A multiple in silico program approach for the prediction of mutagenicity from chemical structure

We have conducted an evaluation of three of the most widely used commercial toxicity prediction programs, Toxicity Prediction by Komputer Assisted Technology (TOPKAT), Deductive Estimation of Risk from Existing Knowledge (DEREK) for Windows (DfW) and CASETOX. The three programs were evaluated for their ability to predict Ames test mutagenicity using 520 proprietary drug candidate (Test set 1) and 94 commercial (Test set 2) compounds. The study demonstrates that these three commercially available programs are useful, with limitations in their ability to predict mutagenicity over a wide range of chemical space, i.e. global predictivity. Individually, each of the programs performed at an acceptable level for overall accuracy, i.e. the ability to predict the correct outcome. However, analysis of the predictions indicates that the overall accuracy figure is heavily weighted by the ability of the programs to correctly predict non-mutagens, whereas none of the programs individually performed well in the prediction of novel mutagenic structures, i.e. Ames positive compounds. The performance of these programs' in predicting Ames positive mutagens appeared to be independent of the chemical utility of the compound, i.e. industrial, agricultural or pharmaceutical. The combination of program predictions provided some improvement in overall accuracy, sensitivity and specificity.     

Exploring the binding properties of agonists interacting with glucocorticoid receptor: an in silico approach

The glucocorticoid receptors (GR) are members of the nuclear receptor superfamily that regulate growth, development, and many of the biological functions, including metabolism and inflammation, in a ligand dependent behavior. Thus, GRs are vital as therapeutic targets with steroid hormones and steroidal analogues, especially including the glucocorticoids. Studying the molecular mechanism of binding between GR and ligands is fundamentally important to develop applications in the pharmacological industry. The present study was carried out via molecular docking and molecular dynamic (MD) simulations of three GR-ligand complexes formed between the ligand binding domain (LBD) of GR with cortisol (a natural steroid), dexamethasone (a well-known synthetic steroid drug), and chonemorphine (a steroid virtually screened from the “Sri Lankan Flora” web-based information system). The investigation was mainly carried out in terms of macroscopic properties of the ligand-protein interactions and conformational fluctuations of the protein. The results indicated greater stability and a similar behavior of the GR protein in the chonemorphine-GR complex, compared to the other two complexes, GR-dexamethasone and GR-cortisol, in an aqueous medium. The integrity of the protein-substrate complexes was preserved by strong hydrogen bonds formed between the amino acid residues of the binding site of the proteins and ligands. The findings revealed that chonemorphine is a promising agonist to GR and may produce a pharmacological effect like that produced by glucocorticoids. Thus, the obtained knowledge could lead to further investigations of the pharmaceutical potential of chonemorphine and biological functions of GR in vivo.     

Sequence analysis,structure and binding site prediction of Sigma 1 receptor protein by in silico method

Sigma 1 Receptor is a subtype of opioid receptor that participates in membrane remodeling and cellular differentiation in the nervous system. Sigma1 Receptor protein with amino acid length ranging from 229 is widely distributed in the liver and moderately in the intestine, kidney, white pulp of the spleen, adrenal gland, brain, placenta and the lung. In this study, the three dimensional structure for sigma 1 receptor protein has been developed by in- silico analysis based on evolutionary trace analysis of 37 sigma proteins from different sources. The present work focus on identification of functionally important residues and its interaction with antipsychotic drugs reported in literature.     

