Protein-small Molecule Interactions Research Articles

Chemical tools to expand the ligandable proteome: Diversity-oriented synthesis-based photoreactive stereoprobes.

Chemical proteomics enables the global analysis of small molecule-protein interactions in native biological systems and has emerged as a versatile approach for ligand discovery. The range of small molecules explored by chemical proteomics has, however, remained limited. Here, we describe a diversity-oriented synthesis (DOS)-inspired library of stereochemically defined compounds bearing diazirine and alkyne units for UV light-induced covalent modification and click chemistry enrichment of interacting proteins, respectively. We find that these "photo-stereoprobes" interact in a stereoselective manner with hundreds of proteins from various structural and functional classes in human cells and demonstrate that these interactions can form the basis for high-throughput screening-compatible NanoBRET assays. Integrated phenotypic screening and chemical proteomics identified photo-stereoprobes that modulate autophagy by engaging the mitochondrial serine protease CLPP. Our findings show the utility of DOS-inspired photo-stereoprobes for expanding the ligandable proteome, furnishing target engagement assays, and facilitating the discovery and characterization of bioactive compounds in phenotypic screens.

Protein-small molecule binding site prediction based on a pre-trained protein language model with contrastive learning.

Predicting protein-small molecule binding sites, the initial step in structure-guided drug design, remains challenging for proteins lacking experimentally derived ligand-bound structures. Here, we propose CLAPE-SMB, which integrates a pre-trained protein language model with contrastive learning to provide high accuracy predictions of small molecule binding sites that can accommodate proteins without a published crystal structure. We trained and tested CLAPE-SMB on the SJC dataset, a non-redundant dataset based on sc-PDB, JOINED, and COACH420, and achieved an MCC of 0.529. We also compiled the UniProtSMB dataset, which merges sites from similar proteins based on raw data from UniProtKB database, and achieved an MCC of 0.699 on the test set. In addition, CLAPE-SMB achieved an MCC of 0.815 on our intrinsically disordered protein (IDP) dataset that contains 336 non-redundant sequences. Case studies of DAPK1, RebH, and Nep1 support the potential of this binding site prediction tool to aid in drug design. The code and datasets are freely available at https://github.com/JueWangTHU/CLAPE-SMB . SCIENTIFIC CONTRIBUTION: CLAPE-SMB combines a pre-trained protein language model with contrastive learning to accurately predict protein-small molecule binding sites, especially for proteins without experimental structures, such as IDPs. Trained across various datasets, this model shows strong adaptability, making it a valuable tool for advancing drug design and understanding protein-small molecule interactions.

Acacetin alleviates rheumatoid arthritis by targeting HSP90 ATPase domain to promote COX-2 degradation

BackgroundInflammation plays a significant role in initiating and sustaining rheumatoid arthritis (RA). Acacetin, a natural flavonoid compound, exhibits excellent anti-inflammatory effects specifically for RA. However, its relevant targets and molecular mechanisms remain to be elucidated. PurposeThis study aims to investigate the mechanism of acacetin in the therapeutic efficacy of acacetin in RA and search for new therapeutic options for RA treatment. MethodsA collagen-induced RA mouse model was established to evaluate the therapeutic effect of acacetin. Acacetin functional probes were synthesized to capture potential target proteins in RAW264.7 cells. Various small molecule-protein interaction methods were conducted to verify the binding of acacetin to target protein. Molecular docking and site directed mutagenesis tests were performed to analyze the specific binding sites. Co-immunoprecipitation, immunofluorescence assay and western blot were engineered to explore the effect of acacetin on COX-2 degradation by targeting HSP90. ResultsAcacetin specifically binds to the ATP domain of HSP90, to facilitate the dissociation between HSP90 and COX-2, inducing the ubiquitin-degradation of COX-2 in macrophages. Acacetin suppressed the production of pro-inflammatory cytokines, as well as inflammatory related pathways, exerting excellent anti-inflammatory effects in RA. ConclusionsThis research proved that acacetin, a novel HSP90 ATPase inhibitor, inhibits the functional folding of the client protein COX-2, promoting its ubiquitin degradation for anti-inflammation. Targeting HSP90 is a viable strategy to inhibit inflammation, affording a distinct way to managing joint inflammation and pains associated with RA.

