Small Molecule Binding Sites Research Articles

μMap Photoproximity Labeling Enables Small Molecule Binding Site Mapping.

The characterization of ligand binding modes is a crucial step in the drug discovery process and is especially important in campaigns arising from phenotypic screening, where the protein target and binding mode are unknown at the outset. Elucidation of target binding regions is typically achieved by X-ray crystallography or photoaffinity labeling (PAL) approaches; yet, these methods present significant challenges. X-ray crystallography is a mainstay technique that has revolutionized drug discovery, but in many cases structural characterization is challenging or impossible. PAL has also enabled binding site mapping with peptide- and amino-acid-level resolution; however, the stoichiometric activation mode can lead to poor signal and coverage of the resident binding pocket. Additionally, each PAL probe can have its own fragmentation pattern, complicating the analysis by mass spectrometry. Here, we establish a robust and general photocatalytic approach toward the mapping of protein binding sites, which we define as identification of residues proximal to the ligand binding pocket. By utilizing a catalytic mode of activation, we obtain sets of labeled amino acids in the proximity of the target protein binding site. We use this methodology to map, in vitro, the binding sites of six protein targets, including several kinases and molecular glue targets, and furthermore to investigate the binding site of the STAT3 inhibitor MM-206, a ligand with no known crystal structure. Finally, we demonstrate the successful mapping of drug binding sites in live cells. These results establish μMap as a powerful method for the generation of amino-acid- and peptide-level target engagement data.

Native American ataxia medicines rescue ataxia-linked mutant potassium channel activity via binding to the voltage sensing domain

There are currently no drugs known to rescue the function of Kv1.1 voltage-gated potassium channels carrying loss-of-function sequence variants underlying the inherited movement disorder, Episodic Ataxia 1 (EA1). The Kwakwaka’wakw First Nations of the Pacific Northwest Coast used Fucus gardneri (bladderwrack kelp), Physocarpus capitatus (Pacific ninebark) and Urtica dioica (common nettle) to treat locomotor ataxia. Here, we show that extracts of these plants enhance wild-type Kv1.1 current, especially at subthreshold potentials. Screening of their constituents revealed that gallic acid and tannic acid similarly augment wild-type Kv1.1 current, with submicromolar potency. Crucially, the extracts and their constituents also enhance activity of Kv1.1 channels containing EA1-linked sequence variants. Molecular dynamics simulations reveal that gallic acid augments Kv1.1 activity via a small-molecule binding site in the extracellular S1-S2 linker. Thus, traditional Native American ataxia treatments utilize a molecular mechanistic foundation that can inform small-molecule approaches to therapeutically correcting EA1 and potentially other Kv1.1-linked channelopathies.

Cannabidiol sensitizes TRPV2 channels to activation by 2-APB.

The cation-permeable TRPV2 channel is important for cardiac and immune cell function. Cannabidiol (CBD), a non-psychoactive cannabinoid of clinical relevance, is one of the few molecules known to activate TRPV2. Using the patch-clamp technique, we discover that CBD can sensitize current responses of the rat TRPV2 channel to the synthetic agonist 2-aminoethoxydiphenyl borate (2-APB) by over two orders of magnitude, without sensitizing channels to activation by moderate (40°C) heat. Using cryo-EM, we uncover a new small-molecule binding site in the pore domain of rTRPV2 in addition to a nearby CBD site that had already been reported. The TRPV1 and TRPV3 channels are also activated by 2-APB and CBD and share multiple conserved features with TRPV2, but we find that strong sensitization by CBD is only observed in TRPV3, while sensitization for TRPV1 is much weaker. Mutations at non-conserved positions between rTRPV2 and rTRPV1 in either the pore domain or the CBD sites failed to confer strong sensitization by CBD in mutant rTRPV1 channels. Together, our results indicate that CBD-dependent sensitization of rTRPV2 channels engages multiple channel regions, and that the difference in sensitization strength between rTRPV2 and rTRPV1 channels does not originate from amino acid sequence differences at the CBD binding site or the pore domain. The remarkably robust effect of CBD on TRPV2 and TRPV3 channels offers a promising new tool to both understand and overcome one of the major roadblocks in the study of these channels - their resilience to activation.

Binding pocket dynamics along the recovery stroke of human β-cardiac myosin.

