Machine Learning, Molecular Docking, and Dynamics-Based Computational Identification of Potential Inhibitors against Lung Cancer.

Lung cancer is the most prevalent cause of cancer deaths worldwide. However, its treatment faces a significant hurdle due to the development of resistance. Phytomolecules are an important source of new chemical entities due to their rich chemical diversity. Therefore, a machine learning (ML) model was developed to computationally identify potential inhibitors using a curated data set of 649 phytomolecules with inhibitory activity against lung cancer cell lines. Four distinct ML approaches, including k-nearest neighbor, random forest, support vector machine, and extreme gradient boosting, were used in conjugation with MACCS and Morgan2 fingerprints to generate the models. It was observed that the random forest model developed by using the MACCS fingerprint shows the best performance. To further explore the chemical space and feature importance, k-means clustering, t-SNE analysis, and mean decrease in impurity had been calculated. Simultaneously, ∼400 000 natural products (NPs) retrieved from the COCONUT database were filtered for pharmacokinetic properties and taken for a multistep screening using docking against epidermal growth factor receptor (EGFR) mutant, a therapeutic drug target of lung cancer. Thereafter, the best-performing random forest model was used to predict the antilung cancer potential of the NPs having binding affinity better than the cocrystal ligand. This allowed the identification of 205 potential inhibitors, wherein the molecules with an indolocarbazole scaffold were enriched in top-scoring molecules. The top three indolocarbazole molecules with the lowest binding energy were further evaluated through 100 ns molecular dynamics (MD) simulations, which suggested that these molecules are strong binders. Also, structural similarity analysis against known drugs revealed that these NPs are similar to staurosporine, which demonstrates potent and selective activity against EGFR mutants. Thereby, the consensus analysis employing ML, molecular docking, and dynamics revealed that the molecules having an indolocarbazole scaffold are the most promising NPs that can act as potential inhibitors against lung cancer.

Identification of Bile Acid-Derived Chemical Chaperone(s) Targeting E46K-Mutated Alpha-Synuclein Protein to Treat Parkinson's Disease: Molecular Modelling, Docking, ADME, and Simulation Studies.

Aggregated α-synuclein (α-syn) present inside small cytoplasmic inclusions in the substantia nigra region marks the major pathological hallmark of Parkinson's disease (PD) and makes it an attractive target for the drug development process. Certain small-molecule chaperones (such as DCA, UDCA, TUDCA) presented the ability to prevent misfolding and aggregation of α-syn as well as to disentangle mature α-syn amyloid fibrils. However, due to toxicity constraints, these small molecules could not be translated into clinical settings. Computational biology methods and bioinformatics approaches allow virtual screening of a large number of molecules, with reduced side effects and better efficacy. In the present study, a library of 10,928 derivatives was generated using DCA, UDCA, and TUDCA bile acid scaffolds and analysed for their binding affinity, pharmacokinetic properties, and drug likeliness profile, to come up with promising compounds with reduced toxicity and better chaperone ability. Molecular docking revealed that with respect to their free binding energy, C1-C25 have the lowest binding energy and bind significantly to recombinantly assembled E46K α-syn fibrils (PDB ID-6UFR). In silico ADME predictions revealed that all these compounds had minimal toxic effects and had good absorption as well as solubility characteristics. Simulation studies further showed that the imidazole ring-based TUDCA derivatives interacted better with the protein in comparison to the others. The proposed study has identified potent chemical chaperones (C2 and C3) as effective therapeutic agents for Parkinson's disease, and further in vitro and in vivo testing will be undertaken to substantiate their potential as novel drugs.

A systematic pipeline of protein structure selection for computer-aided drug discovery: A case study on T790M/L858R mutant EGFR structures.

