scales. These rate constants coupled with MSMs provide a robust representation of the free energy landscape, enabling access to kinetic, and thermodynamic parameters unavailable from current experimental data. These approaches have helped to illuminate the cooperative nature of PKA activation in response to distinct cAMP binding events. Collectively, this approach exemplifies a general strategy for multiscale model development that is applicable to a wide range of biological problems.

The cis conformation of proline leads to weaker binding of a p53 peptide to MDM2 compared to trans

The cis and trans conformations of the Xaa-Pro (Xaa: any amino acid) peptide bond are thermodynamically stable while other peptide bonds strongly prefer trans. The effect of proline cis-trans isomerization on protein binding has not been thoroughly investigated. In this study, computer simulations were used to calculate the absolute binding affinity for a p53 peptide (residues 17-29) to MDM2 for both cis and trans isomers of the p53 proline in position 27. Results show that the cis isomer of p53(17-29) binds more weakly to MDM2 than the trans isomer, and that this is primarily due to the difference in the free energy cost associated with the loss of conformational entropy of p53(17-29) when it binds to MDM2. The population of cis p53(17-29) was estimated to be 0.8% of the total population in the bound state. The stronger binding of trans p53(17-29) to MDM2 compared to cis may leave a minimal level of p53 available to respond to cellular stress. This study demonstrates that it is feasible to estimate the absolute binding affinity for an intrinsically disordered protein fragment binding to an ordered protein that are in good agreement with experimental results.

Investigation of Carbohydrate Recognition via Computer Simulation

Carbohydrate recognition by proteins, such as lectins and other (bio)molecules, can be essential for many biological functions. Recently, interest has arisen due to potential protein and drug design and future bioengineering applications. A quantitative measurement of carbohydrate-protein interaction is thus important for the full characterization of sugar recognition. We focus on the aspect of utilizing computer simulations and biophysical models to evaluate the strength and specificity of carbohydrate recognition in this review. With increasing computational resources, better algorithms and refined modeling parameters, using state-of-the-art supercomputers to calculate the strength of the interaction between molecules has become increasingly mainstream. We review the current state of this technique and its successful applications for studying protein-sugar interactions in recent years.

On the energy components governing molecular recognition in the framework of continuum approaches

Molecular recognition is a process that brings together several biological macromolecules to form a complex and one of the most important characteristics of the process is the binding free energy. Various approaches exist to model the binding free energy, provided the knowledge of the 3D structures of bound and unbound molecules. Among them, continuum approaches are quite appealing due to their computational efficiency while at the same time providing predictions with reasonable accuracy. Here we review recent developments in the field emphasizing on the importance of adopting adequate description of physical processes taking place upon the binding. In particular, we focus on the efforts aiming at capturing some of the atomistic details of the binding phenomena into the continuum framework. When possible, the energy components are reviewed independently of each other. However, it is pointed out that rigorous approaches should consider all energy contributions on the same footage. The two major schemes for utilizing the individual energy components to predict binding affinity are outlined as well.

Energy landscape of all-atom protein-protein interactions revealed by multiscale enhanced sampling

Protein-protein interactions are regulated by a subtle balance of complicated atomic interactions and solvation at the interface. To understand such an elusive phenomenon, it is necessary to thoroughly survey the large configurational space from the stable complex structure to the dissociated states using the all-atom model in explicit solvent and to delineate the energy landscape of protein-protein interactions. In this study, we carried out a multiscale enhanced sampling (MSES) simulation of the formation of a barnase-barstar complex, which is a protein complex characterized by an extraordinary tight and fast binding, to determine the energy landscape of atomistic protein-protein interactions. The MSES adopts a multicopy and multiscale scheme to enable for the enhanced sampling of the all-atom model of large proteins including explicit solvent. During the 100-ns MSES simulation of the barnase-barstar system, we observed the association-dissociation processes of the atomistic protein complex in solution several times, which contained not only the native complex structure but also fully non-native configurations. The sampled distributions suggest that a large variety of non-native states went downhill to the stable complex structure, like a fast folding on a funnel-like potential. This funnel landscape is attributed to dominant configurations in the early stage of the association process characterized by near-native orientations, which will accelerate the native inter-molecular interactions. These configurations are guided mostly by the shape complementarity between barnase and barstar, and lead to the fast formation of the final complex structure along the downhill energy landscape.