177 T-cell vaccine design for Streptococcus pneumoniae: an in silico approach

Streptococcus pneumoniae (pneumococcus) remains an important cause of meningitis, bacteremia, acute otitis media, community acquired pneumonia associated with significant morbidity, and mortality world wide. Conjugated polysaccharide, glycoconjugated, and capsular polysaccharide based vaccines were existent for pneumococcal disease but are still specific and restricted to serotypes of S. pneumoniae. Proteome of eight serotypes of S. pneumoniae was retrieved and identified in common proteins (Munikumar et al., 2012). 18 membrane proteins were distinguished from 1657 common proteins of eight serotypes of S. pneumoniae. Implementing comparative genomic approach and subtractive genomic approach, three membrane proteins were predicted as essential for bacterial survival and non-homologous to human (Munikumar et al., 2012; Umamaheswari et al., 2011). ProPred server was used to propose four promiscuous T-cell epitopes from three membrane proteins and validated through published positive control, SYFPEITHI and immune epitope database (Munikumar et al., in press). The four epitopes docked into peptide binding region of predominant HLA-DRB alleles with good binding affinity in Maestro v9.2. The T-cell epitope 89-VVYLLPILI-97 and HLA-DRB5^?0101 docking complex was with best XPG score (?13.143?kcal/mol). Further, the stability of the complex was checked through molecular dynamics simulations in Desmond v3.3. The simulation results had revealed that the complex was stable throughout 5000?ps (Munikumar et al., in press). Thus, the epitope would be the ideal candidate for T-cell driven subunit vaccine design against selected serotypes of S. pneumoniae.     

RNAi components are required for nuclear clustering of Polycomb group response elements

Drosophila Polycomb group (PcG) proteins silence homeotic genes through binding to Polycomb group response elements (PREs). Fab-7 is a PRE-containing regulatory element from the homeotic gene Abdominal-B. When present in multiple copies in the genome, Fab-7 can induce long-distance gene contacts that enhance PcG-dependent silencing. We show here that components of the RNA interference (RNAi) machinery are involved in PcG-mediated silencing at Fab-7 and in the production of small RNAs at transgenic Fab-7 copies. In general, these mutations do not affect the recruitment of PcG components, but they are specifically required for the maintenance of long-range contacts between Fab-7 copies. Dicer-2, PIWI, and Argonaute1, three RNAi components, frequently colocalize with PcG bodies, and their mutation significantly reduces the frequency of PcG-dependent chromosomal associations of endogenous homeotic genes. This suggests a novel role for the RNAi machinery in regulating the nuclear organization of PcG chromatin targets.     

Candidate genes for barley mutants involved in plant architecture: an in silico approach

To individuate candidate genes (CGs) for a set of barley developmental mutants, a synteny approach comparing the genomes of barley and rice has been introduced. Based on map positions of mutants, sequenced RFLP markers linked to the target loci were selected. The markers were mapped in silico by BLAST searches against the rice genome sequence and chromosomal regions syntenous to barley target intervals were identified. Rice syntenous regions were defined for 15 barley chromosomal intervals hosting 23 mutant loci affecting plant height (brh1; brh2; sld4), shoot and inflorescence branching (als; brc1; cul-2, -3, -5, -15, -16; dub1; mnd6; vrs1), development of leaves (lig) and leaf-like organs (cal-b19, -C15, -d4; lks5; suKD-25; suKE-74; suKF-76; trd; trp). Annotation of 110 Mb of rice genomic sequence made it possible to screen for putative CGs which are listed together with the reasons supporting mutant–gene associations. For two loci, CGs were identified with a clear probability to represent the locus considered. These include FRIZZY PANICLE, a candidate for the brc1 barley mutant, and the rice ortholog of maize Liguleless1 (Lg1), a candidate for the barley lig locus on chromosome 2H. For this locus, the validity of the approach was supported by the PCR-amplification of a genomic fragment of the orthologous barley sequence. SNP mapping located this fragment on chromosome 2H in the region hosting the lig genetic locus. Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

