Docking Methodology Research Articles

Anti-quorum sensing and antibiofilm activity of coumarin derivatives against Pseudomonas aeruginosa PAO1: Insights from in vitro and in silico studies.

Biofilm-associated infections are challenging to manage or treat since the biofilm matrix is impenetrable to most antibiotics. Therefore, the best approach to deal with biofilm infections is to interrupt the construction during the initial levels. Biofilm formation has been regulated through the quorum sensing (QS) network, making it an attractive target for any antibacterial therapy. Here, some coumarin members, including umbelliprenin, 4-farnesyloxycoumarin, gummosin, samarcandin, farnesifrol A, B, C, and auraptan, have been assessed as QS inhibitors in silico and in vitro. Their potential inhibitory effects on biofilm formation and virulence factor production of Pseudomonas aeruginosa PAO1 were evaluated. First, the interaction of these compounds was investigated against one of the major transcriptional regulator proteins, PqsR, using molecular docking and structural analysis methodology. After that, in vitro evaluations indicated that 4-farnesyloxycoumarin and farnesifrol B showed considerable reduction in biofilm formation (62% and 56%, respectively), virulence factor production, and synergistic effects with tobramycin. Moreover, 4-farnesyloxycoumarin significantly (99.5%) reduced PqsR gene expression. The biofilm formation test, virulence factors production assays, gene expression analysis, and molecular dynamic simulations data demonstrated that coumarin derivatives are a potential anti-QS family through PqsR inhibition.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors.

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Multiple-P450 Gene Co-Up-Regulation in the Development of Permethrin Resistance in the House Fly, Musca domestica

This paper reports a study conducted at the whole transcriptome level to characterize the P450 genes involved in the development of pyrethroid resistance, utilizing expression profile analyses of 86 cytochrome P450 genes in house fly strains with different levels of resistance to pyrethroids/permethrin. Interactions among the up-regulated P450 genes and possible regulatory factors in different autosomes were examined in house fly lines with different combinations of autosomes from a resistant house fly strain, ALHF. Eleven P450 genes that were significantly up-regulated, with levels > 2-fold those in the resistant ALHF house flies, were in CYP families 4 and 6 and located on autosomes 1, 3 and 5. The expression of these P450 genes was regulated by trans- and/or cis-acting factors, especially on autosomes 1 and 2. An in vivo functional study indicated that the up-regulated P450 genes also conferred permethrin resistance in Drosophila melanogaster transgenic lines. An in vitro functional study confirmed that the up-regulated P450 genes are able to metabolize not only cis- and trans-permethrin, but also two metabolites of permethrin, PBalc and PBald. In silico homology modeling and the molecular docking methodology further support the metabolic capacity of these P450s for permethrin and substrates. Taken together, the findings of this study highlight the important function of multi-up-regulated P450 genes in the development of insecticide resistance in house flies.

Gramm: A webserver for free and template-based protein docking.

Most biological functions depend on protein interactions. Structural modeling of proteins and protein assemblies is important for understanding the fundamental properties of biomolecular mechanisms and for our ability to manipulate them. Recently released artificial intelligence (AI) based programs, AlphaFold and RoseTTAFold, can accurately predict three-dimensional structures of proteins, even in the absence of the structures of homologous proteins. With the success of these AI-based approaches to protein structure prediction, the research community is anticipating methods/algorithms that will enable us to reliably predict the structures of protein complexes, including those in transient interactions, in the crowded environment inside a cell, accounting for often significant conformational flexibility of the interacting proteins. Prediction of the structure of protein complexes by docking methods is a well-established research field. The intermolecular energy landscape mapped by systematic docking approaches, like the FFT-based docking algorithms, can be used to refine docking predictions, to detect macro characteristics such as the binding funnel, and to generate ensembles of transient interactions for investigation of the protein interaction patterns. We present a user-friendly web interface to our popular GRAMM docking software for structural characterization of protein-protein interactions. Users can choose between the free and the template-based docking methodology, and select a number of advanced features, including mapping of the intermolecular energy landscape by distributions of docking poses, and the dynamic visualization of the docked models.