Dynamic nature of the protein-protein interactions is an important element of cellular processes such as metabolic reactions and signal transduction, but its atomistic details are still unclear. Computational survey using molecular dynamics simulation is a straightforward method to elucidate these atomistic protein-protein interaction processes. However, a sufficient configurational sampling of the large system containing the atomistic protein complex model and explicit solvent remains a great challenge due to the long timescale involved. Here, we demonstrate that the multiscale enhanced sampling (MSES) successfully captured the atomistic details of the association/dissociation processes of a barnase-barstar complex covering the sampled space from the native complex structure to fully non-native configurations. The landscape derived from the simulation indicates that the association process is funnel-like downhill, analogously to the funnel landscape of fast-folding proteins. The funnel was found to be originated from near-native orientations guided by the shape complementarity between barnase and barstar, accelerating the formation of native inter-molecular interactions to complete the final complex structure.

Molecular docking uncovers TSPY binds more efficiently with eEF1A2 compared to eEF1A1

Testis-specific protein, Y-encoded (TSPY) binds to eukaryotic translation elongation factor 1 alpha (eEF1A) at its SET/NAP domain that is essential for the elongation during protein synthesis implicated with normal spermatogenesis. The eEF1A exists in two forms, eEF1A1 (alpha 1) and eEF1A2 (alpha 2), encoded by separate loci. Despite critical interplay of the TSPY and eEF1A proteins, literature remained silent on the residues playing significant roles during such interactions. We deduced 3D structures of TSPY and eEF1A variants by comparative modeling (Modeller 9.13) and assessed protein-protein interactions employing HADDOCK docking. Pairwise alignment using EMBOSS Needle for eEF1A1 and eEF1A2 proteins revealed high degree (~92%) of homology. Efficient binding of TSPY with eEF1A2 as compared to eEF1A1 was observed, in spite of the occurrence of significant structural similarities between the two variants. We also detected strong interactions of domain III followed by domains II and I of both eEF1A variants with TSPY. In the process, seven interacting residues of TSPY's NAP domain namely, Asp 175, Glu 176, Asp 179, Tyr 183, Asp 240, Glu 244, and Tyr 246 common to both eEF1A variants were detected. Additionally, six lysine residues observed in eEF1A2 suggest their possible role in TSPY-eEF1A2 complex formation essential for germ cell development and spermatogenesis. Thus, more efficient binding of TSPY with eEF1A2 as compared to that of eEF1A1 established autonomous functioning of these two variants. Studies on mutated protein following similar approach would uncover the causative obstruction, between the interacting partners leading to deeper understanding on the structure-function relationship.

Protein mixtures of environmentally friendly zein to understand protein–protein interactions through biomaterials synthesis, hemolysis, and their antimicrobial activities

Protein–protein interactions through biomaterials synthesis for biological applications.

The scoring of poses in protein-protein docking: current capabilities and future directions

BACKGROUND

Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling.

RESULTS

We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically.

CONCLUSIONS

All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.

Protein-Protein Docking Using EMAP in CHARMM and Support Vector Machine: Application to Ab/Ag Complexes

In this work, we have (i) evaluated the ability of the EMAP method implemented in the CHARMM program to generate the correct conformation of Ab/Ag complex structures and (ii) developed a support vector machine (SVM) classifier to detect native conformations among the thousands of refined Ab/Ag configurations using the individual components of the binding free energy based on a thermodynamic cycle as input features in training the SVM. Tests on 24 Ab/Ag complexes from the protein-protein docking benchmark version 3.0 showed that based on CAPRI evaluation criteria, EMAP could generate medium-quality native conformations in each case. Furthermore, the SVM classifier could rank medium/high-quality native conformations mostly in the top six among the thousands of refined Ab/Ag configurations. Thus, Ab-Ag docking can be performed using different levels of protein representations, from grid-based (EMAP) to polar hydrogen (united-atom) to all-atom representation within the same program. The scripts used and the trained SVM are available at the www.charmm.org forum script repository.