Accumulating experimental evidence has demonstrated that microRNAs (miRNAs) have a huge impact on numerous critical biological processes and they are associated with different complex human diseases. Nevertheless, the task to predict potential miRNAs related to diseases remains difficult. In this paper, we developed a Kernel Fusion‐based Regularized Least Squares for MiRNA‐Disease Association prediction model (KFRLSMDA), which applied kernel fusion technique to fuse similarity matrices and then utilized regularized least squares to predict potential miRNA‐disease associations. To prove the effectiveness of KFRLSMDA, we adopted leave‐one‐out cross‐validation (LOOCV) and 5‐fold cross‐validation and then compared KFRLSMDA with 10 previous computational models (MaxFlow, MiRAI, MIDP, RKNNMDA, MCMDA, HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA). Outperforming other models, KFRLSMDA achieved AUCs of 0.9246 in global LOOCV, 0.8243 in local LOOCV and average AUC of 0.9175 ± 0.0008 in 5‐fold cross‐validation. In addition, respectively, 96%, 100% and 90% of the top 50 potential miRNAs for breast neoplasms, colon neoplasms and oesophageal neoplasms were confirmed by experimental discoveries. We also predicted potential miRNAs related to hepatocellular cancer by removing all known related miRNAs of this cancer and 98% of the top 50 potential miRNAs were verified. Furthermore, we predicted potential miRNAs related to lymphoma using the data set in the old version of the HMDD database and 80% of the top 50 potential miRNAs were confirmed. Therefore, it can be concluded that KFRLSMDA has reliable prediction performance.     

Binding of type II nuclear receptors and estrogen receptor to full and half-site estrogen response elements in vitro.

The mechanism by which retinoids, thyroid hormone (T3) and estrogens modulate the growth of breast cancer cells is unclear. Since nuclear type II nuclear receptors, including retinoic acid receptor (RAR), retinoid X receptor (RXR) and thyroid hormone receptor (TR), bind direct repeats (DR) of the estrogen response elements (ERE) half-site (5'-AGGTCA-3'), we examined the ability of estrogen receptor (ER) versus type II nuclear receptors, i.e. RARalpha, beta and gamma, RXRbeta, TRalpha and TRbeta, to bind various EREs in vitro . ER bound a consensus ERE, containing a perfectly palindromic 17 bp inverted repeat (IR), as a homodimer. In contrast, ER did not bind to a single ERE half-site. Likewise, ER did not bind two tandem (38 bp apart) half-sites, but low ER binding was detected to three tandem copies of the same half-site. RARalpha,beta or gamma bound both ERE and half-site constructs as a homodimer. RXRbeta did not bind full or half-site EREs, nor did RXRbeta enhance RARalpha binding to a full ERE. However, RARalpha and RXRbeta bound a half-site ERE cooperatively forming a dimeric complex. The RARalpha-RXRbeta heterodimer bound the Xenopus vitellogenin B1 estrogen responsive unit, with two non-consensus EREs, with higher affinity than one or two copies of the full or half-site ERE. Both TRalpha and TRbeta bound the full and the half-site ERE as monomers and homodimers and cooperatively as heterodimers with RXRbeta. We suggest that the cellular concentrations of nuclear receptors and their ligands, and the nature of the ERE or half-site sequence and those of its flanking sequences determine the occupation of EREs in estrogen-regulated genes in vivo .     

In silico prediction of clinical efficacy

Drug development is a high risk and costly process, and the ability to predict clinical efficacy in silico (in a computer) can save the pharmaceutical industry time and resources. Additionally, such an approach will result in more targeted, personalized therapies. To date, a number of in silico strategies have been developed to provide better information about the human response to novel therapies earlier in the drug development process. Some of the most prominent include physiological modeling of disease and disease processes, analytical tools for population pharmacodynamics, tools for the analysis of genomic expression data, Monte Carlo simulation technologies, and predictive biosimulation. These strategies are likely to contribute significantly to reducing the failure rate of drugs entering clinical trials.     