Virtual screening (VS) is a routine method to evaluate chemical libraries for lead identification. Therefore, the selection of appropriate protein structures for VS is an essential prerequisite to identify true actives during docking. But the presence of several crystal structures of the same protein makes it difficult to select one or few structures rationally for screening. Therefore, a computational prioritization protocol has been developed for shortlisting crystal structures that identify true active molecules with better efficiency. As identification of small-molecule inhibitors is an important clinical requirement for the T790M/L858R (TMLR) EGFR mutant, it has been selected as a case study. The approach involves cross-docking of 21 co-crystal ligands with all the structures of the same protein to select structures that dock non-native ligands with lower RMSD. The cross docking performance was then correlated with ligand similarity and binding-site conformational similarity. Eventually, structures were shortlisted by integrating cross-docking performance, and ligand and binding-site similarity. Thereafter, binding pose metadynamics was employed to identify structures having stable co-crystal ligands in their respective binding pockets. Finally, different enrichment metrics like BEDROC, RIE, AUAC, and EF1% were evaluated leading to the identification of five TMLR structures (5HCX, 5CAN, 5CAP, 5CAS, and 5CAO). These structures docked a number of non-native ligands with low RMSD, contain structurally dissimilar ligands, have conformationally dissimilar binding sites, harbor stable co-crystal ligands, and also identify true actives early. The present approach can be implemented for shortlisting protein targets of any other important therapeutic kinases.

Meta-Analysis of 49 SNPs Covering 25,446 Cases and 41,106 Controls Identifies Polymorphisms in Hormone Regulation and DNA Repair Genes Associated with Increased Endometrial Cancer Risk.

Endometrial cancer (EC) is among the most common gynecological disorders globally. As single nucleotide polymorphisms (SNPs) play an important role in the causation of EC, therefore, a comprehensive meta-analysis of 49 SNPs covering 25,446 cases and 41,106 controls was performed to identify SNPs significantly associated with increased EC risk. PubMed was searched to identify case control studies and meta-analysis was performed to compute the pooled odds ratio (OR) at 95% confidence interval (CI). Cochran's Q-test and I2 were used to study heterogeneity, based on which either a random or a fixed effect model was implemented. The meta-analysis identified 11 SNPs (from 10 genes) to be significantly associated with increased EC risk. Among these, seven SNPs were significant in at least three of the five genetic models, as well as three of the polymorphisms (rs1801320, rs11224561, and rs2279744) corresponding to RAD51, PGR, and MDM2 genes, which contained more than 1000 EC cases each and exhibited increased risk. The current meta-analysis indicates that polymorphisms associated with various hormone related genes-SULT1A1 (rs1042028), PGR (rs11224561), and CYP19A1 (rs10046 and rs4775936); DNA repair genes-ERCC2 (rs1799793), OGG1 (rs1052133), MLH1 (rs1800734), and RAD51 (rs1801320) as well as genes like MDM2 (rs2279744), CCND1 (rs9344), and SERPINE1 (rs1799889), are significantly associated with increased EC risk.

Prioritization and Meta-analysis of regulatory SNPs identified IL6, TGFB1, TLR9 and MMP7 as significantly associated with cervical cancer.

Cervical cancer is a leading women cancer globally with respect to both incidence and mortality. Its increased risk has been linked with HPV infection and genetic variations like single nucleotide polymorphisms (SNPs). Although, studies have been published which evaluates the effect of SNPs in a few candidate genes, however the role of number of regulatory SNPs (rSNPs) in cervical cancer is not available. As literature evidence has shown that non-coding rSNPs are related with increasing cervical cancer risk, we undertook this study to prioritize the important rSNPs and elucidate their role. A search was conducted in PubMed up to December 2020, which led to the identification of 263 articles and 969 SNPs in the non-coding region. These 969 SNPs were analysed through rSNPBase and RegulomeDB, leading to identification of 105 rSNPs. Afterwards, a regulatory module was constructed using protein-protein interaction data and a hub of highly interacting 23 target genes (corresponding to 34 rSNPs) was identified using MCODE. To further understand the mechanism of action of the 34 rSNPs, their transcription factor information with respect to cervical cancer was retrieved. To evaluate the pooled effect of these prioritized polymorphisms in cervical cancer patients, a meta-analysis was performed on 10,537 cases and 11,252 controls from 30 studies corresponding to 8 rSNPs. It led to identification of polymorphisms in IL6 (rs2069837), TGFB1 (rs1800469), TLR9 (rs187084) and MMP7 (rs11568818) which are significantly (p < 0.05) associated with increased cervical cancer risk at the population level. Overall, the study demonstrates that rSNPs targeting immune and inflammatory genes (IL1B, IL6, IL10, IL18, TGFB1, CCR5, CD40, TLR9, and MMP7) are associated with cervical cancer.

Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach.

Double mutated epidermal growth factor receptor is a clinically important target for addressing drug resistance in lung cancer treatment. Therefore, discovering new inhibitors against the T790M/L858R (TMLR) resistant mutation is ongoing globally. In the present study, nearly 150 000 molecules from various natural product libraries were screened by employing different ligand and structure-based techniques. Initially, the library was filtered to identify drug-like molecules, which were subjected to a machine learning based classification model to identify molecules with a higher probability of having anti-cancer activity. Simultaneously, rules for constrained docking were derived from three-dimensional protein-ligand complexes and thereafter, constrained docking was undertaken, followed by HYDE binding affinity assessment. As a result, three molecules that resemble interactions similar to the co-crystallized complex were selected and subjected to 100 ns molecular dynamics simulation for stability analysis. The interaction analysis for the 100 ns simulation period showed that the leads exhibit the conserved hydrogen bond interaction with Gln791 and Met793 as in the co-crystal ligand. Also, the study indicated that Y-shaped molecules are preferred in the binding pocket as it enables them to occupy both pockets. The MMGBSA binding energy calculations revealed that the molecules have comparable binding energy to the native ligand. The present study has enabled the identification of a few ADMET adherent leads from natural products that exhibit the potential to inhibit the double mutated drug-resistant EGFR.

A comprehensive meta-analysis of non-coding polymorphisms associated with precancerous lesions and cervical cancer.

OBJECTIVES: To study the risk of polymorphisms present in the non-coding regions of genes related with cervical cancer. METHODS: The PubMed database was extensively searched using text-mining techniques to identify literature containing the association of single nucleotide polymorphisms and cervical cancer. Case-control studies published till June 2020 were considered for the meta-analysis if they fulfilled the selection criteria. The polymorphisms within each case-control study were checked for the presence of genotype data and then divided into groups based on the precancerous and cancerous conditions of the cervix. Odds ratio and 95% confidence intervals (CI) were used to study the effects of polymorphisms with the help of different genetic models (allele, dominant, recessive, heterozygous and homozygous). Also checked heterogeneity along with publication bias and statistical significance using the p-value. RESULTS: 120 papers covering 48 unique non-coding SNPs having 37,123 cases and 39,641 control data was considered for the meta-analysis. The genotype data was categorised into Cancer, Precancer and "Cancer + Precancer" groups, for 43, 8 and 11 SNPs respectively. The meta-analysis identified 21 and 1 SNPs as significant in the Cancer and "Cancer + Precancer" groups. Among all the polymorphisms, rs1143627 (IL1B), rs1800795 (IL6), rs1800871 (IL10), rs568408 (IL12A), rs3312227 (IL12B), rs2275913 (IL17A), rs5742909 (CTLA4), rs1800629 (TNFα), and rs4646903 (CYP1A1) were found to increase risk of cervical cancer in at least three of the five genetic models. CONCLUSION: We identified potential non-coding SNPs corresponding to various cytokines like interleukins (ILs), tumor necrosis factor (TNF), interferon (IFN) and other immune related genes like toll like receptor (TLR), cytotoxic T-lymphocyte associated protein (CTLA) and matrix metalloproteinase (MMP), as significant with increased pooled OR in this meta-analysis pointing to risk association of the immune-related genes in cervical carcinogenesis.

Synthesis of metronidazole based thiazolidinone analogs as promising antiamoebic agents.

Metronidazole and its derivatives are widely used for the treatment of amoebiasis. However, metronidazole is considered as the standard drug but it has many side effects. The present study describes the synthesis of a series of metronidazole based thiazolidinone analogs via Knoevenagel condensation of 4-[2-(2-methyl-5-nitro-1H-imidazole-1-yl)ethoxy]benzaldehyde 1 with various thiazolidinone derivatives 2-14 to get the new scaffold (15-27) having better activity and lesser toxicity. Six compounds have shown better efficacy and lesser cytotoxicity than the standard drug metronidazole towards HM1: IMSS strain of Entamoeba histolytica. These compounds may combat the problem of drug resistance and might be effective in identifying potential alternatives for future drug discovery against EhOASS.