Leveraging Transfer Learning for Predicting Protein-Small Molecule Interactions.

A complex web of intermolecular interactions defines and regulates biological processes. Understanding this web has been particularly challenging because of the sheer number of actors in biological systems: ∼10 4 proteins in a typical human cell offer a plausible 10 8 interactions. This number grows rapidly if we consider metabolites, drugs, nutrients, and other biological molecules. The relative strength of interactions also critically affects these biological processes. However, the small and often incomplete datasets (10 3 -10 4 protein-ligand interactions) traditionally used for binding affinity predictions limit the ability to capture the full complexity of these interactions. To overcome this challenge, we developed Yuel 2, a novel neural network-based approach that leverages transfer learning to address the limitations of small datasets. Yuel 2 is pre-trained on a large-scale dataset to learn intricate structural features and then fine-tuned on specialized datasets like PDBbind to enhance the predictive accuracy and robustness. We show that Yuel 2 predicts multiple binding affinity metrics - Kd, Ki, and IC50 - between proteins and small molecules, offering a comprehensive representation of molecular interactions crucial for drug design and development.

Integration of Hydrogen-Deuterium Exchange Mass Spectrometry with Molecular Dynamics Simulations and Ensemble Reweighting Enables High Resolution Protein-Ligand Modeling.

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.

Measuring Interactions Between Proteins and Small Molecules or Nucleic Acids.

Interactions between proteins and small molecules or nucleic acids play a pivotal role in numerous biological processes critical for human health and are fundamental for advancing our understanding of biological systems. Proteins are the workhorses of the cell, executing various functions ranging from catalyzing biochemical reactions to transmitting signals within the body. Small molecules, including drugs and metabolites, can modulate protein activity, thereby impacting cellular processes and disease pathways. Similarly, nucleic acids, such as DNA and RNA, regulate protein synthesis and function through intricate interactions. Understanding these interactions is crucial for drug discovery and development and can shed light on gene regulation, transcriptional control, and RNA processing, providing insights into genetic diseases and developmental disorders. Moreover, studying protein-small molecule and protein-nucleic acid interactions enhances our comprehension of fundamental biological mechanisms. A wide array of methods to study these interactions range in cost, sensitivity, materials usage, throughput, and complexity. Notably in the last decade, new techniques have been developed that enhance our understanding of these interactions. In this review, we aim to summarize the new state-of-the-art methods for detecting interactions between proteins and small molecules or nucleic acids, as well as discuss older methods that still hold value today. © 2024 Wiley Periodicals LLC.

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.

Synthesis of Monofluorinated 7-Hydroxycoumarin-3-Carboxamides as Cell-Permeable Fluorescent Molecular Probes.

To facilitate studies of engagement of protein targets by small molecules in living cells, we synthesized fluorinated derivatives of the fluorophore 7-hydroxycoumarin-3-carboxylic acid (7OHCCA). Compared to the related difluorinated coumarin Pacific Blue (PB), amide derivatives of 6-fluoro-7-hydroxycoumarin-3-carboxylic acid (6FC) exhibited substantially brighter fluorescence. When linked to the anticancer drug paclitaxel (Taxol) via gamma-aminobutyric acid (GABA), the acidity of the phenol of these coumarins profoundly affected cellular efflux and binding to microtubules in living cells. In contrast to the known fluorescent taxoid PB-GABA-Taxol, the less acidic 6FC-GABA-Taxol was more cell-permeable due to a lower susceptibility to active efflux. In living cells, this facilitated the imaging of microtubules by confocal microscopy and enabled quantification of binding to microtubules by flow cytometry without added efflux inhibitors. The photophysical, chemical, and biological properties of 6FC derivatives make these compounds particularly attractive for the construction of fluorescent molecular probes suitable for quantitative analysis of intracellular small molecule-protein interactions.

