Drug-protein Interactions Research Articles

Identifying Novel Aging-Related Biomarkers for Age-related Macular Degeneration with Integrative Bioinformatics Approaches

Age-related macular degeneration (AMD) is the leading cause of visual impairment in older adults worldwide and is a condition that causes visual deprivation. There exist two subcategories of this disease with the wet form of this disease being the focus of our study. Using bioinformatic analysis, this research conducted investigations to uncover the genes related to aging that may be biomarkers for the development of AMD. First, we compared the expression levels of samples from AMD/CNV patients and a control group using the GEO microarrays (GSE29801) in order to obtain differentially expressed genes (DEGs). WGCNA, combined with functional enrichment analysis, is utilized to discover and validate the gene module crucial for AMD. Differentially expressed aging-related genes (DEARGs) were identified by overlapping significant gene sets. The subcellular location of hub DEARGs and their corresponding cell subpopulations were determined and predicted using the Geo dataset GSE155288. Pan-cancer analyses were used to confirm those hub DEARGs function in other diseases. Moreover, both Protein-Protein Interaction (PPI) and AlphaFold prediction were employed to validate the protein interaction among the key DEARGs. Lastly, a potential target drug was selected, with portions of them validated through drug-protein interactions. In further analysis of our result, the collect gene set of 7 DEARGs was divided into the immune-related group and the non-immune-related group. These groups uncovered two distinct pathways of AMD development, with one triggering inflammatory responses by promoting macrophage proliferation and the other inducing choroidal neovascularization formation due to malfunctioning growth regulator genes

Cytokine Dynamics in Action: A Mechanistic Approach to Assess Interleukin 6 Related Therapeutic Protein-Drug-Disease Interactions.

Understanding cytokine-related therapeutic protein-drug interactions (TP-DI) is crucial for effective medication management in conditions characterized by elevated inflammatory responses. Recent FDA and ICH guidelines highlight a systematic, risk-based approach for evaluating these interactions, emphasizing the need for a thorough mechanistic understanding of TP-DIs. This study integrates the physiologically based pharmacokinetic (PBPK) model for TP (specifically interleukin-6, IL-6) with small-molecule drug PBPK models to elucidate cytokine-related TP-DI mechanistically. The integrated model successfully predicted TP-DIs across a broad range of both constant and fluctuating IL-6 levels, as observed in patients with rheumatoid arthritis, Crohn's disease, HIV-infection, and those undergoing hip-surgery or bone marrow transplantation (all simulated AUC and Cmax ratios were within a twofold error of the observed data). Constant IL-6 levels that would be associated with mild, moderate, and strong inhibitory interactions were estimated. The time-course and extent of TP-DI potential were also assessed in cytokine storm triggered by SARS-CoV-2 infection (COVID-19) and T-cell engager therapies (blinatumomab, mosunetuzumab, and epcoritamab). Additionally, scenarios involving concurrent CYP enzyme suppression by IL-6 and induction by rifampicin were assessed for the magnitude of drug interaction. By providing a robust mechanistic framework for understanding cytokine-drug interactions and establishing reliable exposure-response relationships, this study enhances predictive accuracy and informs human dosing strategies. It demonstrates the potential of PBPK models to improve therapeutic decision making and patient care, particularly in inflammatory conditions.

Neuroinflammation mediates the progression of neonate hypoxia-ischemia brain damage to Alzheimer’s disease: a bioinformatics and experimental study

