Characterizing Compounds Targeting Colorectal Cancer Derived From Monastrol Using High-Through Screening of an Extensive Combinatorial Library

Characterizing Compounds Targeting Colorectal Cancer Derived From Monastrol Using High-Through Screening of an Extensive Combinatorial Library | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Characterizing Compounds Targeting Colorectal Cancer Derived From Monastrol Using High-Through Screening of an Extensive Combinatorial Library View ORCID Profile Alejandro Rodríguez-Martínez , Lucía Giraldo-Ruiz , Maria C. Ramos , Irene Luque , Diogo Ribeiro , Fátima Postigo-Corrales , Begoña Alburquerque-González , Silvia Montoro-García , Ana Belén Arroyo-Rodríguez , Pablo Conesa-Zamora , Ana María Hurtado , Ginés Luengo-Gil , Horacio Pérez-Sánchez doi: https://doi.org/10.1101/2025.08.07.667829 Alejandro Rodríguez-Martínez 1 Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), HiTech Innovation Hub, UCAM Universidad Católica de Murcia , Murcia 30107, Spain; Health Sciences PhD Program, Universidad Católica deMurcia UCAM , Murcia 30107, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alejandro Rodríguez-Martínez Lucía Giraldo-Ruiz 2 Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), UCAM Universidad Católica de Murcia (UCAM) , 30107 Murcia, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria C. Ramos 3 Departamento de Química Física, Instituto de Biotecnología y Unidad de Excelencia de Química aplicada a Biomedicina y Medioambiente Universidad de Granada. Campus Fuentenueva s/n 18071 Granada , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Irene Luque 2 Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), UCAM Universidad Católica de Murcia (UCAM) , 30107 Murcia, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Diogo Ribeiro 4 Genetics, Molecular Pathology and Rare Diseases Research Group, Universidad Católica de Murcia (UCAM); Guadalupe , 30107, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fátima Postigo-Corrales 4 Genetics, Molecular Pathology and Rare Diseases Research Group, Universidad Católica de Murcia (UCAM); Guadalupe , 30107, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Begoña Alburquerque-González 4 Genetics, Molecular Pathology and Rare Diseases Research Group, Universidad Católica de Murcia (UCAM); Guadalupe , 30107, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Silvia Montoro-García 4 Genetics, Molecular Pathology and Rare Diseases Research Group, Universidad Católica de Murcia (UCAM); Guadalupe , 30107, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ana Belén Arroyo-Rodríguez 5 Molecular Pathology and Pharmacogenetics Research Group, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital General Universitario Santa Lucía; Cartagena , 30202, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pablo Conesa-Zamora 4 Genetics, Molecular Pathology and Rare Diseases Research Group, Universidad Católica de Murcia (UCAM); Guadalupe , 30107, Spain 5 Molecular Pathology and Pharmacogenetics Research Group, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital General Universitario Santa Lucía; Cartagena , 30202, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ana María Hurtado 3 Departamento de Química Física, Instituto de Biotecnología y Unidad de Excelencia de Química aplicada a Biomedicina y Medioambiente Universidad de Granada. Campus Fuentenueva s/n 18071 Granada , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ginés Luengo-Gil 4 Genetics, Molecular Pathology and Rare Diseases Research Group, Universidad Católica de Murcia (UCAM); Guadalupe , 30107, Spain 5 Molecular Pathology and Pharmacogenetics Research Group, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital General Universitario Santa Lucía; Cartagena , 30202, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: hperez{at}ucam.edu gluengo{at}ucam.edu Horacio Pérez-Sánchez 1 Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), HiTech Innovation Hub, UCAM Universidad Católica de Murcia , Murcia 30107, Spain; Health Sciences PhD Program, Universidad Católica deMurcia UCAM , Murcia 30107, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: hperez{at}ucam.edu gluengo{at}ucam.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Background Cancer remains a critical global health concern. Among its various forms, colorectal cancer (CRC) stands out due to its high prevalence and mortality rates, emphasizing the urgent need for novel therapeutic agents to enhance treatment efficacy and prolong patient survival. Monastrol, an antimitotic compound known to bind kinesin Eg5, is employed in some cancer therapies. Recent studies have revealed that monastrol also interacts with fascin, a protein implicated in tumor aggressiveness and metastasis, thereby disrupting microtubule dynamics and actin bundling, ultimately impairing cell migration. Methods In this work, we developed a workflow to identify fascin-binding compounds based on a monastrol-derived pharmacophore model, integrating in silico predictions with in vitro validation. We performed ligand-based virtual screening using a pharmacophore model constructed from monastrol, applied to a high-throughput screening (HTS) library of 1.6 million compounds. The top-ranking candidates from the virtual screening were subsequently subjected to physicochemical characterization and cellular assays. Results Two compounds (designated Z118298144 and Z17544625) were identified that exhibited strong binding to fascin and inhibited actin