Targeting leukemic stem cell subpopulation in AML using phytochemicals: An in-silico and in-vitro approach.

It has been indicated that leukemic stem cells (LSCs), a subset of leukaemia cells, are responsible for therapy resistance and relapse in acute myeloid leukaemia (AML). Therefore, the current study aimed to discover an LSC biomarker in AML patients and identify a natural compound that may target the same. By performing the different gene expression analyses, we identified 12 up-regulated and 192 down-regulated genes in LSCs of AML compared to normal bone marrow-derived HSCs. Further STRING interaction, GO enrichment and KEGG pathway analysis were carried out to top hub genes. Wilms' tumour-1 (WT1) transcription factor was pointed out as the top hub gene and a potential biomarker for LSCs in AML. For the targeted inhibition of WT1, we performed screening and stimulation of potential natural compounds. The results revealed Gallic acid (GA) and Chlorogenic acid (CA) as promising WT1 inhibitors. In-vitro validation of cytotoxic effects of both GA and CA on THP-1 and HL-60 cell lines suggested that both these compounds inhibited cell proliferation. Still, GA has a more cytotoxic effect compared to CA. Next, we performed cell cycle analysis and apoptosis analysis and found that both compounds arrested cells in G0/G1 phase and induced apoptosis in both cell lines. Surprisingly, a significant decrease in colony formation and cell migration was also observed. However, GA gave more promising results in all cellular assays than CA. Furthermore, we studied the mRNA expression of WT1 and BCL2, which are transcriptionally activated by it. We found that GA significantly downregulated both these genes compared to CA. Our results suggested that GA is a potential inhibitor of WT1 and might be an excellent anti-LSCs natural drug for AML patients.

Innate sensing pathways: Defining new innate immune and inflammatory cell death pathways has shaped translational applications.

The past 20 years of research has elucidated new innate immune sensing and cell death pathways with disease relevance. Future molecular characterization of these pathways and their crosstalk and functional redundancies will aid in development of therapeutic strategies.

MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints.

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC50 ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC50 values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.

PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments.

Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.

Theaflavin-3-gallate, a natural antagonist for Hsp90: In-silico and in-vitro approach.

Poor prognosis and metastasis have been recognized as the major cause of breast cancer related deaths worldwide. Recent experimental evidence has shown that Hsp90, the prime chaperone, is overexpressed in many cancers and is responsible if reducing the 5-year survival rate of cancer patients. Therefore, targeted inhibition of Hsp90 may be a new and effective way to target cancer as well as enhancing therapeutic outcomes. In the present study, screening and simulation of potential natural compounds result in the identification of theaflavin-3-gallate as a promising inhibitory compound of Hsp90. Further in-vitro validation of the cytotoxic effect of theaflavin-3-gallate in human breast carcinoma cell line MCF7 and normal cell line MCF10A revealed that theaflavin-3-gallate significantly inhibited the cell proliferation of MCF7 cells whereas no cytotoxic effect was observed on MCF10A cells. We also found that theaflavin-3-gallate significantly induced programmed cell death by arresting cells in the G2/M phase of the cell cycle. A significant decrease in cell migration and colony formation by theaflavin-3-gallate treatment was also observed in MCF7 cells. Furthermore, theaflavin-3-gallate significantly downregulated the mRNA expression patterns of the HSP90, MMP9, VEGFA, and SPP1 genes. Collectively, our results demonstrated theaflavin-3-gallate as a potential natural Hsp90 inhibitor that can be used to enhance the therapeutic efficacy of existing breast cancer therapies and improve overall survival of breast cancer patients.

Computational investigation of ginkgetin and theaflavin as potential inhibitors of heat shock protein 90 (Hsp90).

Heat shock protein 90 (Hsp90) is the prime molecular chaperone found to be overexpressed in cancer cells and pose as an anti-cancer therapeutic drug target for cancer chemotherapy. Even drugs are available which inhibit Hsp90, the associated side effects along with multi-drug regimen necessitate the identification of natural molecules to block the activity of Hsp90. In this present investigation, we performed virtual screening of Hsp90 inhibitors from a curated collection of natural molecules with proven pharmacological effects. This process helped in the identification of the top two scoring ligands, ginkgetin and theaflavin with favorable as well as crucial interactions with the Hsp90 ligand-binding pocket. Molecular dynamics simulations of these two natural molecules exhibited minimal fluctuations in the binding pattern of ginkgetin and theaflavin to Hsp90 which retained crucial contacts throughout the simulation time. We anticipate that ginkgetin and theaflavin could act as potent Hsp90 inhibitors which are under current investigation in our laboratory.Communicated by Ramaswamy H. Sarma.