Q-BioLiP: A Comprehensive Resource for Quaternary Structure-based Protein-ligand Interactions.

Since its establishment in 2013, BioLiP has become one of the widely used resources for protein-ligand interactions. Nevertheless, several known issues occurred with it over the past decade. For example, the protein-ligand interactions are represented in the form of single chain-based tertiary structures, which may be inappropriate as many interactions involve multiple protein chains (known as quaternary structures). We sought to address these issues, resulting in Q-BioLiP, a comprehensive resource for quaternary structure-based protein-ligand interactions. The major features of Q-BioLiP include: (1) representing protein structures in the form of quaternary structures rather than single chain-based tertiary structures; (2) pairing DNA/RNA chains properly rather than separation; (3) providing both experimental and predicted binding affinities; (4) retaining both biologically relevant and irrelevant interactions to alleviate the wrong justification of ligands' biological relevance; and (5) developing a new quaternary structure-based algorithm for the modelling of protein-ligand complex structure. With these new features, Q-BioLiP is expected to be a valuable resource for studying biomolecule interactions, including protein-small molecule interaction, protein-metal ion interaction, protein-peptide interaction, protein-protein interaction, protein-DNA/RNA interaction, and RNA-small molecule interaction. Q-BioLiP is freely available at https://yanglab.qd.sdu.edu.cn/Q-BioLiP/.

The density-threshold affinity:Calculating lipid binding affinities from unbiased coarse-grained molecular dynamics simulations.

Many membrane proteins are sensitive to their local lipid environment. As structural methods for membrane proteins have improved, there is growing evidence of direct, specific binding of lipids to protein surfaces. Unfortunately the workhorse of understanding protein-small molecule interactions, the binding affinity for a given site, is experimentally inaccessible for these systems. Coarse-grained molecular dynamics simulations can be used to bridge this gap, and are relatively straightforward to learn. Such simulations allow users to observe spontaneous binding of lipids to membrane proteins and quantify localized densities of individual lipids or lipid fragments. In this chapter we outline a protocol for extracting binding affinities from these localized distributions, known as the "density threshold affinity." The density threshold affinity uses an adaptive and flexible definition of site occupancy that alleviates the need to distinguish between "bound'' lipids and bulk lipids that are simply diffusing through the site. Furthermore, the method allows "bead-level" resolution that is suitable for the case where lipids share binding sites, and circumvents ambiguities about a relevant reference state. This approach provides a convenient and straightforward method for comparing affinities of a single lipid species for multiple sites, multiple lipids for a single site, and/or a single lipid species modeled using multiple forcefields.

Unravelling the molecular interactions of phenyl isothiocyanate and benzoyl isothiocyanate with human lysozyme: Biophysical and computational analyses

Phenyl isothiocyanate and benzoyl isothiocyanate are the phytochemicals present in the Brassicaceae family. They have antibacterial, antiapoptotic and antifungal properties. Protein-small molecule interaction studies are done to assess the changes in structure, dynamics, and functions of protein and to decipher the binding mechanism. This study is based on the comparative binding of PT and BT with human lysozyme using in vitro and computational techniques. UV, fluorescence emission, and FRET spectra gave insight into the complex formation, quenching mechanism, and binding parameters. Both PT and BT quenched the intrinsic fluorescence of Lyz by a static quenching mechanism. Synchronous, 3D fluorescence and CD spectroscopy substantiated conformational and microenvironmental alterations in the Lyz. The metal ions and β-cyclodextrin had a pronounced effect on the binding strength of Lyz-PT and Lyz-BT complexes. Accessible surface area analysis was determined to characterise the amino acid residue packing. Molecular docking further validated the wet lab experimental results.

