Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures.

Machine learning-based protein structure prediction algorithms, such as RosettaFold and AlphaFold2, have greatly impacted the structural biology field, arousing a fair amount of discussion around their potential role in drug discovery. While there are few preliminary studies addressing the usage of these models in virtual screening, none of them focus on the prospect of hit-finding in a real-world virtual screen with a model based on low prior structural information. In order to address this, we have developed an AlphaFold2 version where we exclude all structural templates with more than 30% sequence identity from the model-building process. In a previous study, we used those models in conjunction with state-of-the-art free energy perturbation methods and demonstrated that it is possible to obtain quantitatively accurate results. In this work, we focus on using these structures in rigid receptor-ligand docking studies. Our results indicate that using out-of-the-box Alphafold2 models is not an ideal scenario for virtual screening campaigns; in fact, we strongly recommend to include some post-processing modeling to drive the binding site into a more realistic holo model.

Are Deep Learning Structural Models Sufficiently Accurate for Free-Energy Calculations? Application of FEP+ to AlphaFold2-Predicted Structures.

The availability of AlphaFold2 has led to great excitement in the scientific community─particularly among drug hunters─due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein structure prediction, it remains to be determined whether ligand binding sites are predicted with sufficient accuracy in these structures to be useful in supporting computationally driven drug discovery programs. We explored this question by performing free-energy perturbation (FEP) calculations on a set of well-studied protein-ligand complexes, where AlphaFold2 predictions were performed by removing all templates with >30% identity to the target protein from the training set. We observed that in most cases, the ΔΔG values for ligand transformations calculated with FEP, using these prospective AlphaFold2 structures, were comparable in accuracy to the corresponding calculations previously carried out using crystal structures. We conclude that under the right circumstances, AlphaFold2-modeled structures are accurate enough to be used by physics-based methods such as FEP in typical lead optimization stages of a drug discovery program.

Development of a Highly Selective Plasmodium falciparum Proteasome Inhibitor with Anti-malaria Activity in Humanized Mice.

Plasmodium falciparum proteasome (Pf20S) inhibitors are active against Plasmodium at multiple stages-erythrocytic, gametocyte, liver, and gamete activation stages-indicating that selective Pf20S inhibitors possess the potential to be therapeutic, prophylactic, and transmission-blocking antimalarials. Starting from a reported compound, we developed a noncovalent, macrocyclic peptide inhibitor of the malarial proteasome with high species selectivity and improved pharmacokinetic properties. The compound demonstrates specific, time-dependent inhibition of the ß5 subunit of the Pf20S, kills artemisinin-sensitive and artemisinin-resistant P. falciparum isolates in vitro and reduces parasitemia in humanized, P. falciparum-infected mice.

Rigorous Free Energy Simulations in Virtual Screening.

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

Large-Scale Validation of Mixed-Solvent Simulations to Assess Hotspots at Protein-Protein Interaction Interfaces.

The ability to target protein-protein interactions (PPIs) with small molecule inhibitors offers great promise in expanding the druggable target space and addressing a broad range of untreated diseases. However, due to their nature and function of interacting with protein partners, PPI interfaces tend to extend over large surfaces without the typical pockets of enzymes and receptors. These features present unique challenges for small molecule inhibitor design. As such, determining whether a particular PPI of interest could be pursued with a small molecule discovery strategy requires an understanding of the characteristics of the PPI interface and whether it has hotspots that can be leveraged by small molecules to achieve desired potency. Here, we assess the ability of mixed-solvent molecular dynamic (MSMD) simulations to detect hotspots at PPI interfaces. MSMD simulations using three cosolvents (acetonitrile, isopropanol, and pyrimidine) were performed on a large test set of 21 PPI targets that have been experimentally validated by small molecule inhibitors. We compare MSMD, which includes explicit solvent and full protein flexibility, to a simpler approach that does not include dynamics or explicit solvent (SiteMap) and find that MSMD simulations reveal additional information about the characteristics of these targets and the ability for small molecules to inhibit the PPI interface. In the few cases were MSMD simulations did not detect hotspots, we explore the shortcomings of this technique and propose future improvements. Finally, using Interleukin-2 as an example, we highlight the advantage of the MSMD approach for detecting transient cryptic druggable pockets that exists at PPI interfaces.

