Modeling Membrane Protein-Ligand Binding Interactions: The Human Purinergic Platelet Receptor

Inhibition of polymorphic MexXY-OprM efflux system in Pseudomonas aeruginosa clinical isolates by Berberine derivatives

The MexXY-OprM multidrug efflux pump (EP) in aminoglycosides resistant Pseudomonas aeruginosa is one of the major resistance mechanisms, which is often overexpressed in strains isolated from pulmonary chronic disease such as cystic fibrosis.[1-3] In this research, we focused on the design of potential efflux pumps inhibitors, targeting MexY, the inner membrane component, in an allosteric site. Berberine[4] has been considered as lead molecule since we previously demonstrated its effectiveness in targeting MexY in laboratory reference strains.[5,6] Since this protein is often present in polymorphic variants in clinical strains, we sequenced and modeled all the mutated forms and we synthesized and evaluated by computational techniques, some berberine derivatives carrying an aromatic functionalization in its 13-C ring position. These compounds were tested in vitro against clinical P. aeruginosa strains for antimicrobial and antibiofilm activity. In conclusion, the results demonstrated the importance of the aromatic moiety functionalization in exerting the EP inhibitory activity in synergy with the aminoglycoside tobramycin. More, we found that aminoacidic composition of MexY in different strains must be considered for predicting potential binding site and affects the different activity of berberine derivatives. Finally, the antibiofilm effect of these new EPIs is promising, particularly for o-CH3-berberine derivative.

Targeting the receptor binding domain and heparan sulfate binding for antiviral drug development against SARS-CoV-2 variants

The emergence of SARS-CoV-2 variants diminished the efficacy of current antiviral drugs and vaccines. Hence, identifying highly conserved sequences and potentially druggable pockets for drug development was a promising strategy against SARS-CoV-2 variants. In viral infection, the receptor-binding domain (RBD) proteins are essential in binding to the host receptor. Others, Heparan sulfate (HS), widely distributed on the surface of host cells, is thought to play a central role in the viral infection cycle of SARS-CoV-2. Therefore, it might be a reasonable strategy for antiviral drug design to interfere with the RBD in the HS binding site. In this study, we used computational approaches to analyze multiple sequences of coronaviruses and reveal important information about the binding of HS to RBD in the SARS-CoV-2 spike protein. Our results showed that the potential hot-spots, including R454 and E471, in RBD, exhibited strong interactions in the HS-RBD binding region. Therefore, we screened different compounds in the natural product database towards these hot-spots to find potential antiviral candidates using LibDock, Autodock vina and furthermore applying the MD simulation in AMBER20. The results showed three potential natural compounds, including Acetoside (ACE), Hyperoside (HYP), and Isoquercitrin (ISO), had a strong affinity to the RBD. Our results demonstrate a feasible approach to identify potential antiviral agents by evaluating the binding interaction between viral glycoproteins and host receptors. The present study provided the applications of the structure-based computational approach for designing and developing of new antiviral drugs against SARS-CoV-2 variants.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Identification of potential agonist-like molecules for α2-adrenergic receptor by multi-layer virtual screening to combat sinusitis

Sinusitis is one of the most common respiratory inflammatory conditions and a significant health issue that affects millions of people worldwide with a global prevalence of 10-15%. The side effects of available drug regimens of sinus infection demand the urgent development of new drug candidates to combat sinusitis. With the aim of identifying new drug-like candidates to control sinus, we have conducted multifold comprehensive screening of drug-like molecules targeting α2-adrenergic receptor (α2-AR), which serve as the primary drug target in sinusitis. By structure-based virtual screening of in-house compound's database, ten molecules (CP1-CP10) with agonistic effects for α2-AR were selected, and their binding mechanism with critical residues of α2-AR and their physicochemical properties were studied. Moreover, the process of receptor activation by these compounds and the conformational changes in α2-AR caused by these molecules, were further explored by molecular dynamic simulation. The MM-PBSA estimated free energies of compounds are higher than that of reference agonist (ΔGTOTAL = -39.0 kcal/mol). Among all, CP2-CP3, CP7-CP8 and CP6 have the highest binding free energies of -78.9 kcal/mol, -77.3 kcal/mol, -75.60 kcal/mol, -64.8 kcal/mol, and -61.6 kcal/mol, respectively. While CP4 (-55.0 kcal/mol), CP5 (-49.2 kcal/mol), CP9 (-54.8 ± 0.07 kcal/mol), CP10 (-56.7 ± 0.10 kcal/mol) and CP1 (-46.0 ± 0.08 kcal/mol) also exhibited significant binding free energies. These energetically favorable binding energies indicate strong binding affinity of our compounds for α2-AR as compared to known partial agonist. Therefore, these molecules can serve as excellent drug-like candidates for sinusitis.

