Exhaustively Exploring the Prevalent Interaction Pathways of Ligands Targeting the Ligand-Binding Pocket of Farnesoid X Receptor via Combined Enhanced Sampling

It is well-known that the potency of a drug is heavily associated with its kinetic and thermodynamic properties with the target. Nuclear receptors (NRs), as an important target family, play important roles in regulating a variety of physiological processes in vivo. However, it is hard to understand the drug-NR interaction process because of the closed structure of the ligand-binding domain (LBD) of the NR proteins, which apparently hinders the rational design of drugs with controllable kinetic properties. Therefore, understanding the underlying mechanism of the ligand-NR interaction process seems necessary to help NR drug design. However, it is usually difficult for experimental approaches to interpret the kinetic process of drug-target interactions. Therefore, in silico methods were utilized to explore the optimal binding/dissociation pathways of the NR ligands. Specifically, farnesoid X receptor (FXR) is considered here as the target system since it has been an important target for the treatment of bile acid metabolism-associated diseases, and a series of structures cocrystallized with diverse scaffold ligands were resolved. By using random acceleration molecular dynamics (RAMD) simulation and umbrella sampling (US), 5 main dissociation pathways (pathways I-V) were identified in 11 representative FXR ligands, with most of them (9/11) preferring to go through Pathway III and the remaining two favoring escaping from Pathway I and IV. Furthermore, key residues functioning in the three main dissociation pathways were revealed by the kinetic residue energy analysis (KREA) based on the US trajectories, which may serve as road-marker residues for rapid identification of the (un)binding pathways of FXR ligands. Moreover, the preferred pathways explored by RAMD simulations are in good agreement with the minimum free energy path identified by the US simulations with the Pearson R = 0.76 between the predicted binding affinity and the experimental data, suggesting that RAMD is suitable for applying in large-scale (un)binding-pathway exploration in the case of ligands with obscure binding tunnels to the target.

An affinity prediction approach for the ligand of E3 ligase Cbl-b and an insight into substrate binding pattern

Protein-protein interactions (PPIs) are essentially fundamental to all cellular processes, so that developing small molecule inhibitors of PPIs have great significance despite representing a huge challenge. Studying PPIs with the help of peptide motifs could obtain the structural information and reference significance to reduce the difficulty in the development of small molecules. Computational methods are powerful tools to characterize peptide-protein interactions, especially molecular dynamics simulation and binding free energy calculation. Here, we established an affinity prediction model suitable for Casitas B lymphoma-b (Cbl-b) and phosphorylated motif system. According to the affinity data set of multiple truncated peptides, the force field, solvent model, and internal dielectric constant of molecular mechanics/generalized Born surface area (MM/GBSA) method were optimized. Further, we predicted the affinity of the rationally designed new sequences through this model and obtained a new 6-mer motif with a 7-fold increase in affinity and the comprehensive structure-activity relationship. Moreover, we proposed an insight of unexpected activity of the truncated 5-mer peptide and revealed the possible binding mode of the new highly active 6-mer motif by extended simulation. Our results showed that the activity enhancement of the truncated peptide was caused by the acetyl-mediated conformation change. The side chain of Arg and pTyr in the 6-mer motif co-occupied the site p1 to form numerous hydrogen bond interactions and increased hydrophobic interaction formed with Tyr266, leading to the higher affinity. The present work provided a reference to investigate the PPI of Cbl-b and phosphorylated substrates and guided the development of Cbl-b inhibitors.

The latest automated docking technologies for novel drug discovery

INTRODUCTION

Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence.

AREAS COVERED

In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking.

EXPERT OPINION

There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.