BackgroundTraumatic brain injury (TBI) can generally be divided into focal damage and diffuse damage, and neonate Hypoxia-Ischemia Brain Damage (nHIBD) is one of the causes of diffuse damage. Patients with nHIBD are at an increased risk of developing Alzheimer’s disease (AD). However, the shared pathogenesis of patients affected with both neurological disorders has not been fully elucidated.PurposeWe here aim to identify the shared molecular signatures between nHIBD and AD. We used an integrated analysis of the cortex gene expression data, targeting differential expression of genes related to the mechanisms of neurodegeneration and cognitive impairment following traumatic brain injury.MethodsThe gene expression profiles of Alzheimer’s disease (GSE203206) and that of Neonate Hypoxia-Ischemia Brain Damage (GSE23317) were obtained from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) of Alzheimer’s disease and neonate Hypoxia-Ischemia Brain Damage by limma package analysis, five kinds of analyses were performed on them, namely Gene Ontology (GO) and pathway enrichment analysis, protein–protein interaction network, DEG-transcription factor interactions and DEG-microRNA interactions, protein-drug interactions and protein-disease association analysis, and gene-inflammation association analysis and protein-inflammation association analysis.ResultsIn total, 12 common DEGs were identified including HSPB1, VIM, MVD, TUBB4A, AACS, ANXA6, DIRAS2, RPH3A, CEND1, KALM, THOP1, AREL1. We also identified 11 hub proteins, three central regulatory transcription factors, and three microRNAs encoded by the DEGs. Protein-drug interaction analysis showed that CYC1 and UQCRFS1 are associated with different drugs. Gene-disease association analysis shows Mammary Neoplasms, Neoplasm Metastasis, Schizophrenia, and Brain Ischemia diseases are the most relevant to the hub proteins we identified. Gene-inflammation association analysis shows that the hub gene AREL1 is related to inflammatory response, while the protein-inflammation association analysis shows that the hub proteins AKT1 and MAPK14 are related to inflammatory response.ConclusionThis study provides new insights into the shared molecular mechanisms between AD and nHIBD. These common pathways and hub genes could potentially be used to design therapeutic interventions, reducing the likelihood of Alzheimer’s disease development in survivors of neonatal Hypoxic-Ischemia brain injury.

Drug-target binding affinity prediction based on power graph and word2vec

BackgroundDrug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.ResultsCompared with other methods, regression-based drug target affinity is more representative of the binding ability. Accurate prediction of drug target affinity can effectively reduce the time and cost of drug retargeting and new drug development. In this paper, a drug target affinity prediction model (WPGraphDTA) based on power graph and word2vec is proposed.ConclusionsIn this model, the drug molecular features in the power graph module are extracted by a graph neural network, and then the protein features are obtained by the Word2vec method. After feature fusion, they are input into the three full connection layers to obtain the drug target affinity prediction value. We conducted experiments on the Davis and Kiba datasets, and the experimental results showed that WPGraphDTA exhibited good prediction performance.

Deciphering Saquinavir–Bovine Serum Albumin Interactions: Spectroscopic and Computational Insights

ABSTRACTBovine serum albumin (BSA) plays a crucial role as a carrier protein in plasma, binding various ligands, including drugs. Understanding the interaction between BSA and saquinavir, an antiretroviral drug, is essential for predicting its pharmacokinetics and pharmacodynamics. We employed spectroscopic approaches, including circular dichroism spectrometry and fluorescence spectroscopy, to investigate the binding of saquinavir to BSA. CD studies revealed conformational changes upon saquinavir mesylate binding, and the complex was stable up to 45°C during thermal denaturation. Saquinavir quenched the intrinsic fluorescence of BSA, indicating static quenching due to complex formation. Additionally, molecular docking simulations were performed to elucidate the favored binding site and interactions. The molecular docking results revealed that Subdomains IIA and IIB, which are proximal to Sudlow Site I, are the principal binding sites for the antiviral drug saquinavir. The ligand‐bound pose of BSA also revealed that residue Trp213, which is adjacent to saquinavir, further validated the results of the fluorescence quenching assay, suggesting that residue Trp213 is quenched upon binding with saquinavir. MD simulations allowed us to explore the dynamic behavior of the BSA–saquinavir complex over time. We observed conformational fluctuations, solvent exposure, flexibility of binding pockets, free energy landscape, and binding energy. This study enhances our understanding of drug–protein interactions and contributes to drug development and optimization.

Target protein identification in live cells and organisms with a non-diffusive proximity tagging system.

Identifying target proteins for bioactive molecules is essential for understanding their mechanisms, developing improved derivatives, and minimizing off-target effects. Despite advances in target identification (target-ID) technologies, significant challenges remain, impeding drug development. Most target-ID methods use cell lysates, but maintaining an intact cellular context is vital for capturing specific drug-protein interactions, such as those with transient protein complexes and membrane-associated proteins. To address these limitations, we developed POST-IT (Pup-On-target for Small molecule Target Identification Technology), a non-diffusive proximity tagging system for live cells, orthogonal to the eukaryotic system. POST-IT utilizes an engineered fusion of proteasomal accessory factor A and HaloTag to transfer Pup to proximal proteins upon directly binding to the small molecule. After significant optimization to eliminate self-pupylation and polypupylation, minimize depupylation, and optimize chemical linkers, POST-IT successfully identified known targets and discovered a new binder, SEPHS2, for dasatinib, and VPS37C as a new target for hydroxychloroquine, enhancing our understanding these drugs' mechanisms of action. Furthermore, we demonstrated the application of POST-IT in live zebrafish embryos, highlighting its potential for broad biological research and drug development.

