Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches.

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules. Reviewed the niche area of phytomolecule-based anti-cancer drug discovery with respect to current trends including machine learning.

EGFRisopred: a machine learning-based classification model for identifying isoform-specific inhibitors against EGFR and HER2.

The EGFR kinase pathway is one of the most frequently activated signaling pathways in human cancers. EGFR and HER2 are the two significant members of this pathway, which are attractive drug targets of clinical relevance in lung and breast cancer. Therefore, identifying EGFR- and HER2-specific inhibitors is one of the important challenges in cancer drug discovery. To address this issue, a dataset of 519 compounds having inhibitory activity against both the isoforms, i.e., EGFR and HER2, was collected from the literature and developed a knowledge-based computational classification model for predicting the specificity of a molecule for an isoform (EGFR/HER2) with precision. A total of seventy-two classification models using nine fingerprint types, four classifiers (IBK, NB, SMO and RF) and two different datasets (EGFR and HER2 isoform specific) were developed. It was observed that the models developed using random forest and IBK performed better for EGFR- and HER2-specific datasets, respectively. Scaffold and functional group analysis led to the identification of prevalent core and fragments in each of the datasets. The accuracy of the selected best performing models was also evaluated using the decoy dataset. We have also developed an application EGFRisopred, which integrates the best performing models and permits the user to predict the specificity of a compound as an EGFR-/HER2-specific anticancer agent. It is expected that the tool's availability as a free utility will allow researchers to identify new inhibitors against these targets important in cancer.

Exploring structural features of EGFR-HER2 dual inhibitors as anti-cancer agents using G-QSAR approach.

Simultaneous inhibition of EGFR and HER2 by dual-targeting inhibitors is an established anti-cancer strategy. Therefore, a recent trend in drug discovery involves understanding the features of such dual inhibitors. In this study, three different G-QSAR models were developed corresponding to individual EGFR, HER2 and the dual-model for both receptors. The dual-model provided site-specific information wherein (i) increasing electronegative character and higher index of saturated carbon at R4 position; (ii) presence of chlorine atom at R2 position; (iii) decreasing alpha modified shape index at R1 and R3 positions; and (iv) less electronegativity at R2 position; were found important for enhancing the dual activity. Also, comparison of dual-model with the EGFR/HER2 individual models revealed that it incorporates the properties of both models and, thus, represents a combination of EGFR/HER2. Further, fragment analysis revealed that R2 and R4 are important for imparting high potency while specificity is decided by R1/R3 fragment. We also checked the predictive ability of the dual-model by determining applicability domain using William's plot. Also, analysis of active molecules showed they show favorable substitutions that agree with the constructed dual-model. Thus, we have been successful in developing a single dual-response QSAR model to get an insight into various structural features influencing EGFR/HER2 activity.

Unraveling structural requirements of amino-pyrimidine T790M/L858R double mutant EGFR inhibitors: 2D and 3D QSAR study.

EGFR is an important drug target in cancer. However, the ineffectiveness of first generation inhibitors due to the occurrence of a secondary mutation (T790M) results in the relapse of the disease. Identification of reversible inhibitors against T790M/L858R double mutants (TMLR) thus is a foremost requirement. In this study, various 2 D and 3 D Quantitative Structure-Activity Relationship models were built for amino-pyrimidine compounds with their known biological activity against TMLR mutants. The model developed using multiple linear regression statistical method via stepwise forward-backward variable selection technique showed the best results in terms of internal and external predictivity. The 2D-QSAR model indicated that the presence of electronegative atom, H-bond donors, moderate slogp, count of number of N atoms separated from O (T_N_O_4), 4pathClusterCount and number of S atom connected with two single bonds (SssSE-index), is required for increasing the inhibitory potential of compounds. Also, the 3D-QSAR model suggested that electronegative group at certain positions along with the presence of bulky groups is beneficial for good inhibition activity of the compounds. Thus, the QSAR models developed in the present work can be used for predicting the TMLR bioactivity of a new series of amino-pyrimidine derivatives. To the best of the author's knowledge, this is the first study which deals with the development of 2 D and 3D-QSAR models for double mutant TMLR inhibitors.