Molecular Features for Probing Small Amphiphilic Molecules with Self-Assembled Monolayer-Protected Nanoparticles

The sensing of small molecules poses the challenge of developing devices able to discriminate between compounds that may be structurally very similar. Here, attention has been paid to the use of self-assembled monolayer (SAM)-protected gold nanoparticles since they enable a modular approach to tune single-molecule affinity and selectivity simply by changing functional moieties (i.e., covering ligands), along with multivalent molecular recognition. To date, the discovery of monolayers suitable for a specific molecular target has relied on trial-and-error approaches, with ligand chemistry being the main criterion used to modulate selectivity and sensitivity. By using molecular dynamics, we showcase that either individual molecular characteristics and/or collective features such as ligand flexibility, monolayer organization, ligand local ordering, and interfacial solvent properties can also be exploited conveniently. The knowledge of the molecular mechanisms that drive the recognition of small molecules on SAM-covered nanoparticles will critically expand our ability to manipulate and control such supramolecular systems.

Ensemble docking-based virtual screening toward identifying inhibitors against Wee1 kinase

Aim: Wee1 kinase plays a key role in the arrest of G2/M checkpoint that prevents mitotic entry in response to DNA damage. This work is to discover potent Wee1 inhibitors which can be considered valuable. Materials & Methods: Herein, Ensemble docking using multiple crystal structures was considered an effective strategy in the virtual screening. The performance of 17 scoring functions obtained from different docking software was evaluated for molecular docking. Results: Two novel compounds B1 and A2 were identified as Wee1 inhibitors with IC₅₀ values of 10.23 ± 0.505 and 8.72 ± 0.323 μM, respectively. Further cell viability assay demonstrated that the two active compounds exhibited good anticancer activities. Conclusion: This provides a meaningful starting point for further structure optimization to discover more potent Wee1 inhibitors.

Exploration of the selective binding mechanism of protein kinase Aurora A selectivity via a comprehensive molecular modeling study

Background

The kinase of Aurora A has been regarded as a promising therapeutic target due to its altered expression in various human cancers. However, given the high similarity of the active binding site of Aurora A to other kinases, designing highly selective inhibitors towards Aurora A remains a challenge. Recently, two potential small-molecule inhibitors named AT9283 and Danusertib were reported to exhibit significant selectivity to Aurora A, but not to Gleevec. It was argued that protein dynamics is crucial for drug selectivity to Aurora A. However, little computational research has been conducted to shed light on the underlying mechanisms.

Methods

In this study, MM/GBSA calculations based on conventional molecular dynamics (cMD) simulations and enhanced sampling simulations including Gaussian accelerated MD (GaMD) simulations and umbrella sampling were carried out to illustrate the selectivity of inhibitors to Aurora A.

Results

The calculation results from cMD simulation showed that the binding specificity is primarily controlled by conformational change of the kinase hinge. The protein dynamics and energetic differences were further supported by the GaMD simulations. Umbrella sampling further proved that AT9283 and Danusertib have similar potential of mean force (PMF) profiles toward Aurora A in terms of PMF depth. Compared with AT9283 and Danusertib, Gleevec has much lower PMF depth, indicating that Gleevec is more easily dissociated from Aurora A than AT9283 and Danusertib. These results not only show the selective determinants of Aurora A, but also provide valuable clues for the further development of novel potent Aurora A selective inhibitors.

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.

Drug Discovery Targeting Anaplastic Lymphoma Kinase (ALK)

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.

Importance of protein flexibility in ranking ERK2 Type I1/2 inhibitor affinities: a computational study

Extracellular-regulated kinase (ERK2) has been regarded as an essential target for various cancers, especially melanoma. Recently, pyrrolidine piperidine derivatives were reported as Type I1/2 inhibitors of ERK2, which occupy both the ATP binding pocket and the allosteric pocket. Due to the dynamic behavior of ERK2 upon the binding of Type I1/2 inhibitors, it is difficult to predict the binding structures and relative binding potencies of these inhibitors with ERK2 accurately. In this work, the binding mechanism of pyrrolidine piperidines was discussed by using different simulation techniques, including molecular docking, ensemble docking based on multiple receptor conformation, molecular dynamics simulations and free energy calculations. Our computational results show that the traditional docking method cannot predict the relative binding ability of the studied inhibitors with high accuracy, but incorporating ERK2 protein flexibility into docking is an effective method to improve the prediction accuracy. It is worth noting that the binding free energies predicted by MM/GBSA or MM/PBSA based on the MD simulations for the docked poses have the highest correlation with the experimental data, which highlights the importance of protein flexibility for accurately predicting the binding ability of Type I1/2 inhibitors of ERK2. In addition, the comprehensive analysis of several representative inhibitors indicates that hydrogen bonds and hydrophobic interactions are of significance for improving the binding affinities of the inhibitors. We hope this work will provide valuable information for further design of novel and efficient Type I1/2 ERK2 inhibitors.