What can crystal structures of aminergic receptors tell us about designing subtype-selective ligands?

G protein-coupled receptors (GPCRs) are integral membrane proteins that represent an important class of drug targets. In particular, aminergic GPCRs interact with a significant portion of drugs currently on the market. However, most drugs that target these receptors are associated with undesirable side effects, which are due in part to promiscuous interactions with close homologs of the intended target receptors. Here, based on a systematic analysis of all 37 of the currently available high-resolution crystal structures of aminergic GPCRs, we review structural elements that contribute to and can be exploited for designing subtype-selective compounds. We describe the roles of secondary binding pockets (SBPs), as well as differences in ligand entry pathways to the orthosteric binding site, in determining selectivity. In addition, using the available crystal structures, we have identified conformational changes in the SBPs that are associated with receptor activation and explore the implications of these changes for the rational development of selective ligands with tailored efficacy.

Relative Binding Free Energy Calculations Applied to Protein Homology Models.

A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein-ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points-something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A2A), and a protein-protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the "real" conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.

In search of novel ligands using a structure-based approach: a case study on the adenosine A2A receptor.

In this work, we present a case study to explore the challenges associated with finding novel molecules for a receptor that has been studied in depth and has a wealth of chemical information available. Specifically, we apply a previously described protocol that incorporates explicit water molecules in the ligand binding site to prospectively screen over 2.5 million drug-like and lead-like compounds from the commercially available eMolecules database in search of novel binders to the adenosine A2A receptor (A2AAR). A total of seventy-one compounds were selected for purchase and biochemical assaying based on high ligand efficiency and high novelty (Tanimoto coefficient ≤0.25 to any A2AAR tested compound). These molecules were then tested for their affinity to the adenosine A2A receptor in a radioligand binding assay. We identified two hits that fulfilled the criterion of ~50 % radioligand displacement at a concentration of 10 µM. Next we selected an additional eight novel molecules that were predicted to make a bidentate interaction with Asn2536.55, a key interacting residue in the binding pocket of the A2AAR. None of these eight molecules were found to be active. Based on these results we discuss the advantages of structure-based methods and the challenges associated with finding chemically novel molecules for well-explored targets.

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Discovery and characterization of a G protein-biased agonist that inhibits ß-arrestin recruitment to the D2 dopamine receptor.

A high-throughput screening campaign was conducted to interrogate a 380,000+ small-molecule library for novel D2 dopamine receptor modulators using a calcium mobilization assay. Active agonist compounds from the primary screen were examined for orthogonal D2 dopamine receptor signaling activities including cAMP modulation and ß-arrestin recruitment. Although the majority of the subsequently confirmed hits activated all signaling pathways tested, several compounds showed a diminished ability to stimulate ß-arrestin recruitment. One such compound (MLS1547; 5-chloro-7-[(4-pyridin-2-ylpiperazin-1-yl)methyl]quinolin-8-ol) is a highly efficacious agonist at D2 receptor-mediated G protein-linked signaling, but does not recruit ß-arrestin as demonstrated using two different assays. This compound does, however, antagonize dopamine-stimulated ß-arrestin recruitment to the D2 receptor. In an effort to investigate the chemical scaffold of MLS1547 further, we characterized a set of 24 analogs of MLS1547 with respect to their ability to inhibit cAMP accumulation or stimulate ß-arrestin recruitment. A number of the analogs were similar to MLS1547 in that they displayed agonist activity for inhibiting cAMP accumulation, but did not stimulate ß-arrestin recruitment (i.e., they were highly biased). In contrast, other analogs displayed various degrees of G protein signaling bias. These results provided the basis to use pharmacophore modeling and molecular docking analyses to build a preliminary structure-activity relationship of the functionally selective properties of this series of compounds. In summary, we have identified and characterized a novel G protein-biased agonist of the D2 dopamine receptor and identified structural features that may contribute to its biased signaling properties.

Predicting GPCR promiscuity using binding site features.