A combined ligand and target-based virtual screening strategy to repurpose drugs as putrescine uptake inhibitors with trypanocidal activity

Chagas disease, also known as American trypanosomiasis, is a neglected tropical disease caused by the protozoa Trypanosoma cruzi, affecting nearly 7 million people only in the Americas. Polyamines are essential compounds for parasite growth, survival, and differentiation. However, because trypanosomatids are auxotrophic for polyamines, they must be obtained from the host by specific transporters. In this investigation, an ensemble of QSAR classifiers able to identify polyamine analogs with trypanocidal activity was developed. Then, a multi-template homology model of the dimeric polyamine transporter of T. cruzi, TcPAT12, was created with Rosetta, and then refined by enhanced sampling molecular dynamics simulations. Using representative snapshots extracted from the trajectory, a docking model able to discriminate between active and inactive compounds was developed and validated. Both models were applied in a parallel virtual screening campaign to repurpose known drugs as anti-trypanosomal compounds inhibiting polyamine transport in T. cruzi. Montelukast, Quinestrol, Danazol, and Dutasteride were selected for in vitro testing, and all of them inhibited putrescine uptake in biochemical assays, confirming the predictive ability of the computational models. Furthermore, all the confirmed hits proved to inhibit epimastigote proliferation, and Quinestrol and Danazol were able to inhibit, in the low micromolar range, the viability of trypomastigotes and the intracellular growth of amastigotes.

Investigating the Dynamic Binding Behavior of PMX53 Cooperating with Allosteric Antagonist NDT9513727 to C5a Anaphylatoxin Chemotactic Receptor 1 through Gaussian Accelerated Molecular Dynamics and Free-Energy Perturbation Simulations

C5a anaphylatoxin chemotactic receptor 1 (C5aR1) is an important target in anti-inflammatory therapeutics. The cyclic peptide antagonist PMX53 binds to the orthosteric site located in the extracellular vestibule of C5aR1, and the non-peptide antagonist NDT9513727 binds to the allosteric site formed by the middle region of TM3 (trans-membrane helix), TM4, and TM5. We catch a sight of the variational binding mode of PMX53 during the Gaussian accelerated molecular dynamic (GaMD) simulations. In the binary complex of C5aR1 and PMX53, the PMX53 takes a dynamic binding mechanism during the simulation. Namely, the side chain of Arg⁶ of PMX53 extends to TM6-TM7 (pose 1) or swings to TM5 (pose 2), forming a salt bridge with Glu199. Meanwhile, in the ternary complex of C5aR1 with PMX53 and NDT9513727, the side chain of Arg⁶ of PMX53 swings to TM5 (pose 2) from extending to TM6-TM7 (pose 1) at the beginning of the GaMD simulation. In subsequent simulation, PMX53 stabilizes in the pose 2 binding mode by forming a stable salt bridge with Glu199. The free-energy perturbation (FEP) calculations demonstrate that pose 1 (ΔG_binding = -10.94 kcal/mol) is more stable in the binary complex and pose 2 (ΔG_binding = -7.91 kcal/mol) is unstable because of highly dynamic TM5. NDT9513727 interacts directly with TM4 and TM5 and stabilizes the hydrophobic stack between the extracellular sides of the two helices. Therefore, pose 2 (ΔG_binding = -16.27 kcal/mol) is notably stable than pose 1 (ΔG_binding = -9.78 kcal/mol) in the ternary complex. The identification of a novel binding mode of PMX53 and the detailed structural information of PMX53 interacting with a receptor obtained by GaMD simulations will be helpful in designing potent antagonists of C5aR1.