Importance of Incorporating Protein Flexibility in Molecule Modeling: A Theoretical Study on Type I1/2 NIK Inhibitors

NF-κB inducing kinase (NIK), which is considered as the central component of the non-canonical NF-κB pathway, has been proved to be an important target for the regulation of the immune system. In the past few years, NIK inhibitors with various scaffolds have been successively reported, among which type I^1/2 inhibitors that can not only bind in the ATP-binding pocket at the DFG-in state but also extend into an additional back pocket, make up the largest proportion of the NIK inhibitors, and are worthy of more attention. In this study, an integration protocol that combines molecule docking, MD simulations, ensemble docking, MM/GB(PB)SA binding free energy calculations, and decomposition was employed to understand the binding mechanism of 21 tricyclic type I^1/2 NIK inhibitors. It is found that the docking accuracy is largely dependent on the selection of docking protocols as well as the crystal structures. The predictions given by the ensemble docking based on multiple receptor conformations (MRCs) and the MM/GB(PB)SA calculations based on MD simulations showed higher linear correlations with the experimental data than those given by conventional rigid receptor docking (RRD) methods (Glide, GOLD, and Autodock Vina), highlighting the importance of incorporating protein flexibility in predicting protein–ligand interactions. Further analysis based on MM/GBSA demonstrates that the hydrophobic interactions play the most essential role in the ligand binding to NIK, and the polar interactions also make an important contribution to the NIK-ligand recognition. A deeper comparison of several pairs of representative derivatives reveals that the hydrophobic interactions are vitally important in the structural optimization of analogs as well. Besides, the H-bond interactions with some key residues and the large desolvation effect in the back pocket devote to the affinity distinction. It is expected that our study could provide valuable insights into the design of novel and potent type I^1/2 NIK inhibitors.

Molecular dynamics investigation on the Asciminib resistance mechanism of I502L and V468F mutations in BCR-ABL

Asciminib, a highly selective non-ATP competitive inhibitor of BCR-ABL, has demonstrated to be a promising drug for patients with chronic myeloid leukemia. It is a pity that two resistant mutations (I502L and V468F) have been found during the clinical trial, which is a challenge for the curative effect of Asciminib. In this study, molecular dynamics simulations and molecular mechanics generalized Born surface area (MM-GB/SA) calculations were performed to investigate the molecular mechanism of Asciminib resistance induced by the two mutants. The obtained results indicate that the mutations have adversely influence on the binding of Asciminib to BCR-ABL, as the nonpolar contributions decline in the two mutants. In addition, I502L mutation causes α-helix I' (αI') to shift away from the helical bundle composed of αE, αF, and αH, making the distance between αI' and Asciminib increased. For V468F mutant, the side chain of Phe468 occupies the bottom of the myristoyl pocket (MP), which drives Asciminib to shift toward the outside of MP. Our results provide the molecular insights of Asciminib resistance mechanism in BCR-ABL mutants, which may help the design of novel inhibitors.

Insight on mutation-induced resistance to anaplastic lymphoma kinase inhibitor ceritinib from molecular dynamics simulations

Ceritinib, an advanced anaplastic lymphoma kinase (ALK) next-generation inhibitor, has been proved excellent antitumor activity in the treatment of ALK-associated cancers. However, the accumulation of acquired resistance mutations compromise the therapeutic efficacy of ceritinib. Despite abundant mutagenesis data, the structural determinants for reduced ceritinib binding in mutants remains elusive. Focusing on the G1123S and F1174C mutations, we applied molecular dynamics (MD) simulations to study possible reasons for drug resistance caused by these mutations. The MD simulations predict that the studied mutations allosterically impact the configurations of the ATP-binding pocket. An important hydrophobic cluster is identified that connects P-loop and the αC-helix, which has effects on stabilizing the conformation of ATP-binding pocket. It is suggested, in this study, that the G1123S and F1174C mutations can induce the conformational change of P-loop thereby causing the reduced ceritinib affinity and causing drug resistance.

