By using different evaluation strategies, we systemically evaluated the performance of Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson-Boltzmann Surface ...Area (MM/PBSA) methodologies based on more than 1800 protein-ligand crystal structures in the PDBbind database. The results can be summarized as follows: (1) for the one-protein-family/one-binding-ligand case which represents the unbiased protein-ligand complex sampling, both MM/GBSA and MM/PBSA methodologies achieve approximately equal accuracies at the interior dielectric constant of 4 (with rp = 0.408 ± 0.006 of MM/GBSA and rp = 0.388 ± 0.006 of MM/PBSA based on the minimized structures); while for the total dataset (1864 crystal structures), the overall best Pearson correlation coefficient (rp = 0.579 ± 0.002) based on MM/GBSA is better than that of MM/PBSA (rp = 0.491 ± 0.003), indicating that biased sampling may significantly affect the accuracy of the predicted result (some protein families contain too many instances and can bias the overall predicted accuracy). Therefore, family based classification is needed to evaluate the two methodologies; (2) the prediction accuracies of MM/GBSA and MM/PBSA for different protein families are quite different with rp ranging from 0 to 0.9, whereas the correlation and ranking scores (an averaged rp/rs over a list of protein folds and also representing the unbiased sampling) given by MM/PBSA (rp-score = 0.506 ± 0.050 and rs-score = 0.481 ± 0.052) are comparable to those given by MM/GBSA (rp-score = 0.516 ± 0.047 and rs-score = 0.463 ± 0.047) at the fold family level; (3) for the overall prediction accuracies, molecular dynamics (MD) simulation may not be quite necessary for MM/GBSA (rp-minimized = 0.579 ± 0.002 and rp-1ns = 0.564 ± 0.002), but is needed for MM/PBSA (rp-minimized = 0.412 ± 0.003 and rp-1ns = 0.491 ± 0.003). However, for the individual systems, whether to use MD simulation is depended. (4) both MM/GBSA and MM/PBSA may be unable to give successful predictions for the ligands with high formal charges, with the Pearson correlation coefficient ranging from 0.621 ± 0.003 (neutral ligands) to 0.125 ± 0.142 (ligands with a formal charge of 5). Therefore, it can be summarized that, although MM/GBSA and MM/PBSA perform similarly in the unbiased dataset, for the currently available crystal structures in the PDBbind database, compared with MM/GBSA, which may be used in multi-target comparisons, MM/PBSA is more sensitive to the investigated systems, and may be more suitable for individual-target-level binding free energy ranking. This study may provide useful guidance for the post-processing of docking based studies.
Here, we systematically investigated how the force fields and the partial charge models for ligands affect the ranking performance of the binding free energies predicted by the Molecular ...Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) approaches. A total of 46 small molecules targeted to five different protein receptors were employed to test the following issues: (1) the impact of five AMBER force fields (ff99, ff99SB, ff99SB-ILDN, ff03, and ff12SB) on the performance of MM/GBSA, (2) the influence of the time scale of molecular dynamics (MD) simulations on the performance of MM/GBSA with different force fields, (3) the impact of five AMBER force fields on the performance of MM/PBSA, and (4) the impact of four different charge models (RESP, ESP, AM1-BCC, and Gasteiger) for small molecules on the performance of MM/PBSA or MM/GBSA. Based on our simulation results, the following important conclusions can be obtained: (1) for short time-scale MD simulations (1 ns or less), the ff03 force field gives the best predictions by both MM/GBSA and MM/PBSA; (2) for middle time-scale MD simulations (2–4 ns), MM/GBSA based on the ff99 force field yields the best predictions, while MM/PBSA based on the ff99SB force field does the best; however, longer MD simulations, for example, 5 ns or more, may not be quite necessary; (3) for most cases, MM/PBSA with the Tan’s parameters shows better ranking capability than MM/GBSA (GBOBC1); (4) the RESP charges show the best performance for both MM/PBSA and MM/GBSA, and the AM1-BCC and ESP charges can also give fairly satisfactory predictions. Our results provide useful guidance for the practical applications of the MM/GBSA and MM/PBSA approaches.