bundling in physicochemical assays. Furthermore, cellular experiments demonstrated that both compounds reduced proliferation and impaired migration of CRC cells at micromolar concentrations. Conclusions We established an optimized pipeline combining virtual screening with experimental validation to efficiently identify fascin inhibitors. Using this approach, we discovered two promising compounds with anticancer activity in CRC cell cultures. Moreover, the protocol has been successfully adapted for application to additional cancer-related targets, expanding its potential utility in drug discovery. Download figure Open in new tab Introduction Cancer still poses one of the most significant challenges in healthcare. It is characterized by intricate mechanisms that often circumvent existing treatments, necessitating innovative therapeutic strategies 1 . Over 90% of cancer mortality is due to tumor metastasis, a process underpinned by the capacity of tumoral cells for migration and invasion. This process requires substantial actin-cytoskeleton reorganization to form structures such as filopodia, lamellipodia, and invadopodia, which are crucial for cancer cell mobility 2 . Eg5 is a kinesin-5-family protein whose high expression levels correlate with poor prognosis in various cancers, including breast cancer, renal cell cancer, and gastric adenocarcinoma 3 – 5 . Suppression of this protein triggers activation of the spindle assembly checkpoint, resulting in mitotic arrest at the G2/M transition and ultimately inducing apoptotic cell death. Fascin is a key protein in the bundling of actin filaments (F-actin) process and plays a crucial role in migration and invasion processes 6 , 7 . This protein is significantly upregulated in various human cancers, such as serrated colorectal adenocarcinoma (SAC) 7 – 9 and triple-negative breast cancer, thereby increasing the mortality and metastasis risk 10 , 11 . Monastrol is a small molecule that is recognized as an anticancer agent that arrests mitotic progression by targeting Eg5 12 . Recently, this molecule has also been demonstrated to bind to fascin protein, binding directly and externally to the actin-binding site 13 . In recent years, in silico tools have proven to be efficient in identifying compounds for drug repurposing or even new compounds that act in the desired context. However, in most cases, the search is limited to known libraries of compounds with known drugs or compounds with clinical trials and approval by the FDA. In addition to this type of compound collection, combinatorial chemistry could be useful for investigating new drugs in different contexts. Therefore, we conducted a computational screening of the Enamine HTS chemical library, a collection of 1.368.754 molecules 14 obtained by combinatorial chemistry and based on the monastrol chemical structure. The compounds with the best results in this screening were tested using in vitro physicochemical and cellular assays to assess their effectiveness in halting cancer cell migration and invasion. Methods Virtual Screening Ligand-Based Generation of HTS Enamine library The HTS compound library from Enamine, comprising 1,368,049 compounds with diverse chemical characteristics, was utilized for Ligand-Based Virtual Screening calculations. The library was partitioned into 217 files to optimize and parallelize the computational process. Using the LigandScout ‘idbgen’ command, available under its license and executed via the command-line interface, we generated a .ldb format file for each segmented library file. Pharmacophore model generation In this study, we developed a pharmacophore model for monastrol. The SDF file containing the chemical structure of the inhibitor was sourced from PubChem 15 . This file was uploaded to the LigandScout graphical user interface (GUI) 16 , where the pharmacophore model was generated. Non-essential pharmacophore features that were irrelevant to fascin binding or inhibition were excluded from the model. Additionally, exclusion spheres were added to the filter compounds based on their size and shape. The final refined model was saved in a PMZ format for subsequent screening against the Enamine library prepared in the previous step. Ligand-based Virtual Screening by Ligand Scout We conducted a comprehensive virtual screening using Metascreener, an in-house software developed by our research group (available at https://github.com/bio-hpc/metascreener ). The pharmacophore model was screened against the entire Enamine library, with the maximum number of features omitted (-a, --allow_omit) set to 0, ensuring that all compounds fully matched the pharmacophore features of the G2 model. A post-filtering step was implemented to refine the screening results based on the pharmacophore relative similarity threshold (α = 0.98), as well as economic feasibility and compound availability. As a result, 12 available compounds were selected and ordered from the Enamine shop for subsequent in vitro experiments. Physical Chemistry Differential Scanning Fluorimetry Fascin was assayed at a concentration of 1 μM against 79 compounds in a 384-well plate, with a final assay volume of 10 μL. Using an Echo® 650 Acoustic Liquid Handler (Labcyte, Cat# LP-000061), 400 nL of each compound, prepared from an initial stock of 10 mM in 100% DMSO, was dispensed to achieve a final compound concentration of 400 μM and 4% DMSO. Each assay was performed in quadruplicate to ensure statistically robust measurements. Two controls comprising 1 μM native