Computer prediction of sites associated with various elements of the nuclear matrix

Attachment regions of the eukaryotic chromosomal DNA to the nuclear scaffold/matrix (S/MARs) participate in various important cellular processes. However, no obvious characteristics common for these nucleotide sequences have been revealed, except that S/MARs are non-coding sites containing putative regulatory elements and binding sites of DNA-topoisomerase II. Heterogeneity among S/MARs can be caused by a variety of biological factors. In this paper, the accuracy of two S/MARs prediction programs, MAR-Finder (Singh, Kramer and Krawetz, 1997) and ChrClass (Glazkov, Rogozin and Glazko, 1998) are compared and it is concluded that both programs can be recommended for analysis of eukaryotic genomes. However, results of their prediction should be interpreted with caution since estimation of prediction accuracy of both programs needs further analysis. Problems of S/MARs prediction are illustrated on several examples of human protein-coding genes, repeated elements and the beta-globin locus from different mammalian species. Results of our analysis suggest that the proportion of missed S/MARs is lower for ChrClass, whereas the proportion of wrong S/MARs is lower for MAR-Finder (a default set of parameters).     

Identifying potential GluN2B subunit containing N-Methyl-D-aspartate receptor inhibitors: an integrative in silico and molecular modeling approach

Abstract

N-methyl-D-aspartate receptors (NMDARs), a class of ligand-gated ion channels, are involved in non-selective cation transport across the membrane. These are contained in glutamatergic synapse and produce excitatory effects leading to synaptic plasticity and memory function. GluN1-GluN2B, a subtype of NMDAR(s), has significant role in neurodegeneration, amyloid β (Aβ) induced synaptic dysfunction and loss. Thus, targeting and inhibiting GluN1-GluN2B may be effective in the management of neurodegenerative diseases including Alzheimer’s disease. In the present study, ligand and structure-based approaches were tried to identify the inhibitors. The pharmacophore, developed from co-crystallised ifenprodil, afforded virtual hits, which were further subjected through drug likeliness and PAINS filters to remove interfering compounds. Further comprehensive docking studies, free energy calculations and ADMET studies resulted in two virtual leads. The leads, ZINC257261614 and ZINC95977857 displayed good docking scores of ?12.90 and ?12.20?Kcal/mol and free binding energies of ?60.83 and ?61.83?Kcal/mol, respectively. The compounds were having acceptable predicted ADMET profiles and were subjected to molecular dynamic (MD) studies. The MD simulation produced stable complexes of these ligands with GluN1-GluN2B subunit having protein and ligand RMSD in acceptable limit. Abbreviations AD Alzheimer's disease

ADME Absorption distribution metabolism and excretion

ATD Amino terminal domain

BBB Blood-brain barrier

CNS Central nervous system

CREB cAMP response element binding protein

CTD Carboxy-terminal domain

Glu Glutamate

GMQE Global model quality estimation

HTVS High throughput virtual screening

HIA Human intestinal absorption

LGA Lamarckian genetic algorithm

MD Molecular dynamics

MM-GBSA Molecular mechanics, the Generalised Born model for Solvent Accessibility

NMDAR N-methyl-D-aspartate receptors

PAINS Pan assay interference compounds

RMSD Root-mean square deviation

RMSF Root-mean-square fluctuation

SMARTS SMILES arbitrary target specification

SP standard precision

XP extra precision

Communicated by Ramaswamy H. Sarma     

Molecular dynamics simulations of RNA: an in silico single molecule approach

RNA molecules are now known to be involved in the processing of genetic information at all levels, taking on a wide variety of central roles in the cell. Understanding how RNA molecules carry out their biological functions will require an understanding of structure and dynamics at the atomistic level, which can be significantly improved by combining computational simulation with experiment. This review provides a critical survey of the state of molecular dynamics (MD) simulations of RNA, including a discussion of important current limitations of the technique and examples of its successful application. Several types of simulations are discussed in detail, including those of structured RNA molecules and their interactions with the surrounding solvent and ions, catalytic RNAs, and RNA-small molecule and RNA-protein complexes. Increased cooperation between theorists and experimentalists will allow expanded judicious use of MD simulations to complement conceptually related single molecule experiments. Such cooperation will open the door to a fundamental understanding of the structure-function relationships in diverse and complex RNA molecules. .