The druggability of small-molecule binding sites can be significantly affected by protein motions and conformational changes. Ligand binding, protein dynamics and protein function have been shown to be closely interconnected in myosins. The breakthrough discovery of omecamtiv mecarbil (OM) has led to an increased interest in small molecules that can target myosin and modulate its function for therapeutic purposes (myosin modulators). In this work, we use a combination of computational methods, including steered molecular dynamics, umbrella sampling and binding pocket tracking tools, to follow the evolution of the OM binding site during the recovery stroke transition of human β-cardiac myosin. We found that steering two internal coordinates of the motor domain can recapture the main features of the transition and in particular the rearrangements of the binding site, which shows significant changes in size, shape and composition. Possible intermediate conformations were also identified, in remarkable agreement with experimental findings. The differences in the binding site properties observed along the transition can be exploited for the future development of conformation-selective myosin modulators.

Abstract 5333: Combining chemoproteomics with machine learning identifies functionally active covalent fragments for hard-to-drug cancer drivers

Abstract a) Many cancer drivers are considered “undruggable” and without targeted treatments because they lack binding sites for conventional small molecules. Here, we introduce The FRONTIER™ Platform applying machine learning (ML), chemoproteomics and covalent chemistry to identify binding sites and cell-active covalent fragments across the human proteome, including against most cancer drivers and previously “undruggable” targets. Molecules tested in functional assays are active and can serve as starting points for new drug discovery initiatives. b) We are using mass spectrometry and data analysis workflows to perform high-throughput chemoproteomic profiling experiments. These experiments identify hits across the proteome using different cancer-relevant cell backgrounds and characterize binding sites for drug discovery. Customized ML algorithms using chemoproteomic, genomic and structural data to characterize and prioritize identified binding sites for covalent drug discovery. The performance of the platform allows the profiling of thousands of compounds from a custom-built library that has been optimized by ML for covalent fragment-based drug discovery. The nature of the fragment hits and the ability to map them to and focus on preferred binding sites for covalent drugs enables accelerated lead generation. c) We show details of the platform highlighting library design concepts and the hotspot map with binding site prioritization algorithms. We show coverage and applicability across important cancer target classes and signaling pathways. Covalent fragment hits have been identified for multiple difficult cancer targets, including KRAS, p53, STAT3, KEAP1, PTPN11 and others. The platform also identifies ligands for novel allosteric binding sites in established oncology targets such as CDK4, PI3KCA and BTK. We will highlight how discovered ligands show functional activity in orthogonal assays, demonstrating their fitness for drug discovery campaigns and target validation experiments. d) Undruggable targets across a variety of cancer target classes have become druggable. Citation Format: Johannes C. Hermann, Robert Everley, Laura Marholz, Matthew Berberich, Tzu-Yi Yang, Yu-Hsin Chao, Abduselam K. Awol, Michael Shaghafi, Han Yoon, Rohan Varma, Reed Stein, Karsten Krug, Emily Lachtara, Daniel Erlanson, Chris Varma, Kevin R. Webster. Combining chemoproteomics with machine learning identifies functionally active covalent fragments for hard-to-drug cancer drivers. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5333.

Abstract 1614: Antitumor activity of novel and potent YAP/TAZ-TEAD inhibitorstargeting the Hippo pathway in solid tumors

