Molecular Mechanics Poisson-Boltzmann Surface Area Method Research Articles

Harnessing Brazilian biodiversity database: identification of flavonoids as potential inhibitors of SARS-CoV-2 main protease using computational approaches and all-atom molecular dynamics simulation.

SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is the etiological agent responsible for the global outbreak of COVID-19 (Coronavirus Disease 2019). The main protease of SARS-CoV-2, Mpro, is a key enzyme that plays a vital role in mediating viral replication and transcription. In this study, a comprehensive computational approach was employed to investigate the binding affinity, selectivity, and stability of natural product candidates as potential new antivirals acting on the viral polyprotein processing mediated by SARS-CoV-2 Mpro. A library of 288 flavonoids extracted from Brazilian biodiversity was screened to select potential Mpro inhibitors. An initial filter based on Lipinski's rule of five was applied, and 204 compounds that did not violate any of the Lipinski rules were selected. The compounds were then docked into the active site of Mpro using the GOLD program, and the poses were subsequently re-scored using MM-GBSA (Molecular Mechanics Generalized Born Surface Area) binding free energy calculations performed by AmberTools23. The top five flavonoids with the best MM-GBSA binding free energy values were selected for analysis of their interactions with the active site residues of the protein. Next, we conducted a toxicity and drug-likeness analysis, and non-toxic compounds were subjected to molecular dynamics simulation and free energy calculation using the MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. It was observed that the five selected flavonoids had lower MM-GBSA binding free energy with Mpro than the co-crystal ligand. Furthermore, these compounds also formed hydrogen bonds with two important residues, Cys145 and Glu166, in the active site of Mpro. Two compounds that passed the drug-likeness filter showed stable conformations during the molecular dynamics simulations. Among these, NuBBE_867 exhibited the best MM-PBSA binding free energy value compared to the crystallographic inhibitor. Therefore, this study suggests that NuBBE_867 could be a potential inhibitor against the main protease of SARS-CoV-2 and may be further examined to confirm our results.

Identification of new pentapeptides as potential inhibitors of amyloid–β42 aggregation using virtual screening and molecular dynamics simulations

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease mainly characterized by extracellular accumulation of amyloid-β (Aβ) peptide. Previous studies reported pentapeptide RIIGL as an effective inhibitor of Aβ aggregation and neurotoxicity induced by Aβ aggregates. In this work, a library of 912 pentapeptides based on RIIGL has been designed and assessed for their efficacy to inhibit Aβ42 aggregation using computational techniques. The top hit pentapeptides revealed by molecular docking were further assessed for their binding affinity with Aβ42 monomer using MM-PBSA (molecular mechanics Poisson-Boltzmann surface area) method. The MM-PBSA analysis identified RLAPV, RVVPI, and RIAPA, which bind to Aβ42 monomer with a higher binding affinity −55.80, −46.32, and −44.26 kcal/mol, respectively, as compared to RIIGL (ΔGbinding = −41.29 kcal/mol). The residue-wise binding free energy predicted hydrophobic contacts between Aβ42 monomer and pentapeptides. The secondary structure analysis of the conformational ensembles generated by molecular dynamics (MD) depicted remarkably enhanced sampling of helical and no β–sheet conformations in Aβ42 monomer on the incorporation of RVVPI and RIAPA. Notably, RVVPI and RIAPA destabilized the D23–K28 salt bridge in Aβ42 monomer, which plays a crucial role in Aβ42 oligomer stability and fibril formation. The MD simulations highlighted that the incorporation of proline and arginine in pentapeptides contributed to their strong binding with Aβ42 monomer. Furthermore, RVVPI and RIAPA prevented conformational conversion of Aβ42 monomer to aggregation-prone structures, which, in turn, resulted in a lower aggregation tendency of Aβ42 monomer.

