Discerning Potent CSF-1R inhibitors for Targeting and Therapy of Neuroinflammation using Computational Approaches

Discerning Potent CSF-1R inhibitors for Targeting and Therapy of Neuroinflammation using Computational Approaches | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Discerning Potent CSF-1R inhibitors for Targeting and Therapy of Neuroinflammation using Computational Approaches Anupriya Adhikari, Anwesh Pandey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3903155/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Microglia, the primary cellular mediator of neuroinflammation, plays a pivotal role in numerous neurological disorders. Precise and non-invasive quantification of microglia is of paramount importance. Despite various investigations into cell-specific biomarkers for assessing neuroinflammation, many suffer from poor cellular specificity and low signal-to-noise ratios. Colony stimulating factor-1 receptor (CSF-1R), also known as FMS kinase, has emerged as a promising neuroinflammation biomarker with significant relevance to inflammatory diseases. Additionally, CSF-1R inhibitors (CSF-1Ri) have shown therapeutic potential in central nervous system (CNS) pathological conditions by depleting microglia. Therefore, the development of more specific CSF-1R inhibitors for targeting and treating various CNS insults and neurological disorders is imperative. This study focuses on the search for novel CSF-1R inhibitors. Based on literature for CSF-1R inhibitors, we proposed and investigated ten ligands as novel CSF-1R inhibitors. Among these, the top three ligands, selected based on their maximum binding scores in docking calculations, are subjected to 100 nanoseconds of molecular dynamics (MD) simulation, alongside three reference ligands. All protein-ligand complexes remain stable throughout the dynamics and exhibit minimal fluctuations during the analysis. The results obtained through this study may prove significant for the future design of CSF-1R inhibitors with potential applications in the field of biomedicine. Computational Biology Neuroinflammation microglia Colony stimulating factor receptor (CSF-1R) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Neuroinflammation is the brain's natural defense response when faced with infections, injuries, or disruptions to its normal balance [ 1 ]. Presently, there is a growing focus on research involving the use of immunomodulatory and neuroprotective substances aimed at influencing microglia (brain cells), which are the primary immune cells in the central nervous system (CNS). These microglia exhibit heightened sensitivity to various triggers and rapidly activate themselves following an injury by releasing chemokines, cytokines, and other signaling molecules [ 2 – 5 ]. Microglia (brain cells) are described as follows; we start with M0, which is like resting microglia. When M0 gets activated, it turns into two different forms, viz., M1 - these microglia are like the defenders of the brain. They respond to problems by releasing things like reactive oxygen species (ROS), TNF-α, and prostaglandins to fight off threats; and M2 - these microglia are more like protectors. They respond to problems by releasing substances that help the brain and reduce inflammation [ 6 , 7 ]. The protein CSF-1R, found on the surface of cells and belonging to the tyrosine kinase family, is primarily expressed in microglia, with some presence in neurons. CSF-1R's main role is to regulate the development, survival, and maintenance of microglia. It becomes active when stimulated by two natural ligands, CSF-1 and interleukin-34 (IL-34) [ 9 – 12 ]. This receptor is most prominently found in the cortical regions of the brain. In the context of treating various inflammatory and neuroinflammatory disorders, one promising strategy could involve using small molecules to inhibit CSF-1R. The latest research, conducted on small animals and post-mortem brain samples, has uncovered a significant link between neuroinflammation and an increase in CSF-1R levels in severe neuroinflammatory diseases such as Alzheimer's disease (AD), Multiple Sclerosis (MS), hypoxic-ischemic encephalopathy (HIE), and glioblastomas [ 11 ]. Consequently, the need for in-vivo microglia imaging is paramount for investigating their behavior in different environments and understanding their long-term effects in both normal and diseased conditions within the central nervous system. This is vital for early detection and the prompt initiation of therapeutic interventions. Researchers have extensively explored various markers to identify microglia, but their full potential is hindered by several limitations. These limitations include issues like markers not being specific enough for microglia (sometimes they also highlight other cells like activated astrocytes and myeloid-lineage cells infiltrating the central nervous system), low signal-to-noise ratios, and the presence of genetic variations. Because of these challenges, there is a growing interest in developing biomarkers that exclusively pinpoint microglia. As a result, researchers have created different radiotracers that target CSF-1R (Colony-Stimulating Factor 1 Receptor) in microglia, including [11C]-AZ683, [11C]-CPPC, [11C]-GW2580, [11C]-BLZ945, and [11C]-OMPYO [ 13 ]. Many existing approaches have limitations, including poor penetration of the blood-brain barrier and significant non-specific binding. This presents challenges when trying to accurately detect subtle changes in microglial activation, given the naturally high levels of CSF1R expression. Additionally, there have been recent discoveries of small inhibitor molecules targeting CSF-1R for the removal of microglia in pathological conditions, with some progressing to clinical trials [ 14 – 16 ]. These inhibitors can lead to approximately 95% reduction in microglia in the central nervous system, resulting in improved cognitive function and decreased expression of proinflammatory genes. However, prolonged use of these inhibitors for therapy is not feasible, as microglia play essential roles in brain function [ 11 ]. Hence, using a CSF-1R inhibitor for short-term treatment to remove harmful microglia in the brain could lead to lasting benefits, preventing the ongoing path towards neurodegeneration [ 17 – 19 ]. However, prolonged treatment, lasting over 20 weeks, has shown adverse effects, including hair discoloration (caused by C-KIT inhibition), hepatotoxicity (due to effects on Kupffer cells), and reduced oligodendrocyte progenitors [ 20 – 22 ]. This underscores the persistent need for developing more targeted and efficient CSF-1R inhibitors to eliminate problematic microglia in the therapy of neurodegenerative diseases. Currently, there's a critical need for discovering new inhibitors of CSF-1R. To efficiently complement experimental research and reduce costs in drug development, we're employing computational techniques to investigate how these ligands interact with the receptor. In this study, we've introduced ten cyano-furan and cyano-imidazole derivative ligands that could potentially bind to and inhibit CSF-1R. We've assessed their interactions with the protein using molecular docking and compared the specificity of the most promising ligand with well-established CSF-1R inhibitors found in the literature [ 23 ]. Material and Methodology All the computational Studies were carried out on the XRD crystal structure of cFMS tyrosine kinase with resolution of 1.95 Å (PDB ID: 3DPK) downloaded from Protein Data Bank (PDB). Further to carry out our studies, we initiated by first identifying reference ligands (6) through a comprehensive review of literature. Subsequently, we later proposed novel ligands (10) based on the reference ligands obtained through the literature survey. Thereafter, we performed molecular docking calculations to probe the pocket interactions of the ligands and their binding sites within the target protein. Following this, based on the docking scores and residue interactions, we subjected the most promising ligands, as well as top-performing reference ligands from the docking process, to rigorous molecular dynamics simulations. These simulations enabled us to thoroughly evaluate the binding interactions, stability, and the dynamic behavior of the biomolecular system. Each of these computational methods are elaborated in the following sections. Molecular Docking Calculations Molecular docking methodology are widely employed for exploring the interactions of interactions and binding affinity of biological activity of proteins. In the presented work, Autodock4.0 software was utilized for docking the protein (PDB ID: 3DPK) and proposed CSF-1R ligands (10) and references (6) [ 23 ]. The chemical structures of all the compounds were drawn using Chem Draw 16.0 tool and their energy was minimized using ChemBio3D [ 24 ]. Afterwards these ligand molecules were put as an input in Autodock4.0 software [ 25 ]. Before commencing the docking simulation through Lamarckian Genetic Algorithm (LGA), macromolecule was prepared through standard protocol by firstly abstracting the co-crystallized ligand, water molecules and cofactors from it. Followed by this, a grid box with changing magnitude was created to surround the active site of the macromolecule for carrying the docking simulation. The least binding affinity docked posture was mined out and aligned with CSF-1R for further research following 20 LGA with a maximum cycle of 2500000 energy evaluations for each receptor-ligand complex. Previous studies on structure and dynamics of biomolecular complexes have successfully reproduced results using the mentioned protocols [ 26 – 30 ]. The most favourable conformation with lowest energy was selected were selected and their interaction with the receptor was analysed using Discovery studio visualizer. Molecular Dynamics