In silico multi-filter screening approaches for developing novel beta-secretase inhibitors

A large database of chemical structures was screened for potential inhibitors of β-secretase was carried out using in silico multi-filter techniques. Substructure screening, computer-aided ligand docking, binding free energy calculations, and partial interaction energy analyses were performed successively to identify chemical compounds which could serve as different scaffolds from known β-secretase inhibitors for future drug design. We showed that our in silico multi-filter screening retrieved all known inhibitors from the compound database investigated, which suggests that the other compounds identified as inhibitors by this computerized screening process are potential β-secretase inhibitors.     

In silico investigation of potential SRC kinase ligands from traditional Chinese medicine

Src kinase is an attractive target for drug development based on its established relationship with cancer and possible link to hypertension. The suitability of traditional Chinese medicine (TCM) compounds as potential drug ligands for further biological evaluation was investigated using structure-based, ligand-based, and molecular dynamics (MD) analysis. Isopraeroside IV, 9alpha-hydroxyfraxinellone-9-O-beta-D-glucoside (9HFG) and aurantiamide were the top three TCM candidates identified from docking. Hydrogen bonds and hydrophobic interactions were the primary forces governing docking stability. Their stability with Src kinase under a dynamic state was further validated through MD and torsion angle analysis. Complexes formed by TCM candidates have lower total energy estimates than the control Sacaratinib. Four quantitative-structural activity relationship (QSAR) in silico verifications consistently suggested that the TCM candidates have bioactive properties. Docking conformations of 9HFG and aurantiamide in the Src kinase ATP binding site suggest potential inhibitor-like characteristics, including competitive binding at the ATP binding site (Lys295) and stabilization of the catalytic cleft integrity. The TCM candidates have significantly lower ligand internal energies and are estimated to form more stable complexes with Src kinase than Saracatinib. Structure-based and ligand-based analysis support the drug-like potential of 9HFG and aurantiamide and binding mechanisms reveal the tendency of these two candidates to compete for the ATP binding site.     

In silico screening for antibiotic escort molecules to overcome efflux

Resistance to antibiotics is a growing problem worldwide and occurs in part due to the overexpression of efflux pumps responsible for the removal of antibiotics from bacterial cells. The current study examines complex formation between efflux pump substrates and escort molecules as a criterion for an in silico screening method for molecules that are able to potentiate antibiotic activities. Initially, the SUPERDRUG database was queried to select molecules that were similar to known multidrug resistance (MDR) modulators. Molecular interaction fields generated by GRID and the docking module GLUE were used to calculate the interaction energies between the selected molecules and the antibiotic norfloxacin. Ten compounds forming the most stable complexes with favourable changes to the norfloxacin molecular properties were tested for their potentiation ability by efflux pump modulation assays. Encouragingly, two molecules were proven to act as efflux pump modulators, and hence provide evidence that complex formation between a substrate and a drug can be used for in silico screening for novel escort molecules.     

In silico screening for tumour-specific expressed sequences in human genome.

A computer-based differential display tool named HsAnalyst has been developed and successfully used for the comparison of expression patterns in a set of tumours versus a set of normal tissues. A list of EST clusters highly represented in tumours and rarely observed in normal tissues has been developed as a resulting output file of the program. These differentially expressed EST clusters (genes) can be useful for developing new tumour markers and prognostic indicators for a wide set of human malignancies. Tumour-specific protein-coding genes may be considered a manifestation of tumour-specific gene expression.     

In silico screening of anti-inflammatory compounds from Lichen by targeting cyclooxygenase-2