Computational modeling of cyanobacterial phytoconstituents against toll-like receptors of skin cancer

Melanoma is an extremely dangerous disease. The diagnosis and treatment of it may be difficult because of its diversity and complexity. More than 90% of the marine biomass (microflora and microalgae) constitutes the natural biodiversity reserves. TLR-related research developments indicate possible cancer therapeutic possibilities. In addition to its significant function in innate immunity, TLR activation is connected to the start of pyroptosis, apoptosis, or autophagy in malignance cells. For these reasons, TLR agonists are appealing candidates for the production of cancer medications. From the web databases, the ternary structures of the receptors (TLR3 and TLR4) and ligands are extracted. Sixty-nine compounds were subjected to a drug likeness filter, but only twenty-two were screened further for evaluating ADMET criteria, in which only seven compounds satisfied the pharmacological properties. These compounds are further analyzed for docking parameters against TLRs (TLR3 and TLR4) and molecular simulation investigation of the best cluster to evaluate the complex stability. Molecular docking methodology discovered that Scytonmein has a significant binding potential energy of −5.21 and −7.92 kcal/mol against TLR3 and TLR4, respectively, in comparison to the redock co-crystal structure (−3.98 and −4.30 kcal/mol, respectively). The simulation analysis demonstrates the significant stability of the Scytonemin and TLR4 complexes in terms of average RMSD and RMSF compared to the redock complex, while criteria like solvent-accessible surface area (SASA), gyration (Rg) and hydrogen bonding have further supported the significant interaction and stability of the conformations. Communicated by Ramaswamy H. Sarma

Identification of an immune-related gene prognostic index for predicting prognosis, immunotherapeutic efficacy, and candidate drugs in amyotrophic lateral sclerosis.

Considering the great insufficiency in the survival prediction and therapy of amyotrophic lateral sclerosis (ALS), it is fundamental to determine an accurate survival prediction for both the clinical practices and the design of treatment trials. Therefore, there is a need for more accurate biomarkers that can be used to identify the subtype of ALS which carries a high risk of progression to guide further treatment. The transcriptome profiles and clinical parameters of a total of 561 ALS patients in this study were analyzed retrospectively by analysis of four public microarray datasets. Based on the results from a series of analyses using bioinformatics and machine learning, immune signatures are able to be used to predict overall survival (OS) and immunotherapeutic response in ALS patients. Apart from other comprehensive analyses, the decision tree and the nomogram, based on the immune signatures, were applied to guide individual risk stratification. In addition, molecular docking methodology was employed to screen potential small molecular to which the immune signatures might response. Immune was determined as a major risk factor contributing to OS among various biomarkers of ALS patients. As compared with traditional clinical features, the immune-related gene prognostic index (IRGPI) had a significantly higher capacity for survival prediction. The determination of risk stratification and assessment was optimized by integrating the decision tree and the nomogram. Moreover, the IRGPI may be used to guide preventative immunotherapy for patients at high risks for mortality. The administration of 2MIU IL2 injection in the short-term was likely to be beneficial for the prolongment of survival time, whose dosage should be reduced to 1MIU if the long-term therapy was required. Besides, a useful clinical application for the IRGPI was to screen potential compounds by the structure-based molecular docking methodology. Ultimately, the immune-derived signatures in ALS patients were favorable biomarkers for the prediction of survival probabilities and immunotherapeutic responses, and the promotion of drug development.

To explore the active constituents of Sedum aizoon L in the treatment of coronary heart disease based on network pharmacology and molecular docking methodology.