Flavonoids in Astragali Radix Functions as Regulators of CDK2, VEGFA and MYC in Osteoporosis and Type 1 Diabetes Mellitus

Background: People with type 1 diabetes mellitus (T1DM) are significantly more likely to have osteoporosis (OP). Astragali Radix is a Chinese herbal medicine containing various active ingredients, and several clinical trials have been reported to use it to treat OP and T1DM, respectively. Objective: To evaluate the targets and potential mechanisms of Astragali Radix administration on OP and T1DM. Methods: The targets of Astragali Radix were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The OP and T1DM datasets were downloaded from the Gene Expression Omnibus (GEO) database. The weighted gene correlation network analysis (WGCNA) method was used to identify the co-expression genes associated with OP and T1DM. In addition, the common gene targets of OP and T1DM were screened using two public databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the R tool. After the validation of key genes, molecular docking was performed to visualize small molecule-protein interactions. Results: The compound target network mainly contained 17 compounds and 147 corresponding targets. There were 561 GO items and 154 signaling pathways in KEGG, mainly including the AGE-RAGE signaling pathway in diabetic complications and osteoclast differentiation. The results of molecular docking showed that flavonoids were the top compound of Astragali Radix, which had a high affinity with CDK2, VEGFA, and MYC. Conclusion: Flavonoids in Astragali Radix may regulate multiple signaling pathways through MYC, CDK2, and VEGFA, which may play a therapeutic role in OP and T1DM.

Synthesis and evaluation of Fmoc-amino esters and amides bearing a substrate like quaternary ammonium group as selective butyrylcholinesterase inhibitors

The depletion of the neurotransmitter acetylcholine has been suggested to contribute to the reduced cognitive function observed in individuals suffering from neurodegenerative diseases such as Alzheimer’s Disease (AD). For the two major cholinesterases, butyrylcholinesterase (BChE) and acetylcholinesterase (AChE), increased BChE activity observed in individuals with AD has been suggested to deplete acetylcholine levels. To reduce acetylcholine degradation and help restore the pool of the neurotransmitter, specific and potent BChE inhibitors are sought. Our previous findings have identified 9-fluorenylmethoxycarbonyl (Fmoc) amino acid-based inhibitors as effective BChE inhibitors. The amino acid-based compounds offered the opportunity to survey a range of structural features to enhance interactions with the enzyme active site. As enzymes interact with features of their substrates, incorporation of substrate-like features was predicted to lead to better inhibitors. Specifically, incorporation of a trimethylammonium moiety to mimic the cationic group of acetylcholine may lead to increased potency and selectivity. To test this model, a series of inhibitors bearing a cationic trimethylammonium group were synthesized, purified, and characterized. While the Fmoc-ester derivatives inhibited the enzyme, additional experiments showed the compounds acted as substrates and were enzymatically hydrolyzed. Inhibition studies with the Fmoc-amide derivatives showed that the compounds do not act as substrates and selectively inhibit BChE with IC50 values in the 0.06–10.0 µM range. Computational docking studies suggest that the inhibitors can interact with cholinyl binding site and peripheral site. Overall, the results suggest that introducing substrate-like characteristics within the Fmoc-amino acid-based background increases their potency. The versatile and ready access to amino acid-based compounds offers an attractive system to further our understanding of the relative importance of protein-small molecule interactions while guiding the development of better inhibitors.

Foldamer Nanotubes Mediated Label-Free Detection of Protein-Small Molecule Interactions.

Development of miniaturized lab-on-chip devices for the detection of rapid and specific small molecule-protein binding interactions at very low concentrations holds significant importance in drug discovery and biomedical applications. Here, the label-free detection of small molecule-protein interactions is reported on the surface functionalizable nanotubes of α,γ-hybrid peptide helical foldamers using nanoscale capacitance and impedance spectroscopy. The 12-helix conformation of the α,γ-hybrid peptide observed in the single crystals, self-assembled into nanotubes in an aqueous environment with exposed cysteine thiols for small molecule conjugation. The binding of streptavidin to the covalently linked biotin on the surface of nanotubes was detected at the picomolar concentrations. No change in the capacitance and impedance were observed in the absence of either immobilized biotin or protein streptavidin. The functionalizable hybrid peptide nanotubes reported here pave the way for the label-free detection of various small molecule protein interactions at very low concentrations.