Orthoallosteric EGFR-TKIs: A New Paradigm in NSCLC Treatment Strategy Targeting the C797S Mutation.

The remarkable clinical success of third-generation epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) has significantly advanced the treatment landscape for non-small-cell lung cancer (NSCLC). However, the emergence of the tertiary point mutation C797S poses a substantial obstacle to their clinical efficacy, leading to a dearth of FDA-approved targeted therapies for patients harboring this mutation. Addressing this pressing clinical challenge necessitates the development of novel therapeutic agents targeting the clinically challenging EGFR mutation. This review delves into the design strategies, antitumor activity, and crucial protein-drug interactions of recently introduced Orthoallosteric fourth-generation EGFR-TKIs. These inhibitors are distinguished by their ability to simultaneously engage both the canonical orthosteric (ATP) binding site and the allosteric site. By shedding light on these key aspects, the review serves as a valuable resource for medicinal chemists, empowering them to propel the advancement of fourth-generation EGFR inhibitors.

ProtChat: An AI Multi-Agent for Automated Protein Analysis Leveraging GPT-4 and Protein Language Model.

Large language models (LLMs) have transformed natural language processing, enabling advanced human-machine communication. Similarly, in computational biology, protein sequences are interpreted as natural language, facilitating the creation of protein large language models (PLLMs). However, applying PLLMs requires specialized preprocessing and script development, increasing the complexity of their use. Researchers have integrated LLMs with PLLMs to develop automated protein analysis tools to address these challenges, simplifying analytical workflows. Existing technologies often require substantial human intervention for specific protein-related tasks, maintaining high barriers to implementing automated protein analysis systems. Here, we propose ProtChat, an AI multiagent system for protein analysis that integrates the inference capabilities of PLLMs with the task-planning abilities of LLMs. ProtChat integrates GPT-4 with multiple PLLMs, like ESM and MASSA, to automate tasks such as protein property prediction and protein-drug interactions without human intervention. This AI agent enables users to input instructions directly, significantly improving efficiency and usability, making it suitable for researchers without a computational background. Experiments demonstrate that ProtChat can automate complex protein tasks accurately, avoiding manual intervention and delivering results rapidly. This advancement opens new research avenues in computational biology and drug discovery. Future applications may extend ProtChat's capabilities to broader biological data analysis. Our code and data are publicly available at github.com/SIAT-code/ProtChat.

Biological evaluation of trans-2,3-dihydrofuro[3,2-c]coumarins as potential cathepsin inhibitors and anticancer agents

Novel trans-2,3-dihydrofuro[3,2-c]coumarins were synthesized and assessed for their inhibition potential against cysteine proteases: cathepsin B, H and L which are the possible targets for anticancer activity. In general, the coumarin derivatives were found to be exceptional inhibitors against this class of enzymes. On the basis of molecular modeling data and structure–activity relationships, their inhibition patterns are also discussed. The selectivity of designed compounds as inhibitors against cathepsins B, H and L was demonstrated by enzyme inhibition data. Enzyme kinetics investigations were also on par with in vitro studies when tested on HepG2 carcinoma cell line utilizing 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide. Possible protein–drug interactions responsible for potential inhibition are demonstrated using docking studies.

In Silico Prediction of Quercetin Analogs for Targeting Death-Associated Protein Kinase 1 (DAPK1) Against Alzheimer's Disease.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that greatly affects the health and life quality of the elderly population. Existing drugs mainly alleviate symptoms but fail to halt disease progression, underscoring the urgent need for the development of novel drugs. Based on the neuroprotective effects of flavonoid quercetin in AD, this study was designed to identify potential AD-related targets for quercetin and perform in silico prediction of promising analogs for the treatment of AD. Database mining suggested death-associated protein kinase 1 (DAPK1) as the most promising AD-related target for quercetin among seven protein candidates. To achieve better biological effects for the treatment of AD, we devised a series of quercetin analogs as ligands for DAPK1, and molecular docking analyses, absorption, distribution, metabolism, and excretion (ADME) predictions, as well as molecular dynamics (MD) simulations, were performed. The energy for drug-protein interaction was predicted and ranked. As a result, quercetin-A1a and quercetin-A1a1 out of 19 quercetin analogs exhibited the lowest interaction energy for binding to DAPK1 than quercetin, and they had similar dynamics performance with quercetin. In addition, quercetin-A1a and quercetin-A1a1 were predicted to have better water solubility. Thus, quercetin-A1a and quercetin-A1a1 could be promising agents for the treatment of AD. Our findings paved the way for further experimental studies and the development of novel drugs.