There is a lack of effective drugs for the treatment of coronary heart disease (CHD). Sedum aizoon L (SL) has multiple effects, and there is no report on CHD in SL at present. The aim of this study is to explore the mechanisms of action of SL in the treatment of CHD based on network pharmacology and molecular docking technology. The targets and active ingredients of SL were screened using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and CHD-related targets were obtained by searching GeneCards and DisGeNet databases. The intersection of LS active ingredient targets and CHD targets was used to construct a "drug-ingredient-disease-target" network using the Cytoscape software. The STRING database was used to construct a protein-protein interaction (PPI) network, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. Key targets and core active ingredients were selected and molecular docking was performed using the AutoDock software. According to the predicted results, a total of 134 corresponding target genes for LS, 12 active components, 1,704 CHD-related targets, and 52 intersecting targets were obtained. GO function and KEGG pathway analysis showed that the key targets were involved with signal transducer and activator of transcription 3 (STAT3), tumor protein p53 (TP53), and vascular endothelial growth factor A (VEGFA). The molecular docking results showed that the key targets bound to the important active ingredients in a stable conformation. The core active ingredients of LS in the treatment of CHD were determined to be ursolic acid, myricetin, and beta-sitosterol. SL may act on targets such as STAT3, TP53, and VEGFA through tumor necrosis factor (TNF) signaling pathway, interleukin 17A (IL-17A) signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and other related pathways, thereby playing a role in preventing and treating CHD.

Ligand-Based Discovery of a Small Molecule as Inhibitor of α-Synuclein Amyloid Formation

α-Synuclein (α-Syn) aggregates are implicated in Parkinson's disease (PD), so inhibitors of α-Syn aggregation have been intensively explored. It has been demonstrated that small molecules might be able to reduce α-Syn aggregation in fibrils, thus exerting neuroprotective effects in models of PD. To expand our knowledge about the structural requirements for blocking the recognition process into the oligomeric assembly of α-Syn aggregates, we performed a ligand-based virtual screening procedure using two well-known α-Syn aggregation inhibitors, SynuClean-D and ZPD-2, as query compounds. A collection of thirty-four compounds bearing distinct chemical functionalities and mutual chemical features were studied in a Th-T fluorescence test, thus identifying 5-(2,6-dinitro-4-(trifluoromethyl)benzyl)-1-methyl-1H-tetrazole (named MeSC-04) as a potent α-Syn amyloid formation inhibitor that demonstrated similar behavior when compared to SynuClean-D in the thioflavin-T-monitored kinetic assays, with both molecules reducing the number and size of amyloid fibrils, as evidenced by electron microscopy. Molecular modeling studies suggested the binding mode of MeSC-04 through the identification of putative druggable pockets on α-syn fibrils and a subsequent consensus docking methodology. Overall, this work could furnish new insights in the development of α-Syn amyloid inhibitors from synthetic sources.

Balchaturbhadra Churna as a potential medicine for SARS-CoV2 infection in pediatric setting

Abstract Background: Balachaturbhadra Churna (BC), Ayurvedic powder dosage form, has four ingredients (Aconitum heterophyllum Wall. Ex Royle, Pistacia integerrima stew. Ex brandis, Piper longum Linn., and Cyperus rotundus linn.), which is commonly prescribed for treating fever, cough, and diarrhea in a pediatric setting. Aim: The present study evaluates the possible antiviral effect of phytochemicals from the ingredients of BC on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (MPro), Ribose Nucleic Acid (RNA) dependent RNA polymerase (RdRP), and spike-angiotensin-converting enzyme 2 (SA) complex through in silico evaluation. Materials and methods: X-ray crystal structure of MPro and Spike-Ace (SA) complex with Protein Data Bank (PDB) IDs 6LU7 and 6LZG and the SWISS model of RdRP were used as the drug targets. An enhanced docking methodology has been implemented to perform the molecular docking of these phytochemicals against the above-mentioned drug targets of SARS-CoV-2. DOCK6 was used to perform the docking of these phytochemicals. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) and drug likeliness properties for these selected phytochemicals were performed using SWISS ADME. Results: The ensemble docking studies suggest that aconitine, lappaaconitine from Aconitum heterophyllum Wall. Ex Royle, 2’-hydroxyisorientin from Pistacia integerrima stew. Ex brandis, and guineesine, pipercide and piperoic acid from Piper longum Linn. hold the potential to bind favorably with the SARS-CoV-2 drug targets. The drug-likeliness and ADMET properties were calculated for these phytochemicals indicating that all three phytochemicals from Piper longum were observed to have considerate drug-likeliness and ADMET properties. Aconitine has a molecular weight slightly higher than 500 kDA and has more than 10 hydrogen bond acceptors. Conclusion: The in silico study demonstrates that phytochemicals present in BC viz., aconitine, lappaaconitine from Aconitum heterophyllum, 2’-hydroxyisorientin from Pistacia intergerrima and guineesine, pipercide, and piperoic acid from Piper longum hold the potential to bind favourably with the SARS-CoV-2 drug targets. The study also provides evidence for drug likeliness and ADMET properties for these phytochemicals suggesting that BC can be further studied as a potential drug of choice for treating SARS-CoV-2 infection to avert post-infectious disease, characterized by multisystem inflammatory syndrome in children in rare cases and other similar conditions in the pediatric setting.