Simulation Molecular dynamics simulations of the protein-ligand complexes were performed as per the protocols mentioned in previous studies [ 31 – 34 ] using the GROMACS 5.1.1 package [ 35 ] employing the charmm36-jul2022 force field [ 36 – 41 ]. Ligand topologies were prepared using the CHARMM General Force Field (CGenFF) server [ 42 , 43 ]. MD simulations were performed under periodic boundary conditions (PBCs) with a cubical box of 1.0nm x 1.0nm x 1.0nm dimension. The process began by placing the protein-ligand complexes in a solution of SPC water molecules. To maintain a neutral charge for the system, counter ions were added to the solvated box as needed. Next, we performed 1000 steps of steepest descent to minimize energy and resolve structural clashes within the solvated system. Following this, we subjected the minimized systems to equilibration with position restraints. The systems were gradually heated from 0 to 300 K over a period of 500 picoseconds using a modified Berendsen thermostat [ 44 , 45 ]. After the heating step, we equilibrated the systems for 1 nanosecond under isothermal-isobaric conditions, maintaining a constant pressure of 1.0 bar. For the production run, a 100 ns simulation was conducted without any restraints. To maintain bond lengths, we utilized the LINCS algorithm [ 46 ], and for long-range electrostatics, the particle mesh Ewald (PME) [ 47 ] method was employed. Additionally, the SETTLE algorithm [ 48 ] was used to ensure the geometry of water molecules remained constrained. The trajectory of each system was analysed using GROMACS utilities and VMD software [ 49 ]. Free Energy Calculations The g_mmpbsa package was utilized to determine the binding free energies of protein-ligand complexes using the Molecular mechanics-Poisson-Boltzmann surface area (MMPBSA) method, as described by the equation below: $${\varDelta \text{E}}_{\text{b}\text{i}\text{n}\text{d}}={\text{E}}_{\text{c}\text{o}\text{m}\text{p}\text{l}\text{e}\text{x}}-({\text{E}}_{\text{p}\text{r}\text{o}\text{t}\text{e}\text{i}\text{n}}+{\text{E}}_{\text{l}\text{i}\text{g}\text{a}\text{n}\text{d}})$$ 1 where \({\text{E}}_{\text{c}\text{o}\text{m}\text{p}\text{l}\text{e}\text{x}}\) , \({\text{E}}_{\text{p}\text{r}\text{o}\text{t}\text{e}\text{i}\text{n}}\) , and \({\text{E}}_{\text{l}\text{i}\text{g}\text{a}\text{n}\text{d}}\) are the total MMPBSA energy of protein-ligand complex, total solution free energies of the isolated protein and ligand molecules, respectively. The total free energy of each individual can be expressed using Eq. 2 . $${\text{E}}_{\text{x}}= - \text{T}\text{S} + $$ 2 where x is either protein, ligand or protein-ligand complex. is the mean value of molecular mechanics potential energies when molecules are in a vacuum. T and S represent the absolute temperature and entropy, respectively, and together, they contribute to the entropic part of the total free energy in a vacuum. encompasses both bonded and nonbonded interactions among the molecules, while represents the average free energy of solvation. This average solvation free energy is composed of polar ( \({\text{E}}_{\text{P}\text{o}\text{l}\text{a}\text{r}}\) ) and nonpolar ( \({\text{E}}_{\text{N}\text{o}\text{n}\text{p}\text{o}\text{l}\text{a}\text{r}}\) ) energies. To estimate the polar solvation energy ( \({\text{E}}_{\text{P}\text{o}\text{l}\text{a}\text{r}}\) ), the Poisson-Boltzmann equation is solved using a linear model [ 51 ], while the nonpolar component of the solvation energy ( \({\text{E}}_{\text{N}\text{o}\text{n}\text{p}\text{o}\text{l}\text{a}\text{r}}\) ) is estimated using the solvent-accessible surface area (SASA) method and can be expressed using Eq. 3 : $${\text{E}}_{\text{N}\text{o}\text{n}-\text{P}\text{o}\text{l}\text{a}\text{r}}=\gamma A+b$$ 3 where \(\gamma\) represents the surface tension of the solvent, A is the calculated SASA of the molecule and b is a fitting parameter. We extracted snapshots at every 10ns from the original 100 ns trajectory and implemented it for MMPBSA calculations. MmPbSaStat.py program was used to calculate the binding energies and MmPbSaDecomp.py was used to extract the residue-specific contributions towards binding, [ 50 ] as implemented in the g_mmpbsa package. Results and Discussion Molecular docking Previously well-established CSF-1 R ligands such as CPPC, AZ683, GW2580, OMPyO, JNJ40346527 and PLX5622 were taken as reference ( Fig. 1 ) and docked with CSF-1 R protein. Out of all these references, the docking score of reference 1, 5 and 6 was found to be maximum with values -9.37 kcal/mol, -10.08 kcal/mol and -7.87 kcal/mol respectively ( Table 1 ). Ref-1 (AZ683), a high affinity ligand for CSF1R (Ki = 8 nM; IC50 = 6 nM) and >250-fold selectivity over 95 other kinases. It forms 3 hydrogen bonds with GLU-664 (1.98 Å), THR-663 (1.81 Å) and GLY-669 (3.00 Å) residues of CSF-1 R along with π-sigma interaction with CYS-666. The other interacting residues are TYR-665, ALA-614, VAL-596, PHE-797, ALA-800. [CH 3 ]-CPPC, reference 1 forms three hydrogen bonds with distance 2.16 Å, (2.06 Å & 2.02 Å) with ASP-796 and LYS-616 respectively along with π-π stacking with PHE-797. Other interacting residues involved are VAL-647, CYS-666, ALA-614, LEU-785 and VAL-596 ( Fig. 3 ). For assessing the effect of varied substituents on the binding potential with target protein, different hydrogen donor/acceptor bearing motifs were conjugated with 5-cyano furan-2-carboxamide (CPPC), pyrrole pyridine (PLX3397), cyanoimidazolecarboxamides (JNJ 40346527) core containing such as pyridine, N-phenylacetamide, piperazine, iso-indole were taken. Out of these ligands only three ligands Lig3, Lig4 and Lig5 demonstrated the maximum binding with target. The docking results indicates maximum binding score for Lig3 (-11.98 kcal/mol) bearing the chemical structure based on CPPC that has higher binding affinity towards CSF-1R (IC50 = 0.8 nM). The structure reserved the 5-cyano furan-2-carboxamide moiety with variation by addition of N-phenylacetamide in place of piperazine core. It forms 3 hydrogen bonds and crucial interactions with VAL-596, π-sigma, ALA-800 π -lone pair. The common interacting residues of Lig3 and Ref1(CPPC) are ASP-796, CYS-666, VAL-596, ALA-614, LEU-785. Both forms crucial hydrogen bond with -NH 2 group in ASP-796 acting as hydrogen bond donor and -CN group of Lig3 serving as hydrogen bond acceptor. This is followed by Lig5 (-11.72 kcal/mol) with core structure of well-known CSF-1R inhibitor, JNJ-40346527 having IC50 = 3.2 nM bearing cyanoimidazolecarboxamides moiety. It forms 4 hydrogen bonds with CSF-1R protein along with two important π-sigma interactions with VAL-596 and LEU-785. The common interacting residues of Lig5 and Ref1 are CYS-666, LEU-588, VAL-596, LEU-785, GLU-664, ALA-800. The hydrogen bond between donor, -NH 2 present in CYS-666 and acceptor N atom of piperazine ring of Lig5 is similar to that of Ref5. The oxygen atom present in isoindoline motif of Lig5 is responsible for the formation of an extra hydrogen bond with -NH 2 group of LEU-588. While Ref5, JNJ40346527 forms single hydrogen bond of distance 2.11 Å with CYS-666 and π-sigma interaction with PHE-797. In the similar way, N atom in iso-indole motif present in Lig4 is responsible for crucial interaction for CSF-1 R binding (hydrogen bond with O atom of carbonyl group in THR-663) ( Fig. 4 ). Furthermore, the 3D interactions among the best docked poses for all the protein ligand complexes are represented in Fig. S1 (reference ligands) and Fig. S3 (proposed ligands), whereas the regions showing the H-bonding donor (pink) and acceptor (green) regions among the best docked poses for all the protein ligand complexes are represented in Fig. S2 (reference ligands) and Fig. S4 (proposed ligands), respectively. Structural Rigidity and Protein Dynamics From the results obtained from the docking calculations we observed lig-3, lig-4, lig-5, and ref-1, ref-5, ref-6 to be the top hits based on the docking scores. Their complexes were further pre-processed for molecular dynamics. We performed 100ns molecular dynamics of all the protein-ligand (PL) complexes and carried out their RMSD analysis to ensure the structural intactness and conformational rigidity of protein as well as the protein-ligand complexes during the dynamics. Fig. 5(a-c) represent the RMSD of the protein backbone ( ) ( Fig. 5(a) ), the ligands ( Fig. 5(b) ) and the protein-ligand complexes ( Fig. 5(c) ) for the entire 100ns trajectory. We can see that there are no major distortions in the protein backbone and it mostly remains intact in case of all the complexes, whereas the range of the distortions lie between 0.1 – 0.25 nm. Protein backbone corresponding to ligand-5 shows a slightly higher RMSD between 30ns to 50ns but gets subtle beyond 50ns and remains intact, whereas all the other protein backbones remain intact during the entire course of the simulation. Similar observations were made in the case of radii of gyration as well ( Fig. 6(b) ), that all the protein backbones remained intact during the entire course of the simulation, whereas the range of distortions being insignificant (1.875 – 1.95 nm). Fig. 6 represents a comparison of the RMSD of the protein ( ) and protein Rg for the entire 100ns trajectory. Further, we analysed the RMSD of each of the ligands ( Fig. 5(b) ), and we observed that maximum deviations were observed in the case of ref-5, and ref-6; whereas the remaining ligands were buried deep in the protein cavity showing no or insignificant deviations. A pairwise consolidated representation of the RMSD of the protein backbone ( ), the ligands and the protein-ligand complexes for the entire 100ns trajectory is shown in ( Fig. 7(a-f) ); whereas Fig. S5 represents the RMSD of Protein ( ), Ligand and Complex for entire 100ns trajectory of each protein-ligand complex, individually. In