fascin were included in the assay: a negative control in 4% DMSO and a positive control for inhibitory activity with 400 μM BDP13176 (MedChemExpress, Cat# HY-119739) in 4% DMSO. Two columns per assay plate were allocated to the controls, with 32 replicates each. The thermal stability profile of fascin was evaluated using the CFX384 Touch Real-Time PCR Detection System (BioRad, Cat# 1855485) across a temperature range of 25 to 95°C, with a gradient of 1°C/min. The melting temperature (Tm) and Tm shifts induced by compound binding were determined using HTSDSF Explorer software by fitting the fluorescence data to a Boltzmann sigmoidal curve. The Z’-factor was calculated to measure the robustness and reproducibility of the assay. This Thermofluor assay achieved a Z’-factor greater than 0.75, confirming its high quality. F-actin-Bundling Assay To visualize F-actin bundling mediated by fascin cross-linking, we implemented an image-based assay following the methodology described by Huang et al. (2018) 17 . F-actin filaments and bundles were labeled using phalloidin conjugated to a fluorescent probe (Alexa Fluor 488-Phalloidin, Thermo Fisher, Cat# A12379), which binds to positive charges of F-actin’s, enabling visualization via fluorescence microscopy. Imaging was performed using the Operetta CLS High Content Analysis System (Revvity, Cat# 8900), and data processing and analysis were conducted using the Harmony™ software (Revvity). A total of 79 compounds were tested in this assay. Each compound (300 nL), dissolved in 100% DMSO from a 10 mM stock solution, was dispensed using the Echo® 550 Acoustic Liquid Handling system (Beckman Coulter, Cat# 100027). Following this, 15 μL of pure fascin at a concentration of 0.5 μM in buffer (20 mM HEPES, 100 mM NaCl, pH 7.45) was added to a 384-well plate (Corning, Cat# 3677) using an automatic dispenser (Multidrop Combi, Thermo Fisher, Cat# 5840300) and incubated for 30 minutes. Next, 15 μL of 0.5 μM polymerized actin (prepared in 100 mM KCl, 20 mM Tris-HCl, pH 7.5, 2 mM MgCl₂, 1 mM DTT, and 1 mM ATP; Cytoskeleton Inc., Cat# BK037) was added to achieve a final compound concentration of 100 μM. The plates were then incubated for an additional 30 min. Then, 10 μL Alexa Fluor 488-Phalloidin (diluted 50-fold from 100% methanol stock) was added to stain F-actin, and the samples were incubated in the dark for 1 h. Finally, 25 μL of the resulting solution was transferred to a 384-well poly-D-lysine-coated plate (Corning, Cat# 354663) and incubated for 20 min before imaging. Each assay plate included 16 wells as negative controls (F-actin without fascin) to confirm that actin bundling was fascin-dependent. Positive controls included the known fascin inhibitor BDP-13176 (Cat# 10009022), which was tested at the same concentration as the compounds. The Z’-Factor, a quality control metric derived from image-defined thickness and texture parameters using the Harmony software, was calculated for each plate. A Z’-Factor greater than 0.5 confirmed the assay’s reliability and robustness. The results derived from both physical chemistry techniques are provided in the supplementary materials accompanying this publication Cell-based assays Cell culture The human colorectal adenocarcinoma cell lines DLD-1 (CLS Cat# 300220/p23208_DLD-1, RRID: CVCL_0248) and HCT-116 (CLS Cat# 300195/p19841_HCT116.html, RRID: CVCL_0291) were purchased from the American Type Culture Collection (ATCC). The cells were cultured at 37°C in high-glucose Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum (FBS), 50 U/mL penicillin, and 50 µg/mL streptomycin (Sigma-Aldrich). The cultures were maintained in a 5% CO₂ and 95% humidified atmosphere, and subcultured when the cell confluence reached 90%. Cell line identities were verified using short tandem repeat (STR) profiling. Cell viability assay Exponentially growing cells were seeded into 96-well plates (Nunc, Roskilde, Denmark) at a density of 1500 cells per well. Each condition was tested in triplicate. After a 24 hours attachment period, serial dilutions of the test compounds (0.1-1000 μM) were added to the wells. Control wells were treated with culture medium containing 0.1% dimethyl sulfoxide (DMSO) (drug carrier). The plates were then incubated for three days at 37°C in a humidified environment with 5% CO₂. Cell viability was determined at the end of the incubation period by using the XTT Cell Viability Assay Kit (Biotium) according to the manufacturer’s protocol. Absorbance was measured at 450 nm with background subtraction at 630 nm using a Spectramax ID3 plate reader (Molecular Devices, VWR). Cell migration assay The wound-healing assay was conducted using the DLD-1 and HCT-116 cell lines. Cells were seeded at 200,000 cells per well in 35-mm dishes equipped with culture inserts (Ibidi). The assays were performed in triplicate, each including three technical replicates. Four fields per well were imaged to assess migration. After a 24-h incubation period, the inserts were removed to create a defined wound field approximately 500 µm wide. Cell migration was subsequently monitored at 24 and 48 hours in the presence of 10% FBS, following the manufacturer’s protocol. Migration was quantified as the percentage of wound closure using ImageJ, Structural Validation Molecular Modeling of fascin structure Molecular modeling was performed to investigate the interactions between fascin and the predicted compounds. The crystallographic structure of fascin bound to NP-G2-029, a derivative of G2 (PDB code: 