Huiyong Sun

Assessing the performance of MM/PBSA and MM/GBSA methods. 3. The impact of force fields and ligand charge models.

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 Tans parameters shows better ranking capability than MM/GBSA (GB(OBC1)); (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.

P-loop conformation governed crizotinib resistance in G2032R-mutated ROS1 tyrosine kinase: clues from free energy landscape.

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.

ADMET Evaluation in Drug Discovery. 13. Development of in Silico Prediction Models for P‑Glycoprotein Substrates

P-glycoprotein (P-gp) actively transports a wide variety of chemically diverse compounds out of cells. It is highly associated with the ADMET properties of drugs and drug candidates and, moreover, plays a major role in the multidrug resistance (MDR) phenomenon, which leads to the failure of chemotherapy in cancer treatments. Therefore, the recognition of potential P-gp substrates at the early stages of the drug discovery process is quite important. Here, we compiled an extensive data set containing 423 P-gp substrates and 399 nonsubstrates, which is the largest P-gp substrate/nonsubstrate data set yet published. Comparison of the distributions of eight important physicochemical properties for the substrates and nonsubstrates reveals that molecular weight and molecular solubility are the informative attributes differentiating P-gp substrates from nonsubstrates. Examination of the distributions of eight physicochemical properties for 735 P-gp inhibitors and 423 substrates gives the fact that inhibitors are significantly more hydrophobic than substrates while substrates tend to have more H-bond donors than inhibitors. Then, the classification models based on simple molecular properties, topological descriptors, and molecular fingerprints were developed using the naive Bayesian classification technique. The best naive Bayesian classifier yields a Matthews correlation coefficient of 0.824 and a prediction accuracy of 91.2% for the training set from a 5-fold cross-validation procedure, and a Matthews correlation coefficient of 0.667 and a prediction accuracy of 83.5% for the test set containing 200 molecules. Analysis of the important structural fragments given by the Bayesian classifier shows that the essential H-bond acceptors arranged in distinct spatial patterns and flexibility are quite essential for P-gp substrate-likeness, which affords a deeper understanding on the molecular basis of substrate/P-gp interaction. Finally, the reasons for mispredictions were discussed. It turns out that the presented classifier could be used as a reliable virtual screening tool for identifying potential substrates of P-gp.

Insight into Crizotinib Resistance Mechanisms Caused by Three Mutations in ALK Tyrosine Kinase using Free Energy Calculation Approaches

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.

Assessing an Ensemble Docking-Based Virtual Screening Strategy for Kinase Targets by Considering Protein Flexibility

In this study, to accommodate receptor flexibility, based on multiple receptor conformations, a novel ensemble docking protocol was developed by using the naïve Bayesian classification technique, and it was evaluated in terms of the prediction accuracy of docking-based virtual screening (VS) of three important targets in the kinase family: ALK, CDK2, and VEGFR2. First, for each target, the representative crystal structures were selected by structural clustering, and the capability of molecular docking based on each representative structure to discriminate inhibitors from non-inhibitors was examined. Then, for each target, 50 ns molecular dynamics (MD) simulations were carried out to generate an ensemble of the conformations, and multiple representative structures/snapshots were extracted from each MD trajectory by structural clustering. On average, the representative crystal structures outperform the representative structures extracted from MD simulations in terms of the capabilities to separate inhibitors from non-inhibitors. Finally, by using the naïve Bayesian classification technique, an integrated VS strategy was developed to combine the prediction results of molecular docking based on different representative conformations chosen from crystal structures and MD trajectories. It was encouraging to observe that the integrated VS strategy yields better performance than the docking-based VS based on any single rigid conformation. This novel protocol may provide an improvement over existing strategies to search for more diverse and promising active compounds for a target of interest.

Current developments of macrophage migration inhibitory factor (MIF) inhibitors

The cytokine macrophage migration inhibitory factor (MIF) is regarded as a major regulator of inflammation and a key mediator that counter-regulates the inhibitory effects of glucocorticoids within the immune system. Therefore, MIF is a therapeutic target for the treatment of inflammatory and autoimmune diseases. In addition, MIF was found to be implicated in cancer pathogenesis. Current therapeutic strategies for targeting MIF focus on inhibiting its signaling activity by small molecules or modulating its biological activities using anti-MIF neutralizing antibodies. In this review, the structure and biological functions of MIF are briefly outlined. Then, the available inhibitors of MIF are systematically summarized. Finally, the recent advances that have been made in the computer-aided drug design and molecular modeling studies of MIF are reviewed.

