Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer

Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.

Virtual Screening Process: A Guide in Modern Drug Designing

Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule's ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.

Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach

Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.

A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database

Artificial Intelligence is hailed as a cutting-edge technology for accelerating drug discovery efforts, and our goal was to validate its potential in predicting pharmacological inhibitors of EGLN1 using a deep learning-based architecture, one of its subsidiaries. Egl nine homolog 1 (EGLN1) inhibition prevents poly ubiquitination-mediated proteosomal destruction HIF-1α. The pharmacological interventions aimed at stabilizing HIF-1α have the potential to be a promising treatment option for a range of human diseases, including ischemic stroke. To unveil a novel EGLN1 inhibitor from marine natural products, a custom-based virtual screening was carried out using a Deep Convolutional Neural Network (DCNN) architecture, docking, and molecular dynamics simulation. The custom DCNN model was optimized and further employed to screen marine natural products from the CMNPD database. The docking was performed as a secondary strategy for screened hits. Molecular dynamics (MD) and molecular mechanics/generalized Born surface area (MM-GBSA) were used to analyze inhibitor binding and identify key interactions. The findings support the claim that deep learning-based virtual screening is a rapid, reliable and accurate method of identifying highly contributing drug candidates (EGLN1 inhibitors). This study demonstrates that deep learning architecture can significantly accelerate drug discovery and development, and provides a solid foundation for using (Z)-2-ethylhex-2-enedioic acid [(Z)-2-ethylhex-2-enedioic acid] as a potential EGLN1 inhibitor for treating various health complications.Communicated by Ramaswamy H. Sarma.

Structure and chemistry of enzymatic active sites that play a role in the switch and conformation mechanism

Enzymes, which are biological molecules, are constructed from polypeptide chains, and these molecules are activated through reaction mechanisms. It is the role of enzymes to speed up chemical reactions that are used to build or break down cell structures. Activation energy is reduced by the enzymes' selective binding of substrates in a protected environment. In enzyme tertiary structures, the active sites are commonly situated in a "cleft," which necessitates the diffusion of substrates and products. The amino acid residues of the active site may be far apart in the primary structure owing to the folding required for tertiary structure. Due to their critical role in substrate binding and attraction, changes in amino acid structure at or near the enzyme's active site usually alter enzyme activity. At the enzyme's active site, or where the chemical reactions occur, the substrate is bound. Enzyme substrates are the primary targets of the enzyme's active site, which is designed to assist in the chemical reaction. This chapter elucidates the summary of structure and chemistry of enzymes, their active site features, charges and role of water in the structures to clarify the biochemistry of the enzymes in the depth of atomic features.

Mechanistic Insights into SARS-CoV-2 Main Protease Inhibition Reveals Hotspot Residues

The main protease (M^pro) is a key enzyme responsible for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication that causes the spread of the global pandemic novel coronavirus (nCOVID-19) infection. In the present study, multiple computational approaches such as docking, long-range molecular dynamics (MD) simulations, and binding free-energy (BFE) estimation techniques were employed to investigate the mechanistic basis of the high-affinity inhibitors─GC-376, Calpain XII, and Calpain II (hereafter Calpain as Cal) from the literature─binding to M^pro. Redocking GC-376 and docking Cal XII and Cal II inhibitors to M^pro were able to reproduce all crucial interactions like the X-ray conformation. Subsequently, the apo (ligand-free) and three holo (ligand-bound) complexes were subjected to extensive MD simulations, which revealed that the ligand binding did not alter the overall M^pro structural features, whereas the heatmap analysis showed that the residues located in subsites S1 and S2, the catalytic dyad, and the ⁴⁵TSEDMLN⁵¹ loop in M^pro exhibit a conformational deviation. Moreover, the BFE estimation method was used to elucidate the crucial thermodynamic properties, which revealed that Coulomb, solvation surface accessibility (Solv_SA), and lipophilic components contributed significant energies for complex formation. The decomposition of the total BFE to per-residue showed that H41, H163, M165, Q166, and Q189 residues contributed maximum energies. The overall results from the current investigation might be valuable for designing novel anti-M^pro inhibitors.