Abstract The Hippo pathway is evolutionarily conserved and known to regulate diverse cellular processes, including cell survival, proliferation, differentiation, migration, and organ size. The key regulator of Hippo pathway is transcriptional enhanced associate domain (TEAD) transcription factors, which directly bind with YAP/TAZ and then drive the multiple signaling by activating target gene expression on nuclear. Loss-of-function mutations in the upstream activators, NF2-LATS1/2-MST1/2, trigger YAP/TAZ nuclear translocation and target gene transcription (Hippo-off state). This YAP/TAZ-TEAD complex is overexpressed and leads to metastatic progression in various cancers including malignant mesothelioma, NSCLC, ovarian cancer or cholangiocarcinoma. A recent development in targeting the Hippo pathway has been focused on the discovery of a central lipophilic pocket in TEAD amenable to the small-molecule binding site of autopalmitoylation. Within this lipophilic palmitate pocket, post-translational S-palmitoylation of TEAD at a conserved catalytic cysteine (Cys) residue (e.g., C380) leads to TEAD stabilization and is believed to be critical for maintaining appropriate protein folding to enable the formation of the transcriptionally active YAP/TAZ -TEAD complex. Therefore, targeting the palmitate pocket with allosteric small molecules inhibitor disrupt the formation of the YAP/TAZ-TEAD complex and modulate YAP/TAZ-TEAD driven gene transcription. We have identified a series of novel, potent small-molecule inhibitors of the YAP/TAZ-TEAD transcriptional complex. It showed under 20 nM of potency in the inhibition of TEAD luciferase reporter assay in MCF7-TEAD-luc cells. These TEAD inhibitors inhibited YAP/TAZ-TEAD protein-protein interaction in H226 cells harboring neurofibromin 2 (NF2) alteration. In addition, our lead compounds exhibited dose-dependent growth inhibitory effects in Hippo pathway-altered cancer cell lines and reduced the YAP/TAZ-TEAD target gene expression, CTGF, and CYR61 in H226 cells. Our lead compounds, singled out and optimized based on in vitro functional assay, displayed favorable pharmacokinetic and safety profiles. Furthermore, orally administered lead compound effectively suppressed tumor growth within tolerable doses in xenograft mice with tumors harboring NF2 alteration as a major upstream molecule of the Hippo pathway. In summary, we pointed our novel YAP/TAZ-TEAD inhibitors that showed excellent efficacy in Hippo-altered mutant cancer in vitro and in vivo xenograft models. These data best support a therapeutic option for the treatment of cancers with amplified or overexpressed YAP, TAZ, or TEAD genes. Further preclinical studies will be performed and reported soon after the establishment of a preclinical candidate. Citation Format: Jisook Kim, Seung Hyun Jung, Seon Yeong Han, Jihee Yoon, Minjeong Kim, Jooyun Byun, Heesun Moon, Eunyoung Lee, Yu-Yon Kim, Hyunjin Park, So-Ye Jeon, Young Gil Ahn, Young Hoon Kim, Kwee Hyun Suh. Antitumor activity of novel and potent YAP/TAZ-TEAD inhibitorstargeting the Hippo pathway in solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1614.

SARS-CoV2 billion-compound docking

This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens.

Identification and validation of novel non-nucleoside class of molecules inhibiting the dengue virus replication

There is currently no drug available to treat mosquito-borne dengue. The C-terminal RNA-dependent RNA polymerase (RdRp) domain in the non-structural type 5 (NS5) protein of the dengue virus (DENV) is essential for viral RNA synthesis and replication, and therefore, it is an attractive target for the anti-dengue drug development. We report herein the discovery and validation of two novel non-nucleoside classes of small molecules as DENV RdRp inhibitors. Firstly, using the refined X-ray structure of the DENV NS5 RdRp domain (PDB-ID: 4V0R), we conducted docking, binding free-energy studies, and short-scale molecular dynamics simulation to investigate the binding sites of known small molecules that led to the optimized protein–ligand complex. Subsequently, protein structure-based screening of a commercial database (∼500,000 synthetic compounds), pre-filtered for the drug-likeness, led to the top-ranked 171 molecules, which was then subjected to structural diversity analysis and clustering. This process led to six structurally distinct and best-scored compounds that were procured from the commercial vendor, and then subjected to the in vitro testing in the MTT and dengue infection assays. It revealed two unique and structurally unique compounds, KKR-D-02 and KKR-D-03, exhibiting 84 and 81% reductions, respectively, in DENV copy number in repeated assays in comparison to the virus-infected cell controls. These active compounds represent novel scaffolds for further structure-based discovery of novel candidate molecules for the intervention of dengue.Communicated by Ramaswamy H. Sarma

Physics-based approach to extend a de novo TIM barrel with rationally designed helix-loop-helix motifs.

Computational protein design promises the ability to build tailor-made proteins de novo. While a range of de novo proteins have been constructed so far, the majority of these designs have idealized topologies that lack larger cavities which are necessary for the incorporation of small molecule binding sites or enzymatic functions. One attractive target for enzyme design is the TIM-barrel fold, due to its ubiquity in nature and capability to host versatile functions. With the successful de novo design of a 4-fold symmetric TIM barrel, sTIM11, an idealized, minimalistic scaffold was created. In this work, we attempted to extend this de novo TIM barrel by incorporating a helix-loop-helix motif into its βα-loops by applying a physics-based modular design approach using Rosetta. Further diversification was performed by exploiting the symmetry of the scaffold to integrate two helix-loop-helix motifs into the scaffold. Analysis with AlphaFold2 and biochemical characterization demonstrate the formation of additional α-helical secondary structure elements supporting the successful extension as intended.