Aptamer-based regulation of DNA polymerase activity for the detection of protein-small molecule interactions

Herein, we developed a novel method for identifying protein-small molecule interactions (PSMIs) based on the switching of DNA polymerase activity. This strategy employs the small molecule-linked DNA probe (SMP) that could bind to and disrupt the DNA polymerase aptamer probe (DAP) capable of inhibiting DNA polymerase activity. Without the binding of the target protein, the SMP is readily degraded by the applied exonuclease I (exo I), preventing its binding to the DAP. The DAP then freely inhibits the polymerase activity through its specific binding to the polymerase. In the presence of the target protein, however, the binding between the target protein and the small molecule protects the SMP against the exo I-catalyzed degradation. The intact SMP now binds to the DAP and disrupts its active aptameric structure, preventing the DAP from inhibiting the polymerase activity. The uninhibited DNA polymerase catalyzes the primer extension reaction in conjugation with the TaqMan probe (TP), leading to a significant fluorescence enhancement. Taking advantage of this elaborate design principle, we successfully identified the two model PSMIs, streptavidin/biotin (SA/BTN) and anti-digoxigenin/digoxigenin (ADIG/DIG), as low as 4.01 nM and 6.72 nM, respectively, with excellent specificity. We further validated its practical applicability by reliably identifying the SA/BTN interaction in heterogeneous human serum specimens.

High resolution micro-confocal Raman spectrometer-based photo-affinity microarray technology for the investigation of active ingredients - Target protein recognition strategy

Natural products has been used for the prevention and treatment of diseases for a long history. Research on the bioactive components from natural products and their interaction with target proteins are essential for drug discovery. However, studying the binding ability of natural products' active ingredients to target proteins is usually time-consuming and laborious due to their complex and diverse chemical structures. In this study, we have developed a high resolution micro-confocal Raman spectrometer-based photo-affinity microarray (HRMR-PM) technology for the investigation of active ingredients-target protein recognition strategy. The novel photo-affinity microarray was constructed by photo-cross-linking the small molecule with the photo-affinity group (4-[3-(Trifluoromethyl)-3H-diazirin-3-yl]benzoic acid, TAD) on the photo-affinity linker coated (PALC) slides under 365 nm ultraviolet irradiation. The small molecules on the microarrays with specific binding ability might immobilize target protein, which were characterized by high resolution micro-confocal Raman spectrometer. Using this method, more than a dozen components of Shenqi Jiangtang granules (SJG) were made into small molecule probe (SMP) microarrays. As a result, 8 of them had been identified to have α-glucosidase binding ability according to characteristic Raman shift at about 3060 cm−1. These compounds were further verified by different small molecule-protein interaction analysis methods, including contact angle D-value, surface plasmon resonance (SPR) and molecular docking. The results showed that Ginsenosides Mb, Formononetin and Gomisin D exhibited the strongest binding ability. In conclusion, the HRMR-PM strategy for investigating the interaction between target proteins and small molecules has the advantages such as high throughput, low sample consumption and fast qualitative characterization. This strategy is universal which can be applied in the study of in vitro binding activity of various types of small molecules to target proteins.

Detection of small molecules by extending the terminal protection to the polymerase

Since Terminal Protection of Small Molecule-Linked DNA (abbreviated as TPSMLD) was first found, TPSMLD-based assays have speedily prospered in small molecule-protein interaction study and sensing. However, most of the TPSMLD-based sensing strategies have adopted nuclease-assisted signal amplification, mainly by exonucleases, which adversely affect triggering the downstream reactions. Here we describe a simple, sensitive, and selective strategy for small molecule measurement taking biotin as a model. Due to the extension of the terminal protection mechanism to the polymerase and the introduction of the competition assay, a turn-on and exponential amplification signal was conveniently achieved. Benefiting from the high selectivity of TPSMLD and the exponential signal amplification assisted by terminal deoxynucleotidyl transferase (TdT)-endonuclease IV (Endo IV), biotin could be sensitively detected with a detection limit of 0.21 nM. Additionally, the recovery test was also executed in 1% spiked human serum with a satisfying recovery in the range of 95.8–98.0%, which demonstrated the robust anti-interference ability and potential clinical application of our proposed strategy. Predictably, by changing the small molecule-protein pair, this work could serve as a universal small molecule detection platform.