G protein-coupled receptors (GPCRs) represent a large family of signaling proteins that includes many therapeutic targets. GPCR ligands include odorants, tastants, and neurotransmitters and vary in size and properties. Dramatic chemical diversity may occur even among ligands of the same receptor. Our goal is to unravel the structural and chemical features that determine GPCRs' promiscuity toward their ligands. We perform statistical analysis using more than 30 descriptors related to the sequence, physicochemical, structural, and energetic properties of the GPCR binding sites-we find that the chemical variability of antagonists significantly correlates with the binding site hydrophobicity and anticorrelates with the number of hydrogen bond donors in the binding site. The number of disulfide bridges in the extracellular region of a receptor anticorrelates with the range of molecular weights of its antagonists, highlighting the role of the entrance pathway in determining the size selectivity for GPCR antagonists. The predictive capability of the model is successfully validated using a separate set of GPCRs, using either X-ray structures or homology models.

Selecting an optimal number of binding site waters to improve virtual screening enrichments against the adenosine A2A receptor.

A major challenge in structure-based virtual screening (VS) involves the treatment of explicit water molecules during docking in order to improve the enrichment of active compounds over decoys. Here we have investigated this in the context of the adenosine A2A receptor, where water molecules have previously been shown to be important for achieving high enrichment rates with docking, and where the positions of some binding site waters are known from a high-resolution crystal structure. The effect of these waters (both their presence and orientations) on VS enrichment was assessed using a carefully curated set of 299 high affinity A2A antagonists and 17,337 decoys. We show that including certain crystal waters greatly improves VS enrichment and that optimization of water hydrogen positions is needed in order to achieve the best results. We also show that waters derived from a molecular dynamics simulation - without any knowledge of crystallographic waters - can improve enrichments to a similar degree as the crystallographic waters, which makes this strategy applicable to structures without experimental knowledge of water positions. Finally, we used decision trees to select an ensemble of structures with different water molecule positions and orientations that outperforms any single structure with water molecules. The approach presented here is validated against independent test sets of A2A receptor antagonists and decoys from the literature. In general, this water optimization strategy could be applied to any target with waters-mediated protein-ligand interactions.

Successful prediction of the intra- and extracellular loops of four G-protein-coupled receptors.

We present results of the restoration of all crystallographically available intra- and extracellular loops of four G-protein-coupled receptors (GPCRs): bovine rhodopsin (bRh), the turkey ß-1 adrenergic receptor (ß1Ar), and the human ß-2 adrenergic (ß2Ar) and A2A adenosine (A2Ar) receptors. We use our Protein Local Optimization Program (PLOP), which samples conformational space from first principles to build sets of loop candidates and then discriminates between them using our physics-based, all-atom energy function with implicit solvent. We also discuss a new kind of explicit membrane calculation developed for GPCR loops that interact, either in the native structure or in low-energy false-positive structures, with the membrane, and thus exist in a multiphase environment not previously incorporated in PLOP. Our results demonstrate a significant advance over previous work reported in the literature, and of particular note we are able to accurately restore the extremely long second extracellular loop (ECL2), which is also key for GPCR ligand binding. In the case of ß2Ar, accurate ECL2 restoration required seeding a small helix into the loop in the appropriate region, based on alignment with the ß1Ar ECL2 loop, and then running loop reconstruction simulations with and without the seeded helix present; simulations containing the helix attain significantly lower total energies than those without the helix, and have rmsds close to the native structure. For ß1Ar, the same protocol was used, except the alignment was done to ß2Ar. These results represent an encouraging start for the more difficult problem of accurate loop refinement for GPCR homology modeling.

A single glycine in extracellular loop 1 is the critical determinant for pharmacological specificity of dopamine D2 and D3 receptors.