Revealing the favorable dissociation pathway of type II kinase inhibitors via enhanced sampling simulations and two-end-state calculations

How does a type II inhibitor bind to/unbind from a kinase target is still a confusing question because the small molecule occupies both the ATP pocket and the allosteric pocket of the kinase binding site. Here, by using enhanced sampling simulations (umbrella sampling, US) and two-end-state free energy calculations (MM/GSBA), we systemically studied the dissociation processes of two distinct small molecules escaping from the binding pocket of p38 MAP kinase through the allosteric channel and the ATP channel. The results show that the unbinding pathways along the allosteric channel have much lower PMF depths than those along the ATP channel, suggesting that the allosteric channel is more favorable for the dissociations of the two inhibitors and thereby supporting the general understanding that the largest channel of a target is usually the entry/exit pathway for the binding/dissociation of small molecules. Interestingly, the MM/GBSA approach yielded similar PMF profiles compared with those based on US, a much time consuming approach, indicating that for a general study, such as detecting the important transition state of a ligand binding/unbinding process, MM/GBSA may be a feasible choice.

Development and Evaluation of an Integrated Virtual Screening Strategy by Combining Molecular Docking and Pharmacophore Searching Based on Multiple Protein Structures

In this study, we developed and evaluated a novel parallel virtual screening strategy by integrating molecular docking and complex-based pharmacophore searching based on multiple protein structures. First, the capacity of molecular docking or pharmacophore searching based on any single structure from nine crystallographic structures of Rho kinase 1 (ROCK1) to distinguish the known ROCK1 inhibitors from noninhibitors was evaluated systematically. Then, the naı̈ve Bayesian classification or recursive partitioning technique was employed to integrate the predictions from molecular docking and complex-based pharmacophore searching based on multiple crystallographic structures of ROCK1, and the integrated protocol yields much better performance than molecular docking or complex-based pharmacophore searching based on any single ROCK1 structure. Finally, the well-validated integrated virtual screening protocol was applied to identify potential inhibitors of ROCK1 from traditional chinese medicine (TCM). The obtained potential active compounds from TCM are structurally novel and diverse compared with the known inhibitors of ROCK1, and they may afford valuable clues for the development of potent ROCK1 inhibitors.

Molecular Principle of Topotecan Resistance by Topoisomerase I Mutations through Molecular Modeling Approaches

Originally isolated from natural products, camptothecin (CPT) has provided extensive playing fields for the development of antitumor drugs. Two of the most successful analogs of CPT, topotecan and irinotecan, have been approved by the FDA for the treatment of colon cancer and ovarian cancer, as well as other cancers. However, the emergence of drug resistance mutations in topoisomerase I is a big challenge for the effective therapy of these drugs. Therefore, in this study, a series of computational approaches from molecular dynamics (MD) simulations to steered molecular dynamics (SMD) simulations and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) binding free energy calculations were employed to uncover the molecular principle of the topotecan resistance induced by three mutations in DNA topoisomerase I, including E418K, G503S, and D533G. Our results demonstrate a remarkable correlation between the binding free energies predicted by MM/GBSA and the rupture forces computed by SMD, and moreover, the theoretical results given by MM/GBSA and SMD are in excellent agreement with the experimental data for ranking the inhibitory activities: WT > E418K > G503S > D533G. In order to explore the drug resistance mechanism that underlies the loss of the binding affinity of topotecan, the binding modes of topotecan bound to the WT and mutated receptors were presented, and comparisons of the binding geometries and energy contributions on a per residue basis of topotecan between the WT complex and each mutant were also discussed. The results illustrate that the mutations of E418K, G503S, and D533G have great influence on the binding of topotecan to topoisomerase I bound with DNA, and the variations of the polar interactions play critical roles in the development of drug resistance. The information obtained from this study provides useful clues for designing improved topoisomerase I inhibitors for combating drug resistance.

Directly Binding Rather than Induced-Fit Dominated Binding Affinity Difference in (S)- and (R)-Crizotinib Bound MTH1

As one of the most successful anticancer drugs, crizotinib is found to be efficient in the suppression of MTH1, a new therapeutic target for RAS-dependent cancers. Deep analysis shows that stereospecificity is prevalent in the binding of crizotinib to MTH1, where the target is more preferred to bind with the (S)-enantiomer of crizotinib. Surprisingly, very similar binding modes were found for the two enantiomers (Huber et al. Nature 2014, 508, 222-227), which puzzled us to ask a question as to why such a subtle structural variation could lead to so large of a binding affinity difference. Thereafter, by using advanced all-atom molecular dynamics simulations, we characterized the free energy surfaces of the binding/unbinding processes of the (S) and (R)-crizotinib enantiomers to/from MTH1. Interestingly, we found that rather than the induced-fit process, which is prevalent in drug selectivity and specificity (Wilson et al. Science 2015, 347, 882-886), the directly binding process has dominated impact on the binding affinity difference of the enantiomers, implying a common mechanism of stereoselectivity of enantiomers.