order to further closely observe the distortions in the protein backbone ( ) and the consolidated position of the ligands during the dynamics, we extracted snapshots (SS) of protein and ligand at every 10ns from the entire 100ns MD trajectories of all the protein-ligand complexes. We then superimposed each of the extracted protein SS over each other corresponding to each complex, as represented in Fig. 8(a-c) and Fig. 9(a-c) . The RMSD for each of the extracted SS is mentioned in Table S1 . From table S1 we can see that minimum mean deviation was observed for protein corresponding to ref-6 (1.239 Å), whereas maximum mean deviation was observed for protein corresponding to lig-5 (1.777 Å). The average mean deviation is 1.463 Å for all the proteins, in general. Furthermore, we superimposed each of the extracted ligand SS over each other corresponding to each complex, as represented in Fig. 8(d-f) and Fig. 9(d-f) . The RMSD for each of the extracted ligand SS is mentioned in Table S1 . From table S1 we can see that minimum mean deviation was observed for reference lig-6 (0.963 Å), whereas maximum mean deviation was observed for reference lig-5 (2.059 Å). The average mean deviation for all the ligands in general is 1.654 Å. Amino Acid Fluctuation Analysis Calculation of the root mean square fluctuation (RMSF) revealed the possible movements of amino acid residues inside the binding pocket and outside of the binding site in all the protein-ligand complexes, as shown in Fig. 10(a) . We can see from the figure that the RMSF plot of the protein amino acid residues is almost similar for all the protein-ligand complexes except for the other small fluctuations, irrespective of the nature of the ligand bound to it. This signifies that protein-ligand complexes are stable during the dynamics. To further elucidate the fluctuations in the protein backbone and ligand during the dynamics we plotted the alignment of protein as well as ligand through a heat map as shown in Fig. S6(a-b) . Fig. S6(a) clearly reveals that protein SS corresponding to lig-5 represents highest deviations from 40-80ns whereas protein SS corresponding to reference lig-6 remains subtle and does not show any substantial fluctuations from 20-70ns, while the remaining protein SS does not show any significant fluctuations and remain subtle throughout. Similarly, in the case of ligands, from fig. S6(b) we can clearly observe that ref-5 shows maximum deviations from 80-100ns whereas the remaining ligand SS does not show any significant fluctuations and remain subtle throughout. Hydrogen Bond Occupancy & Other NCIs (H-Bond & Pertinent Residues) NCIs play a significant role in determining the folding, structure, stability, binding and hence govern the dynamics of a plethora of chemical, biological, and supramolecular assemblies [52,53]. We analysed the MD trajectories for the H-bond lengths. H-Bond distance is determined by the bond length (< 3.5 Å) ( Fig. 10(b) ). From Fig. 10(b) , we can see that lig-5 forms a maximum of 5 H-bonds with an average of 4 H-bonds throughout the simulation, followed by reference lig-6 which forms maximum of 4 H-bonds with an average of 4 H-bonds throughout the simulation. Furthermore, table S4(a-c) represent a comprehensive list of all the pertinent residues which take part in H-bonding as well as other non-covalent interactions. From table S4(a) we can see that lig-5 participates in the formation of H-bonding during the entire simulations and involves ASP-796, ASP-802, ALA-590, ARG-801, LYS-616 in formation of H-bonding. Table S4(b) highlights all those residues which take part in π-type of noncovalent interactions. We can see that LYS-616 and PHE-797 are the most prominent ones owing to their structural orientations and the interacting groups present. Further, table S4(c) highlights all the residues which take part in other noncovalent interactions. Free Energy Analysis Free energy calculations were carried out for all the PL complexes using the MMPBSA method. Table 2 represents the mean values of the decomposed Free Energy Components of each protein-ligand complex. We can see from table 2 that van Der Waals’ (ΔG vdw ) components dominate the free energies followed by the electrostatic component (ΔG ele ). Binding energies (ΔG be ) obtained are also significant, however, lig-5 and ref-1 tend to possess positive binding energies which clearly states that they are unstable whereas the remaining binding energies are fairly negative. Fig. 11 represents the comparative variation of the mean free energy components of all the PL complexes. Further, the free energy decomposition at an interval of 10ns for the entire 100ns trajectory was also carried out in order to identify the prominent free energy component involved in the binding of the ligands. Table S2 and Table S3 represent the Free Energy Components for the proposed and reference ligands respectively, taken at interval of 10ns for entire 100ns trajectory of each complex, whereas fig. 12 provides a comparative variation of the same. We can see that the trend remains the same, that is, van Der Waals’ (ΔG vdw ) components dominate the free energies followed by the electrostatic component (ΔG ele ). Binding energies (ΔG be ) obtained are also significant, however, lig-5 and ref-1 tend to possess positive binding energies which clearly states that they are unstable whereas the remaining binding energies are fairly negative. Furthermore, we also calculated the per residue contributions to the overall free energy of the protein-ligand complex represented in Fig. S7 and Fig. S8 , respectively. Fig. S8 gives us a comprehensive list of top ten protein residues (at an interval of 10ns for the entire 100ns trajectory) for each of the PL complex which have higher contribution towards the binding affinity, viz., for the reference ligands, ref-1 (GLU-554, ASP-625, GLU-626, GLU-628, GLU-633, ARG-676, ARG-677, ARG-782, LYS-793, and ASP-837); ref-5 (ASP-565, LYS-586, LYS-612, ASP-625, GLU-626, GLU-628, GLU-633, GLU-644, GLU-664, and ASP-796); and ref-6 (VAL-548, ARG-549, LYS-551, LYS-574, GLU-598, LYS-616, LYS-635, LYS-772, ARG-777, and LYS-793). Similarly, for the case of proposed ligands, lig-3 (GLU-554, ASP-625, GLU-626, GLU-628, GLU-633, ARG-676, ARG-677, ARG-782, LYS-793, and ASP-837); lig-4 (LYS-586, LYS-612, LYS-619, GLU-626, GLU-633, ASP-778, ARG-816, ASP-837, GLU-847, and GLU-912); and lig-5 (LYS-619, VAL-548, LYS-586, LYS-595, LYS-612, ARG-676, ARG-677, ARG-782, LYS-793, and ARG-801). Whereas fig. S7 gives us a heat map representation for a larger number of protein residues which have higher contribution towards the binding affinity. Conclusions and future outlook Microglia, the initial defense system against neuroinflammation and is considered as chief mediator of brain injury and repair. Its overexpression of microglial is directly associated with neuroinflammation involved in variety of disorders. The prolonged microglia activation results in chronic neuronal damage and hinders the regeneration process. Developing strategies to replace faulty microglia or regulate their activity offers a fresh approach for treating neurodegenerative diseases. Here, we concentrate on the most up-to-date preclinical and clinical evidence related to potential microglia-based therapies in neurodegenerative conditions, specifically by targeting the CSF-1Ri. Our study will aid-in getting a deeper insight regarding the CSF-1R binding mechanism and affinity of cyano-furan and cyano-imidazole analogs. The presented analysis will improve existing CSF-1Ri and would also prove helpful in the design of new and potent drugs with therapeutic application. According to the pharmacophore-based virtual screenings, we identified ten ligands with potential in-silico activity against CSF-1R. Molecular docking study indicates that hydrogen-bond interactions as key for affinity. The most potent ligands, Lig3, Lig4 and Lig5 were subjected to MD simulation for assessing the overall stability of the protein–ligand complex. 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M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S. and Olson, A. J. (2009) Autodock4 and AutoDockTools4: automated docking with selective receptor flexiblity. J. Computational Chemistry 2009, 16 : 2785-91. Pandey, A., Misra, M., Yadav, A.K.; Molecular Docking Studies on Binding Specificity of 3,6- and 2,7-Carbazoles With DNA Duplexes. European Journal of Biological Research; 2020, 11, 14-23. Pandey, A., Yadav, R., Shukla, A., Yadav, A.K.; Unveiling the Antimicrobial Activities of Dicationic Carbazoles and Related Analogs Through Computational Docking; Advanced Science, Engineering and Medicine; 2020, 1, 40-44. Yadav, R., Pandey, A., Awasthi, Shukla, A.; Molecular Docking Studies of Enzyme Binding Drugs on Family of Cytochrome P450 Enzymes; Advanced Science, Engineering and Medicine; 2020, 1, 83-87. 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Fuhrmans, M., Sanders, B. P., Marrink, S.-J., & de Vries, A. H. (2010). Effects of bundling on the properties of the SPC water model. Theoretical Chemistry Accounts, 125(3–6), 335–344. K. Vanommeslaeghe, E. Hatcher, C. Acharya, S. Kundu, S. Zhong, J. Shim, E. Darian, O. Guvench, P. Lopes, I. Vorobyov, A. D. MacKerell Jr., CHARMM General Force Field: A Force field for Ligand-Like Molecules Compatible with the CHARMM All-Atom Additive Biological Force Field, J. Comput. Chem. 2010, 31, 671-690. W. Yu, X. He, K. Vanommeslaeghe, A. D. MacKerell Jr., Extension of the CHARMM General Force Field to Sulfonyl-Containing Compounds and Its Utility in Biomolecular Simulations, J. Comput. Chem. 2012, 33, 2451-2468. Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A., & Haak, J. R. (1984). Molecular dynamics with coupling to an external bath. The Journal of Chemical Physics, 81(8), 3684–3690. Berendsen, H. J. C., van der Spoel, D., & van Drunen, R. (1995). GROMACS: A message-passing parallel molecular dynamics implementation. Computer Physics Communications, 91(1–3), 43–56. Hess, B., Bekker, H., Berendsen, H. J. C., & Fraaije, J. G. E. M. (1997). LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry, 18(12), 1463–1472 Darden, T., York, D., & Pedersen, L. (1993). Particle mesh Ewald: An N, log (N) method for Ewald sums in large systems. Journal of Chemical Physics, 98(12), 10089–10092 Miyamoto, S., & Kollman, P. A. (1992). Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. Journal of Computational Chemistry, 13(8), 952–962 Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14(1), 33–38. Kumari, R., Kumar, R., & Lynn, A. (2014). g_mmpbsa-A GROMACS tool for high-throughput MM-PBSA calculations. Journal of Chemical Information and Modeling, 54(7), 1951–1962 Baker, N. A., Sept, D., Joseph, S., Holst, M. J., & McCammon, J. A. (2001). Electrostatics of nano systems: Application to microtubules and the ribosome. Proceedings of the National Academy of Sciences United Sciences, 98(18), 10037–10041. A. Pandey, N. Kumar; Tracing the Transition from Covalent to Non-covalent Functionalization of Pyrene through C-, N-, and O- based Ionic and Radical Substrates using Quantum Mechanical Calculations, RSC Advances, 2023; Y.B. Kumar, A. Pandey, N. Kumar, G.N. Sastry; Binding propensity and selectivity of cationic, anionic, and neutral guests with model hydrophobic hosts: A first principles study; J. Comput. Chem.; 2023, 44, 432. Tables Table 1. Docking scores (kcal/mol) of the reference ligands and the proposed ligands with CSF-1R Reference Ligands Docking Score Proposed Ligands Docking Score Ref-1 -9.37 Lig-1 -9.28 Ref-2 -5.59 Lig-2 -9.48 Ref-3 -7.66 Lig-3 -11.98 Ref-4 -5.10 Lig-4 -11.10 Ref-5 -10.08 Lig-5 -11.72 Ref-6 -7.87 Lig-6 -9.74 Lig-7 -9.15 Lig-8 -8.98 Lig-9 -8.25 Lig-10 -9.29 Table 2. Mean values of the decomposed Free Energy Components of each protein-ligand complex VDW ELE POL SASA BIND Lig-3 -203.26 -63.42 224.84 -22.40 -64.24 Lig-4 -137.72 -32.52 134.10 -15.51 -51.65 Lig-5 -146.53 -95.55 322.62 -17.89 62.66 Ref-1 -138.25 -39.18 326.75 -15.85 133.48 Ref-5 -100.07 -9.87 82.71 -10.38 -37.61 Ref-6 -152.56 -39.78 141.67 -18.00 -68.67 Additional Declarations The authors declare no competing interests. Supplementary Files SupportingInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3903155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269598685,"identity":"3bc4ce33-b4c0-405f-8f81-cb136a0e1dac","order_by":0,"name":"Anupriya 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ligands\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/d506ba369f2a58326f2324b4.png"},{"id":50357603,"identity":"43d126ba-9a79-4bfc-81c6-1186092db658","added_by":"auto","created_at":"2024-01-30 09:23:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36908,"visible":true,"origin":"","legend":"\u003cp\u003eFigure representing the structures of the proposed ligands based on well-established CSF-1 R ligands\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/50c855e08fd6919e77773aee.png"},{"id":50357606,"identity":"27ac0d68-0cdd-46f0-b3ab-19c5987b065e","added_by":"auto","created_at":"2024-01-30 09:23:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158392,"visible":true,"origin":"","legend":"\u003cp\u003e2D representation of the best docked pose of the reference ligands (Green represents H-bonding interactions followed by other noncovalent interactions)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/d541b307b069c4394498ae63.png"},{"id":50358514,"identity":"5466699e-ab32-4fe3-b8f8-8a4737a07b83","added_by":"auto","created_at":"2024-01-30 09:39:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":153236,"visible":true,"origin":"","legend":"\u003cp\u003e2D representation of the best docked pose of the proposed ligands (Green represents H-bonding interactions followed by other noncovalent interactions)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/c71678649164fa936eca4eb8.png"},{"id":50358188,"identity":"d8d746aa-07f2-436f-9210-a06fbc817d89","added_by":"auto","created_at":"2024-01-30 09:31:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104214,"visible":true,"origin":"","legend":"\u003cp\u003eFigure representing the RMSD of (a) Protein (Cα), (b) Ligand and (c) Complex for entire 100ns trajectory\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/755ea92c9991554ab00740f1.png"},{"id":50359272,"identity":"b29cce5c-16e3-444d-b6c3-192ee59fc968","added_by":"auto","created_at":"2024-01-30 09:47:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":111078,"visible":true,"origin":"","legend":"\u003cp\u003eFigure representing the RMSD of (a) Protein (Cα) and (b) Protein Rg for entire 100ns trajectory\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/3c89458e967a7d7388205ec4.png"},{"id":50358190,"identity":"e48b91c9-fb48-42d8-93fb-8ed2b0aadc84","added_by":"auto","created_at":"2024-01-30 09:31:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":188527,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD of Complex, Protein (Cα) and Ligand for entire 100ns trajectory for each complex\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/fe4a8d05de61572f47130086.png"},{"id":50358194,"identity":"34ceca50-3ac2-49d9-9717-ecbe04b77bc9","added_by":"auto","created_at":"2024-01-30 09:31:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":360102,"visible":true,"origin":"","legend":"\u003cp\u003eThe superimposed protein backbones (a-c) and proposed ligands (d-e), taken at interval of 10ns for entire 100ns trajectory of each complex\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/4fda9df219c65b86047e814f.png"},{"id":50357613,"identity":"a7f5a2bf-11d5-47b1-83d9-e0b16fdc6f33","added_by":"auto","created_at":"2024-01-30 09:23:52","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":359666,"visible":true,"origin":"","legend":"\u003cp\u003eThe superimposed protein backbones (a-c) and reference ligands (d-e), taken at interval of 10ns for entire 100ns trajectory of each of the protein-ligand complex\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/5abcbb90be30224348a68373.png"},{"id":50357609,"identity":"2aa5147e-d062-4fe0-8271-aeeb4de0878d","added_by":"auto","created_at":"2024-01-30 09:23:52","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":70252,"visible":true,"origin":"","legend":"\u003cp\u003eFigure representing the (a) RMSF of Protein (Cα) and (b) H-Bond occupancy for entire 100ns trajectory of each complex\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/686b486bf83ba98587551153.png"},{"id":50358516,"identity":"0eb03b3d-2290-4780-a303-2e0a47331192","added_by":"auto","created_at":"2024-01-30 09:39:52","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":8929,"visible":true,"origin":"","legend":"\u003cp\u003eMean values of the decomposed Free Energy Components of each protein-ligand complex\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/032f8c4421de35898134ddc2.png"},{"id":50359271,"identity":"3ddfe848-6dca-414f-bdda-a00df6daa489","added_by":"auto","created_at":"2024-01-30 09:47:52","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":26138,"visible":true,"origin":"","legend":"\u003cp\u003eFigure representing the Free Energy Components for proposed ligands (a-c) and reference ligands (d-e), taken at interval of 10ns for entire 100ns trajectory of each complex\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/8ea81e519076ea7ae120bdad.png"},{"id":50359858,"identity":"2afdb270-231c-4330-b540-7f151a4d20bd","added_by":"auto","created_at":"2024-01-30 09:55:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1868882,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/40ed6a90-48da-4000-863f-9638dbd22f70.pdf"},{"id":50357614,"identity":"a0618975-439c-42ea-8601-4e1233d7721e","added_by":"auto","created_at":"2024-01-30 09:23:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17285900,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3903155/v1/236eccbc372c027a332484f6.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDiscerning Potent CSF-1R inhibitors for Targeting and Therapy of Neuroinflammation using Computational Approaches\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeuroinflammation is the brain's natural defense response when faced with infections, injuries, or disruptions to its normal balance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Presently, there is a growing focus on research involving the use of immunomodulatory and neuroprotective substances aimed at influencing microglia (brain cells), which are the primary immune cells in the central nervous system (CNS). These microglia exhibit heightened sensitivity to various triggers and rapidly activate themselves following an injury by releasing chemokines, cytokines, and other signaling molecules [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Microglia (brain cells) are described as follows; we start with M0, which is like resting microglia. When M0 gets activated, it turns into two different forms, viz., M1 - these microglia are like the defenders of the brain. They respond to problems by releasing things like reactive oxygen species (ROS), TNF-α, and prostaglandins to fight off threats; and M2 - these microglia are more like protectors. They respond to problems by releasing substances that help the brain and reduce inflammation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The protein CSF-1R, found on the surface of cells and belonging to the tyrosine kinase family, is primarily expressed in microglia, with some presence in neurons. CSF-1R's main role is to regulate the development, survival, and maintenance of microglia. It becomes active when stimulated by two natural ligands, CSF-1 and interleukin-34 (IL-34) [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This receptor is most prominently found in the cortical