In-silico strategies for identification of potent inhibitor for MMP-1 to prevent metastasis of breast cancer

Matrix Metalloproteinase-1 (MMP-1) has been often upregulated in advanced breast cancers, known to participate in ECM degradation, migration, invasion, thus leading to metastasis. Due to these effects, the condition is often reported to inversely correlate with survival in advanced breast cancers. In the present study, in-silico method was adopted based on selective non zinc binding inhibitors of MMP-1. ADME properties were predicted for PASS filtered compounds and docking calculations were performed using Glide XP and IFD protocols of Schrodinger program. We identified six ligands as potent inhibitors and validated by observing structures and the interactions of MMP-1. The identified hits were validated using molecular dynamics simulation studies. Electronic structure analysis was performed for two top hit compounds myricetin and quercetin using density function theory (DFT) at B3LYP/6-31**G level to understand their molecular reactivity. Finally, one compound myricetin has emerged as the structurally stable compound with -7.801 kcal/mol and reasonable pose inside the binding site. Molecular dynamics results indicated that myricetin forms a stable interaction with the key amino acid residues such as Glu209, Glu219, Tyr240 and Pro238. In addition, it did not form any binding with the catalytic zinc at its active site. The interaction pattern of myricetin at its substrate binding site exhibited to be potent MMP-1 inhibitor. DFT study also showed that it has more potent inhibitory effect and solubility. These factors altogether show that myricetin could be considered as the best among the compounds evaluated in inhibiting MMP-1 thereby preventing metastasis of breast cancer. Communicated by Ramaswamy H. Sarma.

Identification of Potential Dual Negative Allosteric Modulators of Group I mGluR Family: A Shape Based Screening, ADME Prediction, Induced Fit Docking and Molecular Dynamics Approach Against Neurodegenerative Diseases

BACKGROUND

Glutamate is the principal neurotransmitter in the human brain that exerts its effects through ionotropic glutamate receptors (iGluRs) and metabotropic glutamate receptors (mGluRs). The mGluRs are a class of C GPCRs that play a vital role in various neurobiological functions, mGluR1 and mGluR5 are the two receptors distributed throughout the brain involved in cognition, learning, memory, and other important neurological processes. Dysfunction of these receptors can cause neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, X-fragile syndrome, anxiety, depression, etc., hence these receptors are high profile targets for pharmaceutical industries.

OBJECTIVE

The objective of our study is to find the novel dual negative allosteric modulators to regulate both mGluR1 and mGluR5.

METHODS

In this study, shape screening protocol was used to find the dual negative allosteric modulators for both mGluR1 and mGluR5 followed by ADME prediction, induced-fit docking (IFD) and molecular dynamics simulations. Further, DFT analysis and MESP studies were carried out for the selected compounds.

RESULTS

Around 247 compounds were obtained from the eMolecules database and clustered through the CANVAS module and filtered with ADME properties. Furthermore, IFD revealed that the top four compounds (16059796, 25004252, 4667236 and 11670690) having good protein-ligand interactions and binding free energies. The molecular electrostatic potential of the top compounds shows interactions in the amine group and the oxygen atom in the negative potential regions. Finally, molecular dynamics simulations were performed with all the selected as well as the reported compound 29 indicates that the screened hits have better stability of protein ligand complex.

CONCLUSION

Hence, from the results, it is evident that top hits 16059796, 25004252, 4667236 and 11670690 could be a novel and potent dual negative allosteric modulators for mGluR1 and mGluR5.

Identification of Novel Pancreatic Lipase Inhibitors Using In Silico Studies

BACKGROUND

Obesity is well known multifactorial disorder towards the public health concern in front of the world. Increasing rates of obesity are characterized by liver diseases, chronic diseases, diabetes mellitus, hypertension and stroke, improper function of the heart, reproductive and gastrointestinal diseases, and gallstones. An essential enzyme pancreatic lipase recognized for the digestion and absorption of lipids can be a promising drug target towards the future development of antiobesity therapeutics in the cure of obesity disorders.

OBJECTIVE

The purpose of present study is to identify an effective potential therapeutic agent for the inhibition of pancreatic lipase.

METHODS

A trio of in-silico procedure of HTVS, SP and XP in Glide module, Schrodinger with default parameters, was applied on Specs databases to identify the best potential compound based on receptor grid. Finally, based on binding interaction, docking score and glide energy, selected compounds were taken forward to the platform of IFD, ADME, MMGBSA, DFT, and MDS for analyzing the ligands behavior into the protein binding site.

RESULTS

Using in silico protocol of structure-based virtual screening on pancreatic lipase top two compounds AN-465/43369242 & AN-465/43384139 from Specs database were reported. The result suggested that both the compounds are competitive inhibitors with higher docking score and greatest binding affinity than the reported inhibitor.

CONCLUSION

We anticipate that results could be future therapeutic agents and may present an idea toward the experimental studies against the inhibition of pancreatic lipase.