6B0T), was used as a starting point. The structure was preprocessed using the MAESTRO (Schrödinger) software. Chain A of the fascin structure was extracted, and hydrogens and charges were added to complete the structure. The processed structure was then saved in the mol2 format. A parallel protocol was carried out using AutoDockTools software, where similar preprocessing steps were performed, including the addition of AD-specific atom types. In this case, the structure was saved in the pdbqt format. The predicted ligands were converted into mol2 and pdbqt formats using ChemAxon’s MolConvert tools and AutoDockTools. Hydrogens and their corresponding charges were added to ensure proper molecular representation for the following docking studies. Blind Docking calculations Blind docking calculations were conducted for two compounds, Z17544625 and Z118298144, which were experimentally shown to bind fascin and reduce cancer cell migration and viability. The crystallographic structure of fascin (PDB code: 6B0T) was used to explore the potential binding sites of these compounds. Two different docking algorithms, AutoDock Vina and Lead Finder, were employed to perform docking calculations. Both methods were performed to enhance the reliability and coverage of potential binding sites. After completing the docking simulations, a consensus approach was applied, which combined the results from both algorithms to determine the average binding pose for each compound. Molecular Dynamics simulations Molecular Dynamics (MD) simulations were performed for 100 ns on the fascin structure (PDB code: 6B0T) using the poses with the best docking scores from the Blind Docking (BD) calculations. These poses corresponded to the known binding sites of fascin: actin-binding site 1, actin-binding site 2, and the tubulin-interacting site. The ligand topologies for each pose were generated using ACPYPE 18 , 19 , and were supported by an automated preprocessing script. MD simulations were conducted with GROMACS 2022.3 20 on the Picasso server, utilizing GPU acceleration (NVIDIA A100-SXM4-40GB) and 4 GB of RAM. The following steps were performed: Protein Topology Generation: The gmx pdb2gmx command generated the protein topology, specifying AMBER99SB as the force field. System Preparation: A simulation box was defined, the system was solvated, and ions were added to neutralize the charge. Energy Minimization: A 2000 ps energy minimization stage was performed to remove steric clashes and stabilize the system. Equilibration Stages: A single NVT equilibration stage was conducted for 50,000 ps. Five consecutive NPT equilibration stages were performed, each lasting 50,000 ps. Production Dynamics: The final MD simulation was run for 100 ns. The results of the MD simulations were analyzed using ASGARD 21 , an in-house tool developed by our research group. The analysis focused on calculating non-bonded interactions and hydrogen bonds involving the final ligand and evaluating the stability of the protein-ligand complex. Furthermore, the fascin flexibility and dynamic behavior were examined to understand the impact of ligand interactions at the binding site. Input files were prepared accordingly, and the tool generated the necessary plots and raw data for detailed interpretation. Drug Feasibility ADME property predictions Drug-like properties were predicted using the QikProp module of the MAESTRO Schrödinger suite 22 . Calculations were performed for the best-known fascin inhibitors: imipramine, migrastatin, raltegravir, NP-G2-044, G2, BDP-13176, and monastrol, as well as the predicted compounds Z118298144, and Z17544625. The results were saved in a CSV file, and a comparative table was generated to analyze the data. Toxicology profile predictions Ligand-based screening was conducted using models derived from Z118298144 and Z17544625 against the DrugBank database to identify potential alternative targets of these compounds. Results Pharmacophore screening We obtained 1634 hits from the monastrol pharmacophore model. A threshold of Relative-Pharmacophore score=0.98 was chosen to filter most of the compounds obtained by the calculation. Finally, the compounds Z56754204, Z1316642426, Z1177947062, Z57012338, Z930944046, Z44416195, Z17544625, Z788127954, Z1673093246, Z982131356, Z56977400, and Z118298144 were selected for the in vitro assays due to their availability and price for the supplier. Figures 1 and 2 show the 2D and 3D pharmacophore models representation for the two compounds with the best values obtained by physicochemical and cellular experiments (Z17544625 and Z118298144). Download figure Open in new tab Figure 1 Comparison between the chemical structure and the pharmacophore models of the monastrol (a) and the compounds obtained by Virtual Screening from Enamine library, Z118298144 (b), and Z17544625 (c). Download figure Open in new tab Figure 2. Comparison between the tridimensional chemical structure of monastrol (cyan) and Enamine compounds obtained by Ligand-Based Virtual Screening, Z118298144 (magenta) and Z17544625 (green). Viability of cancer cells Viability assays were performed on DLD-1 and HCT-116 cells to determine the working concentrations of the drugs ( Figure 3 ). These two CRC cell lines exhibit differential endogenous fascin expression levels, with HCT-116 showing higher and DLD-1 lower expression, as previously verified 23 , 24 Overall, HCT-116 was more sensitive than DLD-1 in all tested drugs, with Z118298144 exhibiting higher cytotoxicity than monastrol in