regions of the brain. In the context of treating various inflammatory and neuroinflammatory disorders, one promising strategy could involve using small molecules to inhibit CSF-1R. The latest research, conducted on small animals and post-mortem brain samples, has uncovered a significant link between neuroinflammation and an increase in CSF-1R levels in severe neuroinflammatory diseases such as Alzheimer's disease (AD), Multiple Sclerosis (MS), hypoxic-ischemic encephalopathy (HIE), and glioblastomas [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Consequently, the need for in-vivo microglia imaging is paramount for investigating their behavior in different environments and understanding their long-term effects in both normal and diseased conditions within the central nervous system. This is vital for early detection and the prompt initiation of therapeutic interventions. Researchers have extensively explored various markers to identify microglia, but their full potential is hindered by several limitations. These limitations include issues like markers not being specific enough for microglia (sometimes they also highlight other cells like activated astrocytes and myeloid-lineage cells infiltrating the central nervous system), low signal-to-noise ratios, and the presence of genetic variations. Because of these challenges, there is a growing interest in developing biomarkers that exclusively pinpoint microglia. As a result, researchers have created different radiotracers that target CSF-1R (Colony-Stimulating Factor 1 Receptor) in microglia, including [11C]-AZ683, [11C]-CPPC, [11C]-GW2580, [11C]-BLZ945, and [11C]-OMPYO [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Many existing approaches have limitations, including poor penetration of the blood-brain barrier and significant non-specific binding. This presents challenges when trying to accurately detect subtle changes in microglial activation, given the naturally high levels of CSF1R expression. Additionally, there have been recent discoveries of small inhibitor molecules targeting CSF-1R for the removal of microglia in pathological conditions, with some progressing to clinical trials [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These inhibitors can lead to approximately 95% reduction in microglia in the central nervous system, resulting in improved cognitive function and decreased expression of proinflammatory genes. However, prolonged use of these inhibitors for therapy is not feasible, as microglia play essential roles in brain function [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Hence, using a CSF-1R inhibitor for short-term treatment to remove harmful microglia in the brain could lead to lasting benefits, preventing the ongoing path towards neurodegeneration [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, prolonged treatment, lasting over 20 weeks, has shown adverse effects, including hair discoloration (caused by C-KIT inhibition), hepatotoxicity (due to effects on Kupffer cells), and reduced oligodendrocyte progenitors [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This underscores the persistent need for developing more targeted and efficient CSF-1R inhibitors to eliminate problematic microglia in the therapy of neurodegenerative diseases. Currently, there's a critical need for discovering new inhibitors of CSF-1R. To efficiently complement experimental research and reduce costs in drug development, we're employing computational techniques to investigate how these ligands interact with the receptor. In this study, we've introduced ten cyano-furan and cyano-imidazole derivative ligands that could potentially bind to and inhibit CSF-1R. We've assessed their interactions with the protein using molecular docking and compared the specificity of the most promising ligand with well-established CSF-1R inhibitors found in the literature [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e"},{"header":"Material and Methodology","content":"\u003cp\u003eAll the computational Studies were carried out on the XRD crystal structure of cFMS tyrosine kinase with resolution of 1.95 \u0026Aring; (PDB ID: 3DPK) downloaded from Protein Data Bank (PDB). Further to carry out our studies, we initiated by first identifying reference ligands (6) through a comprehensive review of literature. Subsequently, we later proposed novel ligands (10) based on the reference ligands obtained through the literature survey. Thereafter, we performed molecular docking calculations to probe the pocket interactions of the ligands and their binding sites within the target protein. Following this, based on the docking scores and residue interactions, we subjected the most promising ligands, as well as top-performing reference ligands from the docking process, to rigorous molecular dynamics simulations. These simulations enabled us to thoroughly evaluate the binding interactions, stability, and the dynamic behavior of the biomolecular system. Each of these computational methods are elaborated in the following sections.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Docking Calculations\u003c/h2\u003e \u003cp\u003eMolecular docking methodology are widely employed for exploring the interactions of interactions and binding affinity of biological activity of proteins. In the presented work, Autodock4.0 software was utilized for docking the protein (PDB ID: 3DPK) and proposed CSF-1R ligands (10) and references (6) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The chemical structures of all the compounds were drawn using Chem Draw 16.0 tool and their energy was minimized using ChemBio3D [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Afterwards these ligand molecules were put as an input in Autodock4.0 software [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Before commencing the docking simulation through Lamarckian Genetic Algorithm (LGA), macromolecule was prepared through standard protocol by firstly abstracting the co-crystallized ligand, water molecules and cofactors from it. Followed by this, a grid box with changing magnitude was created to surround the active site of the macromolecule for carrying the docking simulation. The least binding affinity docked posture was mined out and aligned with CSF-1R for further research following 20 LGA with a maximum cycle of 2500000 energy evaluations for each receptor-ligand complex. Previous studies on structure and dynamics of biomolecular complexes have successfully reproduced results using the mentioned protocols [\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The most favourable conformation with lowest energy was selected were selected and their interaction with the receptor was analysed using Discovery studio visualizer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Dynamics Simulation\u003c/h2\u003e \u003cp\u003eMolecular dynamics simulations of the protein-ligand complexes were performed as per the protocols mentioned in previous studies [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] using the GROMACS 5.1.1 package [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] employing the charmm36-jul2022 force field [\u003cspan additionalcitationids=\"CR37 CR38 CR39 CR40\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Ligand topologies were prepared using the CHARMM General Force Field (CGenFF) server [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. MD simulations were performed under periodic boundary conditions (PBCs) with a cubical box of 1.0nm x 1.0nm x 1.0nm dimension. The process began by placing the protein-ligand complexes in a solution of SPC water molecules. To maintain a neutral charge for the system, counter ions were added to the solvated box as needed. Next, we performed 1000 steps of steepest descent to minimize energy and resolve structural clashes within the solvated system. Following this, we subjected the minimized systems to equilibration with position restraints. The systems were gradually heated from 0 to 300 K over a period of 500 picoseconds using a modified Berendsen thermostat [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. After the heating step, we equilibrated the systems for 1 nanosecond under isothermal-isobaric conditions, maintaining a constant pressure of 1.0 bar. For the production run, a 100 ns simulation was conducted without any restraints. To maintain bond lengths, we utilized the LINCS algorithm [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and for long-range electrostatics, the particle mesh Ewald (PME) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] method was employed. Additionally, the SETTLE algorithm [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] was used to ensure the geometry of water molecules remained constrained. The trajectory of each system was analysed using GROMACS utilities and VMD software [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFree Energy Calculations\u003c/h2\u003e \u003cp\u003eThe g_mmpbsa package was utilized to determine the binding free energies of protein-ligand complexes using the Molecular mechanics-Poisson-Boltzmann surface area (MMPBSA) method, as described by the equation below:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\varDelta \\text{E}}_{\\text{b}\\text{i}\\text{n}\\text{d}}={\\text{E}}_{\\text{c}\\text{o}\\text{m}\\text{p}\\text{l}\\text{e}\\text{x}}-({\\text{E}}_{\\text{p}\\text{r}\\text{o}\\text{t}\\text{e}\\text{i}\\text{n}}+{\\text{E}}_{\\text{l}\\text{i}\\text{g}\\text{a}\\text{n}\\text{d}})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}}_{\\text{c}\\text{o}\\text{m}\\text{p}\\text{l}\\text{e}\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}}_{\\text{p}\\text{r}\\text{o}\\text{t}\\text{e}\\text{i}\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}}_{\\text{l}\\text{i}\\text{g}\\text{a}\\text{n}\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e are the total MMPBSA energy of protein-ligand complex, total solution free energies of the isolated protein and ligand molecules, respectively. The total free energy of each individual can be expressed using Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${\\text{E}}_{\\text{x}}= \u0026lt;{\\text{E}}_{\\text{M}\\text{M}}\u0026gt; - \\text{T}\\text{S} + \u0026lt;{\\text{E}}_{\\text{s}\\text{o}\\text{l}\\text{v}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}}\u0026gt;$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere x is either protein, ligand or protein-ligand complex.