MM/PB(GB)SA benchmarks on soluble proteins and membrane proteins

Predicting protein-ligand binding free energy rapidly and accurately remains a challenging question in modern drug discovery. Molecular mechanics/Poisson-Boltzmann (Generalized Born) surface area (MM/PB(GB)SA) has emerged as an essential tool for accelerating cost-efficient binding free energy calculation. This study presents benchmarks with three membrane-bound protein systems and six soluble protein systems. Different parameters were sampled for different benchmarks to explore the highest accuracy. These include ligand charges, protein force fields, extra points, GB models, nonpolar optimization methods, internal dielectric constants and membrane dielectric constants. Comparisons of accuracy were made between MM/PB(GB)SA, docking and free energy perturbation (FEP). The results reveal a competitive performance between MM/PB(GB)SA and FEP. In summary, MM/PB(GB)SA is a powerful approach to predict ligand binding free energy rapidly and accurately. Parameters of MM/PB(GB)SA calculations, such as the GB models and membrane dielectric constants, need to be optimized for different systems. This method can be served as a powerful tool for drug design.

Inhibition of VMAT2 by β2-adrenergic agonists, antagonists, and the atypical antipsychotic ziprasidone

Vesicular monoamine transporter 2 (VMAT2) is responsible for packing monoamine neurotransmitters into synaptic vesicles for storage and subsequent neurotransmission. VMAT2 inhibitors are approved for symptomatic treatment of tardive dyskinesia and Huntington's chorea, but despite being much-studied inhibitors their exact binding site and mechanism behind binding and inhibition of monoamine transport are not known. Here we report the identification of several approved drugs, notably β2-adrenergic agonists salmeterol, vilanterol and formoterol, β2-adrenergic antagonist carvedilol and the atypical antipsychotic ziprasidone as inhibitors of rat VMAT2. Further, plausible binding modes of the established VMAT2 inhibitors reserpine and tetrabenazine and hit compounds salmeterol and ziprasidone were identified using molecular dynamics simulations and functional assays using VMAT2 wild-type and mutants. Our findings show VMAT2 as a potential off-target of treatments with several approved drugs in use today and can also provide important first steps in both drug repurposing and therapy development targeting VMAT2 function.

In Silico Screening of Novel TMPRSS2 Inhibitors for Treatment of COVID-19

COVID-19, a pandemic caused by the virus SARS-CoV-2, has spread globally, necessitating the search for antiviral compounds. Transmembrane protease serine 2 (TMPRSS2) is a cell surface protease that plays an essential role in SARS-CoV-2 infection. Therefore, researchers are searching for TMPRSS2 inhibitors that can be used for the treatment of COVID-19. As such, in this study, based on the crystal structure, we targeted the active site of TMPRSS2 for virtual screening of compounds in the FDA database. Then, we screened lumacaftor and ergotamine, which showed strong binding ability, using 100 ns molecular dynamics simulations to study the stability of the protein–ligand binding process, the flexibility of amino acid residues, and the formation of hydrogen bonds. Subsequently, we calculated the binding free energy of the protein–ligand complex by the MM-PBSA method. The results show that lumacaftor and ergotamine interact with residues around the TMPRSS2 active site, and reached equilibrium in the 100 ns molecular dynamics simulations. We think that lumacaftor and ergotamine, which we screened through in silico studies, can effectively inhibit the activity of TMPRSS2. Our findings provide a basis for subsequent in vitro experiments, having important implications for the development of effective anti-COVID-19 drugs.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Computational Study of Encapsulation of Polyaromatic Hydrocarbons by Endo-Functionalized Receptors in Nonpolar Medium

Polycyclic aromatic hydrocarbons (PAHs) constitute a large group of organic pollutants produced from either natural or artificial sources during the incomplete combustion of fossil fuels or derived from various industrial processes (such as refinery processes of crude petroleum). They are seriously hazardous to human health, and removing them is of major importance. The complexation likeliness with and selective recognition of PAH guests by endo-functionalized molecular tube hosts (host-abu and host-abtu) in a nonpolar medium are investigated using classical molecular dynamics simulation and quantum calculation to probe the factors and the molecular mechanism involved in complexation processes. We examine the role of different guest molecules in the structural changes of hosts, a prelude to van der Waals interactions and binding free energy in the complexation process. These types of host-guest interactions depend on various factors. We find that (i) both the host molecules (host-abtu and host-abu) interact with the guest π-electron cloud almost equally and (ii) these interactions also depend on the molecular size of PAHs. The larger the nonpolar surface area of PAHs, the greater the interactions with the host, and the more extensive the π-electron cloud of the guest, the stronger the interactions. The linear PAHs interact more strongly than isomeric branched/curved PAHs, and the presence of heteroatoms on PAHs decreases the interactions with the host by creating repulsion between the lone pairs of heteroatoms and the π-electron cloud of the host. Noncovalent van der Waals interactions and N-H···π interactions dominate the high affinities of PAHs toward host-abu and host-abtu. The potential of mean force and molecular mechanics Poisson-Boltzmann surface area calculations reveal that all host-guest complexes are energetically stable.

