Estimation of the Binding Free Energy of AC1NX476 to HIV-1 Protease Wild Type and Mutations Using Free Energy Perturbation Method

Molecular basis of ssDNA recognition by RBM45 protein of neurodegenerative disease from multiple molecular dynamics simulations and energy predictions

Neurodegenerative diseases (NDD) are a group of cognitive and behavioral disorders characterized by progressive loss of neuronal structure and function. As the population ages, the incidence is getting higher and higher, but there is currently no effective treatment. The details of RNA/DNA recognition by the RNA-binding protein RBM45 closely related to neurodegenerative diseases through its two tandem RNA-recognition domains at its N-terminus have important implications for structure-based drug discovery against degenerative diseases. To explore the key characteristics of interaction between ssDNA and RBM45, we performed multiple molecular dynamics (MD) simulations along with MM-PBSA energy prediction on the complexes of wild type (WT) and three mutant RBM45s (K100A, F124A/Y165A, and F29A/F70A/F124A/Y165A) with ssDNA, respectively. The findings suggest that these mutated residues of RBM45 modify the interaction of their surrounding residues with ssDNA, thereby affecting RBM45 protein binding to ssDNA. In contrast with WT RBM45 protein, variations in van der Waals and electrostatic interactions with ssDNA caused by these three RBM45 mutants are critical to affect binding between them. In addition, energy analysis showed that RBM45 is a specific ssDNA-binding protein. The results of our work provide valuable theoretical guidance for the design effective drugs of NDD.

Multiple Molecular Dynamics Simulations and Free-Energy Predictions Uncover the Susceptibility of Variants of HIV-1 Protease against Inhibitors Darunavir and KNI-1657

HIV-1 protease (PR) is considered to be the main targets of anti-AIDS drug design because of its role in the proteolytic processing of viral polyproteins. However, the emergence of drug-resistant HIV has become a major problem in the therapy of HIV-1-infected patients. Focused on the complexes of wild type (WT) PR and two mutant PRs (V32I/L33F/I54M/V82I and V32I/L33F/I54M/I84 V) with inhibitors Darunavir (DRV) and KNI-1657 (KNI), respectively, we have conducted research on the conformational dynamics and the resistance mechanism caused by residue mutations through multiple molecular dynamics (MD) simulations combined with an energy (MM-PBSA and solvated interaction energy (SIE)) prediction. The results indicate that mutated residues of PR alter the distance between flap regions and catalytic sites, the volume of the inner catalytic site, and the curling degree of the flap tips, thereby affecting DRV and KNI inhibitor binding to PR. These mutated residues reduced the binding affinity of the two mutant PRs to DRV, resulting in drug resistance, whereas the two mutant PRs increase the binding affinity with KNI, indicating they enhance the sensitivity to KNI. Compared with the WT PR, the changes in van der Waals interaction and electrostatic interaction in the two variant PRs play a vital part in the binding of PR with DRV and KNI. These results may supply valuable guidance for the design of anti-AIDS drugs targeting PR.

Accurate prediction of relative binding affinities of a series of HIV-1 protease inhibitors using semi-empirical quantum mechanical charge

A major challenge in computer-aided drug design is the accurate estimation of ligand binding affinity. Here, a new approach that combines the adaptive steered molecular dynamics (ASMD) and partial atomic charges calculated by semi-empirical quantum mechanics (SQMPC), namely ASMD-SQMPC, is suggested to predict the ligand binding affinities, with 24 HIV-1 protease inhibitors as testing examples. In the ASMD-SQMPC, the relative binding free energy (ΔG) is reflected by the average maximum potential of mean force (<PMF>_max ) between bound and unbound states. The correlation coefficient (R² ) between the <PMF>_max and experimentally determined ΔG is 0.86, showing a significant improvement compared with the conventional ASMD (R² = 0.52). Therefore, this study provides an efficient approach to predict the relative ΔG and reveals the significance of precise partial atomic charges in the theoretical simulations.