Pharmacokinetic profiling of anticancer phytocompounds using computational approach.

INTRODUCTION: Natural products exhibit diverse scaffolds and are considered as suitable candidates for development of leads. However, poor pharmacokinetics often acts as a hindrance during the drug discovery process. OBJECTIVE: With a view of exploring the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of plant-based anticancer compounds, open-access databases (NPACT, CancerHSP and TaxKB) were analysed to identify molecules having properties favourable for drug ability. METHODOLOGY: Our workflow involved identification of molecules capable of passing each of the ADMET barriers based on physicochemical properties of molecules, and physiological barriers and factors. RESULTS: The results revealed that out of 5086 phytomolecules, 63% were orally absorbable and 52% distributable. Also, an appreciable proportion of these compounds (45%) could be metabolised and excreted. Furthermore, 28% were found to be non-toxic for cardio toxicity and central nervous system (CNS) activity. Additionally, comparison against known anticancer drugs (reference dataset) revealed that the three libraries exhibit similar trends, thus providing additional confidence to the predictions. Overall, 28% of the molecular dataset was found to have suitable pharmacokinetic properties. We have also discussed a few natural products which exhibit favourable ADMET as well as low nano-micromolar in vitro anticancer activity. CONCLUSION: We have created an interactive database (ADMETCan), which provides access to predicted ADMET of these anticancer phytomolecules. The ease of availability of this dataset is expected to minimise failure rate of these compounds and thus is expected to be beneficial to the scientific community involved in anticancer identification and development.

A Comprehensive Review of Dysregulated miRNAs Involved in Cervical Cancer.

MicroRNAs(miRNAs) have become the center of interest in oncology. In recent years, various studies have demonstrated that miRNAs regulate gene expression by influencing important regulatory genes and thus are responsible for causing cervical cancer. Cervical cancer being the third most diagnosed cancer among the females worldwide, is the fourth leading cause of cancer related mortality. Prophylactic human papillomavirus (HPV) vaccines and new HPV screening tests, combined with traditional Pap test screening have greatly reduced cervical cancer. Yet, thousands of women continue to be diagnosed with and die of this preventable disease annually. This has necessitated the scientists to ponder over ways of evolving new methods and chalk out novel treatment protocols/strategies. As miRNA deregulation plays a key role in malignant transformation of cervical cancer along with its targets that can be exploited for both prognostic and therapeutic strategies, we have collected and reviewed the role of miRNA in cervical cancer. A systematic search was performed using PubMed for articles that report aberrant expression of miRNA in cervical cancer. The present review provides comprehensive information for 246 differentially expressed miRNAs gathered from 51 published articles that have been implicated in cervical cancer progression. Of these, more than 40 miRNAs have been reported in the literature in several instances signifying their role in the regulation of cancer. We also identified 40 experimentally validated targets, studied the cause of miRNAs dysregulation along with its mechanism and role in different stages of cervical cancer. We also identified and analysed miRNA clusters and their expression pattern in cervical cancer. This review is expected to further enhance our understanding in this field and serve as a valuable reference resource.

Identification and characterization of the in-vivo metabolites of the novel soluble epoxide hydrolase inhibitor EC5026 using liquid chromatography quadrupole time of flight mass spectrometry.