\u003c/p\u003e \u003cp\u003e\u0026lt;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}}_{\\text{M}\\text{M}}\\)\u003c/span\u003e\u003c/span\u003e\u0026gt; is the mean value of molecular mechanics potential energies when molecules are in a vacuum. T and S represent the absolute temperature and entropy, respectively, and together, they contribute to the entropic part of the total free energy in a vacuum. \u0026lt;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}}_{\\text{M}\\text{M}}\\)\u003c/span\u003e\u003c/span\u003e\u0026gt; encompasses both bonded and nonbonded interactions among the molecules, while \u0026lt;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}}_{\\text{s}\\text{o}\\text{l}\\text{v}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e\u0026gt; represents the average free energy of solvation. This average solvation free energy is composed of polar (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}}_{\\text{P}\\text{o}\\text{l}\\text{a}\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e) and nonpolar (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}}_{\\text{N}\\text{o}\\text{n}\\text{p}\\text{o}\\text{l}\\text{a}\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e) energies. To estimate the polar solvation energy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}}_{\\text{P}\\text{o}\\text{l}\\text{a}\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e), the Poisson-Boltzmann equation is solved using a linear model [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], while the nonpolar component of the solvation energy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}}_{\\text{N}\\text{o}\\text{n}\\text{p}\\text{o}\\text{l}\\text{a}\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e) is estimated using the solvent-accessible surface area (SASA) method and can be expressed using Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${\\text{E}}_{\\text{N}\\text{o}\\text{n}-\\text{P}\\text{o}\\text{l}\\text{a}\\text{r}}=\\gamma A+b$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\gamma\\)\u003c/span\u003e\u003c/span\u003e represents the surface tension of the solvent, A is the calculated SASA of the molecule and b is a fitting parameter. We extracted snapshots at every 10ns from the original 100 ns trajectory and implemented it for MMPBSA calculations. \u003cem\u003eMmPbSaStat.py\u003c/em\u003e program was used to calculate the binding energies and \u003cem\u003eMmPbSaDecomp.py\u003c/em\u003e was used to extract the residue-specific contributions towards binding, [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] as implemented in the g_mmpbsa package.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eMolecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreviously well-established CSF-1 R ligands such as CPPC, AZ683, GW2580, OMPyO, JNJ40346527 and PLX5622 were taken as reference (\u003cstrong\u003eFig. 1\u003c/strong\u003e) and docked with CSF-1 R protein. Out of all these references, the docking score of reference 1, 5 and 6 was found to be maximum with values -9.37 kcal/mol, -10.08 kcal/mol and -7.87 kcal/mol respectively (\u003cstrong\u003eTable 1\u003c/strong\u003e). Ref-1 (AZ683), a high affinity ligand for CSF1R (Ki = 8 nM; IC50 = 6 nM) and \u0026gt;250-fold selectivity over 95 other kinases. It forms 3 hydrogen bonds with GLU-664 (1.98 \u0026Aring;), THR-663 (1.81 \u0026Aring;) and GLY-669 (3.00 \u0026Aring;) residues of CSF-1 R along with \u0026pi;-sigma interaction with CYS-666. The other interacting residues are TYR-665, ALA-614, VAL-596, PHE-797, ALA-800. [CH\u003csub\u003e3\u003c/sub\u003e]-CPPC, reference 1 forms three hydrogen bonds with distance 2.16 \u0026Aring;, (2.06 \u0026Aring; \u0026amp; 2.02 \u0026Aring;) with ASP-796 and LYS-616 respectively along with \u0026pi;-\u0026pi; stacking with PHE-797. Other interacting residues involved are VAL-647, CYS-666, ALA-614, LEU-785 and VAL-596 (\u003cstrong\u003eFig. 3\u003c/strong\u003e). For assessing the effect of varied substituents on the binding potential with target protein, different hydrogen donor/acceptor bearing motifs were conjugated with\u0026nbsp;5-cyano furan-2-carboxamide (CPPC), pyrrole pyridine (PLX3397), cyanoimidazolecarboxamides (JNJ 40346527) core containing such as pyridine, N-phenylacetamide, piperazine, iso-indole were taken. Out of these ligands only three ligands Lig3, Lig4 and Lig5 demonstrated the maximum binding with target.\u0026nbsp;The docking results indicates maximum binding score for Lig3 (-11.98 kcal/mol) bearing the chemical structure based on CPPC that has higher binding affinity towards CSF-1R (IC50 = 0.8 nM).\u0026nbsp;The structure reserved the 5-cyano furan-2-carboxamide moiety with variation by addition of N-phenylacetamide in place of piperazine core. It forms 3 hydrogen bonds and crucial interactions with VAL-596, \u0026pi;-sigma, ALA-800 \u0026pi; -lone pair. The common interacting residues of Lig3 and Ref1(CPPC) are ASP-796, CYS-666, VAL-596, ALA-614, LEU-785. Both forms crucial hydrogen bond with -NH\u003csub\u003e2\u003c/sub\u003e group in ASP-796 acting as hydrogen bond donor and -CN group of Lig3 serving as hydrogen bond acceptor. This is followed by Lig5 (-11.72 kcal/mol) with core structure of well-known CSF-1R inhibitor, JNJ-40346527 having IC50 = 3.2 nM bearing cyanoimidazolecarboxamides moiety. It forms 4 hydrogen bonds with CSF-1R protein along with two important \u0026pi;-sigma interactions with VAL-596 and LEU-785. The common interacting residues of Lig5 and Ref1 are CYS-666, LEU-588, VAL-596, LEU-785, GLU-664, ALA-800. The hydrogen bond between donor, -NH\u003csub\u003e2\u003c/sub\u003e present in CYS-666 and acceptor N atom of piperazine ring of Lig5 is similar to that of Ref5. The oxygen atom present in isoindoline motif of Lig5 is responsible for the formation of an extra hydrogen bond with -NH\u003csub\u003e2\u003c/sub\u003e group of LEU-588.\u0026nbsp;While Ref5, JNJ40346527 forms single hydrogen bond of distance 2.11 \u0026Aring; with CYS-666 and \u0026pi;-sigma interaction with PHE-797. In the similar way, N atom in iso-indole motif present in Lig4 is responsible for crucial interaction for CSF-1 R binding (hydrogen bond with O atom of carbonyl group in THR-663) (\u003cstrong\u003eFig. 4\u003c/strong\u003e). Furthermore, the 3D interactions among the best docked poses for all the protein ligand complexes are represented in \u003cstrong\u003eFig. S1\u003c/strong\u003e (reference ligands) and \u003cstrong\u003eFig. S3\u003c/strong\u003e (proposed ligands), whereas the regions showing the H-bonding donor (pink) and acceptor (green) regions among the best docked poses for all the protein ligand complexes are represented in \u003cstrong\u003eFig. S2\u003c/strong\u003e (reference ligands) and \u003cstrong\u003eFig. S4\u003c/strong\u003e (proposed ligands), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural Rigidity and Protein Dynamics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the results obtained from the docking calculations we observed lig-3, lig-4, lig-5, and ref-1, ref-5, ref-6 to be the top hits based on the docking scores. Their complexes were further pre-processed for molecular dynamics. We performed 100ns molecular dynamics of all the protein-ligand (PL) complexes and carried out their RMSD analysis to ensure the structural intactness and conformational rigidity of protein as well as the protein-ligand complexes during the dynamics. \u003cstrong\u003eFig. 5(a-c)\u003c/strong\u003e represent the RMSD of the protein backbone ( ) (\u003cstrong\u003eFig. 5(a)\u003c/strong\u003e), the ligands (\u003cstrong\u003eFig. 5(b)\u003c/strong\u003e) and the protein-ligand complexes (\u003cstrong\u003eFig. 5(c)\u003c/strong\u003e)\u0026nbsp;for the entire 100ns trajectory. We can see that there are no major distortions in the protein backbone and it mostly remains intact in case of all the complexes, whereas the range of the distortions lie between 0.1 \u0026ndash; 0.25 nm. Protein\u0026nbsp;backbone\u0026nbsp;corresponding to ligand-5 shows a slightly higher RMSD between 30ns to 50ns but gets subtle beyond 50ns and remains intact, whereas all the other protein\u0026nbsp;backbones remain intact\u0026nbsp;during the entire course of the simulation. Similar observations were made in the case of radii of gyration as well (\u003cstrong\u003eFig. 6(b)\u003c/strong\u003e), that all the protein backbones remained intact during the entire course of the simulation, whereas the range of distortions being insignificant (1.875 \u0026ndash; 1.95 nm). \u003cstrong\u003eFig. 6\u003c/strong\u003e represents a comparison of the RMSD of the protein ( ) and protein Rg for the entire 100ns trajectory. Further, we analysed the RMSD of each of the ligands (\u003cstrong\u003eFig. 5(b)\u003c/strong\u003e), and we observed that maximum deviations were observed in the case of\u0026nbsp;ref-5, and ref-6; whereas the remaining ligands were buried deep in the protein cavity showing no or insignificant deviations. A pairwise consolidated representation of the RMSD\u0026nbsp;of the\u0026nbsp;protein backbone (\u0026nbsp;), the ligands and the protein-ligand complexes\u0026nbsp;for the entire 100ns trajectory is shown