HCT-116. Furthermore, the obtained IC50 value of monastrol corroborates previously reported findings 13 . Given the differential fascin expression between the cell lines, a comparative evaluation of potential fascin-dependent drug responses was conducted, observing a lower sensitivity in DLD-1 cells that is consistent with their low expression of fascin, as opposed to a superior efficacy in HCT-116. The approximate working concentrations of monastrol, Z17544625, and Z118298144 were set for subsequent in vitro studies at 40 and 100 µM for HCT-116 and DLD-1 cells, respectively. These concentrations were set below the determined IC50 values to minimize cytotoxic effects, enabling reliable assessment of cellular migration in subsequent assays. Download figure Open in new tab Figure 3. Cell viability assays for DLD-1 and HCT-116 cells were performed in three independent experiments with monastrol (A), Z17544625 (B), and Z118298144 (C). Each line represents the normalized curve fit for an independent experiment. Each point was determined in quintuplicate (mean value + SEM). Cancer cells’ migration To investigate the impact of Z17544625 and Z118298144 on cell migration compared with the monastrol effect, DLD-1 and HCT-116 human CRC cell lines were treated with these compounds at previously established working concentrations, as well as at a higher concentration, to evaluate their effects on cellular migration in a dose-dependent manner. Cell motility was analyzed using a wound-healing assay at 24 and 48 hours. As shown in Figure 4A , all the compounds at 200 µM showed a significant decrease in DLD-1 cell migration, with Z118298144 having the most remarkable effect, even after 48 hours of treatment, and it was significantly more pronounced than monastrol (p < 0.05). Conversely, when the concentration of Z17544625 was reduced to 100 µM, there was no observable impact on cell migration at either of the evaluated timepoints. On HCT-116 cells, the effect of these compounds on migration was only significant for monastrol and Z118298144 ( Figure 4 B), with the latter having a similar effect to monastrol at the higher concentration. Notably, Z118298144 exhibited greater cytotoxicity compared to monastrol, which likely contributed to its pronounced inhibitory effect on cell migration at the higher concentration. Download figure Open in new tab Figure 4. Wound healing assays for DLD-1 (A) and HCT-116 (B) colorectal cancer cells at 24 and 48h using monastrol, Z17544625, and Z118298144. Blind Docking detected binding in the Actin-Binding Site 1 and Tubulin Interacting Site for both compounds Blind Docking calculations using AutoDock Vina and LeadFinder algorithms were performed for the two compounds with the best results (Z118298144 and Z175446245) in the experimental assays against the 6b0t fascin structure to compare with the Blind Docking obtained for monastrol 13 . We found poses in the known binding sites for monastrol: actin-binding site 1, tubulin-interaction site ( Figure 3 ), and actin-binding site 2 ( Figure 4 ). First, we identified several possible clusters where compounds bind to some residues within the actin-binding site 1 ( Table 1 ) 25 . Z118298144 shows poses interacting with the actin-binding site 1 in clusters 5 and 8 using Lead Finder AutoDock Vina, respectively. Several key residues interact with the ligand (Ser38, Ser39, Lys41, and Lys43). Regarding the Z17544625 compound, we obtained similar results to Z118298144 with cluster 13 for LeadFinder results, and cluster 3 and 10 for AutoDock Vina and interacting with Ala26, Phe29, Phe31, Gly30, Asn34, Ser38, Ser39, and Lys41, known actin site 1 key residues 25 . Download figure Open in new tab Figure 3. Poses obtained by Blind Docking calculations for Z118298144 and Z17544625 against the fascin protein (6b0t). Green represents the poses obtained for Z118298144 by AutoDock Vina and Lead Finder algorithms. Cian represents the poses obtained for Z17544625. The red and yellow surfaces indicate the actin-binding site 1, the tubulin-interacting site of fascin, respectively. Figure 3.A) shows the poses obtained for the actin-binding site Figure 3. B) shows the poses obtained for the actin-binding site 1 and tubulin-interacting site. View this table: View inline View popup Download powerpoint Table 1. Interactions detected for compounds Z118298144 and Z17544625 in the fascin Actin-Binding Site 1 by AutoDock Vina and Lead Finder Docking calculations. The actin-binding site 2 showed the best results, located in the first pose in both compounds and by both algorithms. The docking scores for Z118298144 are -8.14 and -7.4 kcal/mol for LeadFinder and AutoDock Vina, respectively. Regarding the Z175446245 compound, the docking scores were -9.72 kcal/mol and -9.16 kcal/mol calculated by LeadFinder and AutoDock Vina, respectively. Table 2 shows the interactions detected by both methods, and bold residues correspond to the residues belonging to each site 17 , 26 . Thus, in both cases, several residues participating in each of these domains interacted with the molecules identified by the screening. View this table: View inline View popup Download powerpoint Table 2. Interactions detected for the compounds Z118298144 and Z17544625 in the fascin Actin-Binding Site 2 by AutoDock Vina and Lead Finder Docking calculations Regarding the tubulin-interacting site ( Table 3 ), for compound Z118298144, both methods found fewer possible poses in both docking softwares. Otherwise, the LeadFinder approximation did not