EC5026 is a novel soluble epoxide hydrolase inhibitor being developed clinically to treat neuropathic pain and inflammation. In the current study, we employed the LC-ESI-Q-TOF-MS/MS technique to identify four in-vivo phase-I metabolites of EC5026 in rat model, out of which three were found to be novel. The identified metabolites include aliphatic hydroxylation, di-hydroxylation, terminal desaturation, and carboxylation. No phase-II metabolites were found. The pharmacokinetic profile of identified metabolites was established after a single oral dose of EC5026 to Wistar rats. The Tmax of the drug and metabolites were found to be in the range of 1-2 hours and 4-12 hours, respectively. The major metabolites M1 and M2 were found to have more than 2-fold (263.87% AUC) and equivalent exposure (96.33% AUC) compared to the parent drug, respectively. Further, the docking study revealed that the mono-hydroxylated and terminally desaturated metabolites possess better binding affinity than the parent drug. Therefore, these metabolites may hold sEH inhibition potential and can be followed through future research.

Elucidation of Increased Cervical Cancer Risk Due to Polymorphisms in XRCC1 (R399Q and R194W), ERCC5 (D1104H), and NQO1 (P187S).

Genetic variations like single nucleotide polymorphisms (SNPs) are associated with cervical carcinogenesis. In this study, SNPs have been identified that contribute toward changes in the function and stability of the proteins and show association with cervical cancer. Initially, literature mining identified 114 protein-coding polymorphisms with population-based evidence in cervical cancer. Subsequently, the functional assessment was performed using sequence-dependent tools, and thereafter, protein stability was analyzed using sequence and structural data. Twenty-three non-synonymous SNPs (nsSNPs) found to be damaging and destabilizing were then analyzed to check their risk association at the population level. The meta-analysis indicated that polymorphisms in DNA damage repair genes XRCC1 (rs25487 and rs1799782), ERCC5 (rs17655), and oxidative stress-related gene NQO1 (rs1800566) are significantly associated with increased cervical cancer risk. The XRCC1 rs25487 and rs1799782 polymorphisms showed the highest risk of cervical cancer in the homozygous model having odds ratio (OR) = 1.85, 95% confidence interval (CI) = 1.17-2.92, p = 0.01, and recessive model with OR = 1.81, 95% CI = 1.01-3.24, and p = 0.04 respectively. Similarly, rs17655 polymorphism of ERCC5 and rs1800566 polymorphism of NQO1 showed the highest pooled OR in the homozygous (OR = 1.70, 95% CI = 1.32-2.19, p = 0.00004) and heterozygous model (OR = 1.3, 95% CI = 1.06-1.58, p = 0.01) respectively. Thus, in this study, a comprehensive collection of nsSNPs was collated and assessed, leading to the identification of polymorphisms in DNA damage repair and oxidative stress-related genes, that destabilize the protein and shows increased risk associated with cervical cancer.

In-Silico molecular screening of natural compounds as a potential therapeutic inhibitor for Methicillin-resistant Staphylococcus aureus inhibition.

Methicillin-resistant Staphylococcus aureus (MRSA) is a life-threatening superbug causing infectious diseases such as pneumonia, endocarditis, osteomyelitis, etc. Conventional antibiotics are ineffective against MRSA infections due to their resistance mechanism against the antibiotics. The Penicillin Binding Protein (PBP2a) inhibits the activity of antibiotics by hydrolyzing the ß-lactam ring. Thus, alternate treatment methods are needed for the treatment of MRSA infections. Natural bioactive compounds exhibit good inhibition efficiency against MRSA infections by hindering its enzymatic mechanism, efflux pump system, etc. The present work deals with identifying potential and non-toxic natural bioactive compounds (ligands) through molecular docking studies through StarDrop software. Various natural bioactive compounds which are effective against MRSA infections were docked with the protein (6VVA). The ligands having good binding energy values and pharmacokinetic and drug-likeness properties have been illustrated as potential ligands for treating MRSA infections. From this exploration, Luteolin, Kaempferol, Chlorogenic acid, Sinigrin, Zingiberene, 1-Methyl-4-(6-methylhepta-1,5-dien-2-yl)cyclohex-1-ene, and Curcumin have found with good binding energies of -8.6 kcal/mol, -8.4 kcal/mol, -8.2 kcal/mol, -7.5 kcal/mol, -7.4 kcal/mol, -7.3 kcal/mol, and -7.2 kcal/mol, respectively.