in\u0026nbsp;(\u003cstrong\u003eFig. 7(a-f)\u003c/strong\u003e); whereas \u003cstrong\u003eFig. S5\u003c/strong\u003e represents the RMSD of Protein ( ), Ligand and Complex for entire 100ns trajectory of each protein-ligand complex, individually. In order to further closely observe the distortions in the protein backbone ( ) and the consolidated position of the ligands during the dynamics, we extracted snapshots (SS) of protein and ligand at every 10ns from the entire 100ns MD trajectories of all the protein-ligand complexes. We then superimposed each of the extracted protein SS over each other corresponding to each complex, as represented in \u003cstrong\u003eFig. 8(a-c)\u003c/strong\u003e and \u003cstrong\u003eFig. 9(a-c)\u003c/strong\u003e. The RMSD for each of the extracted SS is mentioned in \u003cstrong\u003eTable S1\u003c/strong\u003e. From \u003cstrong\u003etable S1\u003c/strong\u003e we can see that minimum mean deviation was observed for protein corresponding to ref-6 (1.239 \u0026Aring;), whereas maximum mean deviation was observed for protein corresponding to lig-5 (1.777 \u0026Aring;). The average mean deviation is 1.463 \u0026Aring; for all the proteins, in general. Furthermore, we superimposed each of the extracted ligand SS over each other corresponding to each complex, as represented in \u003cstrong\u003eFig. 8(d-f)\u003c/strong\u003e and \u003cstrong\u003eFig. 9(d-f)\u003c/strong\u003e. The RMSD for each of the extracted ligand SS is mentioned in \u003cstrong\u003eTable S1\u003c/strong\u003e. From \u003cstrong\u003etable S1\u003c/strong\u003e we can see that minimum mean deviation was observed for reference lig-6 (0.963 \u0026Aring;), whereas maximum mean deviation was observed for reference lig-5 (2.059 \u0026Aring;). The average mean deviation for all the ligands in general is 1.654 \u0026Aring;.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmino Acid Fluctuation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCalculation of the root mean square fluctuation (RMSF) revealed the possible movements of amino acid residues inside the binding pocket and outside of the binding site in all the protein-ligand complexes, as shown in \u003cstrong\u003eFig. 10(a)\u003c/strong\u003e. We can see from the figure that the RMSF plot of the protein amino acid residues is almost similar for all the protein-ligand complexes except for the other small fluctuations, irrespective of the nature of the ligand bound to it. This signifies that protein-ligand complexes are stable during the dynamics. To further elucidate the fluctuations in the protein backbone and ligand during the dynamics we plotted the alignment of protein as well as ligand through a heat map as shown in \u003cstrong\u003eFig. S6(a-b)\u003c/strong\u003e. \u0026nbsp;\u003cstrong\u003eFig. S6(a)\u003c/strong\u003e clearly reveals that protein SS corresponding to lig-5 represents highest deviations from 40-80ns whereas protein SS corresponding to reference lig-6 remains subtle and does not show any substantial fluctuations from 20-70ns, while the remaining protein SS does not show any significant fluctuations and remain subtle throughout. Similarly, in the case of ligands, from \u003cstrong\u003efig. S6(b)\u003c/strong\u003e we can clearly observe that ref-5 shows maximum deviations from 80-100ns whereas the remaining ligand SS does not show any significant fluctuations and remain subtle throughout.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHydrogen Bond Occupancy \u0026amp; Other NCIs (H-Bond \u0026amp; Pertinent Residues)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNCIs play a significant role in determining the folding, structure, stability, binding and hence govern the dynamics of a plethora of chemical, biological, and supramolecular assemblies [52,53]. We analysed the MD trajectories for the H-bond lengths. H-Bond distance is determined by the bond length (\u0026lt; 3.5 \u0026Aring;) (\u003cstrong\u003eFig. 10(b)\u003c/strong\u003e). From \u003cstrong\u003eFig. 10(b)\u003c/strong\u003e, we can see that lig-5 forms a maximum of 5 H-bonds with an average of 4 H-bonds throughout the simulation, followed by reference lig-6 which forms maximum of 4 H-bonds with an average of 4 H-bonds throughout the simulation. Furthermore, \u003cstrong\u003etable S4(a-c)\u003c/strong\u003e represent a comprehensive list of all the pertinent residues which take part in H-bonding as well as other non-covalent interactions. From \u003cstrong\u003etable S4(a)\u003c/strong\u003e we can see that lig-5 participates in the formation of H-bonding during the entire simulations and involves ASP-796, ASP-802, ALA-590, ARG-801, LYS-616 in formation of H-bonding. \u003cstrong\u003eTable S4(b)\u003c/strong\u003e highlights all those residues which take part in \u0026pi;-type of noncovalent interactions. We can see that LYS-616 and PHE-797 are the most prominent ones owing to their structural orientations and the interacting groups present. Further, \u003cstrong\u003etable S4(c)\u003c/strong\u003e highlights all the residues which take part in other noncovalent interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFree Energy Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFree energy calculations were carried out for all the PL complexes using the MMPBSA method. \u003cstrong\u003eTable 2\u003c/strong\u003e represents the mean values of the decomposed Free Energy Components of each protein-ligand complex. We can see from \u003cstrong\u003etable 2\u003c/strong\u003e that van\u0026nbsp;Der Waals\u0026rsquo;\u0026nbsp;(\u0026Delta;G\u003csub\u003evdw\u003c/sub\u003e)\u0026nbsp;components dominate the free energies followed by the electrostatic component\u0026nbsp;(\u0026Delta;G\u003csub\u003eele\u003c/sub\u003e). Binding energies\u0026nbsp;(\u0026Delta;G\u003csub\u003ebe\u003c/sub\u003e)\u0026nbsp;obtained are also significant, however, lig-5 and ref-1 tend to possess positive binding energies which clearly states that they are unstable whereas the remaining binding energies are fairly negative. \u003cstrong\u003eFig. 11\u003c/strong\u003e represents the comparative variation of the mean free energy components of all the PL complexes. Further,\u0026nbsp;the free energy decomposition at an interval of 10ns for the entire 100ns trajectory was also carried out in order to identify the prominent free energy component involved in the binding of the ligands. \u003cstrong\u003eTable S2\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Table S3\u003c/strong\u003e represent the Free Energy Components for the proposed and reference ligands respectively, taken at interval of 10ns for entire 100ns trajectory of each complex, whereas \u003cstrong\u003efig. 12\u003c/strong\u003e provides a comparative variation of the same. We can see that the trend remains the same, that is, van\u0026nbsp;Der Waals\u0026rsquo;\u0026nbsp;(\u0026Delta;G\u003csub\u003evdw\u003c/sub\u003e)\u0026nbsp;components dominate the free energies followed by the electrostatic component\u0026nbsp;(\u0026Delta;G\u003csub\u003eele\u003c/sub\u003e). Binding energies\u0026nbsp;(\u0026Delta;G\u003csub\u003ebe\u003c/sub\u003e)\u0026nbsp;obtained are also significant, however, lig-5 and ref-1 tend to possess positive binding energies which clearly states that they are unstable whereas the remaining binding energies are fairly negative. Furthermore, we also calculated the per residue contributions to the overall free energy of the protein-ligand complex represented in \u003cstrong\u003eFig. S7\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Fig. S8\u003c/strong\u003e, respectively. \u003cstrong\u003eFig. S8\u003c/strong\u003e gives us a comprehensive list of top ten protein residues (at an interval of 10ns for the entire 100ns trajectory) for each of the PL complex which have higher contribution towards the binding affinity, viz., for the reference ligands, ref-1 (GLU-554, ASP-625, GLU-626, GLU-628, GLU-633, ARG-676, ARG-677, ARG-782, LYS-793, and ASP-837); ref-5 (ASP-565, LYS-586, LYS-612, ASP-625, GLU-626, GLU-628, GLU-633, GLU-644, GLU-664, and ASP-796); and ref-6 (VAL-548, ARG-549, LYS-551, LYS-574, GLU-598, LYS-616, LYS-635, LYS-772, ARG-777, and LYS-793). Similarly, for the case of proposed ligands, lig-3 (GLU-554, ASP-625, GLU-626, GLU-628, GLU-633, ARG-676, ARG-677, ARG-782, LYS-793, and ASP-837); lig-4 (LYS-586, LYS-612, LYS-619, GLU-626, GLU-633, ASP-778, ARG-816, ASP-837, GLU-847, and GLU-912); and lig-5 (LYS-619, VAL-548, LYS-586, LYS-595, LYS-612, ARG-676, ARG-677, ARG-782, LYS-793, and ARG-801). Whereas \u003cstrong\u003efig. S7\u003c/strong\u003e gives us a heat map representation for a larger number of protein residues which have higher contribution towards the binding affinity.\u003c/p\u003e"},{"header":" Conclusions and future outlook","content":"\u003cp\u003eMicroglia, the initial defense system against neuroinflammation and is considered as chief mediator of brain injury and repair. Its overexpression of microglial is directly associated with neuroinflammation involved in variety of disorders. The prolonged microglia activation results in chronic neuronal damage and hinders the regeneration process. Developing strategies to replace faulty microglia or regulate their activity offers a fresh approach for treating neurodegenerative diseases. Here, we concentrate on the most up-to-date preclinical and clinical evidence related to potential microglia-based therapies in neurodegenerative conditions, specifically by targeting the CSF-1Ri. Our study will aid-in getting a deeper insight regarding the CSF-1R binding mechanism and affinity of cyano-furan and cyano-imidazole analogs. The presented analysis will improve existing CSF-1Ri and would also prove helpful in the design of new and potent drugs with therapeutic application. According to the pharmacophore-based virtual screenings, we identified ten ligands with potential in-silico activity against CSF-1R. Molecular docking study indicates that hydrogen-bond interactions as key for affinity. The most potent ligands, Lig3, Lig4 and Lig5 were subjected to MD simulation for assessing the overall stability of the protein\u0026ndash;ligand complex. According to our investigations, all the three docked complexes were stable for 100 ns. Moreover, the binding energies for these ligands were significant, with van der Waals\u0026rsquo; components being the dominate free energies. These optimistic results are quite crucial for the further designing of the CSF-1Ri that can be used for biomedical applications. We are currently synthesizing these screened compounds in our lab, and in due course of time we hope to conduct more experiments to validate the above findings. \u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDokalis, N., \u0026amp; Prinz, M. (2019). 