Exploring the Reasons for Decrease in Binding Affinity of HIV-2 Against HIV-1 Protease Complex Using Interaction Entropy Under Polarized Force Field

Comparative study of the unbinding process of some HTLV-1 protease inhibitors using unbiased molecular dynamics simulations

The HTLV-1 protease is one of the major antiviral targets to overwhelm this virus. Several research groups have developed protease inhibitors, but none has been successful. In this regard, developing new HTLV-1 protease inhibitors to fix the defects in previous inhibitors may overcome the lack of curative treatment for this oncovirus. Thus, we decided to study the unbinding pathways of the most potent (compound 10, PDB ID 4YDF, Ki = 15 nM) and one of the weakest (compound 9, PDB ID 4YDG, Ki = 7900 nM) protease inhibitors, which are very structurally similar. We conducted 12 successful short and long simulations (totaling 14.8 μs) to unbind the compounds from two monoprotonated (mp) forms of protease using the Supervised Molecular Dynamics (SuMD) without applying any biasing force. The results revealed that Asp32 or Asp32′ in the two forms of mp state similarly exert powerful effects on maintaining both potent and weak inhibitors in the binding pocket of HTLV-1 protease. In the potent inhibitor’s unbinding process, His66′ was a great supporter that was absent in the weak inhibitor’s unbinding pathway. In contrast, in the weak inhibitor’s unbinding process, Trp98/Trp98′ by pi-pi stacking interactions were unfavorable for the stability of the inhibitor in the binding site. In our opinion, these results will assist in designing more potent and effective inhibitors for the HTLV-1 protease.

A fast-slow method to treat solute dynamics in explicit solvent

Aiming to reduce the computational cost in the current explicit solvent molecular dynamics (MD) simulation, this paper proposes a fast-slow method for the fast MD simulation of biomolecules in explicit solvent. This fast-slow method divides the entire system into two parts: a core layer (typically solute or biomolecule) and a peripheral layer (typically solvent molecules). The core layer is treated using standard MD method but the peripheral layer is treated by a slower dynamics method to reduce the computational cost. We compared four different simulation models in testing calculations for several small proteins. These include gas-phase, implicit solvent, fast-slow explicit solvent and standard explicit solvent MD simulations. Our study shows that gas-phase and implicit solvent models do not provide a realistic solvent environment and fail to correctly produce reliable dynamic structures of proteins. On the other hand, the fast-slow method can essentially reproduce the same solvent effect as the standard explicit solvent model while gaining an order of magnitude in efficiency. This fast-slow method thus provides an efficient approach for accelerating the MD simulation of biomolecules in explicit solvent.

Decoding molecular mechanism underlying binding of drugs to HIV-1 protease with molecular dynamics simulations and MM-GBSA calculations

HIV-1 protease (PR) is thought to be efficient targets of anti-AIDS drug design. Molecular dynamics (MD) simulations and multiple post-processing analysis technologies were applied to decipher molecular mechanism underlying binding of three drugs Lopinavir (LPV), Nelfinavir (NFV) and Atazanavir (ATV) to the PR. Binding free energies calculated by molecular mechanics generalized Born surface area (MM-GBSA) suggest that compensation between binding enthalpy and entropy plays a vital role in binding of drugs to PR. Dynamics analyses show that binding of LPV, NFV and ATV highly affects structural flexibility, motion modes and dynamics behaviour of the PR, especially for two flaps. Computational alanine scanning and interaction network analysis verify that although three drugs have structural difference, they share similar binding modes to the PR and common interaction clusters with the PR. The current findings also confirm that residues located interaction clusters, such as Asp25/Asp25', Gly27/Gly27', Ala28/Ala28', Asp29, Ile47/Ile47', Gly49/Gly49', Ile50/Ile50', Val82/Val82' and Ile84/Ile84, can be used as efficient targets of clinically available inhibitors towards the PR.

A systematic strategy for the investigation of vaccines and drugs targeting bacteria

Infectious and epidemic diseases induced by bacteria have historically caused great distress to people, and have even resulted in a large number of deaths worldwide. At present, many researchers are working on the discovery of viable drug and vaccine targets for bacteria through multiple methods, including the analyses of comparative subtractive genome, core genome, replication-related proteins, transcriptomics and riboswitches, which plays a significant part in the treatment of infectious and pandemic diseases. The 3D structures of the desired target proteins, drugs and epitopes can be predicted and modeled through target analysis. Meanwhile, molecular dynamics (MD) analysis of the constructed drug/epitope-protein complexes is an important standard for testing the suitability of these screened drugs and vaccines. Currently, target discovery, target analysis and MD analysis are integrated into a systematic set of drug and vaccine analysis strategy for bacteria. We hope that this comprehensive strategy will help in the design of high-performance vaccines and drugs.

An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein-ligand binding affinity

The molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method is constantly used to calculate the binding free energy of protein-ligand complexes, and has been shown to effectively balance computational cost against accuracy. The relative binding affinities obtained by the MM/PBSA approach are acceptable, while it usually overestimates the absolute binding free energy. This paper proposes four free energy estimators based on the MM/PBSA for enthalpy change combined with interaction entropy (IE) for entropy change using different weights for individual energy terms. The ΔG_{PBSA_IE} method is determined to be an optimal estimator based on its performance in terms of the correlation between experimental and theoretical values and error estimations. This approach is optimized using high-quality experimental values from a training set containing 84 protein-ligand systems, and the coefficients for the sum of electrostatic energy and polar solvation free energy, van der Waals (vdW) energy, non-polar solvation energy and entropy change are obtained by multivariate linear fitting to the corresponding experimental values. A comparison between the traditional MM/PBSA method and this method shows that the correlation coefficient is improved from 0.46 to 0.72 and the slope of the regression line increases from 0.10 to 1.00. More importantly, the mean absolute error (MAE) is significantly reduced from 22.52 to 1.59 kcal mol^-1. Furthermore, the numerical stability of this method is validated on a test set with a similar correlation coefficient, slope and MAE to those of the training set. Based on the above advantages, the ΔG_{PBSA_IE} method can be a powerful tool for a reliable and accurate estimation of binding free energy and plays a significant role in a detailed energetic investigation of protein-ligand interaction.