An activity prediction model for steroidal and triterpenoidal inhibitors of Acetylcholinesterase enzyme

Nowadays, the importance of computational methods in the design of therapeutic agents in a more efficient way is indisputable. Particularly, these methods have been important in the design of novel acetylcholinesterase enzyme inhibitors related to Alzheimer's disease. In this sense, in this report a computational model of linear prediction of acetylcholinesterase inhibitory activity of steroids and triterpenes is presented. The model is based in a correlation between binding energies obtained from molecular dynamic simulations (after docking studies) and [Formula: see text] values of a training set. This set includes a family of natural and semi-synthetic structurally related alkaloids reported in bibliography. These types of compounds, with some structural complexity, could be used as building blocks for the synthesis of many important biologically active compounds Therefore, the present study proposes an alternative based on the use of conventional and easily accessible tools to make progress on the rational design of molecules with biological activity.

Effective estimation of the inhibitor affinity of HIV-1 protease via a modified LIE approach

The inhibition of the Human Immunodeficiency Virus Type 1 Protease (HIV-1 PR) can prevent the synthesis of new viruses. Computer-aided drug design (CADD) would enhance the discovery of new therapies, through which the estimation of ligand-binding affinity is critical to predict the most efficient inhibitor. A time-consuming binding free energy method would reduce the usefulness of CADD. The modified linear interaction energy (LIE) approach emerges as an appropriate protocol that performs this task. In particular, the polar interaction free energy, which is obtained via numerically resolving the linear Poisson-Boltzmann equation, plays as an important role in driving the binding mechanism of the HIV-1 PR + inhibitor complex. The electrostatic interaction energy contributes to the attraction between two molecules, but the vdW interaction acts as a repulsive factor between the ligand and the HIV-1 PR. Moreover, the ligands were found to adopt a very strong hydrophobic interaction with the HIV-1 PR. Furthermore, the results obtained corroborate the high accuracy and precision of computational studies with a large correlation coefficient value R = 0.83 and a small RMSE δ RMSE = 1.25 kcal mol-1. This method is less time-consuming than the other end-point methods, such as the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and free energy perturbation (FEP) approaches. Overall, the modified LIE approach would provide ligand-binding affinity with HIV-1 PR accurately, precisely, and rapidly, resulting in a more efficient design of new inhibitors.

Adequate prediction for inhibitor affinity of Aβ40 protofibril using the linear interaction energy method

The search for efficient inhibitors targeting Aβ oligomers and fibrils is an important issue in Alzheimer's disease treatment. As a consequence, an accurate and computationally cheap approach to estimate the binding affinity for many ligands interacting with Aβ peptides is very important. Here, the calculated binding free energies of 30 ligands interacting with 12Aβ11-40 peptides using the linear interaction energy (LIE) approach are found to be in good correlation with experimental data (R = 0.79). The binding affinities of these complexes are also calculated by using free energy perturbation (FEP) and molecular mechanic/Poisson-Boltzmann surface area (MM/PBSA) methods. The time-consuming FEP method provides results with similar correlation (R = 0.72), whereas MM/PBSA calculations show very low correlation with experimental data (R = 0.27). In all complexes, van der Waals interactions contribute much more than electrostatic interactions. The LIE model, which is much less time-consuming than both the FEP and MM/PBSA methods, opens the door to accurate and rapid affinity prediction of ligands with Aβ peptides and the design of new ligands.