Structure-activity Relationship Study on Therapeutically Relevant EGFR Double Mutant Inhibitors.

BACKGROUND: EGFR is a clinically approved drug target in cancer. The first generation tyrosine kinase inhibitors targeting L858R mutated EGFR are routinely used to treat non-small cell lung cancer (NSCLC). However, the presence of a secondary mutation (T790M) tenders these inhibitors ineffective and thus results in the relapse of the disease. OBJECTIVE: New reversible inhibitors are required, which act against T790M/L858R (TMLR) double mutants and overcome resistance. METHOD: In the present study, various Fragment based QSAR (G-QSAR) models along with interaction terms have been studied for amino-pyrimidine derivatives having biological activity against TMLR mutant enzyme. RESULTS: The G-QSAR models developed using partial least squares regression via stepwise forward- backward variable selection technique showed the best results. The model showed a high correlation coefficient (r² = 0.86), cross-validation coefficient (q² = 0.81) and predicted correlation (predicted r² = 0.62), which indicated that the model is robust and predictive. Based on the model, it was revealed that at R1 position increasing saturated carbon (number of -CH atom connected with 3 single bonds i.e. SsssCHcount) and retention index (chi3) is desired for the enhancement of bioactivity. Additionally, at the R2 position, increasing lipophilic character (slogp) and at site R3, the polarizability of compound need to be increased for better inhibitory activity. We further studied the contribution of interactions among significant descriptors in enhancing the activity of the compounds. It revealed that the presence of Sum((R1-SsssCHcount, R2-slogp) and Mult(R1-chi3, R3-polarizabilityAHC) are the most significantly influencing descriptors. We further compared the variation in the most and least active compounds which established that retention of the above properties is essential for imparting significant inhibitory activity to these molecules. CONCLUSION: The study provides site specific information wherein chemical group variation influences the inhibitory potency of TMLR amino-pyrimidine inhibitors, which can be used for designing new molecules with the desired activity.

ADMET profiling of geographically diverse phytochemical using chemoinformatic tools.

Aim: Phytocompounds are important due to their uniqueness, however, only few reach the development phase due to their poor pharmacokinetics. Therefore, preassessing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties is essential in drug discovery. Methodology: Biologically diverse databases (Phytochemica, SerpentinaDB, SANCDB and NuBBEDB) covering the region of India, Brazil and South Africa were considered to predict the ADMET using chemoinformatic tools (Qikprop, pkCSM and DataWarrior). Results: Screening through each of pharmacokinetic criteria resulted in identification of 24 compounds that adhere to all the ADMET properties. Furthermore, assessment revealed that five have potent anticancer biological activity against cancer cell lines. Conclusion: We have established an open-access database (ADMET-BIS) to enable identification of promising molecules that follow ADMET properties and can be considered for drug development.

Control of apoptosis by SMAR1.

The nuclear matrix associated protein SMAR1 is sensitive to p53 and acts as a stress inducer as well as a regulator in the p53 regulatory network. Depending on the amount of stress SMAR1 stimulates, it can drive the p53 dynamics in the system to various dynamical states which correspond to various cellular states. The behavior of p53 in these dynamical states is found to be multifractal, due to the mostly long range correlations and large scale fluctuations imparted by stress. This fractal behavior is exhibited in the topological properties of the networks constructed from these dynamical states, and is a signature of self-organization to optimize information flow in the dynamics. The assortativity found in these networks is due to perturbation induced by stress, and indicates that the hubs in the time series play a significant role in stress management. SMAR1 can also regulate apoptosis in the presence of HDAC1, depending on the stress induced by it.

Metronidazole hydrazone conjugates: Design, synthesis, antiamoebic and molecular docking studies.