DFT, molecular docking and molecular dynamics simulation studies on some recent natural products revealing their EGFR tyrosine kinase inhibition potential

Phytochemicals are important chemical compounds in pharmaceutical chemistry. These natural compounds have interesting biological activities, including anticancer, as well as many other functions. EGFR (epidermal growth factor receptor) tyrosine kinase inhibition is emerging as one of the accepted methods in the treatment of cancer. On the other hand, computer-aided drug design has become an increasingly important field of study due to its many important advantages such as efficient use of time and other resources. In this study, fourteen phytochemicals which have triterpenoid structure and have recently entered the literature were investigated computationally for their potential as EGFR tyrosine kinase inhibitors. In the study, DFT (density functional theory) calculations, molecular docking, molecular dynamics simulations, binding free energy calculations with the use of MM-PBSA (molecular mechanics Poisson-Boltzmann Surface Area) method, and ADMET predictions were performed. The obtained results were compared to the results obtained for reference drug Gefitinib. Results showed that the investigated natural compounds are promising structures for EGFR tyrosine kinase inhibition. Communicated by Ramaswamy H. Sarma

Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol-water partition coefficient (logP) for druglike molecules using MM-PBSA method.

The logarithm of n-octanol-water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time-consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy-based logP prediction model-FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water to n-octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), thus this method is named as free energy-based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly-used QSPR or machine learning-based logP models, as well as some continuum solvation model-based methods. The root-mean-square error (RMSE) and Pearson correlation coefficient(R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner-up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.

PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important. For the first time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding affinities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding affinities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding affinities.

Computational repurposing approach for targeting the critical spike mutations in B.1.617.2 (delta), AY.1 (delta plus) and C.37 (lambda) SARS-CoV-2 variants using exhaustive structure-based virtual screening, molecular dynamic simulations and MM-PBSA methods

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the contagious coronavirus disease 2019 (COVID-19) which was first identified in Wuhan, China, in December 2019. Around the world, many researchers focused their research on identifying inhibitors against the druggable SARS-CoV-2 targets. The reported genomic mutations have a direct effect on the receptor-binding domain (RBD), which interacts with host angiotensin-converting enzyme 2 (ACE-2) for viral cell entry. These mutations, some of which are variants of concern (VOC), lead to increased morbidity and mortality rates. The newest variants including B.1.617.2 (Delta), AY.1 (Delta plus), and C.37 (Lambda) were considered in this study. Thus, an exhaustive structure-based virtual screening of a ligand library (in which FDA approved drugs are also present) using the drug-likeness screening, molecular docking, ADMET profiling was performed followed by molecular dynamics (MD) simulation, and Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculation to identify compounds or drugs can be repurposed for inhibiting the wild type, Delta, Delta plus and Lambda variants of RBD of the spike protein. Based on the virtual screening steps, two FDA approved drugs, Atovaquone (atv) and Praziquantel (prz), were selected and repurposed as the best candidates of SARS-CoV-2 RBD inhibitors. Molecular docking results display that both atv and prz contribute in different interaction with binding site residues (Gln493, Asn501 and Gly502 in the hydrogen bond formation, Phe490 and Tyr505 in the π- π stacking and Tyr449, Ser494, and Phe497 in the vdW interactions) in the wild type, Delta, Delta plus and Lambda variants of RBD of the spike protein. MD simulations revealed that among the eight studied complexes, the wild type-atv and Delta-prz complexes have the most structural stability over the simulation time. Furthermore, MM-PBSA calculation showed that in the atv containing complexes, highest binding affinity is related to the wild type-atv complex and in the prz containing complexes, it is related to the Delta-prz complex. The validation of docking results was done by comparing with experimental data (heparin in complex with wild type and Delta variants). Also, comparison of the obtained results with the result of simulation of the k22 with the studied proteins showed that atv and prz are suitable inhibitors for these proteins, especially wild type t and Delta variant, respectively. Thus, we found that atv and prz are the best candidate for inhibition of wild type and Delta variant of the spike protein. Also, atv can be an appropriate inhibitor for the Lambda variant. Obtained in silico results may help the development of new anti-COVID-19 drugs.

Biomimetic design of inhibitors of immune checkpoint LILRB4.