Ultra-high-throughput screening of antimicrobial combination therapies using a two-stage transparent machine learning model.

Here, we present M2D2, a two-stage machine learning (ML) pipeline that identifies promising antimicrobial drug combinations, which are crucial for combating drug resistance. M2D2 addresses key challenges in drug combination discovery by predicting drug synergies using computationally generated drug-protein interaction data, thereby circumventing the need for expensive omics data. The model improves the accuracy of drug target identification using high-throughput experimental and computational methods via feedback between ML stages. M2D2's transparent framework provides mechanistic insights into drug interactions and was benchmarked against chemogenomics, transcriptomics, and metabolomics datasets. We experimentally validated M2D2 using high-throughput screening of 946 combinations of Food and Drug Administration (FDA)- approved drugs and antibiotics against Escherichia coli . We discovered synergy between a cerebrovascular drug and a widely used penicillin antibiotic and validated predicted mechanisms of action using genome-wide CRISPR inhibition screens. M2D2 offers a transparent ML tool for rapidly designing combination therapies and guides repurposing efforts while providing mechanistic insights.

Accurate and transferable drug-target interaction prediction with DrugLAMP.

Accurate prediction of drug-target interactions (DTIs), especially for novel targets or drugs, is crucial for accelerating drug discovery. Recent advances in pretrained language models (PLMs) and multi-modal learning present new opportunities to enhance DTI prediction by leveraging vast unlabeled molecular data and integrating complementary information from multiple modalities. We introduce DrugLAMP (PLM-assisted multi-modal prediction), a PLM-based multi-modal framework for accurate and transferable DTI prediction. DrugLAMP integrates molecular graph and protein sequence features extracted by PLMs and traditional feature extractors. We introduce two novel multi-modal fusion modules: (i) pocket-guided co-attention (PGCA), which uses protein pocket information to guide the attention mechanism on drug features, and (ii) paired multi-modal attention (PMMA), which enables effective cross-modal interactions between drug and protein features. These modules work together to enhance the model's ability to capture complex drug-protein interactions. Moreover, the contrastive compound-protein pre-training (2C2P) module enhances the model's generalization to real-world scenarios by aligning features across modalities and conditions. Comprehensive experiments demonstrate DrugLAMP's state-of-the-art performance on both standard benchmarks and challenging settings simulating real-world drug discovery, where test drugs/targets are unseen during training. Visualizations of attention maps and application to predict cryptic pockets and drug side effects further showcase DrugLAMP's strong interpretability and generalizability. Ablation studies confirm the contributions of the proposed modules. Source code and datasets are freely available at https://github.com/Lzcstan/DrugLAMP. All data originate from public sources.

The Long and Short of Coupling and Uncoupling via 2D IR Spectroscopy.

Determining dynamic structural changes along with the functional movements in biological systems has been a significant challenge for scientists for several decades. Utilizing vibrational coupling with the aid of 2D IR probe pairs has aided in uncovering structural dynamics and functional roles of chemical moieties involved in actions such as membrane peptide folding and transport, ion and water transport, and drug-protein interactions. Both native and non-native vibrational probe pairs have been developed for infrared studies, and their efficacy has been tested in various systems. With these probe pairs, 2D IR spectroscopy captures frozen snapshots of the structural events involved in biological function through vibrational coupling and correlated spectral diffusion. In this Perspective, different treatments of vibrational coupling and coupling models will be addressed, and a review of some of the specific vibrational probe pairs used to study these coupling mechanisms is presented. Overall, the intrinsic molecular dynamics detected on these ultrafast time scales will provide an atomic level view of how chosen structures traverse reaction paths. Thus, it is important to evaluate and assess the accuracy of the different vibrational coupling models and their consistency with the prediction of different molecular structures.