Effective Estimation of Ligand-Binding Affinity Using Biased Sampling Method

The binding between two biomolecules is one of the most critical factors controlling many bioprocesses. Therefore, it is of great interest to derive a reliable method to calculate the free binding energy between two biomolecules. In this work, we have demonstrated that the binding affinity of ligands to proteins can be determined through biased sampling simulations. The umbrella sampling (US) method was applied on 20 protein-ligand complexes, including the cathepsin K (CTSK), type II dehydroquinase (DHQase), heat shock protein 90 (HSP90), and factor Xa (FXa) systems. The ligand-binding affinity was evaluated as the difference between the largest and smallest values of the free-energy curve, which was obtained via a potential of mean force analysis. The calculated affinities differ sizably from the previously reported experimental values, with an average difference of ∼3.14 kcal/mol. However, the calculated results are in good correlation with the experimental data, with correlation coefficients of 0.76, 0.87, 0.96, and 0.97 for CTSK, DHQase, HSP90, and FXa, respectively. Thus, the binding free energy of a new ligand can be reliably estimated using our US approach. Furthermore, the root-mean-square errors (RMSEs) of binding affinity of these systems are 1.13, 0.90, 0.37, and 0.25 kcal/mol, for CTSK, DHQase, HSP90, and FXa, respectively. The small RMSE values indicate the good precision of the biased sampling method that can distinguish the ligands exhibiting similar binding affinities.

Etersalate prevents the formations of 6Aβ16-22 oligomer: An in silico study

Oligomerization of amyloid beta (Aβ) peptides has been considered as the crucially causative agent in the development of Alzheimer's disease. Etersalate, a nonsteroidal anti-inflammatory oral drug (United State Food and Drug Administration—Unique Ingredient Identifier: 653GN04T2G) was previously suggested to bind well to proto-fibrils of Aβ peptides in silico. Here, the effect of etersalate on the oligomerization of soluble Aβ_16–22 hexamer (6Aβ_16–22) were extensively investigated using temperature replica exchange molecular dynamics (REMD) simulations over ~16.8 μs in total for 48 replicas (350 ns per replica). The results reveal that etersalate can enter the inner space or bind on the surface of 6Aβ_16–22 conformations, which destabilizes the hexamer. Etersalate was predicted to able to cross the blood brain barrier using prediction of absorption, distribution, metabolism, and excretion—toxicity (preADMET) tools. Overall, although the investigation was performed with the low concentration of trial inhibitor, the obtained results indicate that etersalate is a potential drug candidate for AD through inhibiting formation of Aβ oligomers with the average binding free energy of -11.7 kcal/mol.

The influences of E22Q mutant on solvated 3Aβ11-40 peptide: A REMD study

The residue E22 plays a critical role in the aggregation process of Amyloid beta (Aβ) peptides. The effect of E22Q mutant on the shapes of the solvated Aβ_11-40 trimer is clarified using a replica exchange molecular dynamics (REMD) simulation employing ∼20.6 μs of MD simulations with 48 disparate replicas. The increase of intramolecular polar contacts and salt bridge between the residue D23 to residues (24-29) was observed. The residual secondary structure of the mutated trimer is shifted in a similar way to the picture observed in previous investigations of F19W mutant. The free energy surface (FES) of the mutated E22Q system has a fewer number of minima in comparison with the wild-type trimer. The optimized shapes of the mutated E22Q form a significant increase in beta structure (47%) and serious decrease in coil content (46%) compared with the wild-type (of 36 and 56%, respectively). The binding affinity of constituting chains to the rest is of -43.7 ± 6.5 kcal/mol, implying that the representative structure of E22Q is more stable than the wild-type one. Furthermore, the E22Q mutant increases the size of stable structures due to larger collision cross section (CCS) and solvent accessible area (SASA). The observed results may enhance the Aβ inhibition throughout the contribution to the knowledge of the Aβ oligomerization/aggregation.

Probing the Binding Affinity by Jarzynski's Nonequilibrium Binding Free Energy and Rupture Time

Binding affinity of a small ligand to a receptor is the important quantity in drug design, and it might be characterized by different quantities. The most popular one is the binding free energy, which can be estimated by several methods in conventional molecular dynamics simulation. So far in steered molecular dynamics (SMD), one can use either the rupture force or nonequilibrium pulling work as a measure for binding affinity. In this paper, we have shown that the nonequilibrium binding free energy Δ G_neq^Jar, obtained by Jarzynski's equality at a finite pulling speed, has good correlation with experimental data on inhibition constants, implying that this quantity can be used as a good scoring function for binding affinity. A similar correlation has also been disclosed for binding and unbinding free energy barriers. Applying the SMD method to unbinding of 23 small compounds from the binding site of β-lactamase protein, a bacteria-produced enzyme, we have demonstrated that the rupture or unbinding time strongly correlates with experimental data with correlation level R ≈ 0.84. As follows from the Jarzynski's equality, the rupture time depends on the unbinding barrier exponentially. We show that Δ G_neq^Jar, the rupture time, and binding and unbinding free energy barriers are good descriptors for binding affinity. Our observation may be useful for fast screening of potential leads as the SMD simulation is not time-consuming. On the basis of nonequilibrium simulation, we disclosed that, in agreement with the experiment, the binding time is much longer than the unbinding one.