Preclinical findings reveal the pharmacological targets of ferulic acid in the treatment of traumatic brain injury.

Traumatic brain injury (TBI) is characterized by cellular damage and inflammation in lesioned brain tissue. Ferulic acid has been shown to have a melioration effect on neurological functions. However, the active pharmacological effects and the underlying mechanisms of ferulic acid against TBI remain unclear. On the basis of network pharmacology and molecular docking methodology, this study aimed to investigate the beneficial effects of ferulic acid in treating TBI, and characterized the detailed biotargets and mechanisms of these actions. The identified core targets were validated via in silico simulation. We identified 91 overlapping targets associated with ferulic acid and TBI. In-silico simulation analysis validated the putative core targets of tumor protein p53, mitogen-activated protein kinase (MAPK) 1, and estrogen receptor 1. The Gene Ontology-enriched annotations and findings were largely associated with cell proliferation, apoptosis, and inflammation in nerve cells. Additional Kyoto Encyclopedia of Genes and Genomes enrichment analysis unmasked the pharmacological pathways of ferulic acid in treating TBI, including the MAPK signaling pathway and hypoxia-inducible factor-1 signaling pathway. Bioinformatic analyses and findings provide a new preclinical strategy for revealing the core targets and network pathways of ferulic acid in treating TBI. Moreover, some bioinformatic findings were computationally validated in silico for exhibiting the neuroprotective action of ferulic acid against TBI.

Induced-Fit Docking Enables Accurate Free Energy Perturbation Calculations in Homology Models.

Homology models have been used for virtual screening and to understand the binding mode of a known active compound; however, rarely have the models been shown to be of sufficient accuracy, comparable to crystal structures, to support free-energy perturbation (FEP) calculations. We demonstrate here that the use of an advanced induced-fit docking methodology reliably enables predictive FEP calculations on congeneric series across homology models ≥30% sequence identity. Furthermore, we show that retrospective FEP calculations on a congeneric series of drug-like ligands are sufficient to discriminate between predicted binding modes. Results are presented for a total of 29 homology models for 14 protein targets, showing FEP results comparable to those obtained using experimentally determined crystal structures for 86% of homology models with template structure sequence identities ranging from 30 to 50%. Implications for the use and validation of homology models in drug discovery projects are discussed.

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors.

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.

Design, synthesis, and in silico studies of tetrahydropyrimidine analogs as urease enzyme inhibitors.