Prediction of SHP2-E76K binding sites based on molecular dynamics simulation and Markov algorithm

SHP2-E76K, a mutant encoded by the PTPN11 gene, was associated with various solid tumors, such as lung cancer, glioblastoma, and intellectual disability. SHP2-E76K has become potential drug targets, while there was no effective inhibitor against the mutant currently. At present, the crystal complex structure of SHP099 with SHP2-E76K has been reported in the RCSB PDB protein data bank, however, the dynamic structure of SHP099 binding to the active center of SHP2-E76K protein was still lacking. Therefore, this study used molecular dynamics simulation and Markov model to characterize the kinetics of the inhibitor SHP099 with SHP2-E76K enzyme and to determine the active binding site, which would give a hint of a vital enzyme-substrate interaction in atomistic detail that proposed the potential to be applied for the discovery of more effective SHP2-E76K inhibitors and, in broader terms, dynamic protein-drug interactions.

MDRepo-an open data warehouse for community-contributed molecular dynamics simulations of proteins.

Molecular Dynamics (MD) simulation of biomolecules provides important insights into conformational changes and dynamic behavior, revealing critical information about folding and interactions with other molecules. The collection of simulations stored in computers across the world holds immense potential to serve as training data for future Machine Learning models that will transform the prediction of structure, dynamics, drug interactions, and more. Ideally, there should exist an open access repository that enables scientists to submit and store their MD simulations of proteins and protein-drug interactions, and to find, retrieve, analyze, and visualize simulations produced by others. However, despite the ubiquity of MD simulation in structural biology, no such repository exists; as a result, simulations are instead stored in scattered locations without uniform metadata or access protocols. Here, we introduce MDRepo, a robust infrastructure that provides a relatively simple process for standardized community contribution of simulations, activates common downstream analyses on stored data, and enables search, retrieval, and visualization of contributed data. MDRepo is built on top of the open-source CyVerse research cyber-infrastructure, and is capable of storing petabytes of simulations, while providing high bandwidth upload and download capabilities and laying a foundation for cloud-based access to its stored data.

AutoRNSRF-DTI: a recommendation model for predicting drug–protein interactions using reliable negative samples

AutoRNSRF-DTI: a recommendation model for predicting drug–protein interactions using reliable negative samples

Exploration of common pathogenesis and candidate hub genes between HIV and monkeypox co-infection using bioinformatics and machine learning

This study explored the pathogenesis of human immunodeficiency virus (HIV) and monkeypox co-infection, identifying candidate hub genes and potential drugs using bioinformatics and machine learning. Datasets for HIV (GSE 37250) and monkeypox (GSE 24125) were obtained from the GEO database. Common differentially expressed genes (DEGs) in co-infection were identified by intersecting DEGs from monkeypox datasets with genes from key HIV modules screened using Weighted Gene Co-Expression Network Analysis (WGCNA). After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and construction of protein-protein interaction (PPI) network, candidate hub genes were further screened based on machine learning algorithms. Transcriptional factors (TFs) and miRNA-candidate hub gene networks were constructed to understand regulatory mechanisms and protein-drug interactions to identify potential therapeutic drugs. Seven candidate hub genes—MX2, ADAR, POLR2H, RPL5, IFI16, IFIT2, and RPS5—were identified. TFs and miRNAs associated with these hub genes, playing a key role in regulating viral infection and inflammation due to the activation of antiviral innate immunity, were also identified through network analysis. Potential therapeutic drugs were screened based on these hub genes: AZT, a nucleotide reverse transcriptase inhibitor, suppressed viral replication in HIV and monkeypox co-infection, while mefloquine inhibited inflammation due to the activation of antiviral innate immunity. In conclusion, the study identified candidate hub genes, their transcriptional regulation, signaling pathways, and small-molecule drugs in HIV and monkeypox co-infection, contributing to understanding the pathogenesis of HIV and monkeypox co-infection and informing precise therapeutic strategies.