Subtype-selective agents for the dopamine D3 receptor (D3R) have been considered as potential medications for drug addiction and other neuropsychiatric disorders. Medicinal chemistry efforts have led to the discovery of 4-phenylpiperazine derivatives that are >100-fold selective for the dopamine D3 receptor over dopamine D2 receptor (D2R), despite high sequence identity (78% in the transmembrane domain). Based on the recent crystal structure of D3R, we demonstrated that the 4-phenylpiperazine moiety in this class of D3R-selective compounds binds to the conserved orthosteric binding site, whereas the extended aryl amide moiety is oriented toward a divergent secondary binding pocket (SBP). In an effort to further characterize molecular determinants of the selectivity of these compounds, we modeled their binding modes in D3R and D2R by comparative ligand docking and molecular dynamics simulations. We found that the aryl amide moiety in the SBP differentially induces conformational changes in transmembrane segment 2 and extracellular loop 1 (EL1), which amplify the divergence of the SBP in D3R and D2R. Receptor chimera and site-directed mutagenesis studies were used to validate these binding modes and to identify a divergent glycine in EL1 as critical to D3R over D2R subtype selectivity. A better understanding of drug-dependent receptor conformations such as these is key to the rational design of compounds targeting a specific receptor among closely related homologs, and may also lead to discovery of novel chemotypes that exploit subtle differences in protein conformations.

Steric hindrance mutagenesis in the conserved extracellular vestibule impedes allosteric binding of antidepressants to the serotonin transporter.

The serotonin transporter (SERT) controls synaptic serotonin levels and is the primary target for antidepressants, including selective serotonin reuptake inhibitors (e.g. (S)-citalopram) and tricyclic antidepressants (e.g. clomipramine). In addition to a high affinity binding site, SERT possesses a low affinity allosteric site for antidepressants. Binding to the allosteric site impedes dissociation of antidepressants from the high affinity site, which may enhance antidepressant efficacy. Here we employ an induced fit docking/molecular dynamics protocol to identify the residues that may be involved in the allosteric binding in the extracellular vestibule located above the central substrate binding (S1) site. Indeed, mutagenesis of selected residues in the vestibule reduces the allosteric potency of (S)-citalopram and clomipramine. The identified site is further supported by the inhibitory effects of Zn(2+) binding in an engineered site and the covalent attachment of benzocaine-methanethiosulfonate to a cysteine introduced in the extracellular vestibule. The data provide a mechanistic explanation for the allosteric action of antidepressants at SERT and suggest that the role of the vestibule is evolutionarily conserved among neurotransmitter:sodium symporter proteins as a binding pocket for small molecule ligands.

Loop prediction for a GPCR homology model: algorithms and results.

We present loop structure prediction results of the intracellular and extracellular loops of four G-protein-coupled receptors (GPCRs): bovine rhodopsin (bRh), the turkey ß1-adrenergic (ß1Ar), the human ß2-adrenergic (ß2Ar) and the human A2a adenosine receptor (A2Ar) in perturbed environments. We used the protein local optimization program, which builds thousands of loop candidates by sampling rotamer states of the loops' constituent amino acids. The candidate loops are discriminated between with our physics-based, all-atom energy function, which is based on the OPLS force field with implicit solvent and several correction terms. For relevant cases, explicit membrane molecules are included to simulate the effect of the membrane on loop structure. We also discuss a new sampling algorithm that divides phase space into different regions, allowing more thorough sampling of long loops that greatly improves results. In the first half of the paper, loop prediction is done with the GPCRs' transmembrane domains fixed in their crystallographic positions, while the loops are built one-by-one. Side chains near the loops are also in non-native conformations. The second half describes a full homology model of ß2Ar using ß1Ar as a template. No information about the crystal structure of ß2Ar was used to build this homology model. We are able to capture the architecture of short loops and the very long second extracellular loop, which is key for ligand binding. We believe this the first successful example of an RMSD validated, physics-based loop prediction in the context of a GPCR homology model.

Ligand-dependent activation and deactivation of the human adenosine A(2A) receptor.