Portability of a Small-Molecule Binding Site between Disordered Proteins.

Intrinsically disordered proteins (IDPs) are important in both normal and disease states. Small molecules can be targeted to disordered regions, but we currently have only a limited understanding of the nature of small-molecule binding sites in IDPs. Here, we show that a minimal small-molecule binding sequence of eight contiguous residues derived from the Myc protein can be ported into a different disordered protein and recapitulate small-molecule binding activity in the new context. We also find that the residue immediately flanking the binding site can have opposing effects on small-molecule binding in the different disordered protein contexts. The results demonstrate that small-molecule binding sites can act modularly and are portable between disordered protein contexts but that residues outside of the minimal binding site can modulate binding affinity.

RLBind: a deep learning method to predict RNA-ligand binding sites.

Identification of RNA-small molecule binding sites plays an essential role in RNA-targeted drug discovery and development. These small molecules are expected to be leading compounds to guide the development of new types of RNA-targeted therapeutics compared with regular therapeutics targeting proteins. RNAs can provide many potential drug targets with diverse structures and functions. However, up to now, only a few methods have been proposed. Predicting RNA-small molecule binding sites still remains a big challenge. New computational model is required to better extract the features and predict RNA-small molecule binding sites more accurately. In this paper, a deep learning model, RLBind, was proposed to predict RNA-small molecule binding sites from sequence-dependent and structure-dependent properties by combining global RNA sequence channel and local neighbor nucleotides channel. To our best knowledge, this research was the first to develop a convolutional neural network for RNA-small molecule binding sites prediction. Furthermore, RLBind also can be used as a potential tool when the RNA experimental tertiary structure is not available. The experimental results show that RLBind outperforms other state-of-the-art methods in predicting binding sites. Therefore, our study demonstrates that the combination of global information for full-length sequences and local information for limited local neighbor nucleotides in RNAs can improve the model's predictive performance for binding sites prediction. All datasets and resource codes are available at https://github.com/KailiWang1/RLBind.

ProBiS-Fold Approach for Annotation of Human Structures from the AlphaFold Database with No Corresponding Structure in the PDB to Discover New Druggable Binding Sites.

ProBiS (Protein Binding Sites), a local structure-based comparison algorithm, is used in the new ProBiS-Fold web server to annotate human structures from the AlphaFold database without a corresponding structure in the Protein Data Bank (PDB) to discover new druggable binding sites. The ProBiS algorithm is used to compare each query protein structure predicted by the AlphaFold approach with the protein structures from the PDB to identify similarities between known binding sites found in the PDB and yet unknown binding sites in the AlphaFold database. Ligands bound in these identified similar PDB sites are then transferred to each query protein from the AlphaFold database, and binding sites are identified as ligand clusters on an AlphaFold protein. Small molecule binding sites are assigned druggability scores based on the similarity of their ligands to known drugs, allowing them to be ranked according to their perceived and actual potential for drug development. ProBiS-Fold provides interactive and downloadable binding sites for the entire human structural proteome, including more than 3000 new druggable binding sites that have no corresponding structure in the PDB, taking into account AlphaFold's model quality, to enable protein structure-function relationship studies and pharmaceutical drug discovery research. The web server is freely accessible to academic users at http://probis-fold.insilab.org.

Deep Learning-Based Label-Free Surface-Enhanced Raman Scattering Screening and Recognition of Small-Molecule Binding Sites in Proteins.

