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.
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.
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.
•Identification of differentially expressed genes in LSCs of AML.•Primary strategy to develop effective therapy for AML using natural medicine.•WT1 MD stimulation to identify natural drug target.•Gallic acid could be a natural anti-LSCs drug for AML patients with high WT1 expression.
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/.
The cross‐talk between amino acids present in a protein cavity coordinates ligand binding by coupling its residue motions. A new approach termed energetic contributions of amino acid residues and its possible cross‐talk (ECONTACT) is proposed to capture the cross‐talks, which is modeled using joint probability density function. The top two cross‐talks corroborates with the experimental findings and mechanism‐based mode of ligand binding.
Nucleotide-binding oligomerization domain (NOD)-like receptors, also known as nucleotide-binding leucine-rich repeat receptors (NLRs), are a family of cytosolic pattern recognition receptors that ...detect a wide variety of pathogenic and sterile triggers. Activation of specific NLRs initiates pro- or anti-inflammatory signaling cascades and the formation of inflammasomes—multi-protein complexes that induce caspase-1 activation to drive inflammatory cytokine maturation and lytic cell death, pyroptosis. Certain NLRs and inflammasomes act as integral components of larger cell death complexes—PANoptosomes—driving another form of lytic cell death, PANoptosis. Here, we review the current understanding of the evolution, structure, and function of NLRs in health and disease. We discuss the concept of NLR networks and their roles in driving cell death and immunity. An improved mechanistic understanding of NLRs may provide therapeutic strategies applicable across infectious and inflammatory diseases and in cancer.
Innate immunity relies on the NLR family of cytosolic pattern recognition receptors to detect pathogenic and sterile insults. In this issue of Immunity, Kanneganti and colleagues review the evolution and molecular characteristics of NLRs and discuss the concept of NLR networks in the context of the roles of these proteins in innate immunity, cell death, and disease.
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.
Abstract
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 (M
pro
) 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 M
pro
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 M
pro
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 M
pro
inhibitors.
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.
A machine‐learning scoring function to identify protein‐ligand complexes with desirable pharmacophoric elements with the potential to secure high active enrichments in database screening of small molecules.
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is significantly impacting human lives, overburdening the healthcare system and ...weakening global economies. Plant-derived natural compounds are being largely tested for their efficacy against COVID-19 targets to combat SARS-CoV-2 infection. The SARS-CoV-2 Main protease (Mpro) is considered an appealing target because of its role in replication in host cells. We curated a set of 7809 natural compounds by combining the collections of five databases viz Dr Duke's Phytochemical and Ethnobotanical database, IMPPAT, PhytoHub, AromaDb and Zinc. We applied a rigorous computational approach to identify lead molecules from our curated compound set using docking, dynamic simulations, the free energy of binding and DFT calculations. Theaflavin and ginkgetin have emerged as better molecules with a similar inhibition profile in both SARS-CoV-2 and Omicron variants.
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•Constructing the library of anticancer compounds from various database.•Assessing the potency of these compounds for masking Main protease (Mpro) of SARS-CoV-2.•Molecular docking, dynamics simulation, MM/GBSA and DFT calculation.
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.
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.
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)hydrazinopyrimidin-4- ylbenzohydrazide as the probable caspase-3 inhibitors.
N'-6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl-4 methylbenzenesulfonohydrazide and 2-nitro-N'-5-nitro-6-N'-(p-tolylsulfonyl)hydrazinopyrimidin-4-ylbenzohydrazide 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.
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