Identification of antiviral phytochemicals as a potential SARS-CoV-2 main protease (Mpro) inhibitor using docking and molecular dynamics simulations.

Novel SARS-CoV-2, an etiological factor of Coronavirus disease 2019 (COVID-19), poses a great challenge to the public health care system. Among other druggable targets of SARS-Cov-2, the main protease (Mpro) is regarded as a prominent enzyme target for drug developments owing to its crucial role in virus replication and transcription. We pursued a computational investigation to identify Mpro inhibitors from a compiled library of natural compounds with proven antiviral activities using a hierarchical workflow of molecular docking, ADMET assessment, dynamic simulations and binding free-energy calculations. Five natural compounds, Withanosides V and VI, Racemosides A and B, and Shatavarin IX, obtained better binding affinity and attained stable interactions with Mpro key pocket residues. These intermolecular key interactions were also retained profoundly in the simulation trajectory of 100 ns time scale indicating tight receptor binding. Free energy calculations prioritized Withanosides V and VI as the top candidates that can act as effective SARS-CoV-2 Mpro inhibitors.

Identification of potential inhibitors of coronavirus hemagglutinin-esterase using molecular docking, molecular dynamics simulation and binding free energy calculation.

The pandemic outbreak of the Corona viral infection has become a critical global health issue. Biophysical and structural evidence shows that spike protein possesses a high binding affinity towards host angiotensin-converting enzyme 2 and viral hemagglutinin-acetylesterase (HE) glycoprotein receptor. We selected HE as a target in this study to identify potential inhibitors using a combination of various computational approaches such as molecular docking, ADMET analysis, dynamics simulations and binding free energy calculations. Virtual screening of NPACT compounds identified 3,4,5-Trihydroxy-1,8-bis[(2R,3R)-3,5,7-trihydroxy-3,4-dihydro-2H-chromen-2-yl]benzo[7]annulen-6-one, Silymarin, Withanolide D, Spirosolane and Oridonin as potential HE inhibitors with better binding energy. Furthermore, molecular dynamics simulations for 100 ns time scale revealed that most of the key HE contacts were retained throughout the simulations trajectories. Binding free energy calculations using MM/PBSA approach ranked the top-five potential NPACT compounds which can act as effective HE inhibitors.

Energetic contributions of amino acid residues and its cross-talk to delineate ligand-binding mechanism.

Receptor-based QSAR approaches can enumerate the energetic contributions of amino acid residues toward ligand binding only when experimental binding affinity is associated. The structural data of protein-ligand complexes are witnessing a tremendous growth in the Protein Data Bank deposited with a few entries on binding affinity. We present here a new approach to compute the Energetic CONTributions of Amino acid residues and its possible Cross-Talk (ECONTACT) to study ligand binding using per-residue energy decomposition, molecular dynamics simulations and rescoring method without the need for experimental binding affinity. This approach recognizes potential cross-talks among amino acid residues imparting a nonadditive effect to the binding affinity with evidence of correlative motions in the dynamics simulations. The protein-ligand interaction energies deduced from multiple structures are decomposed into per-residue energy terms, which are employed as variables to principal component analysis and generated cross-terms. Out of 16 cross-talks derived from eight datasets of protein-ligand systems, the ECONTACT approach is able to associate 10 potential cross-talks with site-directed mutagenesis, free energy, and dynamics simulations data strongly. We modeled these key determinants of ligand binding using joint probability density function (jPDF) to identify cross-talks in protein structures. The top two cross-talks identified by ECONTACT approach corroborated with the experimental findings. Furthermore, virtual screening exercise using ECONTACT models better discriminated known inhibitors from decoy molecules. This approach proposes the jPDF metric to estimate the probability of observing cross-talks in any protein-ligand complex. The source code and related resources to perform ECONTACT modeling is available freely at https://www.gujaratuniversity.ac.in/econtact/.

A multiparametric organ toxicity predictor for drug discovery.

The assessment of major organ toxicities through in silico predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of in silico predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, clinical adverse impacts and metabolism of pharmaceutically active substances has gained widespread acceptance in academia and pharmaceutical industries. With unrestricted accessibility to powerful biomarkers, researchers have an opportunity to contemplate the most accurate predictive scores to evaluate drug's adverse impact on various organs.A multiparametric model involving physico-chemical properties, quantitative structure-activity relationship predictions and docking score was found to be a more reliable predictor for estimating chemical toxicities with potential to reflect atomic-level insights. These in silico models provide informed decisions to carry out in vitro and in vivo studies and subsequently confirms the molecules clues deciphering the cytotoxicity, pharmacokinetics, and pharmacodynamics and organ toxicity properties of compounds. Even though the drugs withdrawn by USFDA at later phases of drug discovery which should have passed all the state-of-the-art experimental approaches and currently acceptable toxicity filters, there is a dire need to interconnect all these molecular key properties to enhance our knowledge and guide in the identification of leads to drug optimization phases. Current computational tools can predict ADMET and organ toxicities based on pharmacophore fingerprint, toxicophores and advanced machine-learning techniques.