A pharmacokinetic study to correlate the hypoglycemic effect of phlorizin in rats: Identification of metabolites as inhibitors of sodium/glucose cotransporters.

Phlorizin (PRZ) is a natural product that belongs to a class of dihydrochalcones. The unique pharmacological property of PRZ is to block glucose absorption or reabsorption through specific and competitive inhibitors of the sodium/glucose cotransporters (SGLTs) in the intestine (SGLT1) and kidney (SGLT2). This results in glycosuria by inhibiting renal reabsorption of glucose and can be used as an adjuvant treatment for type 2 diabetes. The pharmacokinetic profile, metabolites of the PRZ, and efficacy of metabolites towards SGLTs are unknown. Therefore, the present study on the characterization of hitherto unknown in vivo metabolites of PRZ and pharmacokinetic profiling using liquid chromatography-electrospray ionization tandem mass spectrometry (LC/ESI/MS/MS) and accurate mass measurements is undertaken. Plasma, urine, and feces samples were collected after oral administration of PRZ to Sprague-Dawley rats to identify in vivo metabolites. Furthermore, in silico efficacy of the identified metabolites was evaluated by docking study. PRZ at an intraperitoneal dose of 400 mg/kg showed maximum concentration in the blood to 439.32 ± 8.84 ng/mL at 1 h, while phloretin showed 14.38 ± 0.33 ng/mL at 6 h. The pharmacokinetic profile of PRZ showed that the maximum concentration lies between 1 and 2 h after dosing. Decreased blood glucose levels and maximum excretion of glucose in the urine were observed when the PRZ and metabolites were observed in plasma. The identification and characterization of PRZ metabolites by LC/ESI/MS/MS further revealed that the phase I metabolites of PRZ are hydroxy (mono-, di-, and tri-) and reduction. Phase II metabolites are O-methylated, O-acetylated, O-sulfated, and glucuronide metabolites of PRZ. Further docking study revealed that the metabolites diglucuronide metabolite of mono-hydroxylated PRZ and mono-glucuronidation of PRZ could be considered novel inhibitors of SGLT1 and SGLT2, respectively, which show better binding affinities than their parent compound PRZ and the known inhibitors.

Genome sequencing of SARS-CoV-2 omicron variants in Delhi reveals alterations in immunogenic regions in spike glycoprotein.

The SARS-CoV-2 omicron variants keep accumulating a large number of mutations in the spike (S) protein, which contributes to greater transmissibility and a rapid rise to dominance within populations. The identification of mutations and their affinity to the cellular angiotensin-converting enzyme-2 (ACE-2) receptor and immune evasion in the Delhi NCR region was under-acknowledged. The study identifies some mutations (Y505 reversion, G339H, and R346T/N) in genomes from Delhi, India, and their probable implications for altering the immune response and binding affinity for ACE-2. The spike mutations have influenced the neutralizing activity of antibodies against the omicron variant, which shows partial immune escape. However, researchers are currently exploring various mitigation strategies to tackle the potential decline in efficacy or effectiveness against existing and future variants of SARS-CoV-2. These strategies include modifying vaccines to target specific variants, such as the omicron variant, developing multivalent vaccine formulations, and exploring alternative delivery methods. To address this, it is also necessary to understand the impact of these mutations from a different perspective, especially in terms of alterations in antigenic determinants. In this study, we have done whole genome sequencing (WGS) of SARS-CoV-2 in COVID-19 samples from Delhi, NCR, and analyzed the spike's mutation with an emphasis on antigenic alterations. The impact of mutation in terms of epitope formation, loss/gain of efficiency, and interaction of epitopes with antibodies has been studied. Some of the mutations or variant genomes seem to be the progenitors of the upcoming variants in India. Our analyses suggested that weakening interactions with antibodies may lead to immune resistance in the circulating genomes.

TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors.