Understanding G Protein Selectivity of Muscarinic Acetylcholine Receptors Using Computational Methods

The neurotransmitter molecule acetylcholine is capable of activating five muscarinic acetylcholine receptors, M1 through M5, which belong to the superfamily of G-protein-coupled receptors (GPCRs). These five receptors share high sequence and structure homology; however, the M1, M3, and M5 receptor subtypes signal preferentially through the Gαq/11 subset of G proteins, whereas the M2 and M4 receptor subtypes signal through the Gαi/o subset of G proteins, resulting in very different intracellular signaling cascades and physiological effects. The structural basis for this innate ability of the M1/M3/M5 set of receptors and the highly homologous M2/M4 set of receptors to couple to different G proteins is poorly understood. In this study, we used molecular dynamics (MD) simulations coupled with thermodynamic analyses of M1 and M2 receptors coupled to both Gαi and Gαq to understand the structural basis of the M1 receptor’s preference for the Gαq protein and the M2 receptor’s preference for the Gαi protein. The MD studies showed that the M1 and M2 receptors can couple to both Gα proteins such that the M1 receptor engages with the two Gα proteins in slightly different orientations and the M2 receptor engages with the two Gα proteins in the same orientation. Thermodynamic studies of the free energy of binding of the receptors to the Gα proteins showed that the M1 and M2 receptors bind more strongly to their cognate Gα proteins compared to their non-cognate ones, which is in line with previous experimental studies on the M3 receptor. A detailed analysis of receptor–G protein interactions showed some cognate-complex-specific interactions for the M2:Gαi complex; however, G protein selectivity determinants are spread over a large overlapping subset of residues. Conserved interaction between transmembrane helices 5 and 6 far away from the G-protein-binding receptor interface was found only in the two cognate complexes and not in the non-cognate complexes. An analysis of residues implicated previously in G protein selectivity, in light of the cognate and non-cognate structures, shaded a more nuanced role of those residues in affecting G protein selectivity. The simulation of both cognate and non-cognate receptor–G protein complexes fills a structural gap due to difficulties in determining non-cognate complex structures and provides an enhanced framework to probe the mechanisms of G protein selectivity exhibited by most GPCRs.

Long-range electron-electron interaction and charge transfer in protein complexes: a numerical approach

With application to the nitrite reductase hexameric protein complex of Desulfovibrio vulgaris, NrfH2A4, we suggest a strategy to compute the energy landscape of electron transfer in large systems of biochemical interest. For small complexes, the energy of all electronic configurations can be scanned completely on the level of a numerical solution of the Poisson-Boltzmann equation. In contrast, larger systems have to be treated using a pair approximation, which is verified here. Effective Coulomb interactions between neighbouring sites of excess electron localization may become as large as 200 meV, and they depend in a nontrivial manner on the intersite distance. We discuss the implications of strong Coulomb interactions on the thermodynamics and kinetics of charging and decharging a protein complex. Finally, we turn to the effect of embedding the system into a biomembrane.

NFBTA: A Potent Cytotoxic Agent against Glioblastoma

Piplartine (PPL), also known as piperlongumine, is a biologically active alkaloid extracted from the Piper genus which has been found to have highly effective anticancer activity against several tumor cell lines. This study investigates in detail the antitumoral potential of a PPL analogue; (E)-N-(4-fluorobenzyl)-3-(3,4,5-trimethoxyphenyl) acrylamide (NFBTA). The anticancer potential of NFBTA on the glioblastoma multiforme (GBM) cell line (U87MG) was determined by 3-(4,5-dimethyl-2-thia-zolyl)-2, 5-diphenyl-2H-tetrazolium bromide (MTT), and lactate dehydrogenase (LDH) release analysis, and the selectivity index (SI) was calculated. To detect cell apoptosis, fluorescent staining via flow cytometry and Hoechst 33258 staining were performed. Oxidative alterations were assessed via colorimetric measurement methods. Alterations in expressions of key genes related to carcinogenesis were determined. Additionally, in terms of NFBTA cytotoxic, oxidative, and genotoxic damage potential, the biosafety of this novel agent was evaluated in cultured human whole blood cells. Cell viability analyses revealed that NFBTA exhibited strong cytotoxic activity in cultured U87MG cells, with high selectivity and inhibitory activity in apoptotic processes, as well as potential for altering the principal molecular genetic responses in U87MG cell growth. Molecular docking studies strongly suggested a plausible anti-proliferative mechanism for NBFTA. The results of the experimental in vitro human glioblastoma model and computational approach revealed promising cytotoxic activity for NFBTA, helping to orient further studies evaluating its antitumor profile for safe and effective therapeutic applications.