An integrated computational approach to identify GC minor groove binders using various molecular docking scoring functions, dynamics simulations and binding free energy calculations.

Understanding the DNA-ligand interaction mechanism is of utmost importance to design selective inhibitors targeting the GC- and AT-rich DNA. This forms a primary strategy to block the association of transcription factors to promoters and subsequently, reduce the expression of genes. We present here an integrated approach combining various docking scoring functions, selective ligand-based pharmacophore models, molecular dynamics simulations and binding free energy calculations to prioritize natural compounds specific to GC minor groove binding. The approach initially applies a selective ligand-based pharmacophore model built upon known GC minor groove binders to identify potential GC minor groove binders from natural compound repositories. These GC minor groove binders were then cross-examined with selective pharmacophore models (controls) based on AT-rich binders and GC intercalators to assess its unfitness. This approach involves the calculation of binding energies of known GC- and AT minor groove binders using three scoring functions without any constraint on groove specificity of GC- and AT-rich DNA. The evaluation of empirical scoring functions led to enumeration of a new parameter, the energy difference computed using Glide (sensitivity = 80%) to recognize GC-rich binders effectively. Molecular dynamics simulations and binding free energy calculations (MM/GBSA) constituted the final phase of this approach to analyze the interactions of natural molecules (hits) with GC-rich DNA comprehensively. Seven natural molecules were selected which exhibited fewer fluctuations in RMSD and RMSF profiles and better GC-rich DNA binding with low free energies of binding. These natural hits prioritized by this integrated approach can be tested in DNA binding assay.Communicated by Ramaswamy H. Sarma.

Cardiotonic steroids as potential Na+/K+-ATPase inhibitors - a computational study.

Cardiotonic steroids (CTS) are steroidal drugs, processed from the seeds and dried leaves of the genus Digitalis as well as from the skin and parotid gland of amphibians. The most commonly known CTS are ouabain, digoxin, digoxigenin and bufalin. CTS can be used for safer medication of congestive heart failure and other related conditions due to promising pharmacological and medicinal properties. Ouabain isolated from plants is widely utilized in in vitro studies to specifically block the sodium potassium (Na+/K+-ATPase) pump. For checking, whether ouabain derivatives are robust inhibitors of Na+/K+-ATPase pump, molecular docking simulation was performed between ouabain and its derivatives using YASARA software. The docking energy falls within the range of 8.470 kcal/mol to 7.234 kcal/mol, in which digoxigenin was found to be the potential ligand with the best docking energy of 8.470 kcal/mol. Furthermore, pharmacophore modeling was applied to decipher the electronic features of CTS. Molecular dynamics simulation was also employed to determine the conformational properties of Na+/K+-ATPase-ouabain and Na+/K+-ATPase-digoxigenin complexes with the plausible structural integrity through conformational ensembles for 100 ns which promoted digoxigenin as the most promising CTS for treating conditions of congestive heart failure patients.

Receptor pharmacophore ensemble (REPHARMBLE): a probabilistic pharmacophore modeling approach using multiple protein-ligand complexes.

Ensemble methods are gaining more importance in structure-based approaches as single protein-ligand complexes strongly influence the outcomes of virtual screening. Structure-based pharmacophore modeling based on a single protein-ligand complex with complex feature combinations is often limited to certain chemical classes. The REPHARMBLE (receptor pharmacophore ensemble) approach presented here examines the ability of an ensemble of selected protein-ligand complexes to populate pharmacophore space in the ligand binding site, rigorously assesses the importance of pharmacophore features using Poisson statistic and information theory-based entropy calculations, and generates pharmacophore models with high probabilities. In addition, an ensemble scoring function that combines all the resultant high-scoring pharmacophore models to score molecules is derived. The REPHARMBLE approach was evaluated on ten DUD-E benchmark datasets and afforded good screening performance, as measured by receiver operating characteristic, enrichment factor and Güner-Henry score. Although one of the high-scoring models achieved superior statistical results in each dataset, the ensemble scoring function balanced the shortcomings of each model and passed with close performance measures. This approach offers a reliable way of choosing the best-scoring features to build four-feature pharmacophore queries and customize a target-biased 'pharmacophore ensemble' scoring function for subsequent virtual screening.