Understanding protein-protein interactions (PPIs) is quite important to elucidate crucial biological processes and even design compounds that interfere with PPIs with pharmaceutical significance. ...Protein-protein docking can afford the atomic structural details of protein-protein complexes, but the accurate prediction of the three-dimensional structures for protein-protein systems is still notoriously difficult due in part to the lack of an ideal scoring function for protein-protein docking. Compared with most scoring functions used in protein-protein docking, the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) methodologies are more theoretically rigorous, but their overall performance for the predictions of binding affinities and binding poses for protein-protein systems has not been systematically evaluated. In this study, we first evaluated the performance of MM/PBSA and MM/GBSA to predict the binding affinities for 46 protein-protein complexes. On the whole, different force fields, solvation models, and interior dielectric constants have obvious impacts on the prediction accuracy of MM/GBSA and MM/PBSA. The MM/GBSA calculations based on the ff02 force field, the GB model developed by Onufriev et al. and a low interior dielectric constant (εin = 1) yield the best correlation between the predicted binding affinities and the experimental data (rp = -0.647), which is better than MM/PBSA (rp = -0.523) and a number of empirical scoring functions used in protein-protein docking (rp = -0.141 to -0.529). Then, we examined the capability of MM/GBSA to identify the possible near-native binding structures from the decoys generated by ZDOCK for 43 protein-protein systems. The results illustrate that the MM/GBSA rescoring has better capability to distinguish the correct binding structures from the decoys than the ZDOCK scoring. Besides, the optimal interior dielectric constant of MM/GBSA for re-ranking docking poses may be determined by analyzing the characteristics of protein-protein binding interfaces. Considering the relatively high prediction accuracy and low computational cost, MM/GBSA may be a good choice for predicting the binding affinities and identifying correct binding structures for protein-protein systems.
As one of the most popular computational approaches in modern structure-based drug design, molecular docking can be used not only to identify the correct conformation of a ligand within the target ...binding pocket but also to estimate the strength of the interaction between a target and a ligand. Nowadays, as a variety of docking programs are available for the scientific community, a comprehensive understanding of the advantages and limitations of each docking program is fundamentally important to conduct more reasonable docking studies and docking-based virtual screening. In the present study, based on an extensive dataset of 2002 protein-ligand complexes from the PDBbind database (version 2014), the performance of ten docking programs, including five commercial programs (LigandFit, Glide, GOLD, MOE Dock, and Surflex-Dock) and five academic programs (AutoDock, AutoDock Vina, LeDock, rDock, and UCSF DOCK), was systematically evaluated by examining the accuracies of binding pose prediction (sampling power) and binding affinity estimation (scoring power). Our results showed that GOLD and LeDock had the best sampling power (GOLD: 59.8% accuracy for the top scored poses; LeDock: 80.8% accuracy for the best poses) and AutoDock Vina had the best scoring power (rp/rs of 0.564/0.580 and 0.569/0.584 for the top scored poses and best poses), suggesting that the commercial programs did not show the expected better performance than the academic ones. Overall, the ligand binding poses could be identified in most cases by the evaluated docking programs but the ranks of the binding affinities for the entire dataset could not be well predicted by most docking programs. However, for some types of protein families, relatively high linear correlations between docking scores and experimental binding affinities could be achieved. To our knowledge, this study has been the most extensive evaluation of popular molecular docking programs in the last five years. It is expected that our work can offer useful information for the successful application of these docking tools to different requirements and targets.
As a receptor tyrosine kinase of insulin receptor (IR) subfamily, anaplastic lymphoma kinase (ALK) has been validated to play important roles in various cancers, especially anaplastic large cell ...lymphoma (ALCL), nonsmall cell lung cancer (NSCLC), and neuroblastomas. Currently, five small-molecule inhibitors of ALK, including Crizotinib, Ceritinib, Alectinib, Brigatinib, and Lorlatinib, have been approved by the U.S. Food and Drug Administration (FDA) against ALK-positive NSCLCs. Novel type-I1/2 and type-II ALK inhibitors with improved kinase selectivity and enhanced capability to combat drug resistance have also been reported. Moreover, the “proteolysis targeting chimera” (PROTAC) technique has been successfully applied in developing ALK degraders, which opened a new avenue for targeted ALK therapies. This review provides an overview of the physiological and biological functions of ALK, the discovery and development of drugs targeting ALK by focusing on their chemotypes, activity, selectivity, and resistance as well as potential therapeutic strategies to overcome drug resistance.
With the rapid development of computational techniques and hardware, more rigorous and precise theoretical models have been used to predict the binding affinities of a large number of small molecules ...to biomolecules. By employing continuum solvation models, the MM/GBSA and MM/PBSA methodologies achieve a good balance between low computational cost and reasonable prediction accuracy. In this study, we have thoroughly investigated the effects of interior dielectric constant, molecular dynamics (MD) simulations, and the number of top-scored docking poses on the performance of the MM/GBSA and MM/PBSA rescoring of docking poses for three tyrosine kinases, including ABL, ALK, and BRAF. Overall, the MM/PBSA and MM/GBSA rescoring achieved comparative accuracies based on a relatively higher solute (or interior) dielectric constant (i.e. ε = 2, or 4), and could markedly improve the 'screening power' and 'ranking power' given by Autodock. Moreover, with a relatively higher solute dielectric constant, the MM/PBSA or MM/GBSA rescoring based on the best scored docking poses and the multiple top-scored docking poses gave similar predictions, implying that much computational cost can be saved by considering the best scored docking poses only. Besides, compared with the rescoring based on the minimized structures, the rescoring based on the MD simulations might not be completely necessary due to its negligible impact on the docking performance. Considering the much higher computational demand of MM/PBSA, MM/GBSA with a high solute dielectric constant (ε = 2 or 4) is recommended for the virtual screening of tyrosine kinases.