find any cluster with interactions in some of the residues corresponding to the tubulin-interacting site of Z17544625. AutoDock Vina obtained a pose close to the tubulin-interacting site in cluster 13 with -6.17 kcal/mol of docking score. Regarding the key residues, some residues indicated as belonging to the tubulin-interacting site in the literature 26 were found by both techniques (Thr239, Lys241, Lys244, Ala245, and Lys247). View this table: View inline View popup Download powerpoint Table 3. Interactions detected for the compounds Z118298144 and Z17544625 in the fascin Tubulin-Interacting Site by AutoDock Vina and Lead finder Docking calculations. Molecular Dynamics simulations verify the binding in our site We performed three different simulations for each compound, targeting each one to the known monastrol-binding sites (actin-binding sites 1 and 2, and tubulin-interaction site). We carried out MM-PBSA and RMSD analyses to prove the stability of the ligand in the fascin complexes. The poses obtained in the tubulin-interacting site and actin-binding site 1 did not remain in the corresponding binding sites. In these cases, the complexes with fascin showed poor stability, and the compounds left the complex from the first 10-15 ns simulation. In contrast, compounds Z118298144 and Z17544625 were stable in complex with fascin binding to the actin-binding site 2. MD simulation results demonstrated stable binding at the actin-binding site 2 for both compounds, Z118298144 and Z17544625. According to the data obtained by MD, both compounds had a higher affinity for the actin-binding site 2. The RMSD (Root Mean Square Deviation) of the ligand for both MD (Molecular Dynamics) simulations showed apparent stabilization, remaining in the range of 0.1 to 0.2 nm. Regarding the protein, fascin is known for its high flexibility, displaying fluctuations, especially in complex with Z17544625. However, these fluctuations do not exceed 5 nm. Download figure Open in new tab Figure X. RMSD of fascin and the Z17544 compound for each corresponding MD simulation. The blue color indicates the protein in both cases, the red color indicates the Z17544625 compound in the MD simulation for the complex in the actin-binding site 2, and the green color indicates the Z118298144 compounds in the same case for its simulation. Finally, MMPBSA demonstrated the stability of this complex binding. As shown in Figure Y, the binding affinities of both complexes are stable between -300 and -350 kJ/mol in the Z17544625 compound and between -200 and -250 kJ/mol for the Z118298144 compound. In addition, there were no strong fluctuations in the energy, which could mean a loss of binding and the formation of the complex. The difference in mean energy during the simulation between both compounds correlated with the data obtained by IC50 assays for in vitro assays, showing Z17544625 as a more effective compound. Download figure Open in new tab Figure Y. MMPBSA analysis showed the binding energies of the compounds Z17544625 and Z11729144 in the actin-binding site 2 for fascin during the simulation time. The plots indicate the van der Waals (blue), electrostatic (red), and total binding (green) energies . Comparing ADMET properties against monastrol The compounds obtained by the screening had 0 stars in the values of the descriptors determined by QuikProp, similar to monastrol. This rating indicates that all the ADMET-predicted properties of the compounds fall within the optimal range for 95% of the known drugs. Most of the predicted properties displayed similar values for the three compounds analyzed. Table 4 displays the values of the most notable predicted properties. All the values were within the acceptable range for a drug. Additionally, there were no significant differences between the values obtained for the compounds and those obtained for monastrol. These results suggest that the new compounds possess favorable druggability profiles, with ADMET properties closely resembling those of the reference compound, monastrol. View this table: View inline View popup Download powerpoint Table 4. ADMET properties calculated for monastrol, Z118298144, and Z17544625 by QikProp. Drug-Target Similarity Prediction Finally, we performed ligand-based calculations for Z118298144 and Z17544625 against the DrugBank compound library to complement ADMET analysis. Thus, some possible alternative drug targets were searched for similarities to other drugs. First, we studied the Z118298144 compound. Here, we observed some interesting targets that obtained high relative pharmacophore scores. First, any possible target that could cause compliance due to the administration of the compounds was identified. The possible targets of interest are Glucose Transporter 1 (GLUT1) and epidermal growth factor receptor (EGFR), two targets studied to block cell proliferation and migration in the context of cancer 27 , 28 . These targets were identified because of the high similarity of known inhibitors with Z118298144, WZB-117, and Tyrphostin B48, which interact with GLUT1 and EGFR, respectively. View this table: View inline View popup Table 5. Drug-Target Prediction by Ligand-Based LigandScout of Z118298144. The study of possible drug targets for the Z17544625 compound also revealed interesting information. As with the Z118298144 compound, any target that could cause adverse effects was found. Tryphostin AG 555 was identified as the second-best hit by pharmacophore similarity for the entire DrugBank library. This compound