Multi-Pharmacophore Modeling of Caspase-3 Inhibitors using Crystal, Dock and Flexible Conformation Schemes.

AIM AND OBJECTIVE: Numerous caspase-3 drug discovery projects were found to have relied on single receptor as the template to recognize most promising small molecule candidates using docking approach. Alternatively, some researchers were contingent upon ligand-based alignment to build up an empirical relationship between ligand functional groups and caspase-3 inhibitory activity quantitatively. To connect both caspase-3 receptor details and its inhibitors chemical functionalities, this study was undertaken to develop receptor- and ligand-pharmacophore models based on different conformational schemes. MATERIAL AND METHODS: A multi-pharmacophore modeling strategy is carried out based on three conformational schemes of pharmacophore hypothesis generation to screen caspase-3 inhibitors from database. The schemes include (i) flexible (conformations unrestricted or flexible during pharmacophore mapping), (ii) dock (conformations obtained using FlexX docking method) and (iii) crystal (extracted from multiple caspase-3-ligand complexes from PDB repository) conformations of query ligands. The pharmacophore models developed using these conformational schemes were then used to identify probable caspase-3 inhibitors from ZINC database. RESULTS: We noticed better sensitivity with good specificity measures returned by candidate pharmacophore hypotheses across each conformation type and recognized crucial pharmacophore features that enable caspase-3 binding. Pharmacophore modeling based on flexible conformational scheme indicated that the crystal structure 3KJF (AAAADH) is the best receptor structure to perform receptor-based pharmacophore screening of caspase-3 inhibitors. When multiple crystal structures were included, the hypothesis (HAAA) is more generalized. Superimposition of multiple co-crystal ligands from various caspase-3 PDB entries in crystallographic binding mode revealed similar hypothesis (HAAA). Further, FlexX-guided dock conformations of validation dataset showed that the crystal structure 1RE1 is the best-suited for dock-based pharmacophore models. Database screening using these pharmacophore hypotheses identified N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4- yl]benzohydrazide as the probable caspase-3 inhibitors. CONCLUSION: N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4-yl]benzohydrazide may be tested for caspase-3 inhibition. We believe that potential caspase-3 inhibitors can be recognized efficiently by adapting multi-pharmacophore models in database screening.

PLHINT: A knowledge-driven computational approach based on the intermolecular H bond interactions at the protein-ligand interface from docking solutions.

The tendency of docking scoring functions to generate crystal close conformations of ligands bound to protein structures face limitations in not reproducing the exact crystal intermolecular contacts in dock poses. Intermolecular H bond contacts enumerated at the protein-docked ligand interface can be used to train scoring models and improve virtual screening performance. There is a need to incorporate additional knowledge of protein-ligand H bond contacts in extension to crystal contacts from docking solutions within the reproducibility efficiency of the docking program. A computational approach PLHINT (Protein-ligand H bond interaction pattern) is presented here which extracts intermolecular H bond interactions from native-like docked ligand poses, transform into the scoring scheme and apply over the virtual screening results of database molecules. The basic premise of the PLHINT approach is to score the most observed H bond patterns with the high score to achieve high recovery rates. Tested on ten diverse DUD-E benchmark datasets, the approach has demonstrated better overall performance and ligand enrichment competency over virtual screening results generated by three genetic algorithm-based docking programs viz. AutoDock Vina, FlexAID and PLANTS. Furthermore, the approach has successfully recovered the poor and random virtual screening results with better enrichments.

Exploration of Mycobacterium tuberculosis structural proteome: An in-silico approach.

Pharmacophore approaches are of central contour in drug discovery. However, the dependence of ligand-based pharmacophore model on appropriate training set molecules and typical use of apo-protein or single protein-ligand complex for the construction of structure-based pharmacophore models might skip some vital information. Therefore, multiple-complex based approach was employed for the construction of pharmacophore models of the Mycobacterium structural proteome. Moreover, the strategy of clustering of common pharmacophore hypotheses was made to gain an insight about the pharmacophore-similarity across the protein classes and share of features among the inhibitors. Rationale behind the present work was to present the scenario of virtual screening and guiding principle for designing efficient inhibitor by taking into account the available pharmacophoric space.

Molecular dynamics-assisted pharmacophore modeling of caspase-3-isatin sulfonamide complex: Recognizing essential intermolecular contacts and features of sulfonamide inhibitor class for caspase-3 binding.