The EGFR is a clinically important therapeutic drug target in lung cancer. The first-generation tyrosine kinase inhibitors used in clinics are effective against L858R-mutated EGFR. However, relapse of the disease due to the presence of resistant mutation (T790M) makes these inhibitors ineffective. This has necessitated the need to identify new potent EGFR inhibitors against the resistant double mutants. Therefore, various machine learning techniques ((instance-based learner (IBK), naïve Bayesian (NB), sequential minimal optimization (SMO), and random forest (RF)) were employed to develop twelve classification models on three different datasets (high, moderate, and weakly active inhibitors). The models were validated using fivefold cross-validation and independent validation datasets. It was observed that the random forest-based models showed best performance. Also, functional groups, PubChem fingerprints, and substructure of highly active inhibitors were compared to inactive to identify structural features which are important for activity. To promote open-source drug discovery, a tool has been developed, which incorporates the best performing models and allows users to predict the potential of chemical molecules as anti-TMLR inhibitor. It is expected that the machine learning classification models developed in this study will pave way for identifying novel inhibitors against the resistant EGFR double mutants.

QSAR of clinically important EGFR mutant L858R/T790M pyridinylimidazole inhibitors.

EGFR is a well-established therapeutic target of clinical relevance in cancer. However, acquisition of secondary mutation (T790M) makes first-generation inhibitors ineffective. Therefore, to circumvent the problem of resistance, new T790M/L858R (TMLR) double mutant inhibitors are required. In this study, fragment-based QSAR models (GQSAR) were generated for pyridinylimidazole derivatives having biological activity against TMLR mutants. The GQSAR model developed using partial least squares regression via stepwise forward-backward variable selection technique showed best results as judged using statistical parameters (r2 , q2 , and pred_r2 ). Additionally, applicability domain of the model was verified using Williams plot, which indicated that the predicted data are reliable. The GQSAR provided site-specific clues wherein modifications related to decreasing lipophilic character and rotatable bonds and increasing SaaCHE-index are required for improving inhibitory activity. Overall, the study indicated that the presence of acrylamide at R5 is essential for covalent bond formation with Cys797 and occurrence of aromatic residue at R2 is required for occupying hydrophobic region next to Met790 gatekeeper residue. Based on this information, new derivatives were designed that show better inhibitory activity than the experimentally reported most active molecules. Thus, the model developed can be used to design new pyridinylimidazole derivatives with improved TMLR bioactivity.

Insight into structural requirements of antiamoebic flavonoids: 3D-QSAR and G-QSAR studies.

Plant-based flavonoids have been found to exhibit strong inhibitory capability against Entamoeba histolytica. So, various QSAR models have been developed to identify the critical features that are responsible for the potency of these molecules. 3D-QSAR analysis using k-nearest neighbour molecular field analysis via stepwise forward-backward variable selection method showed best results for both internal and external predictive ability of the model (i.e., q2  = 0.64 and pred_r2  = 0.56). Also, a group-based QSAR (G-QSAR) model was developed based on partial least squares regression combined with stepwise forward-backward variable selection method. It gave best parametric results (r2  = 0.74, q2  = 0.56 and pred_r2  = 0.54) which implied that the model is highly predictive. 3D-QSAR established that presence/absence of bulk near rings B and C is important in deciding the inhibitory potential of these molecules. Additionally, G-QSAR provided site-specific clue wherein modifications related to molecular weight, electronegativity and separation of an oxygen atom in rings A and C can result in enhanced biological activity. To the best of the author's knowledge, this is the first QSAR study of antiamoebic flavonoids, and therefore, we expect the results to be useful in the design of more potent antiamoebic inhibitors.

Insight into Discovery of Next Generation Reversible TMLR Inhibitors Targeting EGFR Activating and Drug Resistant T790M Mutants.