Entropic effect and residue specific entropic contribution to the cooperativity in streptavidin-biotin binding

Molecular dynamics (MD) simulations were performed employing the polarized protein-specific charge (PPC) to explore the origin of the cooperativity in streptavidin-biotin systems (wild type, two single mutations and one double-mutation). The results of the experiment found that the existence of cooperativity is mainly the result of the entropic effect. In this study, the entropic contribution to the binding free energy was calculated using the recently developed interaction entropy (IE) method, and computational results are in excellent agreement with the experimental observations and are further verified by the calculation of the thermodynamic integration. Comparison of different force fields in terms of predicted binding strength ordering, cooperativity of energy and the stability of hydrogen bonding suggests that the PPC force field combined IE method is a suitable choice. In addition, the IE method enables us to obtain the residue-specific entropic contributions to the streptavidin-biotin binding affinity and identify ten hot-spot residues providing the dominant contribution to the cooperative binding. Importantly, the overall cooperativity obtained from the ten residues also comes mainly from the entropic effect in our study. The calculation of the potential of mean force shows that the unbinding of streptavidin-biotin is a multi-step process, and each step corresponds to the formation and rupture of the hydrogen bond network. And S45A mutation may increase the rigidity of the linker region, making the flap region relatively difficult to open. The present study provides significant molecular insight into the binding cooperativity of the streptavidin-biotin complex.

Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression

The random forest regression (RFR) model was introduced to predict the multiple spin state charges of a heme model, which is important for the molecular dynamic simulation of the spin crossover phenomenon. In this work, a multiple spin state structure data set with 39,368 structures of the simplified heme-oxygen binding model was built from the non-adiabatic dynamic simulation trajectories. The ESP charges of each atom were calculated and used as the real-valued response. The conformational adapted charge model (CAC) of three spin states was constructed by an RFR model using symmetry functions. The results show that our RFR model can effectively predict the on the fly atomic charges with the varying conformations as well as the atomic charge of different spin states in the same conformation, thus achieving the balance of accuracy and efficiency. The average mean absolute error of the predicted charges of each spin state is <0.02 e. The comparison studies on descriptors showed a maximum 0.06 e improvement in prediction of the charge of Fe 2+ by using 11 manually selected structural parameters. We hope that this model can not only provide variable parameters for developing the force field of the multi-spin state but also facilitate automation, thus enabling large-scale simulations of atomistic systems.

Improving the performance of the MM/PBSA and MM/GBSA methods in recognizing the native structure of the Bcl-2 family using the interaction entropy method

Correct discrimination of native structure plays an important role in drug design. IE method significantly improves the performance of MM/PB(GB)SA method in discriminating native and decoy structures in protein–ligand/protein systems of Bcl-2 family.

Insight Into the Binding Mechanism of p53/pDIQ-MDMX/MDM2 With the Interaction Entropy Method

The study of the p53-MDMX/MDM2 binding sites is a research hotspot for tumor drug design. The inhibition of p53-targeted MDMX/MDM2 has become an effective approach in anti-tumor drug development. In this paper, a theoretically rigorous and computationally accurate method, namely, the interaction entropy (IE) method, combined with the polarized protein-specific charge (PPC) force field, is used to explore the difference in the binding mechanism between p53-MDMX and p53-MDM2. The interaction of a 12mer peptide inhibitor (pDIQ), which is similar to p53 in structure, with MDMX/MDM2 is also studied. The results demonstrate that p53/pDIQ with MDM2 generates a stronger interaction than with MDMX. Compared to p53, pDIQ has larger binding free energies with MDMX and MDM2. According to the calculated binding free energies, the differences in the binding free energy among the four complexes that are obtained from the combination of PPC and IE are more consistent with the experimental values than with the results from the combination of the non-polarizable AMBER force field and IE. In addition, according to the decomposition of the binding free energy, the van der Waals (vdW) interactions are the main driving force for the binding of the four complexes. They are also the main source of the weaker binding affinity of p53/pDIQ-MDMX relative to p53/pDIQ-MDM2. Compared with p53-MDMX/MDM2, according to the analysis of the residue decomposition, the predicated total residue contributions are higher in pDIQ-MDMX/MDM2 than in p53-MDMX/MDM2, which explains why pDIQ has higher binding affinity than p53 with MDMX/MDM2. The current study provides theoretical guidance for understanding the binding mechanisms and designing a potent dual inhibitor that is targeted to MDMX/MDM2.

Drug-resistance mechanisms of three mutations in anaplastic lymphoma kinase against two inhibitors based on MM/PBSA combined with interaction entropy

As a promising drug target in the treatment of lung cancer, anaplastic lymphoma kinase (ALK) and its mutations have been studied widely. This work explored the origin of the resistance mechanism of the ALK mutants again two inhibitors.