Solvent-induced proteome profiling for proteomic quantitation and target discovery of small molecular drugs.

Target identification by modification-free proteomic approaches can potentially reveal the pharmacological mechanism of small molecular compounds. By combining the recent solvent-induced protein precipitation (SIP) method with TMT-labeling quantitative proteomics, we propose solvent-induced proteome profiling (SIPP) approach to identify small molecule-protein interactions. The SIPP approach enables to depict denaturation curves of the target protein by varying concentrations of organic solvents to induce unfolding and precipitation of the cellular proteome. By using this approach, we have successfully identified the known targets of market drugs and natural products and extended the proteome information of SIP for target identification.

Applications of hydrogen deuterium exchange ensemble reweighting to model native protein ensembles, macromolecular interactions and protein drug interactions.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a powerful approach to probe the structural dynamics of a protein in solution. It simultaneously provides time-resolved information on the structural environment of the amide hydrogen of every residue of a protein except Prolines. As such, HDX-MS has been successfully applied to provide insight into the characterization of native state ensembles of proteins, macromolecular interactions, protein-small molecule interactions or protein folding and unfolding studies. However, it remains largely limited to peptide-level resolution information. On the other hand, molecular dynamics (MD) simulations provide a high-resolution view of a protein's structural dynamics by simulating the motion of a protein's atoms over time starting from a high-resolution structure. However, the time scales accessible to MD simulations are limited and can lead to inadequate exploration of a protein's conformational landscape. To bridge this gap, efforts have aimed at developing quantitative and integrative approaches that leverage HDX-MS data to assess and refine computationally generated structural ensembles. HDX ensemble reweighting (HDXer) is one such approach which uses a post hoc maximum entropy approach to adjusts the weights of individual frames of a structural ensemble to conform to HDX-MS data. We had previously applied HDXer to model the native ensemble of the cytoplasmic heme binding protein, PhuS. Here, we have validated the proposed model ensemble by using structure-based drug design approach to successfully target novel, cryptic drug binding sites that would have otherwise not been targetable using HDX-MS or MD simulations alone. Finally, we demonstrate the broader applicability of such HDX-MS guided integrative approach to model macromolecular interactions, missing electron density and protein-small molecule drug interactions

Virtual Screening of Soybean Protein Isolate-Binding Phytochemicals and Interaction Characterization.

Soybean protein isolate (SPI) and small molecule interactions have drawn more and more attention regarding their benefits for both parts, while research on large-scale investigations and comparisons of different compounds is absent. In this study, a high throughput virtual screening was applied on a phytochemical database with 1130 compounds to pinpoint the potential SPI binder. Pentagalloylglucose, narcissoside, poliumoside, isoginkgetin, and avicurin were selected as the top-five ranking molecules for further validation. Fluorescence quenching assays illustrated that isoginkgetin has a significantly higher apparent binding constant (Ka) of (0.060 ± 0.020) × 106 L·mol-1, followed by avicularin ((0.058 ± 0.010) × 106 L·mol-1), pentagalloylglucose ((0.049 ± 0.010) × 106 L·mol-1), narcissoside ((0.0013 ± 0.0004) × 106 L·mol-1), and poliumoside ((0.0012 ± 0.0006) × 106 L·mol-1). Interface characterization by MD simulation showed that protein residues E172, H173, G202, and V204 are highly involved in hydrogen bonding with the two carbonyl oxygens of isoginketin, which could be the crucial events in SPI binding. Van der Waals force was identified as the major driven force for isoginketin binding. Our study explored SPI-phytochemical interaction through multiple strategies, revealing the molecular binding details of isoginkgetin as a novel SPI binder, which has important implications for the utilization of the SPI-phytochemical complex in food applications.