Immune checkpoint inhibitors have become a hot spot in the treatment of acute myeloid leukemia (AML), the most common acute leukemia (blood cancer) in adults. In the present study, molecular insights into the molecular interactions between an immune checkpoint leukocyte immunoglobulin-like receptor b4 (LILRB4) and its mAb h128-3 was explored using molecular dynamics (MD) simulation for the biomimetic design of peptide inhibitor of LILRB4. Both hydrophobic interaction and electrostatic interaction were found favorable for the binding of the mAb h128-3 on LILRB4, and hydrophobic interaction was identified as the main driving force. The key amino acid residues for the binding of mAb h128-3 on LILRB4 were identified as Y93, D94, D106, Y34, S103, W107, Y61, N30, E27, Y33, Y59, W95, S92 through MM-PBSA (molecular mechanics-Poisson-Boltzmann surface area) method. Based on this, an inhibitor library with the sequence of SXDXYXSY (Where X is an arbitrary amino acid residue) were designed. Two peptide inhibitors, SADHYHSY and SVDWYHSY were obtained through screening using molecular docking and MD simulations, and then validated by successful blocking of LILRB4 through the covering of LILRB4 surface by these inhibitors. These results would be helpful for the research and development of therapies for AML.

In Silico Studies of Potential Selective Inhibitors of Thymidylate Kinase from Variola virus.

Continuing the work developed by our research group, in the present manuscript, we performed a theoretical study of 10 new structures derived from the antivirals cidofovir and ribavirin, as inhibitor prototypes for the enzyme thymidylate kinase from Variola virus (VarTMPK). The proposed structures were subjected to docking calculations, molecular dynamics simulations, and free energy calculations, using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method, inside the active sites of VarTMPK and human TMPK (HssTMPK). The docking and molecular dynamic studies pointed to structures 2, 3, 4, 6, and 9 as more selective towards VarTMPK. In addition, the free energy data calculated through the MM-PBSA method, corroborated these results. This suggests that these compounds are potential selective inhibitors of VarTMPK and, thus, can be considered as template molecules to be synthesized and experimentally evaluated against smallpox.

Molecular Dynamics and MM-PBSA Studies for Deciphering Molecular Interactions of Valproic Acid with CYP2C9 Mutants F114L and I207T

Valproic Acid (VPA) is a widely used drug, particularly in neuropsychiatric disorders, while showing promise in other types of diseases such as cancer. VPA metabolism via cytochrome P450 (CYP) pathway is responsible from only ~10% of the total drug dose. However, due to high risk of severe adverse reactions in liver and pancreas, interaction of VPA with CYP2C9 remains to be delineated chiefly in CYP2C9 mutants. Hence, here we implemented a molecular dynamics study, followed by MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method based relative binding energy estimation to understand how F114L and I207T CYP2C9 mutants changed their binding mode towards VPA in comparison to wild type (WT) CYP2C9. Results indicated that while F114L and I207T mutants have showed significant decrease in total relative binding energy, compared with WT, there were a clear shift of occupied amino acids responsible for VPA interaction in mutants vs WT. Overall, here for the first time in literature, this novel shift of VPA interacting amino acids in F114L and I207T mutants were reported. Limitations and future perspective of the data were also discussed.

How Well Does the Extended Linear Interaction Energy Method Perform in Accurate Binding Free Energy Calculations?