Identification of small-molecule binding sites in proteins is of great significance in analysis of protein function and drug design. Modified sites can be recognized via proteolytic cleavage followed by liquid chromatography-mass spectrometry (LC-MS); however, this has always been impeded by the complexity of peptide mixtures and the elaborate synthetic design for tags. Here, we demonstrate a novel technique for identifying protein binding sites using a deep learning-based label-free surface-enhanced Raman scattering (SERS) screening (DLSS) strategy. In DLSS, the deep learning model that was trained with large SERS signals could detect signal features of small molecules with high accuracy (>99%). Without any secondary tag, the small molecules are directly complexed with proteins. After proteolysis and LC, SERS signals of all LC fractions are collected and input into the model, whereby the fractions containing the small-molecule-modified peptides can be recognized by the model and sent to MS/MS to identify the binding site(s). By using an automated DLSS system, we successfully identified the modification sites of fomepizole in alcohol dehydrogenase, which is coordinated with zinc along with three peptides. We also showed that the DLSS strategy works for identification of amino-acid residues that covalently bond with ibrutinib in Bruton tyrosine kinase. These results suggest that the DLSS strategy, which provides high molecular recognition capability to LC-MS analysis, has potential in drug discovery, proteomics, and metabolomics.

SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules.

RNA molecules can act as potential drug targets in different diseases, as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for ∼70% of the human genome, and these molecules can have complex tertiary structures that present a great opportunity for targeting by small molecules. In the present study, the site identification by ligand competitive saturation (SILCS) computational approach is extended to target RNA, termed SILCS-RNA. Extensions to the method include an enhanced oscillating excess chemical potential protocol for the grand canonical Monte Carlo calculations and individual simulations of the neutral and charged solutes from which the SILCS functional group affinity maps (FragMaps) are calculated for subsequent binding site identification and docking calculations. The method is developed and evaluated against seven RNA targets and their reported small molecule ligands. SILCS-RNA provides a detailed characterization of the functional group affinity pattern in the small molecule binding sites, recapitulating the types of functional groups present in the ligands. The developed method is also shown to be useful for identification of new potential binding sites and identifying ligand moieties that contribute to binding, granular information that can facilitate ligand design. However, limitations in the method are evident including the ability to map the regions of binding sites occupied by ligand phosphate moieties and to fully account for the wide range of conformational heterogeneity in RNA associated with binding of different small molecules, emphasizing inherent challenges associated with applying computer-aided drug design methods to RNA. While limitations are present, the current study indicates how the SILCS-RNA approach may enhance drug discovery efforts targeting RNAs with small molecules.

Computational approaches to identify a novel binding site of BHPI on estrogen receptor alpha

3,3-bis(4-hydroxyphenyl)-7-methyl-1,3,dihydro-2H-indol-2-one (BHPI) is a biomodulator of Estrogen Receptor alpha (ERα) that targets ERα positive cancer cells by activating the unfolded protein response (UPR). BHPI induces strong and sustained activation of this pathway, eventually resulting in necrotic cell death. While much is known about how BHPI triggers the UPR leading to necrotic cell death, it is not known how BHPI binds to its putative molecular target, ERα. In an effort to identify the binding site of BHPI on ERα, molecular docking studies in AutoDock Vina were utilized. Unexpectedly, BHPI was found to dock more frequently and with significantly better binding affinity to a newly described surface pocket on the ERα ligand-binding domain, compared to the ligand-binding pocket. This work uncovers a novel binding site for small molecules on ERα that is not targeted by classical ligands, such as estrogen and tamoxifen, and may allow for the design of additional anti-cancer drugs that work in distinct ways.

Structural landscapes of PPI interfaces.

Proteins are capable of highly specific interactions and are responsible for a wide range of functions, making them attractive in the pursuit of new therapeutic options. Previous studies focusing on overall geometry of protein–protein interfaces, however, concluded that PPI interfaces were generally flat. More recently, this idea has been challenged by their structural and thermodynamic characterisation, suggesting the existence of concave binding sites that are closer in character to traditional small-molecule binding sites, rather than exhibiting complete flatness. Here, we present a large-scale analysis of binding geometry and physicochemical properties of all protein–protein interfaces available in the Protein Data Bank. In this review, we provide a comprehensive overview of the protein–protein interface landscape, including evidence that even for overall larger, more flat interfaces that utilize discontinuous interacting regions, small and potentially druggable pockets are utilized at binding sites.

ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction.

Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two classes of methods prevail: machine learning models built on top of handcrafted features and comparative modeling. They are, respectively, limited by the expressivity of the handcrafted features and the availability of similar proteins. Here, we introduce ScanNet, an end-to-end, interpretable geometric deep learning model that learns features directly from 3D structures. ScanNet builds representations of atoms and amino acids based on the spatio-chemical arrangement of their neighbors. We train ScanNet for detecting protein-protein and protein-antibody binding sites, demonstrate its accuracy-including for unseen protein folds-and interpret the filters learned. Finally, we predict epitopes of the SARS-CoV-2 spike protein, validating known antigenic regions and predicting previously uncharacterized ones. Overall, ScanNet is a versatile, powerful and interpretable model suitable for functional site prediction tasks. A webserver for ScanNet is available from http://bioinfo3d.cs.tau.ac.il/ScanNet/ .