Abstract

Non-steroidal anti-inflammatory drugs (NSAID) targeting cyclooxygenase-2 are clinically effective. However, they lack anti-thrombotic activity resulting in incidences of adverse effects like myocardial infarction, gastrointestinal and abdominal discomfort which necessitate for discovering new drug candidates with improved therapeutic effects and tolerability. Various recent researches have suggested that many lichens offer a vast reservoir for anti-inflammatory drug candidates which are natural as well as safe for human consumption. Drug discovery is a very complex and time-consuming process; however, in silico techniques can make this process simple and economic. Hence to find out natural anti-inflammatory compounds, we have carried out the virtual screening of 412 lichen compounds by molecular docking with human Cox-2 enzyme and validated the docking score by X-Score followed by ADMET and Drug-likeness analysis. The resulting 6 top-scored compounds were subjected to Molecular dynamics simulation (MDS) to analyze the stability of docked protein-ligand complex, to assess the fluctuation and conformational changes during protein-ligand interaction. The values of RMSD, Rg, and interaction energy after 30?ns of MDS revealed the good stability of these Lichen compounds in the active site pocket of Cox-2 in compare to reference, JMS. Additionally, we have done the pharmacophore analysis which found many common pharmacophore features between Lichen compounds and well known anti-inflammatory compounds. Our result shows that these lichen compounds are potential anti-inflammatory candidates and could be further modified and evaluated to develop more effective anti-inflammatory drugs with fewer side effects for the treatment of inflammatory diseases.

Communicated by Ramaswamy H. Sarma     

In silico screening of mutational effects on enzyme-proteic inhibitor affinity: a docking-based approach

Background  

Molecular recognition between enzymes and proteic inhibitors is crucial for normal functioning of many biological pathways. Mutations in either the enzyme or the inhibitor protein often lead to a modulation of the binding affinity with no major alterations in the 3D structure of the complex.     

In silico screening of archaeal tRNA-encoding genes having multiple introns with bulge-helix-bulge splicing motifs

In archaeal species, several transfer RNA genes have been reported to contain endogenous introns. Although most of the introns are located at anticodon loop regions between nucleotide positions 37 and 38, a number of introns at noncanonical sites and six cases of tRNA genes containing two introns have also been documented. However, these tRNA genes are often missed by tRNAscan-SE, the software most widely used for the annotation of tRNA genes. We previously developed SPLITS, a computational tool to identify tRNA genes containing one intron at a noncanonical position on the basis of its discriminative splicing motif, but the software was limited in the detection of tRNA genes with multiple introns at noncanonical sites. In this study, we initially updated the system as SPLITSX in order to correctly predict known tRNA genes as well as novel ones with multiple introns. By a comprehensive search for tRNA genes in 29 archaeal genomes using SPLITSX, we listed 43 novel candidates that contain introns at noncanonical sites. As a result, 15 contained two introns and three contained three introns within the respective putative tRNA genes. Moreover, the candidates completely complemented all the codons of two archaeal species of uncultured methanogenic archaeon, RC-I and Thermofilum pendens Hrk 5, with novel candidates that were not detectable by tRNAscan-SE alone.     

In silico zebrafish pattern formation

Mutations that affect pattern formation in the zebrafish have been widely studied over the past few decades, leading to speculations as to the mechanism by which stripes, spots and other skin patterns are formed. Recent in silico developments in modeling of cellular dynamics now permit explicit analysis of how cells migrate and interact, and we describe here an in silico simulation that appears to reproduce many of the surface patterns previously reported in zebrafish. We find that many observed zebrafish patterns are necessarily associated with expression of repulsive as well as attractive cellular ligands, and we make predictions for the detailed effects of changes in expression of these ligands.     

In silico pharmacogenetics of warfarin metabolism

Pharmacogenetic approaches can be instrumental for predicting individual differences in response to a therapeutic intervention. Here we used a recently developed murine haplotype-based computational method to identify a genetic factor regulating the metabolism of warfarin, a commonly prescribed anticoagulant with a narrow therapeutic index and a large variation in individual dosing. After quantification of warfarin and nine of its metabolites in plasma from 13 inbred mouse strains, we correlated strain-specific differences in 7-hydroxywarfarin accumulation with genetic variation within a chromosomal region encoding cytochrome P450 2C (Cyp2c) enzymes. This computational prediction was experimentally confirmed by showing that the rate-limiting step in biotransformation of warfarin to its 7-hydroxylated metabolite was inhibited by tolbutamide, a Cyp2c isoform-specific substrate, and that this transformation was mediated by expressed recombinant Cyp2c29. We show that genetic variants responsible for interindividual pharmacokinetic differences in drug metabolism can be identified by computational genetic analysis in mice.     