Metronidazole hydrazone conjugates (2-13) were synthesized and screened in vitro for antiamoebic activity against HM1: IMSS strain of Entamoeba histolytica. Six compounds were found to be better inhibitors of E. histolytica than the reference drug metronidazole. These compounds showed greater than 50-60% viability against HeLa cervical cancer cell line after 72 h treatment. Also, molecular docking study was undertaken on E. histolytica thioredoxin reductase (EhTHRase) protein which showed significant binding affinity in the active site. Out of the six actives, some of the compounds showed lipophilic characteristics.

QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest.

BACKGROUND: Epidermal Growth Factor Receptor (EGFR) is a well-characterized cancer drug target. In the past, several QSAR models have been developed for predicting inhibition activity of molecules against EGFR. These models are useful to a limited set of molecules for a particular class like quinazoline-derivatives. In this study, an attempt has been made to develop prediction models on a large set of molecules (~3500 molecules) that include diverse scaffolds like quinazoline, pyrimidine, quinoline and indole. RESULTS: We train, test and validate our classification models on a dataset called EGFR10 that contains 508 inhibitors (having inhibition activity IC50 less than 10 nM) and 2997 non-inhibitors. Our Random forest based model achieved maximum MCC 0.49 with accuracy 83.7% on a validation set using 881 PubChem fingerprints. In this study, frequency-based feature selection technique has been used to identify best fingerprints. It was observed that PubChem fingerprints FP380 (C(~O) (~O)), FP579 (O = C-C-C-C), FP388 (C(:C) (:N) (:N)) and FP 816 (ClC1CC(Br)CCC1) are more frequent in the inhibitors in comparison to non-inhibitors. In addition, we created different datasets namely EGFR100 containing inhibitors having IC50 < 100 nM and EGFR1000 containing inhibitors having IC50 < 1000 nM. We trained, test and validate our models on datasets EGFR100 and EGFR1000 datasets and achieved and maximum MCC 0.58 and 0.71 respectively. In addition, models were developed for predicting quinazoline and pyrimidine based EGFR inhibitors. CONCLUSIONS: In summary, models have been developed on a large set of molecules of various classes for discriminating EGFR inhibitors and non-inhibitors. These highly accurate prediction models can be used to design and discover novel EGFR inhibitors. In order to provide service to the scientific community, a web server/standalone EGFRpred also has been developed ( http://crdd.osdd.net/oscadd/egfrpred/ ).

Identification of critical microRNA gene targets in cervical cancer using network properties.

miRNAs are short non-coding RNAs which function as oncogenes or tumour suppressor gene and regulate gene expression by controlling targets that play role in cancer development and progression. Numerous recent studies have established an association of abnormal expression of miRNA with cervical cancer progression. Although the number of reported deregulated miRNA in cervical cancer is increasing, only a few associations between miRNA and their targets have been studied in cervical cancer. Therefore, we performed a systematic analysis of known dysregulated miRNAs involved in cervical cancer so as to identify critical miRNA targets that could pave way for therapeutic solutions. In this study, miRNAs reported to be dysregulated in cervical cancer were collected and their targets predicted using TargetScan, PicTar and miRanda. These targets were subsequently compared with previously curated gene dataset involved in cervical cancer to derive the putative target dataset. We then compared network properties (composed of degree, betweenness centrality, closeness centrality and clustering coefficient) of the putative, validated and human protein-protein interaction network. Based on the topological properties genes were ranked and observed that the gene targets BIRC5 (survivin), HOXA1 and RARB presenting with high Novoseek score of Genecards were enriched in cervical cancer. BIRC5 is an anti- apoptotic protein while HOXA1 and RARB are transcription factors which play critical role in altering the level of cell cycle and apoptosis associated proteins. Also, miRNA-mRNA network was constructed and it was found that miR-203 and miR-30b could target these genes. The analysis indicates that the genes BIRC5, HOXA1 and RARB are critical targets that play an important regulatory role in cervical cancer pathogenesis.

QSAR-based models for designing quinazoline/imidazothiazoles/pyrazolopyrimidines based inhibitors against wild and mutant EGFR.