Protonation and pK changes in protein-ligand binding

Formation of protein-ligand complexes causes various changes in both the receptor and the ligand. This review focuses on changes in pK and protonation states of ionizable groups that accompany protein-ligand binding. Physical origins of these effects are outlined, followed by a brief overview of the computational methods to predict them and the associated corrections to receptor-ligand binding affinities. Statistical prevalence, magnitude and spatial distribution of the pK and protonation state changes in protein-ligand binding are discussed in detail, based on both experimental and theoretical studies. While there is no doubt that these changes occur, they do not occur all the time; the estimated prevalence varies, both between individual complexes and by method. The changes occur not only in the immediate vicinity of the interface but also sometimes far away. When receptor-ligand binding is associated with protonation state change at particular pH, the binding becomes pH dependent: we review the interplay between sub-cellular characteristic pH and optimum pH of receptor-ligand binding. It is pointed out that there is a tendency for protonation state changes upon binding to be minimal at physiologically relevant pH for each complex (no net proton uptake/release), suggesting that native receptor-ligand interactions have evolved to reduce the energy cost associated with ionization changes. As a result, previously reported statistical prevalence of these changes - typically computed at the same pH for all complexes - may be higher than what may be expected at optimum pH specific to each complex. We also discuss whether proper account of protonation state changes appears to improve practical docking and scoring outcomes relevant to structure-based drug design. An overview of some of the existing challenges in the field is provided in conclusion.

Reduced atomic pair-interaction design (RAPID) model for simulations of proteins

Increasingly, theoretical studies of proteins focus on large systems. This trend demands the development of computational models that are fast, to overcome the growing complexity, and accurate, to capture the physically relevant features. To address this demand, we introduce a protein model that uses all-atom architecture to ensure the highest level of chemical detail while employing effective pair potentials to represent the effect of solvent to achieve the maximum speed. The effective potentials are derived for amino acid residues based on the condition that the solvent-free model matches the relevant pair-distribution functions observed in explicit solvent simulations. As a test, the model is applied to alanine polypeptides. For the chain with 10 amino acid residues, the model is found to reproduce properly the native state and its population. Small discrepancies are observed for other folding properties and can be attributed to the approximations inherent in the model. The transferability of the generated effective potentials is investigated in simulations of a longer peptide with 25 residues. A minimal set of potentials is identified that leads to qualitatively correct results in comparison with the explicit solvent simulations. Further tests, conducted for multiple peptide chains, show that the transferable model correctly reproduces the experimentally observed tendency of polyalanines to aggregate into β-sheets more strongly with the growing length of the peptide chain. Taken together, the reported results suggest that the proposed model could be used to succesfully simulate folding and aggregation of small peptides in atomic detail. Further tests are needed to assess the strengths and limitations of the model more thoroughly.

Biomolecular electrostatics and solvation: a computational perspective

An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view toward describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g. solvent structure, polarization, ion binding, and non-polar behavior) in order to provide a background to understand the different types of solvation models.

Coarse-grained simulations of protein-protein association: an energy landscape perspective

Understanding protein-protein association is crucial in revealing the molecular basis of many biological processes. Here, we describe a theoretical simulation pipeline to study protein-protein association from an energy landscape perspective. First, a coarse-grained model is implemented and its applications are demonstrated via molecular dynamics simulations for several protein complexes. Second, an enhanced search method is used to efficiently sample a broad range of protein conformations. Third, multiple conformations are identified and clustered from simulation data and further projected on a three-dimensional globe specifying protein orientations and interacting energies. Results from several complexes indicate that the crystal-like conformation is favorable on the energy landscape even if the landscape is relatively rugged with metastable conformations. A closer examination on molecular forces shows that the formation of associated protein complexes can be primarily electrostatics-driven, hydrophobics-driven, or a combination of both in stabilizing specific binding interfaces. Taken together, these results suggest that the coarse-grained simulations and analyses provide an alternative toolset to study protein-protein association occurring in functional biomolecular complexes.

Effect of interprotein polarization on protein-protein binding energy

Molecular dynamics simulation in explicit water for the binding of the benchmark barnase-barstar complex was carried out to investigate the effect polarization of interprotein hydrogen bonds on its binding free energy. Our study is based on the AMBER force field but with polarized atomic charges derived from fragment quantum mechanical calculation for the protein complex. The quantum-derived atomic charges include the effect of polarization of interprotein hydrogen bonds, which was absent in the standard force fields that were used in previous theoretical calculations of barnase-barstar binding energy. This study shows that this polarization effect impacts both the static (electronic) and dynamic interprotein electrostatic interactions and significantly lowers the free energy of the barnase-barstar complex.