Evaluation of the absolute affinity of neuraminidase inhibitor using steered molecular dynamics simulations

The absolute free energy difference of binding (ΔG) between neuraminidase and its inhibitor was evaluated using fast pulling of ligand (FPL) method over steered molecular dynamics (SMD) simulations. The metric was computed through linear interaction approximation. Binding nature was described by free energy differences of electrostatic and van der Waals (vdW) interactions. The finding indicates that vdW metric is dominant over electrostatics in binding process. The computed values are in good agreement with experimental data with a correlation coefficient of R=0.82 and error of σΔG_exp=2.2kcal/mol. The results were observed using Amber99SB-ILDN force field in comparison with CHARMM27 and GROMOS96 43a1 force fields. Obtained results may stimulate the search for an Influenza therapy.

EGCG inhibits the oligomerization of amyloid beta (16-22) hexamer: Theoretical studies

An extensive replica exchange molecular dynamics (REMD) simulation was performed to investigate the progress patterns of the inhibition of (-)-epigallocatechin-3-gallate (EGCG) on the Aβ_16-22 hexamer. Structural variations of the oligomers without and with EGCG were monitored and analyzed in detail. It has been found that EGCG prevents the formation of Aβ oligomer through two different ways by either accelerating the Aβ oligomerization or reducing the β-content of the hexamer. It also decreases the potential "highly toxic" conformations of Aβ oligomer, which is related to the conformations having high order β-sheet sizes. Both electrostatic and van der Waals interaction energies are found to be involved to the binding process. Computed results using quantum chemical methods show that the π-π stacking is a critical factor of the interaction between EGCG and the peptides. As a result, the binding free energy of the EGCG to the Aβ peptides is slightly larger than that of the curcumin.

Replica exchange molecular dynamics study of the amyloid beta (11–40) trimer penetrating a membrane

The transmembrane Aβ_11–40 trimer is investigated for the first time using REMD and FEP.

In silico studies of solvated F19W amyloid β (11–40) trimer

REMD studies shows that F19W mutation does not change in the overall structure of Aβ_11–40 trimer significantly but increases it flexibility, consistent with the observed formation of the same fibril structures at slower rates.

Replica exchange molecular dynamics study of the truncated amyloid beta (11–40) trimer in solution

The structure of the 3Aβ_11–40 oligomer is determined for the first time using T-REMD simulations.

Anti-arrhythmic Medication Propafenone a Potential Drug for Alzheimer's Disease Inhibiting Aggregation of Aβ: In Silico and in Vitro Studies

Alzheimer's disease (AD) is the most common form of dementia caused by the formation of Aβ aggregates. So far, no effective medicine for the treatment of AD is available. Many efforts have been made to find effective medicine to cope with AD. Curcumin is a drug candidate for AD, being a potent anti-amyloidogenic compound, but the results of clinical trials for it were either negative or inclusive. In the present study, we took advantages from accumulated knowledge about curcumin and have screened out four compounds that have chemical and structural similarity with curcumin more than 80% from all FDA-approved oral drugs. Using all-atom molecular dynamics simulation and the free energy perturbation method we showed that among predicted compounds anti-arrhythmic medication propafenone shows the best anti-amyloidogenic activity. The in vitro experiment further revealed that it can inhibit Aβ aggregation and protect cells against Aβ induced cytotoxicity to almost the same extent as curcumin. Our results suggest that propafenone may be a potent drug for the treatment of Alzheimer's disease.