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P., Marrink, S.-J., \u0026amp; de Vries, A. H. (2010). Effects of bundling on the properties of the SPC water model. Theoretical Chemistry Accounts, 125(3\u0026ndash;6), 335\u0026ndash;344.\u003c/li\u003e\n\u003cli\u003eK. Vanommeslaeghe, E. Hatcher, C. Acharya, S. Kundu, S. Zhong, J. Shim, E. Darian, O. Guvench, P. Lopes, I. Vorobyov, A. D. MacKerell Jr., CHARMM General Force Field: A Force field for Ligand-Like Molecules Compatible with the CHARMM All-Atom Additive Biological Force Field, J. Comput. Chem. 2010, 31, 671-690.\u003c/li\u003e\n\u003cli\u003eW. Yu, X. He, K. Vanommeslaeghe, A. D. MacKerell Jr., Extension of the CHARMM General Force Field to Sulfonyl-Containing Compounds and Its Utility in Biomolecular Simulations, J. Comput. Chem. 2012, 33, 2451-2468.\u003c/li\u003e\n\u003cli\u003eBerendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A., \u0026amp; Haak, J. R. (1984). Molecular dynamics with coupling to an external bath. The Journal of Chemical Physics, 81(8), 3684\u0026ndash;3690.\u003c/li\u003e\n\u003cli\u003eBerendsen, H. J. C., van der Spoel, D., \u0026amp; van Drunen, R. (1995). GROMACS: A message-passing parallel molecular dynamics implementation. Computer Physics Communications, 91(1\u0026ndash;3), 43\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eHess, B., Bekker, H., Berendsen, H. J. C., \u0026amp; Fraaije, J. G. E. M. (1997). LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry, 18(12), 1463\u0026ndash;1472\u003c/li\u003e\n\u003cli\u003eDarden, T., York, D., \u0026amp; Pedersen, L. (1993). Particle mesh Ewald: An N, log (N) method for Ewald sums in large systems. Journal of Chemical Physics, 98(12), 10089\u0026ndash;10092\u003c/li\u003e\n\u003cli\u003eMiyamoto, S., \u0026amp; Kollman, P. A. (1992). Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. Journal of Computational Chemistry, 13(8), 952\u0026ndash;962\u003c/li\u003e\n\u003cli\u003eHumphrey, W., Dalke, A., \u0026amp; Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14(1), 33\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eKumari, R., Kumar, R., \u0026amp; Lynn, A. (2014). g_mmpbsa-A GROMACS tool for high-throughput MM-PBSA calculations. Journal of Chemical Information and Modeling, 54(7), 1951\u0026ndash;1962\u003c/li\u003e\n\u003cli\u003eBaker, N. A., Sept, D., Joseph, S., Holst, M. J., \u0026amp; McCammon, J. A. (2001). Electrostatics of nano systems: Application to microtubules and the ribosome. Proceedings of the National Academy of Sciences United Sciences, 98(18), 10037\u0026ndash;10041.\u003c/li\u003e\n\u003cli\u003eA. Pandey, N. Kumar; Tracing the Transition from Covalent to Non-covalent Functionalization of Pyrene through C-, N-, and O- based Ionic and Radical Substrates using Quantum Mechanical Calculations, RSC Advances, 2023;\u003c/li\u003e\n\u003cli\u003eY.B. Kumar, A. Pandey, N. Kumar, G.N. Sastry; Binding propensity and selectivity of cationic, anionic, and neutral guests with model hydrophobic hosts: A first principles study; J. Comput. Chem.; 2023, 44, 432.\u003cstrong\u003e\u003cbr\u003e \u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDocking scores (kcal/mol) of the reference ligands and the proposed ligands with CSF-1R\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference Ligands\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocking Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProposed Ligands\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocking Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-9.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-9.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef-3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-11.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef-4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-11.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef-5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-10.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-11.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef-6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e-9.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Mean values of the decomposed Free Energy Components of each protein-ligand complex\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"384\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVDW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eELE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSASA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBIND\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-203.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-63.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e224.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-22.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-64.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-137.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-32.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e134.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-15.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-51.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLig-5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-146.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-95.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e322.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-17.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e62.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-138.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-39.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e326.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-15.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e133.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef-5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-100.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-9.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e82.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-10.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-37.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef-6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-152.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-39.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e141.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-18.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-68.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neuroinflammation, microglia, Colony stimulating factor receptor (CSF-1R)","lastPublishedDoi":"10.21203/rs.3.rs-3903155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3903155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicroglia, the primary cellular mediator of neuroinflammation, plays a pivotal role in numerous neurological disorders. Precise and non-invasive quantification of microglia is of paramount importance. Despite various investigations into cell-specific biomarkers for assessing neuroinflammation, many suffer from poor cellular specificity and low signal-to-noise ratios. Colony stimulating factor-1 receptor (CSF-1R), also known as FMS kinase, has emerged as a promising neuroinflammation biomarker with significant relevance to inflammatory diseases. Additionally, CSF-1R inhibitors (CSF-1Ri) have shown therapeutic potential in central nervous system (CNS) pathological conditions by depleting microglia. Therefore, the development of more specific CSF-1R inhibitors for targeting and treating various CNS insults and neurological disorders is imperative. This study focuses on the search for novel CSF-1R inhibitors. Based on literature for CSF-1R inhibitors, we proposed and investigated ten ligands as novel CSF-1R inhibitors. Among these, the top three ligands, selected based on their maximum binding scores in docking calculations, are subjected to 100 nanoseconds of molecular dynamics (MD) simulation, alongside three reference ligands. All protein-ligand complexes remain stable throughout the dynamics and exhibit minimal fluctuations during the analysis. The results obtained through this study may prove significant for the future design of CSF-1R inhibitors with potential applications in the field of biomedicine.\u003c/p\u003e","manuscriptTitle":"Discerning Potent CSF-1R inhibitors for Targeting and Therapy of Neuroinflammation using Computational Approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 09:23:46","doi":"10.21203/rs.3.rs-3903155/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85a62d5b-d304-4b0c-9073-1ecaa3136229","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28406728,"name":"Computational Biology"}],"tags":[],"updatedAt":"2024-01-30T09:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 09:23:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3903155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3903155","identity":"rs-3903155","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}