Portability of Small Molecule Binding Sites in Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) lack stable secondary and tertiary structure. Because they are associated with a range of diseases, IDPs are desirable therapeutic targets and have been targeted by small molecules. The transcription factor c‐Myc (Myc) is an IDP that is implicated in over 70% of cancers. Previously, we have shown that several small molecules can bind to short, contiguous sequences on Myc. These sequences remain unstructured when binding to small molecules, potentially indicating only a limited dependence on residues outside of the contiguous binding residues. This mode of binding differs from that common in structured proteins where contact residues are often noncontiguous and scaffolded by extensive protein structure. Here we test the portability of a small molecule‐IDP interaction implied by its dependence on only a short, linear sequence. We compare the binding of a small molecule to its specific recognition sequence in a native Myc context, in a minimalist peptide context, and by porting the binding sequence into the context of a different IDP. An ability of short, disordered sequences to bind small molecules in a context agnostic manner would have implications for the prediction and discovery of IDP binding sites broadly.

Functional inhibition of c-Myc using novel inhibitors identified through “hot spot” targeting

Protein–protein interactions drive various biological processes in healthy as well as disease states. The transcription factor c-Myc plays a crucial role in maintaining cellular homeostasis, and its deregulated expression is linked to various human cancers; therefore, it can be considered a viable target for cancer therapeutics. However, the structural heterogeneity of c-Myc due to its disordered nature poses a major challenge to drug discovery. In the present study, we used an in silico alanine scanning mutagenesis approach to identify “hot spot” residues within the c-Myc/Myc-associated factor X interface, which is highly disordered and has not yet been systematically analyzed for potential small molecule binding sites. We then used the information gained from this analysis to screen potential inhibitors using a conformation ensemble approach. The fluorescence-based biophysical experiments showed that the identified hit molecules displayed noncovalent interactions with these hot spot residues, and further cell-based experiments showed substantial in vitro potency against diverse c-Myc-expressing cancer/stem cells by deregulating c-Myc activity. These biophysical and computational studies demonstrated stable binding of the hit compounds with the disordered c-Myc protein. Collectively, our data indicated effective drug targeting of the disordered c-Myc protein via the determination of hot spot residues in the c-Myc/Myc-associated factor X heterodimer.

How does a small molecule bind at a cryptic binding site?

Protein-protein interactions (PPIs) are ubiquitous biomolecular processes that are central to virtually all aspects of cellular function. Identifying small molecules that modulate specific disease-related PPIs is a strategy with enormous promise for drug discovery. The design of drugs to disrupt PPIs is challenging, however, because many potential drug-binding sites at PPI interfaces are “cryptic”: When unoccupied by a ligand, cryptic sites are often flat and featureless, and thus not readily recognizable in crystal structures, with the geometric and chemical characteristics of typical small-molecule binding sites only emerging upon ligand binding. The rational design of small molecules to inhibit specific PPIs would benefit from a better understanding of how such molecules bind at PPI interfaces. To this end, we have conducted unbiased, all-atom MD simulations of the binding of four small-molecule inhibitors (SP4206 and three SP4206 analogs) to interleukin 2 (IL2)—which performs its function by forming a PPI with its receptor—without incorporating any prior structural information about the ligands’ binding. In multiple binding events, a small molecule settled into a stable binding pose at the PPI interface of IL2, resulting in a protein–small-molecule binding site and pose virtually identical to that observed in an existing crystal structure of the IL2-SP4206 complex. Binding of the small molecule stabilized the IL2 binding groove, which when the small molecule was not bound emerged only transiently and incompletely. Moreover, free energy perturbation (FEP) calculations successfully distinguished between the native and non-native IL2–small-molecule binding poses found in the simulations, suggesting that binding simulations in combination with FEP may provide an effective tool for identifying cryptic binding sites and determining the binding poses of small molecules designed to disrupt PPI interfaces by binding to such sites.