With continually increased computer power, molecular mechanics force field-based approaches, such as the endpoint methods of molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA), have been routinely applied in both drug lead identification and optimization. However, the MM-PB/GBSA method is not as accurate as the pathway-based alchemical free energy methods, such as thermodynamic integration (TI) or free energy perturbation (FEP). Although the pathway-based methods are more rigorous in theory, they suffer from slow convergence and computational cost. Moreover, choosing adequate perturbation routes is also crucial for the pathway-based methods. Recently, we proposed a new method, coined extended linear interaction energy (ELIE) method, to overcome some disadvantages of the MM-PB/GBSA method to improve the accuracy of binding free energy calculation. In this work, we have systematically assessed this approach using in total 229 protein-ligand complexes for eight protein targets. Our results showed that ELIE performed much better than the molecular docking and MM-PBSA method in terms of root-mean-square error (RMSE), correlation coefficient (R), predictive index (PI), and Kendall's τ. The mean values of PI, R, and τ are 0.62, 0.58, and 0.44 for ELIE calculations. We also explored the impact of the length of simulation, ranging from 1 to 100 ns, on the performance of binding free energy calculation. In general, extending simulation length up to 25 ns could significantly improve the performance of ELIE, while longer molecular dynamics (MD) simulation does not always perform better than short MD simulation. Considering both the computational efficiency and achieved accuracy, ELIE is adequate in filling the gap between the efficient docking methods and computationally demanding alchemical free energy methods. Therefore, ELIE provides a practical solution for the routine ranking of compounds in lead optimization.

Discovery of Novel Tankyrase Inhibitors through Molecular Docking-Based Virtual Screening and Molecular Dynamics Simulation Studies.

Tankyrase enzymes (TNKS), a core part of the canonical Wnt pathway, are a promising target in the search for potential anti-cancer agents. Although several hundreds of the TNKS inhibitors are currently known, identification of their novel chemotypes attracts considerable interest. In this study, the molecular docking and machine learning-based virtual screening techniques combined with the physico-chemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) profile prediction and molecular dynamics simulations were applied to a subset of the ZINC database containing about 1.7 M commercially available compounds. Out of seven candidate compounds biologically evaluated in vitro for their inhibition of the TNKS2 enzyme using immunochemical assay, two compounds have shown a decent level of inhibitory activity with the IC50 values of less than 10 nM and 10 μM. Relatively simple scores based on molecular docking or MM-PBSA (molecular mechanics, Poisson-Boltzmann, surface area) methods proved unsuitable for predicting the effect of structural modification or for accurate ranking of the compounds based on their binding energies. On the other hand, the molecular dynamics simulations and Free Energy Perturbation (FEP) calculations allowed us to further decipher the structure-activity relationships and retrospectively analyze the docking-based virtual screening performance. This approach can be applied at the subsequent lead optimization stages.

Investigation of Binding Affinity between Potential Antiviral Agents and PB2 Protein of Influenza A: Non-equilibrium Molecular Dynamics Simulation Approach.

The PB2 protein of the influenza virus RNA polymerase is a major virulence determinant of influenza viruses. It binds to the cap structure at the 5' end of host mRNA to generate short capped RNA fragments that are used as primers for viral transcription named cap-snatching. A large number of the compounds were shown to bind the minimal cap-binding domain of PB2 to inhibit the cap-snatching machinery. However, their binding in the context of an extended form of the PB2 protein has remained elusive. A previous study reported some promising compounds including azaindole and hydroxymethyl azaindole, which were analyzed here to predict binding affinity to PB2 protein using the steered molecular dynamics (SMD) and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) methods. The results show that the rupture force (Fmax) value of three complexes is in agreement with the binding free energy value (ΔGbind) estimated by the MM-PBSA method, whereas for the non-equilibrium pulling work (Wpull) value a small difference between A_PB2-4 and A_PB2-12 was observed. The binding affinity results indicate the A_PB2-12 complex is more favorable than the A_PB2-4 and A_PB2-16 complexes, which means the inhibitor (12) has the potential to be further developed as anti-influenza agents in the treatment of influenza A.

Study of the effectiveness of various cannabinoid receptor 1 (CB1) agonists using molecular docking and molecular dynamics modeling

The binding of a series of small organic molecules, acting as agonists of the cannabinoid receptor CB1, was investigated by means of three methods of computational chemistry. Binding modes were predicted by means of molecular docking, and binding free energy was estimated via docking, molecular-mechanics Poisson-Boltzmann surface area method, and multistate Bennett acceptance ratio. No evident correlation was observed for the molecules between the experimental characteristics of affinity and three computed binding free energy estimates. The reasons for the discrepancy were discussed.