The urease enzyme, a metalloenzyme having Ni2+ ions, is recognized in some bacteria, fungi, and plants. Particularly, it is vital to the progress of infections induced by pathogenic microbes, such as Proteus mirabilis and Helicobacter pylori. Herein, we reported the synthesis of a series of tetrahydropyrimidine derivatives and evaluated their antiurease activity. Finally, quantitative and qualitative analyses of the derivatives were performed via in silico studies. Urease inhibitory activity was determined as the reaction of H. pylori urease with different concentrations of compounds, and thiourea was used as a standard compound. Docking and dynamics methodologies were applied to study the interactions of the best compounds with the amino acids in the active site. All compounds showed good to excellent antiurease activity. The potent compounds were not cytotoxic against the HUVEC normal cell line. Based on the docking study, compound 4e with the highest urease inhibitory activity (IC50 = 6.81 ± 1.42 µM) showed chelates with both Ni2+ ions of the urease active site. Further, compound 4f displayed a very good inhibitory activity (IC50 = 8.45 ± 1.64 μM) in comparison to thiourea (IC50 = 22.03 ± 1.24 μM). The molecular docking and dynamics simulation results were correlated with the in vitro assay results. Moreover, the derivatives 4a-n followed Lipinski's rule-of-five and had drug-likeness properties.

Enantioselective bioaccumulation, oxidative stress, and thyroid disruption assessment of cis-metconazole enantiomers in zebrafish (Danio rerio).

Chiral triazole pesticides may cause enantioselectively adverse effects to non-targetorganisms. In this work, we employed zebrafish as an aquatic organism model to explore stereoselective acute toxicity, bioaccumulation, oxidativestress, and thyroid disruption of cis-metconazole enantiomers. The median lethal concentration values of (1S, 5R)-metconazole, (1R, 5S)-metconazole, and the mixture of them against zebrafish were 4.01, 2.61 and 3.17mg⋅L-1, respectively. (1R, 5S)-Metconazole was preferentially bioaccumulated in zebrafish than (1S, 5R)-metconazole, and the bioconcentration factor of (1R, 5S)-metconazole was 1.28-fold larger than that of (1S, 5R)-metconazole. Then, the activity order of catalase, superoxide dismutase, and glutathione-S transferase enzymes in zebrafish was expressed as (1S, 5R)-metconazole > the mixture > (1R, 5S)-metconazole, while the order of malondialdehyde content in zebrafish was (1R, 5S)-metconazole > the mixture > (1S, 5R)-metconazole. Moreover, cis-metconazole exhibited enantioselective regulation effects on the levels of triiodothyronine and thyroxine in zebrafish, and (1R, 5S)-metconazole possessed stronger thyroid disruption ability to zebrafish than the others. By virtue of molecular docking methodology, the binding affair and docking energy results supported that interactions between (1R, 5S)-metconazole and thyroid hormone receptors were much stronger than those between (1S, 5R)-metconazole and same receptors. This study of enantioselective evaluation of cis-metconazole in zebrafish can provide favorable information for risk assessments of chiral pesticides toward environment and health of aquatic organisms.

Synthesis, Antimicrobial, Cytotoxic and Molecular Docking Studies of Bis(azolylsulfonyl)pyrrole Dicarboxamides

Bis(azolylsulfonyl)pyrrole dicarboxamides were prepared from azolylsulfonamides and pyrrole-2,4-dicarboxylic acid under ultrasonication and studied their antimicrobial and cytotoxic activities. Docking methodology was used to predict their binding conformation thereby to explain the biological activities. Chloro substituted bis(oxazolylsulfonyl)pyrrole dicarboxamide (16c), unsubstituted, methyl and chloro substituted bis(imidazolylsulfonyl)pyrrole dicarboxamides (18a, 18b, 18c) showed high affinity toward colchicine binding site and could be used as possible leads for therapies against cancers. All the compounds were screened in vitro for their cytotoxic effect on three cancer cell lines. In fact, 16c, 18a and 18c pretreatment was shown to modulate the cytotoxic activity against lung, colon and prostate cancer cells while 18b against lung and prostate cancer cell lines. Further, antimicrobial studies revealed that unsubstituted, chloro substituted bis(thiazolylsulfonyl)pyrrole dicarboxamides (17a, 17c) and chloro substituted bis(imidazolylsulfonyl)pyrrole dicarboxamide (18c) displayed antibacterial activity against B. subtilis greater than the standard drug Chloramphenicol. Compounds 17c and 18c showed antifungal activity on A. niger greater than the standard drug Ketoconazole.