BACKGROUND: Epidermal growth factor receptor (EGFR) is a well-recognised drug target exploited for treating non-small cell lung cancer (NSCLC). Gefitinib and erlotinib are first generation clinically employed inhibitors used against EGFR activating mutants. However, during course of treatment these inhibitors become ineffective due to the emergence of an acquired secondary mutation. Subsequently, in order to overcome non-responsiveness second and third generation inhibitors were designed having covalent bond and irreversible mode of action. However, these inhibitors were shown to be toxic. This led to the discovery of lead candidates with completely different mode of action and therapeutic efficacy. OBJECTIVE: We have reviewed the recent efforts undertaken by researchers in discovering newer noncovalent reversible next generation inhibitors for treating NSCLC. METHODS: We first studied the optimization steps and pharmacokinetic variables of the synthesised molecules. We also analysed bonds and interactions using PDB X-ray crystal structures as well as scaffold and selectivity analysis was undertaken. RESULTS: We identified that ligand lipophilic efficiency driven potency is a preferable optimisation parameter for maintaining drug likeliness of the molecule. Also, few h-bonds were recognised as major players in affecting the binding of compound. The scaffold analysis revealed that ligand molecules with pyrimidine core exhibit higher inhibitory activity against TMLR, as well as higher selectivity with respect to other kinases. CONCLUSION: Next generation reversible inhibitors exhibited unique binding mode and were found to occupy three major pockets (ribose pocket, back pocket and hinge region), which is critical for increasing the selectivity of the compound against TMLR mutants.

Acetogenins as Potential Anticancer Agents.

Acetogenins (ACG) are naturally occurring compounds that are chemically one of the least investigated families. In the review, we have provided a comprehensive listing of 133 of these compounds for which anti-tumor activity has been documented within the literature. We have compiled and studied their chemical structure, in-vitro as well as in-vivo anticancer biological activity. We observed that the relative potency of acetogenins can be categorized as adjacent bis-THF ACGs > nonadjacent bis-THF ACGs > mono-THF ACGs > linear-THF ACGs. Among adjacent bis-THF ACGs, asiminocin (A100), asiminecin (A101), asiminacin (A102) and asimin (A103) are the most active compounds with in-vitro activity (ED50) in the range of 10(-9) to 10(-12) µg/mL. For the nonadjacent bis-THF ACGs, gigantecin (A53) exhibited better cytotoxicity as compared to others in the series with an ED50 in the range of 10(-6) to 10(-8) µg/mL. Similarly, muricatetrocin-C (A36), a mono-THF and coriadienin (A116) a linear ACG has been reported to show promising cytotoxicity with an ED50 of 10(-5) µg/mL. Moreover, in-vivo studies indicate that compounds like bullatacin (A83), desacetyluvaricin (A76), bullatalicin (A58) and annonacin (A8) have demonstrated significant activity in mouse models and thereby exhibiting potential for lead development as a potential anticancer agent/drug. Also, globally oncologists are looking towards compounds from natural origin that inhibits the growth of resistant tumor cells. We find that several acetogenins like bullatacin (A83), motrilin (A95), asimicin (A77), trilobacin (A96), annonacin (A8), gigantetronenin (A108) and squamocin (A73) are efficacious in suppressing the proliferation of the MDR MCF-7/Adr cells. The present analysis suggests that acetogenins can act as yet another important source for obtaining promising lead compounds in order to contribute to cancer prevention, however, in future extensive in-vivo studies in animal models will be needed to provide insight for lead development.

Understanding the transcriptional regulation of cervix cancer using microarray gene expression data and promoter sequence analysis of a curated gene set.