is related to some cancers, interacting with known cancer-regulating targets, such as tyrosine kinase and EGFR 30 . The other compound was found to be A-674563. This molecule inhibits the CDK2 enzyme, blocking the cell growth in some types of lung cancer 31 . View this table: View inline View popup Table 6. Drug-Target Prediction by Ligand-Based LigandScout of Z17544625. Discussion Monastrol is a small-molecule inhibitor of the mitotic kinesin Eg5, which is essential for maintaining spindle bipolarity during cell division 12 . Recent studies have demonstrated that this molecule also effectively inhibits fascin and blocks migration and metastasis processes 13 . Fascin plays a causative role in tumor migration and invasion and is associated with tumor subtypes with adverse prognosis. This protein is considered a promising therapeutic target and clinical trials with fascin inhibitors are ongoing 34 , 35 . Progress in drug discovery in the fascin context is closely linked to screening FDA-approved compound libraries in search of drug repurposing opportunities. This approach identifies existing drugs that may present fewer challenges when advancing clinical trials 36 . However, exploration of increasingly extensive chemical spaces is an underexploited avenue for Drug Discovery. In this study, we performed virtual screening calculations against a library of over one million compounds, such as Enamine, to identify a compound with activity similar to monastrol binding, such as the fascin protein, and inhibiting tumor migration. The workflow to obtain these possible hits resulted in two compounds with similar activities to monastrol: Z118298144 and Z17544625. In vitro assays demonstrated that these compounds also prevented the actin-bundling mediated by fascin and colorectal tumor cell viability and migration, especially Z118298144, whose effect was even more pronounced than that of monastrol. The in silico structure-based analysis showed the stability for both compounds in at least one possible binding site. From 6b0t Blind Docking, we observed that both compounds bind and remain in the known fascin-binding sites for actin and tubulin, which are related to actin-bundling and posterior cancer cell migration. The posterior 100 ns MD simulation for each best pose indicated a stable and strong binding in the actin-binding site 2 for the two compounds. In addition, the compounds obtained share many drug-like properties, converting these molecules into possible alternative molecules to monastrol and obtaining an excellent value for future rounds of screening to optimize the process and obtain better hits. In this way, two potential compounds were identified using a screening workflow, including ligand-based virtual screening from the monastrol model, in vitro physicochemical and cellular assays, and Structure-Based computational techniques: Blind Docking and Molecular Dynamics. The ADMET properties and similarity with other drug targets were described to check the drug capability of these compounds, demonstrating a positive evaluation of this methodology, which could help subsequent screening cycles to obtain more effective compounds. We also introduced a new workflow designed to enhance prediction accuracy of virtual screening for fascin-binding compounds. This workflow integrates several experimental validation methods, including physicochemical analyses and cell culture-based assays. Our results demonstrated that this tool is easy to implement and efficient. The computational pipeline begins with a single input file representing the molecular structure of a known fascin inhibitor monastrol and proceeds through a series of automated steps that enable the identification of top-scoring hits from an HTS chemical library. Notably, two of these hits exhibited enhanced inhibition of actin bundling and improved antiproliferative and antimigratory activities compared with monastrol. This strategy may serve as a valuable platform for the discovery of novel compounds with higher activity than that of the reference molecule. Through iterative application, this workflow could facilitate the identification of candidates with micromolar and even nanomolar IC50 values and significant potential for cancer treatment. We intend to apply this methodology to other established anticancer targets, thus contributing to the ongoing evolution of drug discovery practices. Despite its promise, the current workflow has limitations that should be addressed in future studies. For example, it relies solely on pharmacophore similarity for ligand-based hit identification. Incorporating additional ligand-based approaches, such as fingerprint similarity (DRAGON, PubChem, Circular fingerprints) or shape/electrostatic similarity (ROCS, EON), could strengthen the prediction pipeline 37 , 38 . A consensus strategy combining these techniques may improve the robustness and predictive power. From a structure-based perspective, this workflow does not incorporate docking due to the flexibility of the fascin binding site. However, for other targets or alternate conformations of fascin, it would be feasible to include structure-based methods, such as blind docking, standard docking, or molecular dynamics simulations with MM/PBSA scoring. These enhancements would allow for a more comprehensive evaluation of ligand-target interactions, thereby improving the predictive accuracy and translational relevance of the workflow. In summary, we developed an automated pipeline for the discovery of novel