Computational study on the affinity of potential drugs to SARS-CoV-2 main protease

Herein, we report a computational investigation of the binding affinity of dexamethasone, betamethasone, chloroquine and hydroxychloroquine to SARS-CoV-2 main protease using molecular and quantum mechanics as well as molecular docking methodologies. We aim to provide information on the anti-COVID-19 mechanism of the abovementioned potential drugs against SARS-CoV-2 coronavirus. Hence, the 6w63 structure of the SARS-CoV-2 main protease was selected as potential target site for the docking analysis. The study includes an initial conformational analysis of dexamethasone, betamethasone, chloroquine and hydroxychloroquine. For the most stable conformers, a spectroscopic analysis has been carried out. In addition, global and local reactivity indexes have been calculated to predict the chemical reactivity of these molecules. The molecular docking results indicate that dexamethasone and betamethasone have a higher affinity than chloroquine and hydroxychloroquine for their theoretical 6w63 target. Additionally, dexamethasone and betamethasone show a hydrogen bond with the His41 residue of the 6w63 protein, while the interaction between chloroquine and hydroxychloroquine with this amino acid is weak. Thus, we confirm the importance of His41 amino acid as a target to inhibit the SARS-CoV-2 Mpro activity.

Plant-derived active compounds as a potential nucleocapsid protein inhibitor of SARS-CoV-2: an in-silico study

The coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2. This virus has a high mismatch repair proofreading ability due to its unique exonuclease activity, making it knotty to treat. The nucleocapsid protein can serve as a potential antiviral drug target, as this protein is responsible for multiple captious functions during the viral life cycle. Herein, we have investigated the potential to repurpose active antiviral compounds of plant origins for treating the SARS-CoV-2 infection. In the present study, we followed the molecular docking methodology to screen druggable natural plants’ active compounds against the nucleocapsid protein of SARS-CoV-2. The virtual screening of all 68 compounds revealed that the top seven active compounds, such as withanolide D, hypericin, silymarin, oxyacanthine, withaferin A, Acetyl aleuritolic acid, and rhein, exhibit good binding affinity with druggable ADME properties, toxicity, and Pass prediction. The stability of the docked complexes was studied by conducting molecular simulations of 100 ns. MM-GBSA calculated the binding free energy uncovered that withanolide D, hypericin, and silymarin result in highly stable binding conformations in three different sites of the nucleocapsid protein. However, further investigation is needed in order to validate the candidacy of these inhibitors for clinical trials. Communicated by Ramaswamy H. Sarma

Water Mapping and Scoring Approaches to Predict the Role of Hydration Sites in the Binding Affinity of PAK1 Inhibitors