Probing the Druggablility on the Interface of the Protein-Protein Interaction and Its Allosteric Regulation Mechanism on the Drug Screening for the CXCR4 Homodimer

Modulating protein–protein interactions (PPIs) with small drug-like molecules targeting it exhibits great promise in modern drug discovery. G protein-coupled receptors (GPCRs) are the largest family of targeted proteins and could form dimers in living biological cells through PPIs. However, compared to drug development of the orthosteric site, there has been lack of investigations on the druggability of the PPI interface for GPCRs and its functional implication on experiments. Thus, in order to address these issues, we constructed a novel computational strategy, which involved in molecular dynamics simulation, virtual screening and protein structure network (PSN), to study one representative GPCR homodimer (CXCR4). One druggable pocket was identified in the PPI interface and one small molecule targeting it was screened, which could strengthen PPI mainly through hydrophobic interaction between the benzene rings of the PPI molecule and TM4 of the receptor. The PSN results further reveals that the PPI molecule could increase the number of the allosteric regulation pathways between the druggable pocket of the dimer interface to the orthostatic site for the subunit A but only play minor role for the other subunit B, leading to the asymmetric change in the volume of the binding pockets for the two subunits (increase for the subunit A and minor change for the subunit B). Consequently, the screening performance of the subunit A to the antagonists is enhanced while the subunit B is unchanged nearly, implying that the PPI molecule may be beneficial to enhance the drug efficacies of the antagonists. In addition, one main regulation pathway with the highest frequency was identified for the subunit A, which consists of Trp195^5.34–Tyr190^ECL2–Val196^5.35–Gln200^5.39–Asp262^6.58–Cys28^N-term, revealing their importance in the allosteric regulation from the PPI molecule. The observations from the work could provide valuable information for the development of the PPI drug-like molecule for GPCRs.

An In silico Approach to Identify High Affinity Small Molecule Targeting m-TOR Inhibitors for the Clinical Treatment of Breast Cancer

Breast cancer is the most frequent malignancy among women. It is a heterogeneous disease with different subtypes defined by its hormone receptor. A hormone receptor is mainly concerned with the progression of the PI3K/AKT/mTOR pathway which is often dysregulated in breast cancer. This is a major signaling pathway that controls the activities such as cell growth, cell division, and cell proliferation. The present study aims to suppress mTOR protein by its various inhibitors and to select one with the highest binding affinity to the receptor protein. Out of 40 inhibitors of mTOR against breast cancer, SF1126 was identified to have the best docking score of -8.705, using Schrodinger Suite which was further subjected for high throughput screening to obtain best similar compound using Lipinski’s filters. The compound obtained after virtual screening, ID: ZINC85569445 is seen to have the highest affinity with the target protein mTOR. The same result based on the binding free energy analysis using MM-GBSA showed that the compound ZINC85569445 to have the the highest binding free energy. The next study of interaction between the ligand and receptor protein with the pharmacophore mapping showed the best conjugates, and the ZINC85569445 can be further studied for future benefits of treatment of breast cancer.

Atom-based 3D-QSAR, induced fit docking, and molecular dynamics simulations study of thieno[2,3-b]pyridines negative allosteric modulators of mGluR5

Atom-based three dimensional-quantitative structure-activity relationship (3D-QSAR) model was developed on the basis of 5-point pharmacophore hypothesis (AARRR) with two hydrogen bond acceptors (A) and three aromatic rings for the derivatives of thieno[2,3-b]pyridine, which modulates the activity to inhibit the mGluR5 receptor. Generation of a highly predictive 3D-QSAR model was performed using the alignment of predicted pharmacophore hypothesis for the training set (R² = 0.84, SD = 0.26, F = 45.8, N = 29) and test set (Q² = 0.74, RMSE = 0.235, Pearson-R = 0.94, N = 9). The best pharmacophore hypothesis AARRR was selected, and developed three dimensional-quantitative structure activity relationship (3D-QSAR) model also supported the outcome of this study by means of favorable and unfavorable electron withdrawing group and hydrophobic regions of most active compound 42d and least active compound 18b. Following, induced fit docking and binding free energy calculations reveals the reliable binding orientation of the compounds. Finally, molecular dynamics simulations for 100 ns were performed to depict the protein-ligand stability. We anticipate that the resulted outcome could be supportive to discover potent negative allosteric modulators for metabotropic glutamate receptor 5 (mGluR5).