In silico characterization of thermostable lipases

Thermostable lipases are of high priority for industrial applications as they are endowed with the capability of carrying out diversified reactions at elevated temperatures. Extremophiles are their potential source. Sequence and structure annotation of thermostable lipases can elucidate evolution of lipases from their mesophilic counterparts with enhanced thermostability hence better industrial potential. Sequence analysis highlighted the conserved residues in bacterial and fungal thermostable lipases. Higher frequency of AXXXA motif and poly Ala residues in lid domain of thermostable Bacillus lipases were distinguishing characteristics. Comparison of amino acid composition among thermostable and mesostable lipases brought into light the role of neutral, charged and aromatic amino acid residues in enhancement of thermostability. Structural annotation of thermostable lipases with that of mesostable lipases revealed some striking features which are increment of gamma turns in thermostable lipases; being first time reported in our paper, longer beta strands, lesser beta-branched residues in helices, increase in charged-neutral hydrogen bonding pair, hydrophobic-hydrophobic contact and differences in the N-cap and C-cap residues of the α helices. Conclusively, it can be stated that subtle changes in the arrangement of amino acid residues in the tertiary structure of lipases contributes to enhanced thermostability.     

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.     

In silico selection of RNA aptamers

In vitro selection of RNA aptamers that bind to a specific ligand usually begins with a random pool of RNA sequences. We propose a computational approach for designing a starting pool of RNA sequences for the selection of RNA aptamers for specific analyte binding. Our approach consists of three steps: (i) selection of RNA sequences based on their secondary structure, (ii) generating a library of three-dimensional (3D) structures of RNA molecules and (iii) high-throughput virtual screening of this library to select aptamers with binding affinity to a desired small molecule. We developed a set of criteria that allows one to select a sequence with potential binding affinity from a pool of random sequences and developed a protocol for RNA 3D structure prediction. As verification, we tested the performance of in silico selection on a set of six known aptamer–ligand complexes. The structures of the native sequences for the ligands in the testing set were among the top 5% of the selected structures. The proposed approach reduces the RNA sequences search space by four to five orders of magnitude—significantly accelerating the experimental screening and selection of high-affinity aptamers.     

In silico screening and biological evaluation of inhibitors of Src-SH3 domain interaction with a proline-rich ligand

Src signalling and transduction are directly involved in cell growth, cell cycle, malignant transformation and cell migration, providing therapeutic opportunities through inhibition of Src. Here we report virtual screening for novel compounds that inhibit the Src-SH3 protein-protein interaction with a proline-rich peptide ligand. Computational docking of the ZINC compound database was performed using GOLD. Top-scoring compounds were assayed using a fluorescence polarization-based assay. A benzoquinoline derivative showed micromolar inhibition of binding between Src-SH3 and the proline-rich peptide. Several analogues were subsequently assayed showing the requirement of a linker between the benzoquinoline and phenyl rings, and electron donating substituents on the phenyl ring.     

In silico prediction of yeast deletion phenotypes

Analysis of gene deletions is a fundamental approach for investigating gene function. We evaluated an algorithm that uses classification techniques to predict the phenotypic effects of gene deletions in yeast. We used a modified simulated annealing algorithm for feature selection and weighting. The selected features with high weights were phylogenetic conservation scores for bacteria, fungi (excluding Ascomycota), Ascomycota (excluding Saccharomyces cerevisiae), plants, and mammals, degree of paralogy, and number of protein-protein interactions. Classification was performed by weighted k-nearest neighbor and with support vector machine algorithms. To demonstrate how this approach might complement existing experimental procedures, we applied our algorithm to predict essential genes and genes causing morphological alterations in yeast.     

In silico simulation of biological network dynamics

Realistic simulation of biological networks requires stochastic simulation approaches because of the small numbers of molecules per cell. The high computational cost of stochastic simulation on conventional microprocessor-based computers arises from the intrinsic disparity between the sequential steps executed by a microprocessor program and the highly parallel nature of information flow within biochemical networks. This disparity is reduced with the Field Programmable Gate Array (FPGA)-based approach presented here. The parallel architecture of FPGAs, which can simulate the basic reaction steps of biological networks, attains simulation rates at least an order of magnitude greater than currently available microprocessors.