Overexpression of EGFR is responsible for causing a number of cancers, including lung cancer as it activates various downstream signaling pathways. Thus, it is important to control EGFR function in order to treat the cancer patients. It is well established that inhibiting ATP binding within the EGFR kinase domain regulates its function. The existing quinazoline derivative based drugs used for treating lung cancer that inhibits the wild type of EGFR. In this study, we have made a systematic attempt to develop QSAR models for designing quinazoline derivatives that could inhibit wild EGFR and imidazothiazoles/pyrazolopyrimidines derivatives against mutant EGFR. In this study, three types of prediction methods have been developed to design inhibitors against EGFR (wild, mutant and both). First, we developed models for predicting inhibitors against wild type EGFR by training and testing on dataset containing 128 quinazoline based inhibitors. This dataset was divided into two subsets called wild_train and wild_valid containing 103 and 25 inhibitors respectively. The models were trained and tested on wild_train dataset while performance was evaluated on the wild_valid called validation dataset. We achieved a maximum correlation between predicted and experimentally determined inhibition (IC50) of 0.90 on validation dataset. Secondly, we developed models for predicting inhibitors against mutant EGFR (L858R) on mutant_train, and mutant_valid dataset and achieved a maximum correlation between 0.834 to 0.850 on these datasets. Finally, an integrated hybrid model has been developed on a dataset containing wild and mutant inhibitors and got maximum correlation between 0.761 to 0.850 on different datasets. In order to promote open source drug discovery, we developed a webserver for designing inhibitors against wild and mutant EGFR along with providing standalone (http://osddlinux.osdd.net/) and Galaxy (http://osddlinux.osdd.net:8001) version of software. We hope our webserver (http://crdd.osdd.net/oscadd/ntegfr/) will play a vital role in designing new anticancer drugs.

EGFRIndb: epidermal growth factor receptor inhibitor database.

BACKGROUND: Aberrant activity of epidermal growth factor receptor (EGFR) family proteins has been found to be associated with a number of human cancers including that of lung and breast. Consequently, the search for EGFR family inhibitors, a well established target of pharmacological and therapeutic value has been ongoing. Therefore, over the years several small molecules, which compete for ATP in the kinase domain have been synthesised and some of them have proved to be effective in attenuating EGFR mediated proliferation. Thus, there exists in literature a vast amount of experimental data on EGFR tyrosine kinase inhibitors. In this paper, we describe a comprehensive database EGFRIndb that contains details of the small molecular inhibitors of EGFR family. DESCRIPTION: EGFRIndb is a literature curated database of small synthetic molecular inhibitors of EGFR. It consists of 4581 compounds showing in vitro inhibitory activities (IC50, IC80, GI50, GI90, EC50, Ki, Kd and percentage inhibition) either against EGFR or its different isoforms i.e. Erbb2 (v-erb-b2 avian erythroblastic leukaemia viral oncogene homolog 2) and Erbb4 (v-erb-b2 avian erythroblastic leukaemia viral oncogene homolog 4) or various mutants. For each compound, database provides information on structure, experimentally determined inhibitory activity of compound against kinase as well as various cell lines, properties (physical, elemental and topological) and drug likeness. Additionally, it provides information on irreversible as well as dual inhibitors that have gained importance in recent years due to the emergence of clinical resistance to known drugs. As compound activity against similar kinases is a measure of its selectivity and specificity, the database also provides this information. It also provides simple search, advanced search, browse facility as well as a tool for structure based searching. CONCLUSION: EGFRIndb gathers biological and chemical information on EGFR inhibitors from the literature. It is hoped that it will serve as a useful resource in drug discovery and provide data for docking, virtual screening and Quantitative structure-activity relationship (QSAR) model development to the cancer researchers.

Synthesis and evaluation of 7-chloro-4-(piperazin-1-yl)quinoline-sulfonamide as hybrid antiprotozoal agents.