Advantages of Structure-Based Drug Design Approaches in Neurological Disorders

OBJECTIVE

The purpose of the review is to portray the theoretical concept on neurological disorders from research data.

BACKGROUND

The freak changes in chemical response of nerve impulse causes neurological disorders. The research evidence of the effort done in the older history suggests that the biological drug targets and their effective feature with responsive drugs could be valuable in promoting the future development of health statistics structure for improved treatment for curing the nervous disorders.

METHODS

In this review, we summarized the most iterative theoretical concept of structure based drug design approaches in various neurological disorders to unfathomable understanding of reported information for future drug design and development.

RESULTS

On the premise of reported information we analyzed the model of theoretical drug designing process for understanding the mechanism and pathology of the neurological diseases which covers the development of potentially effective inhibitors against the biological drug targets. Finally, it also suggests the management and implementation of the current treatment in improving the human health system behaviors.

CONCLUSION

With the survey of reported information we concluded the development strategies of diagnosis and treatment against neurological diseases which leads to supportive progress in the drug discovery.

Structural insights into the binding mode of flavonols with the active site of matrix metalloproteinase-9 through molecular docking and molecular dynamic simulations studies

Cartilage degradation in rheumatoid arthritis is mediated principally by the collagenases and gelatinases. Gelatinase B (also called matrix metalloproteinase 9 - MMP-9), is a valid target molecule which is known to participate in cartilage degradation as well as angiogenesis associated with the disease and inhibition of its activity shall prevent cartilage damage and angiogenesis. The focus of this study is to investigate the possibilities of MMP-9 inhibition by flavonol class of bioflavonoids by studying their crucial binding interactions at the active site of MMP 9 using molecular docking (Glide XP and QPLD) and further improvisation by post-docking MM-GBSA and molecular dynamic (MD) simulations. The results show that flavonols can convincingly bind to active site of MMP-9 as demonstrated by their stable interactions at the S1' specificity pocket and favourable binding energies. Gossypin has emerged as a promising candidate with a docking score of -14.618 kcal/mol, binding energy of -79.97 kcal/mol and a stable MD pattern over 15 ns. In addition, interaction mechanisms with respect to catalytic site zinc are also discussed. Further, the drug-like characters of the ligands were also analysed using ADME analysis.

Structure-based virtual screening toward the discovery of novel inhibitors for impeding the protein-protein interaction between HIV-1 integrase and human lens epithelium-derived growth factor (LEDGF/p75)

HIV-1 integrase is a unique promising component of the viral replication cycle, catalyzing the integration of reverse transcribed viral cDNA into the host cell genome. Generally, IN activity requires both viral as well as a cellular co-factor in the processing replication cycle. Among them, the human lens epithelium-derived growth factor (LEDGF/p75) represented as promising cellular co-factor which supports the viral replication by tethering IN to the chromatin. Due to its major importance in the early steps of HIV replication, the interaction between IN and LEDGF/p75 has become a pleasing target for anti-HIV drug discovery. The present study involves the finding of novel inhibitor based on the information of dimeric CCD of IN in complex with known inhibitor, which were carried out by applying a structure-based virtual screening concept with molecular docking. Additionally, Free binding energy, ADME properties, PAINS analysis, Density Functional Theory, and Enrichment Calculations were performed on selected compounds for getting a best lead molecule. On the basis of these analyses, the current study proposes top 3 compounds: Enamine-Z742267384, Maybridge-HTS02400, and Specs-AE-848/37125099 with acceptable pharmacological properties and enhanced binding affinity to inhibit the interaction between IN and LEDGF/p75. Furthermore, Simulation studies were carried out on these molecules to expose their dynamics behavior and stability. We expect that the findings obtained here could be future therapeutic agents and may provide an outline for the experimental studies to stimulate the innovative strategy for research community.

Binding mode exploration of LuxR-thiazolidinedione analogues, e-pharmacophore-based virtual screening in the designing of LuxR inhibitors and its biological evaluation