Predicting protein interactions by Brownian dynamics simulations

We present a newly adapted Brownian-Dynamics (BD)-based protein docking method for predicting native protein complexes. The approach includes global BD conformational sampling, compact complex selection, and local energy minimization. In order to reduce the computational costs for energy evaluations, a shell-based grid force field was developed to represent the receptor protein and solvation effects. The performance of this BD protein docking approach has been evaluated on a test set of 24 crystal protein complexes. Reproduction of experimental structures in the test set indicates the adequate conformational sampling and accurate scoring of this BD protein docking approach. Furthermore, we have developed an approach to account for the flexibility of proteins, which has been successfully applied to reproduce the experimental complex structure from the structure of two unbounded proteins. These results indicate that this adapted BD protein docking approach can be useful for the prediction of protein-protein interactions.

Clustering of HIV-1 Subtypes Based on gp120 V3 Loop electrostatic properties

BACKGROUND

The V3 loop of the glycoprotein gp120 of HIV-1 plays an important role in viral entry into cells by utilizing as coreceptor CCR5 or CXCR4, and is implicated in the phenotypic tropisms of HIV viruses. It has been hypothesized that the interaction between the V3 loop and CCR5 or CXCR4 is mediated by electrostatics. We have performed hierarchical clustering analysis of the spatial distributions of electrostatic potentials and charges of V3 loop structures containing consensus sequences of HIV-1 subtypes.

RESULTS

Although the majority of consensus sequences have a net charge of +3, the spatial distribution of their electrostatic potentials and charges may be a discriminating factor for binding and infectivity. This is demonstrated by the formation of several small subclusters, within major clusters, which indicates common origin but distinct spatial details of electrostatic properties. Some of this information may be present, in a coarse manner, in clustering of sequences, but the spatial details are largely lost. We show the effect of ionic strength on clustering of electrostatic potentials, information that is not present in clustering of charges or sequences. We also make correlations between clustering of electrostatic potentials and net charge, coreceptor selectivity, global prevalence, and geographic distribution. Finally, we interpret coreceptor selectivity based on the N6X7T8|S8X9 sequence glycosylation motif, the specific positive charge location according to the 11/24/25 rule, and the overall charge and electrostatic potential distribution.

CONCLUSIONS

We propose that in addition to the sequence and the net charge of the V3 loop of each subtype, the spatial distributions of electrostatic potentials and charges may also be important factors for receptor recognition and binding and subsequent viral entry into cells. This implies that the overall electrostatic potential is responsible for long-range recognition of the V3 loop with coreceptors CCR5/CXCR4, whereas the charge distribution contributes to the specific short-range interactions responsible for the formation of the bound complex. We also propose a scheme for coreceptor selectivity based on the sequence glycosylation motif, the 11/24/25 rule, and net charge.

Protein-protein binding affinity prediction on a diverse set of structures

MOTIVATION

Accurate binding free energy functions for protein-protein interactions are imperative for a wide range of purposes. Their construction is predicated upon ascertaining the factors that influence binding and their relative importance. A recent benchmark of binding affinities has allowed, for the first time, the evaluation and construction of binding free energy models using a diverse set of complexes, and a systematic assessment of our ability to model the energetics of conformational changes.

RESULTS

We construct a large set of molecular descriptors using commonly available tools, introducing the use of energetic factors associated with conformational changes and disorder to order transitions, as well as features calculated on structural ensembles. The descriptors are used to train and test a binding free energy model using a consensus of four machine learning algorithms, whose performance constitutes a significant improvement over the other state of the art empirical free energy functions tested. The internal workings of the learners show how the descriptors are used, illuminating the determinants of protein-protein binding.

AVAILABILITY

The molecular descriptor set and descriptor values for all complexes are available in the Supplementary Material. A web server for the learners and coordinates for the bound and unbound structures can be accessed from the website: http://bmm.cancerresearchuk.org/~Affinity.

CONTACT

paul.bates@cancer.org.uk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.