Aim: This study aims to develop and establish a computational model that can identify potent molecules for p21-activating kinase 1 (PAK1). Background: PAK1 is a well-established drug target that has been explored for various therapeutic interventions. Control of this protein requires an indispensable inhibitor to curb the structural changes and subsequent activation of signalling effectors responsible for the progression of diseases, such as cancer, inflammatory, viral, and neurological disorders. Objective: To establish a computational model that could identify active molecules which will further provide a platform for developing potential PAK1 inhibitors. Method: A congeneric series of 27 compounds was considered for this study with Ki (nm) covering a minimum of 3 log range. The compounds were developed based on a previously reported Group-I PAK inhibitor, namely G-5555. The 27 compounds were subjected to the SP and XP mode of docking, to understand the binding mode, its conformation and interaction patterns. To understand the relevance of biological activity from computational approaches, the compounds were scored against generated water maps to obtain WM/MM ΔG binding energy. Moreover, molecular dynamics analysis was performed for the highly active compound, to understand the conformational variability and complex’s stability. We then evaluate the predictable binding pose obtained from the docking studies. Result: From the SP and XP modes of docking, the common interaction pattern with the amino acid residues Arg299 (cation-π), Glu345 (Aromatic hydrogen bond), hinge region Leu347, salt bridges Asp393 and Asp407 was observed, among the congeneric compounds. The interaction pattern was compared with the co-crystal inhibitor FRAX597 of the PAK1 crystal structure (PDB id: 4EQC). The correlation with different docking parameters in the SP and XP modes was insignificant and thereby revealed that the SP and XP’s scoring functions could not predict the active compounds. This was due to the limitations in the docking methodology that neglected the receptor flexibility and desolvation parameters. Hence, to recognise the desolvation and explicit solvent effects, as well as to study the Structure-Activity Relationships (SARs) extensively, WaterMap (WM) calculations were performed on the congeneric compounds. Based on displaceable unfavourable hydration sites (HS) and their associated thermodynamic properties, the WM calculations facilitated to understand the significance of correlation in the folds of activity of highly (19 and 17), moderate (16 and 21) and less active (26 and 25) compounds. Furthermore, the scoring function from WaterMap, namely WM/MM, led to a significant R2 value of 0.72, due to a coupled conjunction with MM treatment and displaced unfavourable waters at the binding site. To check the “optimal binding conformation”, molecular dynamics simulation was carried out with the highly active compound 19 to explain the binding mode, stability, interactions, solvent accessible area, etc., which could support the predicted conformation with bioactive conformation. Conclusion: This study determined the best scoring function, established SARs and predicted active molecules through a computational model. This will contribute towards development of the most potent PAK1 inhibitors.

Drug repurposing in silico screening platforms

Over the last decade, for the first time, substantial efforts have been directed at the development of dedicated in silico platforms for drug repurposing, including initiatives targeting cancers and conditions as diverse as cryptosporidiosis, dengue, dental caries, diabetes, herpes, lupus, malaria, tuberculosis and Covid-19 related respiratory disease. This review outlines some of the exciting advances in the specific applications of in silico approaches to the challenge of drug repurposing and focuses particularly on where these efforts have resulted in the development of generic platform technologies of broad value to researchers involved in programmatic drug repurposing work. Recent advances in molecular docking methodologies and validation approaches, and their combination with machine learning or deep learning approaches are continually enhancing the precision of repurposing efforts. The meaningful integration of better understanding of molecular mechanisms with molecular pathway data and knowledge of disease networks is widening the scope for discovery of repurposing opportunities. The power of Artificial Intelligence is being gainfully exploited to advance progress in an integrated science that extends from the sub-atomic to the whole system level. There are many promising emerging developments but there are remaining challenges to be overcome in the successful integration of the new advances in useful platforms. In conclusion, the essential component requirements for development of powerful and well optimised drug repurposing screening platforms are discussed.

Structure prediction of eukaryotic elongation factor-2 kinase and identification of the binding mechanisms of its inhibitors: homology modeling, molecular docking, and molecular dynamics simulation

Protein kinases emerged as one of the most successful families of drug targets due to their increased activity and involvement in mediating critical signal transduction pathways in cancer cells. Recent evidence suggests that eukaryotic elongation factor 2 kinase (eEF-2K) is a potential therapeutic target for treating some highly aggressive solid cancers, including lung, pancreatic and triple-negative breast cancers. Thus, several compounds have been developed for the inhibition of the enzyme activity, but they are not sufficiently specific and potent. Besides, the crystal structure of this kinase remains unknown. Hence, the functional organization and regulation of eEF-2K remain poorly characterized. For this purpose, we constructed a homology model of eEF-2K and then used docking methodology to better understanding the binding mechanism of eEF-2K with 58 compounds that have been proposed as existing inhibitors. The results of this analysis were compared with the experimental results and the compounds effective against eEF-2K were determined against eEF-2K as a result of both studies. And finally, molecular dynamics (MD) simulations were performed for the stability of eEF-2K with these compounds. According to these study defined that the binding mechanism of eEF-2K with inhibitors at the molecular level and elucidated the residues of eEF-2K that play an important role in enzyme selectivity and ligand affinity. This information may lead to new selective and potential drug molecules to be for inhibition of eEF-2K. Communicated by Ramaswamy H. Sarma