anticancer compounds using a known molecule as a reference. This tool enabled the identification of two novel compounds with improved bioactivity compared to monastrol. Beyond fascin, this approach holds promise for broader applications in anticancer drug discovery and may serve as a foundational methodology for exploring inhibitors of other cancer-relevant targets. Conclusion By implementing an efficient Ligand-Based Virtual Screening workflow, this study led to the discovery of two novel anti-fascin agents from the extensive combinatorial library of Enamine. Unlike previous studies that focused on smaller libraries of FDA-approved compounds, our approach explored a broader chemical space. By using the pharmacophore features of monastrol, the model identified two new fascin inhibitors with unique chemical properties and potential freedom from intellectual property constraints. Furthermore, we provided molecular insights into the structural interactions of these compounds with fascin, indicating that this protocol could be applied to discovering additional anti-fascin agents. Finally, we described a novel pipeline that integrates in silico and experimental validations to identify promising anticancer compounds. This approach could be extended to other cancer targets in the future, and may contribute to drug discovery efforts against different classes of therapeutic targets. Data availability No datasets were generated or analysed during the current study. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used ChatGPT and Paperpal: AI Academic Writing Tool in order to improve language and readability. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. Author contribution Alejandro Rodríguez-Martínez : Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization. Lucía Giraldo-Ruiz : Validation, Investigation, Writing - Original Draft, Writing - Review & Editing, Visualization. Maria C. Ramos : Investigation, Resources, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration, Funding acquisition. Irene Luque : Methodology, Formal analysis, Investigation, Resources, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration, Funding acquisition. Diogo Ribeiro : Validation, Investigation, Writing - Original Draft, Writing - Review & Editing, Visualization. Fátima Postigo-Corrales : Validation, Investigation, Writing - Original Draft, Writing - Review & Editing, Visualization. Begoña Alburquerque-González : Investigation, Writing - Original Draft, Writing - Review & Editing. Silvia Montoro-García : Methodology, Investigation, Resources, Writing - Original Draft, Writing - Review & Editing. Ana Belén Arroyo-Rodríguez : Validation, Investigation, Writing - Original Draft, Writing - Review & Editing. Pablo Conesa-Zamora : Methodology, Formal analysis, Investigation, Resources, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration, Funding acquisition. Ana María Hurtado : Validation, Investigation, Writing - Original Draft, Writing - Review & Editing. Ginés Luengo-Gil : Methodology, Formal analysis, Investigation, Resources, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, Funding acquisition. Horacio Pérez-Sánchez : Conceptualization, Software, Formal analysis, Investigation, Resources, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration, Funding acquisition. Competing interest statement The authors declare no competing interests. Acknowledgments This study was supported by a grant from the Scientific Foundation of the Spanish Association against Cancer (Predoctoral Fellowship). This research was funded by JUNTA ANDALUCIA GRANT, the Andalusian Regional Government, through Grant Proyectos de Excelencia (P18-RT-1193). Project support for this research was provided in part by the European Union Horizon 2020 Grant (REVERT project GA848098), Instituto de Salud Carlos III (PI23/00601), and FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades (PY20_00678 and B-BIO-18-UGR20). The authors acknowledge the computing resources and technical support provided by the Plataforma Andaluza de Bioinformática at the University of Málaga. Powered@NLHPC research was partially supported by the super-computing infrastructure of NLHPC (ECM-02). The author(s) gratefully acknowledge the computational resources provided by the Barcelona Supercomputing Center at MareNostrum5 under project BCV-2025-1-0004. This work was supported by Ministerio de Ciencia e Innovation through the grant Ramón and Cajal program (Grant RYC2022-037702-I, funded by MCIU/AEI/10.13039/501100011033 and by the ESF+) Funder Information Declared Spanish Association against Cancer , PRDMU246084RODR Regional Government of Andalusia, https://ror.org/01jem9c82 , P18-RT-1193 Carlos III University of Madrid, https://ror.org/03ths8210 , PI23/00601 FEDER/Junta de Andalucía , PY20_00678 , B-BIO-18-UGR20 Barcelona Supercomputing Center, https://ror.org/05sd8tv96 , BCV-2025-1-0004 Ramón and Cajal Program , RYC2022-037702-I European Union Horizon 2020 , GA848098 European Union NextGenerationEU , PRTR-C17.I1 Fundación Séneca - Agencia de Ciencia y Tecnología de la Región de Murcia, https://ror.org/03q68pf65 , 22616/PI/24 Abbreviations BD Blind Docking CRC Colorectal cancer EGFR Epidermal growth factor receptor GLUT1 Glucose Transporter 1 GUI Graphical user interface HTS High-throughput screening MD Molecular Dynamics SAC Serrated colorectal adenocarcinoma Tm Melting Temperature References 1. ↵ The global challenge of cancer . 2020 . 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