Polyphenol Tannin‐Induced Protein Precipitation for Drug Target Discovery by Chemical Proteomics

AbstractHigh‐throughput drug discovery is highly dependent on the targets available to accelerate the process of candidate screening. Unbiased drug target identification is of crucial importance to reveal the mechanisms of protein–drug interactions. In recent years, energetics‐based protein conformation methods have become an alternative strategy for protein target discovery of bioactive drugs. In this study, we present a novel tannin protein precipitation method (TAP) for identifying target proteins of small molecules using the effect on protein solubility of tannin. The feasibility of this method was validated using a drug model of methotrexate. It was found that the known target DHFR yields a significant solubility shift. This method was further investigated with Annexin A2, the target protein of protopanaxadiol (PPD)‐S by western blot, and by screening potential target proteins of PPD‐S in K562 cells by quantitative proteomics. Finally, the TAP method was employed to differentiate the target proteins of pair chiral molecules PPD‐S and PPD‐R, which can bring up novel insights into the mechanism of drug–protein interactions. This study not only provides an effective tool for drug research and development but also offers new ideas for understanding the mechanism of action of natural chiral bioactive compounds.

Insights into the Binding of Metadoxine with Bovine Serum Albumin: A Multi-Spectroscopic Investigation Combined with Molecular Docking.

Metadoxine, also known as pyruvate dehydrogenase activator, is a small molecule drug that has been used in the treatment of various medical conditions. Bovine serum albumin is a commonly studied protein that serves as a plasmatic for understanding protein-drug interactions due to its abundance. This research suggests that metadoxine can bind to bovine serum albumin with moderate affinity, leading to an alteration in the secondary structure of the protein, which may also influence the protein's stability and function, which could provide a comprehensive understanding of the interaction at a molecular level. In this study, a variety of methodologies wereused to determine various thermodynamic parameters. The study uses UV-visible, Fluorescence, Fourier-transform infrared, Circular dichroism spectroscopy, and Molecular docking to analyze the interaction between bovine serum albumin and metadoxine, providing thermodynamic parameters for understanding the protein structure and its binding. The binding of metadoxine with bovine serum albumin, causes a hyperchromic shift. In fluorescence spectroscopy, the value of the Stern Volmer increases constantly with an increase in temperature, suggesting a stronger interaction between the Metadoxine and the Bovine serum albumin, leading to dynamic quenching. Additionally, Fourier-transform infrared and circular dichroism indicated a reduction in the secondary structure of Bovine serum albumin. The interactions between metadoxine and bovine serum albumin, cause hyperchromic shift revealed by UV-visible spectroscopy, whereas in Fluorescence spectroscopy, the value of the Stern Volmer constant increases with an increase in temperature, suggesting a stronger interaction between the MD and the BSA, leading to dynamic quenching. Additionally, Fourier-transform infrared and circular dichroism spectroscopy indicated a reduction in the secondary structure of the protein, as evidenced by the shifting of the amide II band and leading to a slight decrease in the αhelix content. The molecular docking shows that metadoxine was docked in the subdomain IIA binding pocket of BSA.

An Insilco approach on Trachyspermum Ammi to Discover a Drug that Reduces the Burden of Gallstone and Gallbladder Tumor

Nowadays, after pandemic circumstances peoples from developing countries and in particular Indians have faith towards herbal medicine to treat chronic disorders. Based on the previous literature it is stated that Indians has genetic impact for the formation of gallstones in liver. The mutation on a single gene in the liver may leads to liver malfunctions and stone formation. In Tamil Nadu most commonly peoples were affected with two types of stone with the major compositions of bilirubin, cholesterol, and calcium with 26%, 7%, and 8 % respectively. Initially gallstone is asymptomatic under medical diagnosis; the burden of disease increases day-by-day prolonged irritation of gall stone in gall bladder may leads to cancer. The mortality rate increases as the disease progresses. Hence, peoples are in need of alternate medicine to treat gall stone initially. In this regard, peoples of south part of India believe herbal medicine to treat and reduce the burden without any side effect. Based on the above, we made an attempt to find the bioactive compounds from Trachyspermum ammi (Ajwain seeds) using ethanol, petroleum ether and water for extraction. Gas chromatography-mass spectroscopy was used to analyze the plant's ethanol extract (GC-MS). Out of the thirty compounds that were chosen based on retention time, five aromatic functionally active compounds underwent docking studies using the bicyclo [3.1.0] of the tumor suppressor protein's SMAD4 receptor. hexan-2-ol, oleyl alcohol, trifluoroacetate, 3-cyclohexen-1-ol, 4-methyl-1-(1-methylethyl)-, thymol, phosphinoline, 1,2,3,4-tetrahydro-. The Insilco studies with protein-drug interaction significantly interact with 10 different proteins as a cascade process for biological changes. The result of the present study concluded that the extract has anti-inflammatory, antimicrobial, antioxidant, anticancer activity, specifically for gallbladder.