G-protein-coupled receptors (GPCRs) are membrane proteins with critical functions in cellular signal transduction, representing a primary class of drug targets. Acting by direct binding, many drugs modulate GPCR activity and influence the signaling pathways associated with numerous diseases. However, complete details of ligand-dependent GPCR activation/deactivation are difficult to obtain from experiments. Therefore, it remains unclear how ligands modulate a GPCR's activity. To elucidate the ligand-dependent activation/deactivation mechanism of the human adenosine A2A receptor (AA2AR), a member of the class A GPCRs, we performed large-scale unbiased molecular dynamics and metadynamics simulations of the receptor embedded in a membrane. At the atomic level, we have observed distinct structural states that resemble the active and inactive states. In particular, we noted key structural elements changing in a highly concerted fashion during the conformational transitions, including six conformational states of a tryptophan (Trp246(6.48)). Our findings agree with a previously proposed view that, during activation, this tryptophan residue undergoes a rotameric transition that may be coupled to a series of coherent conformational changes, resulting in the opening of the G-protein binding site. Further, metadynamics simulations provide quantitative evidence for this mechanism, suggesting how ligand binding shifts the equilibrium between the active and inactive states. Our analysis also proposes that a few specific residues are associated with agonism/antagonism, affinity, and selectivity, and suggests that the ligand-binding pocket can be thought of as having three distinct regions, providing dynamic features for structure-based design. Additional simulations with AA2AR bound to a novel ligand are consistent with our proposed mechanism. Generally, our study provides insights into the ligand-dependent AA2AR activation/deactivation in addition to what has been found in crystal structures. These results should aid in the discovery of more effective and selective GPCR ligands.

Improved docking of polypeptides with Glide.

Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein-protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM-GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 Å for the interface backbone atoms) increased from 21% with default Glide SP settings to 58% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.

Structure-based virtual screening of MT2 melatonin receptor: influence of template choice and structural refinement.

Developing GPCR homology models for structure-based virtual screening requires the choice of a suitable template and refinement of binding site residues. We explored this systematically for the MT2 melatonin receptor, with the aim to build a receptor homology model that is optimized for the enrichment of active melatoninergic ligands. A set of 12 MT2 melatonin receptor models was built using different GPCR X-ray structural templates and submitted to a virtual screening campaign on a set of compounds composed of 29 known melatonin receptor ligands and 2560 drug-like decoys. To evaluate the effect of including a priori information in receptor models, 12 representative melatonin receptor ligands were placed into the MT2 receptor models in poses consistent with known mutagenesis data and with assessed pharmacophore models. The receptor structures were then adapted to the ligands by induced-fit docking. Most of the 144 ligand-adapted MT2 receptor models showed significant improvements in screening enrichments compared to the unrefined homology models, with some template/refinement combinations giving excellent enrichment factors. The discriminating ability of the models was further tested on the 29 active ligands plus a set of 21 inactive or low-affinity compounds from the same chemical classes. Rotameric states of side chains for some residues, presumed to be involved in the binding process, were correlated with screening effectiveness, suggesting the existence of specific receptor conformations able to recognize active compounds. The top MT2 receptor model was able to identify 24 of 29 active ligands among the first 2% of the screened database. This work provides insights into the use of refined GPCR homology models for virtual screening.

Identification of a small-molecule inhibitor of the PICK1 PDZ domain that inhibits hippocampal LTP and LTD.

Proteins containing PSD-95/Discs-large/ZO-1 homology (PDZ) domains play key roles in the assembly and regulation of cellular signaling pathways and represent putative targets for new pharmacotherapeutics. Here we describe the first small-molecule inhibitor (FSC231) of the PDZ domain in protein interacting with C kinase 1 (PICK1) identified by a screening of approximately 44,000 compounds in a fluorescent polarization assay. The inhibitor bound the PICK1 PDZ domain with an affinity similar to that observed for endogenous peptide ligands (K(i) approximately 10.1 microM). Mutational analysis, together with computational docking of the compound in simulations starting from the PDZ domain structure, identified the binding mode of FSC231. The specificity of FSC231 for the PICK1 PDZ domain was supported by the lack of binding to PDZ domains of postsynaptic density protein 95 (PSD-95) and glutamate receptor interacting protein 1 (GRIP1). Pretreatment of cultured hippocampal neurons with FSC231 inhibited coimmunopreciptation of the AMPA receptor GluR2 subunit with PICK1. In agreement with inhibiting the role of PICK1 in GluR2 trafficking, FSC231 accelerated recycling of pHluorin-tagged GluR2 in hippocampal neurons after internalization in response to NMDA receptor activation. FSC231 blocked the expression of both long-term depression and long-term potentiation in hippocampal CA1 neurons from acute slices, consistent with inhibition of the bidirectional function of PICK1 in synaptic plasticity. Given the proposed role of the PICK1/AMPA receptor interaction in neuropathic pain, excitotoxicity, and cocaine addiction, FSC231 might serve as a lead in the future development of new therapeutics against these conditions.