Cervical cancer, the malignant neoplasm of the cervix uteri is the second most common cancer among women worldwide and the top-most cancer in India. Several factors are responsible for causing cervical cancer, which alter the expression of oncogenic genes resulting in up or down-regulation of gene expression and inactivation of tumor-suppressor genes/gene products. Gene expression is regulated by interactions between transcription factors (TFs) and specific regulatory elements in the promoter regions of target genes. Thus, it is important to decipher and analyze TFs that bind to regulatory regions of diseased genes and regulate their expression. In the present study, computational methods involving the combination of gene expression data from microarray experiments and promoter sequence analysis of a curated gene set involved in the cervical cancer causation have been utilized for identifying potential regulatory elements. Consensus predictions of two approaches led to the identification of twelve TFs that might be crucial to the regulation of cervical cancer progression. Subsequently, TF enrichment and oncomine expression analysis suggested that the transcription factor family E2F played an important role for the regulation of genes involve in cervical carcinogenesis. Our results suggest that E2F possesses diagnostic/prognostic value and can act as a potential drug target in cervical cancer.

Ensemble docking and molecular dynamics identify knoevenagel curcumin derivatives with potent anti-EGFR activity.

Epidermal growth factor receptor tyrosine kinase (EGFR-TK) is an attractive target for cancer therapy. Despite a number of effective EGFR inhibitors that are constantly expanding and different methods being employed to obtain novel compounds, the search for newer EGFR inhibitors is still a major scientific challenge. In the present study, a molecular docking and molecular dynamics investigation has been carried out with an ensemble of EGFR-TK structures against a synthetically feasible library of curcumin analogs to discover potent EGFR inhibitors. To resolve protein flexibility issue we have utilized 5 EGFR wild type crystal structures during docking as this gives improved possibility of identifying an active compound as compared to using a single crystal structure. We then identified five curcumin analogs representing different scaffolds that can serve as lead molecules. Finally, the 5 ns molecular dynamics simulation shows that knoevenagel condensate of curcumin specifically C29 and C30 can be used as starting blocks for developing effective leads capable of inhibiting EGFR.

Structural investigation of deleterious non-synonymous SNPs of EGFR gene.

Epidermal Growth Factor Receptor (EGFR), a member of the receptor tyrosine kinase family has shown to be implicated in the development and progression of various cancers due to mutations in the tyrosine kinase domain (TKD). It is important to understand the functional significance of amino acid variation occurring within TKD due to non-synonymous Single Nucleotide Polymorphism (nsSNPs). Therefore, we have evaluated the influence of nsSNPs on the structure of EGFR-TKD using computational methods. Out of 2,493 SNPs in the EGFR gene, only 41 were found to be non-synonymous. In silico evaluation of these nsSNPs using a sequence based SIFT tool and structure based PolyPhen algorithm revealed that 13 nsSNPs disrupted the conformation of EGFR-TKD. Protein stability analysis using CUPSAT, I-mutant2.0 and iPTree-STAB identified 6 mutants that are less stable than the wild structure. Thereafter, to evaluate the structural impact of 5 mutants (G719A, P733L, V742A, S768I and H773R) the molecular dynamics (MD) simulation for 2 ns was performed. The MD trajectories showed that the native EGFR was stabilized after 0.9 ns while the stability of mutants was achieved after longer simulation. The RMSF profile of P-loop and A-loop shows an increased flexibility for all the mutants. We also observed that the 3 mutants (V742A, P733L and H773R) showed large root mean square deviation (2.075, 2.59 and 2.752 Å respectively) compared to the native EGFR. Further docking studies indicate that gefitinib can be administered for combating cancer occurring due to presence of these mutations.

Human intronless disease associated genes are slowly evolving.

In the present study we have examined human-mouse homologous intronless disease and non-disease genes alongside their extent of sequence conservation, tissue expression, domain and gene ontology composition to get an idea regarding evolutionary and functional attributes. We show that selection has significantly discriminated between the two groups and the disease associated genes in particular exhibit lower K(a) and K(a)/K(s) while K(s) although smaller is not significantly different. Our analyses suggest that majority of disease related intronless human genes have homology limited to eukaryotic genomes and their expression is localized. Also we observed that different classes of intronless disease related genes have experienced diverse selective pressures and are enriched for higher level functionality that is essentially needed for developmental processes in complex organisms. It is expected that these insights will enhance our understanding of the nature of these genes and also improve our ability to identify disease related intronless genes.