Reliability of Docking-Based Virtual Screening for GPCR Ligands with Homology Modeled Structures: A Case Study of the Angiotensin II Type I Receptor

The number of solved G-protein-coupled receptor (GPCR) crystal structures has expanded rapidly, but most GPCR structures remain unsolved. Therefore, computational techniques, such as homology modeling, have been widely used to produce the theoretical structures of various GPCRs for structure-based drug design (SBDD). Due to the low sequence similarity shared by the transmembrane domains of GPCRs, accurate prediction of GPCR structures by homology modeling is quite challenging. In this study, angiotensin II type I receptor (AT1R) was taken as a typical case to assess the reliability of class A GPCR homology models for SBDD. Four homology models of angiotensin II type I receptor (AT1R) at the inactive state were built based on the crystal structures of CXCR4 chemokine receptor, CCR5 chemokine receptor, and δ-opioid receptor, and refined through molecular dynamics (MD) simulations and induced-fit docking, to allow for backbone and side-chain flexibility. Then, the quality of the homology models was assessed relative to the crystal structures in terms of two criteria commonly used in SBDD: prediction accuracy of ligand-binding poses and screening power of docking-based virtual screening. It was found that the crystal structures outperformed the homology models prior to any refinement in both assessments. MD simulations could generally improve the docking results for both the crystal structures and homology models. Moreover, the optimized homology model refined by MD simulations and induced-fit docking even shows a similar performance of the docking assessment to the crystal structures. Our results indicate that it is possible to establish a reliable class A GPCR homology model for SBDD through the refinement by integrating multiple molecular modeling techniques.

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

Comprehensive assessment of nine docking programs on type II kinase inhibitors: prediction accuracy of sampling power, scoring power and screening power

Protein kinases have been regarded as important therapeutic targets for many diseases. Currently, a total of 41 kinase inhibitors have been approved by the Food and Drug Administration, along with a large number of kinase inhibitors being evaluated in clinical and preclinical trials. Among all, allosteric inhibitors, such as type II kinase inhibitors, have attracted extensive attention owing to their potential high selectivity. Nowadays, molecular docking has become a powerful tool to search for novel kinase inhibitors. However, as for type II kinase inhibitors, their allosteric characteristics may exert a deep influence on docking accuracy. In this study, a comprehensive assessment was conducted to evaluate the effectiveness of nine docking algorithms towards type II kinase inhibitors. The calculation results showed that most tested docking programs, especially Glide with XP scoring, LeDock and Surflex-Dock, succeeded in the accurate identification of near-native binding poses, with the success rates ranging from 0.80 to 0.90, and the scoring functions in GOLD and LeDock outperformed the others in the prediction of relative binding affinities. In terms of the P-values, areas under the curve and enrichment factors, Glide with XP scoring, Surflex-Dock, GOLD with Astex Statistical Potential scoring and LeDock had better screening power to discriminate between active compounds and decoys. However, the screening power is sensitive to different initial conformations of the same target. It is expected that our study can provide some guidance for docking-based virtual screening to discover novel type II kinase inhibitors, as well as other allosteric inhibitors.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

The influence of residue in the position of 116 on the inhibitory potency of TH588 for MTH1