Entropy effects play an important role in drug-target interactions, but the entropic contribution to ligand-binding affinity is often neglected by end-point binding free energy calculation methods, ...such as MM/GBSA and MM/PBSA, due to the expensive computational cost of normal mode analysis (NMA). Here, we systematically investigated entropy effects on the prediction power of MM/GBSA and MM/PBSA using >1500 protein-ligand systems and six representative AMBER force fields. Two computationally efficient methods, including NMA based on truncated structures and the interaction entropy approach, were used to estimate the entropic contributions to ligand-target binding free energies. In terms of the overall accuracy, we found that, for the minimized structures, in most cases the inclusion of the conformational entropies predicted by truncated NMA (enthalpynmode_min_9Å) compromises the overall accuracy of MM/GBSA and MM/PBSA compared with the enthalpies calculated based on the minimized structures (enthalpymin). However, for the MD trajectories, the binding free energies can be improved by the inclusion of the conformation entropies predicted by either truncated-NMA for a relatively high dielectric constant (εin = 4) or the interaction entropy method for εin = 1-4. In terms of reproducing the absolute binding free energies, the binding free energies estimated by including the truncated-NMA entropies based on the MD trajectories (ΔGnmode_md_9Å) give the lowest average absolute deviations against the experimental data among all the tested strategies for both MM/GBSA and MM/PBSA. Although the inclusion of the truncated NMA based on the MD trajectories (ΔGnmode_md_9Å) for a relatively high dielectric constant gave the overall best result and the lowest average absolute deviations against the experimental data (for the ff03 force field), it needs too much computational time. Alternatively, considering that the interaction entropy method does not incur any additional computational cost and can give comparable (at high dielectric constant, εin = 4) or even better (at low dielectric constant, εin = 1-2) results than the truncated-NMA entropy (ΔGnmode_md_9Å), the interaction entropy approach is recommended to estimate the entropic component for MM/GBSA and MM/PBSA based on MD trajectories, especially for a diverse dataset. Furthermore, we compared the predictions of MM/GBSA with six different AMBER force fields. The results show that the ff03 force field (ff03 for proteins and gaff with AM1-BCC charges for ligands) performs the best, but the predictions given by the tested force fields are comparable, implying that the MM/GBSA predictions are not very sensitive to force fields.
Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of ...hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.
Tyrosine kinases are regarded as excellent targets for chemical drug therapy of carcinomas. However, under strong purifying selection, drug resistance usually occurs in the cancer cells within a ...short term. Many cases of drug resistance have been found to be associated with secondary mutations in drug target, which lead to the attenuated drug-target interactions. For example, recently, an acquired secondary mutation, G2032R, has been detected in the drug target, ROS1 tyrosine kinase, from a crizotinib-resistant patient, who responded poorly to crizotinib within a very short therapeutic term. It was supposed that the mutation was located at the solvent front and might hinder the drug binding. However, a different fact could be uncovered by the simulations reported in this study. Here, free energy surfaces were characterized by the drug-target distance and the phosphate-binding loop (P-loop) conformational change of the crizotinib-ROS1 complex through advanced molecular dynamics techniques, and it was revealed that the more rigid P-loop region in the G2032R-mutated ROS1 was primarily responsible for the crizotinib resistance, which on one hand, impaired the binding of crizotinib directly, and on the other hand, shortened the residence time induced by the flattened free energy surface. Therefore, both of the binding affinity and the drug residence time should be emphasized in rational drug design to overcome the kinase resistance.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
As a safe and efficacious drug, crizotinib was approved by the U.S. Food and Drug Administration (FDA) in 2011 for the treatment of advanced fusion-type nonsmall-cell lung cancer. Although high ...response ratio was detected from the patients treated with crizotinib, the cancer has eventually conferred resistance to crizotinib. Several drug resistance mutations have been found in the anaplastic lymphoma kinase (ALK) tyrosine kinase domain as the target for crizotinib, but the drug resistance mechanisms remain unclear. Therefore, in this study, the adaptive biasing force (ABF) method and two-end-state free energy calculation approaches were employed to elucidate the resistance mechanisms of crizotinib induced by the mutations L1152R, G1202R, and S1206Y. The ABF simulation results suggest that the reaction coordinates for the unbinding processes of crizotinib from the binding pockets of the mutated ALKs is different from that of the wild type ALK. The potentials of mean force for the crizotinib unbinding and the binding free energies predicted by the two-end-state free energy calculations are consistent with the experimental data. Our results indicate that the three mutations weaken the binding affinity of crizotinib obviously and lead to drug resistance. The free energy decomposition analysis illustrates the importance of the loss of two important H-bonds in the L1152R and S1206Y mutants on drug resistance. The entropy analysis shows that the entropy term plays a critical role in the substantial change of the conformational entropies of G1202R and L1152R. Our results reveal the mechanisms of drug resistance and provide vital clues for the development of new inhibitors to combat drug resistance.
PDF