Heterogeneous Dielectric Implicit Membrane Model for the Calculation of MMPBSA Binding Free Energies

Membrane-bound protein receptors are a primary biological drug target, but the computational analysis of membrane proteins has been limited. In order to improve molecular mechanics Poisson-Boltzmann surface area (MMPBSA) binding free energy calculations for membrane protein-ligand systems, we have optimized a new heterogeneous dielectric implicit membrane model, with respect to free energy simulations in explicit membrane and explicit water, and implemented it into the Amber software suite. This new model supersedes our previous uniform, single dielectric implicit membrane model by allowing the dielectric constant to vary with depth within the membrane. We calculated MMPBSA binding free energies for the human purinergic platelet receptor (P2Y₁₂R) and two of the muscarinic acetylcholine receptors (M2R and M3R) bound to various antagonist ligands using both membrane models, and we found that the heterogeneous dielectric membrane model has a stronger correlation with experimental binding affinities compared to the older model under otherwise identical conditions. This improved membrane model increases the utility of MMPBSA calculations for the rational design and improvement of future drug candidates.

Robustness and Efficiency of Poisson-Boltzmann Modeling on Graphics Processing Units

Poisson-Boltzmann equation (PBE) based continuum electrostatics models have been widely used in modeling electrostatic interactions in biochemical processes, particularly in estimating protein-ligand binding affinities. Fast convergence of PBE solvers is crucial in binding affinity computations as numerous snapshots need to be processed. Efforts have been reported to develop PBE solvers on graphics processing units (GPUs) for efficient modeling of biomolecules, though only relatively simple successive over-relaxation and conjugate gradient methods were implemented. However, neither convergence nor scaling properties of the two methods are optimal for large biomolecules. On the other hand, geometric multigrid (MG) has been shown to be an optimal solver on CPUs, though no MG have been reported for biomolecular applications on GPUs. This is not a surprise as it is a more complex method and depends on simpler but limited iterative methods such as Gauss-Seidel in its core relaxation procedure. The robustness and efficiency of MG on GPUs are also unclear. Here we present an implementation and a thorough analysis of MG on GPUs. Our analysis shows that robustness is a more pronounced issue than efficiency for both MG and other tested solvers when the single precision is used for complex biomolecules. We further show how to balance robustness and efficiency utilizing MG's overall efficiency and conjugate gradient's robustness, pointing to a hybrid GPU solver with a good balance of efficiency and accuracy. The new PBE solver will significantly improve the computational throughput for a range of biomolecular applications on the GPU platforms.

Computational Analysis for the Rational Design of Anti-Amyloid Beta (Aβ) Antibodies

Alzheimer's disease (AD) is a neurodegenerative disorder that lacks effective treatment options. Anti-amyloid beta (Aβ) antibodies are the leading drug candidates to treat AD, but the results of clinical trials have been disappointing. Introducing rational mutations into anti-Aβ antibodies to increase their effectiveness is a way forward, but the path to take is unclear. In this study, we demonstrate the use of computational fragment-based docking and MMPBSA binding free energy calculations in the analysis of anti-Aβ antibodies for rational drug design efforts. Our fragment-based docking method successfully predicts the emergence of the common EFRH epitope. MD simulations coupled with MMPBSA binding free energy calculations are used to analyze scenarios described in prior studies, and we computationally introduce rational mutations into PFA1 to predict mutations that can improve its binding affinity toward the pE3-Aβ_3-8 form of Aβ. Two out of our four proposed mutations are predicted to stabilize binding. Our study demonstrates that a computational approach may lead to an improved drug candidate for AD in the future.