Prediction and validation of HIV-1 gp41 ecto-transmembrane domain post-fusion trimeric structure using molecular modeling

The glycoproteins on the surface of human immunodeficiency virus (HIV) undergoes cascade of conformational transitions to evade the human immune system. The virus replicates inside the host and infects the T-cells instigating acquired immunodeficiency syndrome (AIDS). The glycoprotein 41 (gp41) of HIV helps to mediate the fusion of virus and host membranes. The detailed mechanism of host cell invasion by virus remains obscure due to the unavailability of experimental structure of complete gp41. In the current study, the post-fusion (PoF) trimeric structure of ecto-domain including transmembrane domain of gp41 was modeled using multiple homologous templates of Simian immunodeficiency virus (SIV) and HIV-1. In order to validate the gp41 model, interactions of three peptide inhibitors: T20, C37 and C34; were studied using all-atom molecular dynamics (MD) simulations, binding free-energy calculation and per-residue energy decomposition analysis. The binding free energy calculated using MM-PBSA (Molecular Mechanics Poisson-Boltzmann surface area) method predicts maximum affinity for C34 and minimum by T20 for gp41, which is in good agreement with the available computational and experimental studies. The van der Waals interaction is a dominant contributor for the peptide-gp41 complexes. The per-residue decomposition of energy confirmed the role of Trp117, Trp120 and Ile124, present in C34 and C37, for the strong hydrophobic interactions with the deep pocket localized around the N-terminal of gp41, which is lacking in T20. The HIV-1 gp41 structure developed in this work can be used in future study to gain insight into the mechanism of virus invasion and probing potent inhibitor to eliminate AIDS.Communicated by Ramaswamy H. Sarma

Investigation of Binding Affinity Between Prokaryotic Proteins (AHU-IHF) and DNAs: Steered Molecular Dynamics Approach.

The aim of this work is to investigate the binding affinity between the prokaryotic proteins-AHU-IHF proteins (AHU (AHU2, TR3, and AHU6) and IHF (IHF-WT and IHF-βE44A))-and DNAs (DNA, H'-DNA, and H'44A-DNA) by using the steered molecular dynamics (SMD) simulation and the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method. The gained results show that although the fluctuation of the pulling force yielded the change of the pulling work, the higher pulling work of the AHU/DNA complexes in comparison to those of the IHF/DNA complexes is not only dependent on the pulling force but also controlled by the change of the trajectory in SMD simulation process. In this study, the pulling work profile not only described the pulling pathway of the complexes but also reflected the hindered process of DNAs when AHU-IHF proteins come out from the binding pocket of DNAs. Additionally, the binding free energy (estimated by the MM-PBSA method) is more confident in giving a true effect to the experimental results in comparison to the pulling force and the pulling work values. These results have also shown a fact that the AHU/DNA complexes were more stable than the IHF/DNA complexes.

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.

Binding affinity of the L-742,001 inhibitor to the endonuclease domain of A/H1N1/PA influenza virus variants: Molecular simulation approaches

The steered molecular dynamics (SMD), molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and free energy perturbation (FEP) methods were used to determine the binding affinity of the L-742,001 inhibitor to the endonuclease domain of the A/H1N1/PA influenza viruses (including wild type (WT) and three mutations I79L, E119D and F105S for both pH1N1 PA and PR8 PA viruses). Calculated results showed that the L-742,001 inhibitor not only binds to the PR8 PAs (1934 A influenza virus) better than to the pH1N1 PAs (2009 A influenza virus) but also more strongly interacts with the WT endonuclease domain than with three mutant variants for both pH1N1 PA and PR8 PA viruses. The binding affinities obtained by the SMD, MM-PBSA and FEP methods attain high correlation with available experimental data. Here the FEP method appears to provide a more accurate determination of the binding affinity than the SMD and MM-PBSA counterparts.

Cyclocurcumin, a curcumin derivative, exhibits immune-modulating ability and is a potential compound for the treatment of rheumatoid arthritis as predicted by the MM-PBSA method.