As one of the first-in-class inhibitor, TH588 was found to be efficient in the suppression of MutT homolog1 (MTH1). A recent work shows that the inhibitory potency of TH588 against human MTH1 (hsMTH1) is approximately 20-fold over that of mouse MTH1 (mmMTH1) and identifies residue in position 116 in MTH1 has an important contribution to TH588 affinity. But the effect of residue Leu or Met in position 116 on the binding affinity remains unclear. In this study, molecular dynamics (MD) simulations and free energy calculations were used to elucidate the mechanism about the effect of residue 116 to the different inhibitory potency of TH588 against MTH1. The binding free energy of TH588 in M116 complexes predicated by the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) is much lower than that in L116 complexes, which is consistent with the experiment results. The analysis of the individual energy terms suggests that the non-polar interactions are important for distinguishing the binding of TH588. The MD results show that the Leu116 disrupts the interactions between Asn33 and TH588, thus induces the conformational changes of Asn33 as well as TH588. The altered interactions between TH588 and mmMTH1 change the flexibility of TH588, which could induce the remarkable conformational fluctuation of mmMTH1. The conformations of the two loops covering the binding pocket have obvious influence on the opening or closure of the active site. The more open binding site may explain the lower inhibitor potency of TH588 against mmMTH1 than hsMTH1. Our results provide mechanistic insight into the effect of different residue Leu or Met in position 116 on the binding affinity of TH588 for MTH1, which is expected to contribute to the further rational design of more potent inhibitors.

Conformational Transition of Key Structural Features Involved in Activation of ALK Induced by Two Neuroblastoma Mutations and ATP Binding: Insight from Accelerated Molecular Dynamics Simulations

Deregulated kinase activity of anaplastic lymphoma kinase (ALK) has been observed to be implicated in the development of tumor progression. The activation mechanism of ALK is proposed to be similar to other receptor tyrosine kinases (RTKs), but the distinct static X-ray crystal conformation of ALK suggests its unique conformational transition. Herein, we have illustrated the dynamic conformational property of wild-type ALK as well as the kinase activation equilibrium variation induced by two neuroblastoma mutations (R1275Q and Y1278S) and ATP binding by performing enhanced sampling accelerated Molecular Dynamics (aMD) simulations. The results suggest that the wild-type ALK is mostly favored in the inactive state, whereas the mutations and ATP binding promote a clear shift toward the active-like conformation. The R1275Q mutant stabilizes the active conformation by rigidifying the αC-in conformation. The Y1278S mutant promotes activation at the expense of a π-stacking hydrophobic cluster, which plays a critical role in the stabilization of the inactive conformation of native ALK. ATP produces a more compact active site and thereby facilitates the activation of ALK. Taken together, these findings not only elucidate the diverse conformations in different ALKs but can also shed light on new strategies for protein engineering and structural-based drug design for ALK.

Role of Molecular Interactions and Protein Rearrangement in the Dissociation Kinetics of p38α MAP Kinase Type-I/II/III Inhibitors

Understanding the governing factors of fast or slow inhibitor binding/unbinding assists in developing drugs with preferred kinetic properties. For inhibitors with the same binding affinity targeting different binding sites of the same protein, the kinetic behavior can profoundly differ. In this study, we investigated unbinding kinetics and mechanisms of fast (type-I) and slow (type-II/III) binders of p38α mitogen-activated protein kinase, where the crystal structures showed that type-I and type-II/III inhibitors bind to pockets with different conformations of the Asp-Phe-Gly (DFG) motif. The work used methods that combine conventional molecular dynamics (MD), accelerated molecular dynamics (AMD) simulations, and the newly developed pathway search guided by internal motions (PSIM) method to find dissociation pathways. The study focuses on revealing key interactions and molecular rearrangements that hinder ligand dissociation by using umbrella sampling and post-MD processing to examine changes in free energy during ligand unbinding. As anticipated, the initial dissociation steps all require breaking interactions that appeared in crystal structures of the bound complexes. Interestingly, for type-I inhibitors such as SB2, p38α keeps barrier-free conformational fluctuation in the ligand-bound complex and during ligand dissociation. In contrast, with a type-II/III inhibitor such as BIRB796, with the rearrangements of p38α in its bound state, ligand unbinding features energetically unfavorable protein-ligand concerted movement. Our results also show that the type-II/III inhibitors preferred dissociation pathways through the allosteric channel, which is consistent with an existing publication. The study suggests that the level of required protein rearrangement is one major determining factor of drug binding kinetics in p38α systems, providing useful information for development of inhibitors.