A new series of 4-aminochloroquinoline based sulfonamides were synthesized and evaluated for antiamoebic and antimalarial activities. Out of the eleven compounds evaluated (F1-F11), two of them (F3 and F10) showed good activity against Entamoeba histolytica (IC50 <5 µM). Three of the compounds (F5, F7 and F8) also displayed antimalarial activity against the chloroquine-resistant (FCR-3) strain of Plasmodium falciparum with IC50 values of 2 µM. Compound F7, whose crystal structure was also determined, inhibited ß-haematin formation more potently than quinine. To further understand the action of hybrid molecules F7 and F8, molecular docking was carried out against the homology model of P. falciparum enzyme dihydropteroate synthase (PfDHPS). The complexes showed that the inhibitors place themselves nicely into the active site of the enzyme and exhibit interaction energy which is in accordance with our activity profile data. Application of Lipinski 'rule of five' on all the compounds (F1-F11) suggested high drug likeness of F7 and F8, similar to quinine.

The dynamics of stress p53-Mdm2 network regulated by p300 and HDAC1.

We construct a stress p53-Mdm2-p300-HDAC1 regulatory network that is activated and stabilised by two regulatory proteins, p300 and HDAC1. Different activation levels of [Formula: see text] observed due to these regulators during stress condition have been investigated using a deterministic as well as a stochastic approach to understand how the cell responds during stress conditions. We found that these regulators help in adjusting p53 to different conditions as identified by various oscillatory states, namely fixed point oscillations, damped oscillations and sustain oscillations. On assessing the impact of p300 on p53-Mdm2 network we identified three states: first stabilised or normal condition where the impact of p300 is negligible, second an interim region where p53 is activated due to interaction between p53 and p300, and finally the third regime where excess of p300 leads to cell stress condition. Similarly evaluation of HDAC1 on our model led to identification of the above three distinct states. Also we observe that noise in stochastic cellular system helps to reach each oscillatory state quicker than those in deterministic case. The constructed model validated different experimental findings qualitatively.

Switching p53 states by calcium: dynamics and interaction of stress systems.

The integration of calcium and a p53-Mdm2 oscillator model is studied using a deterministic as well as a stochastic approach, to investigate the impact of a calcium wave on single cell dynamics and on the inter-oscillator interaction. The high dose of calcium in the system activates the nitric oxide synthase, synthesizing nitric oxide which then downregulates Mdm2 and influences drastically the p53-Mdm2 network regulation, lifting the system from a normal to a stressed state. The increase in calcium level switches the system to different states, as identified by the different behaviours of the p53 temporal dynamics, i.e. oscillation death to sustain the oscillation state via a mixed state of dampened and oscillation death states. Further increase of the calcium dose in the system switches the system from sustained to oscillation death state again, while an excess of calcium shifts the cell to an apoptotic state. Another important property of the calcium ion is its ability to behave as a synchronizing agent among the interacting systems. The time evolution of the p53 dynamics of the two diffusively coupled systems at stress condition via Ca(2+) shows synchronization between the two systems. The noise contained in the system interestingly helps the system to maintain its stabilized state (normal condition). However, noise has the tendency to destruct the synchronization effect, which means that it tries to restrict the system from external signals to maintain its normal condition. However, at the stress condition, the synchronization rate is found to be faster.

NPACT: Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database.

Plant-derived molecules have been highly valued by biomedical researchers and pharmaceutical companies for developing drugs, as they are thought to be optimized during evolution. Therefore, we have collected and compiled a central resource Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database (NPACT, http://crdd.osdd.net/raghava/npact/) that gathers the information related to experimentally validated plant-derived natural compounds exhibiting anti-cancerous activity (in vitro and in vivo), to complement the other databases. It currently contains 1574 compound entries, and each record provides information on their structure, manually curated published data on in vitro and in vivo experiments along with reference for users referral, inhibitory values (IC(50)/ED(50)/EC(50)/GI(50)), properties (physical, elemental and topological), cancer types, cell lines, protein targets, commercial suppliers and drug likeness of compounds. NPACT can easily be browsed or queried using various options, and an online similarity tool has also been made available. Further, to facilitate retrieval of existing data, each record is hyperlinked to similar databases like SuperNatural, Herbal Ingredients' Targets, Comparative Toxicogenomics Database, PubChem and NCI-60 GI(50) data.