Molecular insights into substrate binding mechanism of undecaprenyl pyrophosphate with membrane integrated phosphatidyl glycerophosphate phosphatase B (PgpB) using molecular dynamics simulation approach

Undecaprenyl phosphate (C55-P) acts as carrier lipid in the synthesis of peptidoglycan, which is de novo synthesized from dephosphorylation of undecaprenyl pyrophosphate (C55-PP). The phosphatidylglycerol phosphate phosphatase B (PgpB) catalyzes the dephosphorylation of C55-PP and forms C55-P. As no structural study has been made regarding the binding of C55-PP to PgpB, in the current study, in silico molecular docking, followed by 150 ns molecular dynamics simulation of the putative binding complex in membrane/solvent environment has been performed to understand conformational dynamics. Results are compared with simulated apo form and PE inhibitor-bound form. Analysis of correlated residual fluctuation network in apo form, C55-PP bound and PE inhibitor-bound form suggests that difference in dynamic coupling between TM domain and α2 and α3 helix of periplasmic domain provides ligand binding to facilitate catalysis or to show inhibitory activity. Distance distribution in catalytic residual pair, H207-R104; H207-R201 and H207-D211 which stabilizes phosphate-enzyme intermediate shows a narrow peak in 2.4-3.6 Å in substrate-bound compared to apo form. Binding interactions and binding free energy analyses complement the partial inhibition of PE where PE has less binding free energy compared to the C55-PP substrate as well as the difference in binding interaction with catalytic pocket. Thus, the present study provides how substrate binding couples the movement in TM domain and periplasmic domain which might help in the understanding of active site communication in PgpB. C55-PP phosphatase interactions with a catalytic pocket of PgpB provide new insight for designing drugs against bacterial infection.

A Finite Element Solution of Lateral Periodic Poisson-Boltzmann Model for Membrane Channel Proteins

Membrane channel proteins control the diffusion of ions across biological membranes. They are closely related to the processes of various organizational mechanisms, such as: cardiac impulse, muscle contraction and hormone secretion. Introducing a membrane region into implicit solvation models extends the ability of the Poisson–Boltzmann (PB) equation to handle membrane proteins. The use of lateral periodic boundary conditions can properly simulate the discrete distribution of membrane proteins on the membrane plane and avoid boundary effects, which are caused by the finite box size in the traditional PB calculations. In this work, we: (1) develop a first finite element solver (FEPB) to solve the PB equation with a two-dimensional periodicity for membrane channel proteins, with different numerical treatments of the singular charges distributions in the channel protein; (2) add the membrane as a dielectric slab in the PB model, and use an improved mesh construction method to automatically identify the membrane channel/pore region even with a tilt angle relative to the z-axis; and (3) add a non-polar solvation energy term to complete the estimation of the total solvation energy of a membrane protein. A mesh resolution of about 0.25 Å (cubic grid space)/0.36 Å (tetrahedron edge length) is found to be most accurate in linear finite element calculation of the PB solvation energy. Computational studies are performed on a few exemplary molecules. The results indicate that all factors, the membrane thickness, the length of periodic box, membrane dielectric constant, pore region dielectric constant, and ionic strength, have individually considerable influence on the solvation energy of a channel protein. This demonstrates the necessity to treat all of those effects in the PB model for membrane protein simulations.

Recent Developments and Applications of the MMPBSA Method

The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach has been widely applied as an efficient and reliable free energy simulation method to model molecular recognition, such as for protein-ligand binding interactions. In this review, we focus on recent developments and applications of the MMPBSA method. The methodology review covers solvation terms, the entropy term, extensions to membrane proteins and high-speed screening, and new automation toolkits. Recent applications in various important biomedical and chemical fields are also reviewed. We conclude with a few future directions aimed at making MMPBSA a more robust and efficient method.

A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins

Membrane proteins constitute a large portion of the human proteome and perform a variety of important functions as membrane receptors, transport proteins, enzymes, signaling proteins, and more. Computational studies of membrane proteins are usually much more complicated than those of globular proteins. Here, we propose a new continuum model for Poisson-Boltzmann calculations of membrane channel proteins. Major improvements over the existing continuum slab model are as follows: (1) The location and thickness of the slab model are fine-tuned based on explicit-solvent MD simulations. (2) The highly different accessibilities in the membrane and water regions are addressed with a two-step, two-probe grid-labeling procedure. (3) The water pores/channels are automatically identified. The new continuum membrane model is optimized (by adjusting the membrane probe, as well as the slab thickness and center) to best reproduce the distributions of buried water molecules in the membrane region as sampled in explicit water simulations. Our optimization also shows that the widely adopted water probe of 1.4 Å for globular proteins is a very reasonable default value for membrane protein simulations. It gives the best compromise in reproducing the explicit water distributions in membrane channel proteins, at least in the water accessible pore/channel regions. Finally, we validate the new membrane model by carrying out binding affinity calculations for a potassium channel, and we observe good agreement with the experimental results.