The control and treatment of rheumatoid arthritis is a challenge in today's world. Therefore, the pursuit of natural disease-modifying antirheumatic drugs (DMRDs) remains a top priority in rheumatology. The present study focused on curcumin and its derivatives in the search for new DMRDs. We focused on prominent p38 mitogen-activated protein (MAP) kinase p38α which is a prime regulator of tumor necrosis factor-α (TNF-α), a key mediator of rheumatoid arthritis. In the present study, we used the X-ray crystallographic structure of p38α for molecular docking simulations and molecular dynamic simulations to study the binding modes of curcumin and its derivatives with the active site of p38α. The ATP-binding domain was used for evaluating curcumin and its derivatives. Molecular docking simulation results were used to select 4 out of 8 compounds. These 4 compounds were simulated using GROMACS molecular simulation platform; the results generated were subjected to molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) calculations. The results showed cyclocurcumin as a potential natural compound for development of a potent DMRD. These data were further supported by inhibition of TNF-α release from lipopolysaccharide (LPS)-stimulated human macrophages following cyclocurcumin treatment.

In silico investigation on the inhibition of Aβ42 aggregation by Aβ40 peptide by potential of mean force study

Recent experimental data revealed that small, soluble Amyloid beta (Aβ42) oligomers, especially dimers impair synaptic plasticity and memory leading to Alzheimer’s disease. Here, we have studied dimerization of Aβ42/Aβ42 homo-dimer and Aβ40/Aβ42 hetero-dimer in terms of free energy profile by all-atom simulations using the ff99SB force field. We have found that in the presence of Aβ40 peptide, there exists a strong tendency to form a hetero-dimer with Aβ42 peptide, suggesting that a possible co-oligomerization. Furthermore, we have investigated the effects of Aβ40 on the Aβ42 peptide. Our study also shows that in presence of Aβ40, the beta-content of Aβ42 monomer is reduced. Additionally, certain residues important for bending in Aβ42 peptide attained an increased flexibility in the presence of Aβ40. The salt-bridge destabilization also manifested the impact of Aβ40 on Aβ42 peptide as a whole. Based on this, one may expect that Aβ40 inhibits the aggregation propensity of Aβ42. Moreover, the binding free energy obtained by the molecular mechanics–Poisson–Boltzmann surface area method also revealed a strong affinity between the two isoforms thereby suggests that Aβ40 binding induces conformational change in Aβ42. Our results suggest that co-oligomerization of Aβ isoforms may play a substantial role in Alzheimer’s disease.

Calculating protein-ligand binding affinities with MMPBSA: Method and error analysis.

Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) methods have become widely adopted in estimating protein-ligand binding affinities due to their efficiency and high correlation with experiment. Here different computational alternatives were investigated to assess their impact to the agreement of MMPBSA calculations with experiment. Seven receptor families with both high-quality crystal structures and binding affinities were selected. First the performance of nonpolar solvation models was studied and it was found that the modern approach that separately models hydrophobic and dispersion interactions dramatically reduces RMSD's of computed relative binding affinities. The numerical setup of the Poisson-Boltzmann methods was analyzed next. The data shows that the impact of grid spacing to the quality of MMPBSA calculations is small: the numerical error at the grid spacing of 0.5 Å is already small enough to be negligible. The impact of different atomic radius sets and different molecular surface definitions was further analyzed and weak influences were found on the agreement with experiment. The influence of solute dielectric constant was also analyzed: a higher dielectric constant generally improves the overall agreement with experiment, especially for highly charged binding pockets. The data also showed that the converged simulations caused slight reduction in the agreement with experiment. Finally the direction of estimating absolute binding free energies was briefly explored. Upon correction of the binding-induced rearrangement free energy and the binding entropy lost, the errors in absolute binding affinities were also reduced dramatically when the modern nonpolar solvent model was used, although further developments were apparently necessary to further improve the MMPBSA methods. © 2016 Wiley Periodicals, Inc.