Master quorum sensing (QS) regulator LuxR of Vibrio harveyi is a unique member of the TetR protein superfamily. Recent studies have demonstrated the contribution of thiazolidinedione analogues in blocking QS by decreasing the DNA-binding ability of LuxR. However, the precise mechanism of thiazolidinedione analogues binding to LuxR is still unclear. In the present study, molecular docking combined with molecular dynamics (MD) simulations was performed to understand the mechanism of ligand binding to the protein. The binding pattern of thiazolidinedione analogues showed strong hydrogen bonding interactions with the amine group (NH) of polar amino acid residue Asn133 and carbonyl (C=O) interaction with negatively charged amino acid residue Gln137 in the binding site of LuxR. The stability of the protein-ligand complexes was confirmed by running 50 ns of MD simulations. Further, the four-featured pharmacophore hypothesis (AHHD) consists of one acceptor (A), two hydrophobic regions (HH) and one donor (D) group was used to screen compounds from ChemBridge database. The identified hit molecules were shown to have excellent pharmacokinetic properties under the acceptable range. Based on the computational studies, ChemBridge_5343641 was selected for in vitro assays. The 1-(4-chlorophenoxy)-3-[(4,6-dimethyl-2-pyrimidinyl)thio]-2-propanol (ChemBridge_5343641) showed significant reduction in bioluminescence in a dose-dependent manner. In addition, ChemBridge_5343641 inhibits biofilm formation and motility in V. harveyi. The result from the study suggests that ChemBridge_5343641 could serve as an anti-QS molecule.

Molecular docking and structure-based virtual screening studies of potential drug target, CAAX prenyl proteases, of Leishmania donovani

Targeting CAAX prenyl proteases of Leishmania donovani can be a good approach towards developing a drug molecule against Leishmaniasis. We have modeled the structure of CAAX prenyl protease I and II of L. donovani, using homology modeling approach. The structures were further validated using Ramachandran plot and ProSA. Active site prediction has shown difference in the amino acid residues present at the active site of CAAX prenyl protease I and CAAX prenyl protease II. The electrostatic potential surface of the CAAX prenyl protease I and II has revealed that CAAX prenyl protease I has more electropositive and electronegative potentials as compared CAAX prenyl protease II suggesting significant difference in their activity. Molecular docking with known bisubstrate analog inhibitors of protein farnesyl transferase and peptidyl (acyloxy) methyl ketones reveals significant binding of these molecules with CAAX prenyl protease I, but comparatively less binding with CAAX prenyl protease II. New and potent inhibitors were also found using structure-based virtual screening. The best docked compounds obtained from virtual screening were subjected to induced fit docking to get best docked configurations. Prediction of drug-like characteristics has revealed that the best docked compounds are in line with Lipinski's rule. Moreover, best docked protein-ligand complexes of CAAX prenyl protease I and II are found to be stable throughout 20 ns simulation. Overall, the study has identified potent drug molecules targeting CAAX prenyl protease I and II of L. donovani whose drug candidature can be verified further using biochemical and cellular studies.

Development of HIV-1 integrase inhibitors: recent molecular modeling perspectives

Of the three viral enzymes essential to HIV replication, HIV-1 integrase (IN) is gaining popularity as a target for the antiviral therapy of AIDS. Substantial work focusing on IN has been done over the past three decades, which has facilitated and led to the approval of three drugs. Here, we discuss in detail the development of IN inhibitors between January 2012 and May 2014, with a particular focus on molecular simulation. We highlight controversial aspects of computational drug design and refer to alternative practices where appropriate. The analysis of these computational approaches provides some useful clues to the possible future discovery of novel IN inhibitors.

Mechanistic insights of SrtA–LPXTG blockers targeting the transpeptidase mechanism in Streptococcus mutans

The SrtA–LPXTG interaction plays a key role in transpeptidation reaction, cell wall and biofilm formations. This study explains the blocking of LEU interactions with SrtA will results as SrtA inhibitors through MD simulation and energy calculations methods.

Newly approved integrase inhibitors for clinical treatment of AIDS

The current therapy for the human immunodeficiency virus (HIV) infection is a combination of anti-HIV drugs targeting multiple steps of virus replication. The drugs for the acquired immunodeficiency syndrome (AIDS) treatment include reverse transcriptase inhibitors, protease inhibitors, fusion inhibitors, co-receptor inhibitor and the newly added integrase inhibitors. Raltegravir, elvitegravir and dolutegravir are the three Food and Drug Administration (FDA) approved integrase strand transfer inhibitors for clinical treatment of HIV infection. The addition of these integrase inhibitors benefits a lot to HIV infected patients. Although it is only seven years from the first integrase inhibitor, which was approved by FDA to now, multiple drug resistant HIV strains have emerged in clinical treatment. Most of the drug resistant virus strains are against raltegravir. Some are cross-resistant to elvitegravir. Dolutegravir is effective for suppression of the current drug resistant viruses. A number of clinical trials have been performed on the three integrase inhibitors. In this study, the application of the three integrase inhibitors in clinical treatment and the findings of drug resistance to integrase inhibitors are summarized.