The identification of isatin sulfonamide as a potent small molecule inhibitor of caspase-3 had fuelled the synthesis and characterization of the numerous sulfonamide class of inhibitors to optimize for potency. Recent works that relied on the ligand-based approaches have successfully shown the regions of optimizations for sulfonamide scaffold. We present here molecular dynamics-based pharmacophore modeling of caspase-3-isatin sulfonamide crystal structure, to elucidate the essential non-covalent contacts and its associated pharmacophore features necessary to ensure caspase-3 optimal binding. We performed 20ns long dynamics of this crystal structure to extract global conformation states and converted into structure-based pharmacophore hypotheses which were rigorously validated using an exclusive focussed library of experimental actives and inactives of sulfonamide class by Receiver Operating Characteristic (ROC) statistic. Eighteen structure-based pharmacophore hypotheses with better sensitivity and specificity measures (>0.6) were chosen which collectively showed the role of pocket residues viz. Cys163 (S1 sub-site; required for covalent and H bonding with Michael acceptor of inhibitors), His121 (S1; π stack with bicyclic isatin moiety), Gly122 (S1; H bond with carbonyl oxygen) and Tyr204 (S2; π stack with phenyl group of the isatin sulfonamide molecule) as stringent binding entities for enabling caspase-3 optimal binding. The introduction of spatial pharmacophore site points obtained from dynamics-based pharmacophore models in a virtual screening strategy will be helpful to screen and optimize molecules belonging to sulfonamide class of caspase-3 inhibitors.

Exploration of interaction zones of ß-tubulin colchicine binding domain of helminths and binding mechanism of anthelmintics.

Numerous studies postulated the possible modes of anthelmintic activity by targeting alternate or extended regions of colchicine binding domain of helminth ß-tubulin. We present three interaction zones (zones vide -1 to -3) in the colchicine binding domain of Haemonchus contortus (a helminth) ß-tubulin homology model and developed zone-wise structure-based pharmacophore models coupled with molecular docking technique to unveil the binding hypotheses. The resulted ten structure-based hypotheses were then refined to essential three point pharmacophore features that captured recurring and crucial non-covalent receptor contacts and proposed three characteristics necessary for optimal zone-2 binding: a conserved pair of H bond acceptor (HBA to form H bond with Asn226 residue) and an aliphatic moiety of molecule separated by 3.75±0.44Å. Further, an aliphatic or a heterocyclic group distant (11.75±1.14Å) to the conserved aliphatic site formed the third feature component in the zone-2 specific anthelmintic pharmacophore model. Alternatively, an additional HBA can be substituted as a third component to establish H bonding with Asn204. We discern that selective zone-2 anthelmintics can be designed effectively by closely adapting the pharmacophore feature patterns and its geometrical constraints.

Multi-level structure-based pharmacophore modelling of caspase-3-non-peptide complexes: Extracting essential pharmacophore features and its application to virtual screening.

Enormous caspase-3-non-peptide crystal structures have been developed to study the structural basis of caspase-3 enzyme inhibition using active site directed small molecular design. These complexes have not been explored thoroughly to decipher the essential non-covalent interactions made by crystal ligands. We present here a multi-level analysis of these caspase-3 complexes using structure-based pharmacophore approach wherein numerous candidate pharmacophore hypotheses were assessed for its ability to cover available caspase-3 small molecular inhibitor dataset. The reliability of the resultant pharmacophores was evaluated using three different validation sets comprising focussed caspase-3 inhibitors, focussed + random decoys, and focussed + structurally similar random decoys and its performance was measured by the Güner-Henry (GH) scoring and enrichment statistics. Furthermore, the effect on excluded volumes toward caspase-3 inhibitors mapping was investigated by an iterative deletion in the structure-based models and created optimal structure-based pharmacophore models to enable effective design of caspase-3 small molecular inhibitor design.

Applications of Receptor- and Ligand-based Models in Inverse Docking Experiments: Recognition of Dihydrofolate Reductase Using 7,8-Dialkyl- 1,3-Diaminopyrrolo[3,2-f]Quinazolines.

Inverse (or reverse) docking approach which involves docking of a ligand against a set of protein structures to predict possible protein target(s), possess limitations, including inefficient empirical scoring schemes and similarities in protein active site shape and physico-chemical properties. To overcome this limitation, we combined receptor- and ligand-based methods to predict probable protein targets. We showed that the experimental protein target along with possible offtargets can be effectively retrieved if the docking energy of the reference molecule and probe molecules based scaled energy profiles were combined and clustered together. The present method was validated using 7,8-dialkyl-1,3-diaminopyrrolo[3,2-f]quinazolines that inhibit Candida albicans dihydrofolate reductase (DHFR) in vitro.