SEARCH · Search PubMed

Results for “In silico”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6Linked to original sources

PubMed2003

Genome-scale microbial in silico models: the constraints-based approach.

Genome sequencing and annotation has enabled the reconstruction of genome-scale metabolic networks. The phenotypic functions that these networks allow for can be defined and studied using constraints-based models and in silico simulation. Several useful predictions have been obtained from such in silico models, including substrate preference, consequences of gene deletions, optimal growth patterns, outcomes of adaptive evolution and shifts in expression profiles. The success rate of these predictions is typically in the order of 70-90% depending on the organism studied and the type of prediction being made. These results are useful as a basis for iterative model building and for several practical applications.

Animals↗

PubMed1999

In silico design for protein stabilization.

Newly developed in silico protein design methods have recently been applied to problems in protein stabilization. Stabilized protein sequences can be designed by combining potential functions that model a protein sequence's compatibility with a structure and fast optimization tools that can search the enormous number of sequence possibilities. The experimental testing of several sequence-design strategies has demonstrated that a wide range of protein structures can be stabilized. The primary advantage of in silico design is the vast number of sequences that can be rapidly screened in the search for an optimal design, far exceeding non-computational methods. This feature allows very large changes in protein properties to be discovered.

Biotechnology↗

PubMedDate not supplied

Progress in predicting human ADME parameters in silico.

Understanding the development of a scientific approach is a valuable exercise in gauging the potential directions the process could take in the future. The relatively short history of applying computational methods to absorption, distribution, metabolism and excretion (ADME) can be split into defined periods. The first began in the 1960s and continued through the 1970s with the work of Corwin Hansch et al. Their models utilized small sets of in vivo ADME data. The second era from the 1980s through 1990s witnessed the widespread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpretable computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vitro data for absorption, drug-drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain penetration, which can theoretically be used to predict the situation in vivo in humans. Combinatorial synthesis, high throughput screening and computational approaches have emerged as a result of continual pressure on pharmaceutical companies to accelerate drug discovery while decreasing drug development costs. The goal has become to reduce the drop-out rate of drug candidates in the latter, most expensive stages of drug development. This is accomplished by increasing the failure rate of candidate compounds in the preclinical stages and increasing the speed of nomination of likely clinical candidates. The industry now understands the reasons for clinical failure other than efficacy are mainly related to pharmacokinetics and toxicity. The late 1990s saw significant company investment in ADME and drug safety departments to assess properties such as metabolic stability, cytochrome P-450 inhibition, absorption and genotoxicity earlier in the drug discovery paradigm. The next logical step in this process is the evaluation of higher throughput data to determine if computational (in silico) models can be constructed and validated from it. Such models would allow an exponential increase in the number of compounds screened virtually for ADME parameters. A number of researchers have started to utilize in silico, in vitro and in vivo approaches in parallel to address intestinal permeability and cytochrome P-450-mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.

Catalysis↗

PubMed2002

In silico identification of novel therapeutic targets.

The availability of the human genome sequence is a 'once in a lifetime' opportunity for scientists to uncover all possible human drug-targets. As the sequence is very large, the best way to identify new genes rapidly is by computational (in silico) methods. There are now many examples in which pharmaceutical companies have identified genes of interest initially by in silico analysis. High-throughput data-generation techniques, such as microarray analysis, are key to the generation of human genome data. Bioinformatics techniques are therefore certain to play an increasingly important role in drug discovery.

Computational Biology↗

PubMedDate not supplied

An ontology for pharmaceutical ligands and its application for in silico screening and library design.

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.

Combinatorial Chemistry Techniques↗

PubMedDate not supplied

Can 'Bacterial-Metabolite-Likeness' model improve odds of 'in silico' antibiotic discovery?

'Inductive' QSAR descriptors have been used to develop the series of QSAR models enabling 'in silico' distinguishing between antimicrobial compounds, conventional drugs, and druglike substances. The constructed neural network-based models operating by 30 'inductive' parameters have been validated on an extensive set of 2686 chemical structures and resulted in up to 97% accurate separation of the three types of molecular activities. The demonstrated ability of 'inductive' parameters to adequately capture molecular features determining 'antibiotic-like' and 'druglike' potentials have been further utilized to construct a model of 'Bacterial-Metabolite-Likeness' (BML). The same 'inductive' descriptors have been used to train a neural network that could very accurately recognize substances involved into bacterial metabolism (that have been experimentally identified). When the developed model has been applied to the mixed set of antimicrobials, drugs, and druglike chemicals (not used for training the BML model), it exhibited a 2-5-fold recognition preference toward antimicrobial compounds compared to general drugs and an 18- to 45-fold preference when compared to a druglike substance (depending on the model stringency). These results illustrate immanent similarity between conventional antimicrobials and native bacterial metabolites and suggest that the developed BML model can be an effective classification tool for 'in silico' antibiotic studies.

Anti-Bacterial Agents↗

PubMed2006

In silico discovery of beta-secretase inhibitors.

Alzheimer's disease, the most common amyloid-associated disorder, accounts for the majority of the dementia diagnosed after the age of 60. The cleavage of the beta-amyloid precursor protein is initiated by beta-secretase (BACE-1), a membrane-bound aspartic protease, which has emerged as an important but difficult protein target. Here, an in silico screening approach consisting of fragment-based docking, ligand conformational search by a genetic algorithm, and evaluation of free energy of binding was used to identify low-molecular-weight inhibitors of BACE-1. More than 300,000 small molecules were docked and about 15,000 prioritized according to a linear interaction energy model with evaluation of solvation by continuum electrostatics. Eighty-eight compounds were tested in vitro, and 10 of them showed an IC(50) value lower than 100 microM in a BACE-1 enzymatic assay. Interestingly, the 10 active compounds shared a triazine scaffold. Moreover, four of them were active in an assay with mammalian cells (EC(50) < 20 microM), indicating that they are cell-permeable. Therefore, these triazine derivatives are very promising lead candidates for BACE-1 inhibition. The discoveries of this series and two other series of nonpeptidic BACE-1 inhibitors demonstrate the usefulness of our in silico high-throughput screening approach.

Amyloid Precursor Protein Secretases↗

PubMed2001

Design and synthesis of non-peptidic inhibitors for the Syk C-terminal SH2 domain based on structure-based in-silico screening.

Structure-based in-silico screening was carried out for the Syk C-terminal SH2 domain. Fragments that could interact with the pY or pY+1 pockets were selected by our in-silico screening. After tethering two fragments bound to these pockets, we have designed and synthesized new compounds that show favorable interaction with the pY+3 pocket. One such compound, having a cyclohexylmalonic acid moiety identified as a novel potent phosphotyrosyl mimetic, exhibited an affinity comparable to that of the monophosphorylated ligand peptide.

Binding, Competitive↗

PubMed2005

Successful in silico discovery of novel nonsteroidal ligands for human sex hormone binding globulin.

Using "in silico" drug design methodologies, we have discovered several nonsteroidal compounds of natural origin that bind to human sex hormone binding globulin (SHBG) with affinity constants of 0.1 x 10(6) to 1.2 x 10(6) M(-1). The computational solutions we developed involved pharmacophore-aided database search, virtual protein-ligand docking, and structure-activity modeling with "inductive" QSAR descriptors. By screening 23 836 natural substance structures, we identified 29 potential SHBG ligands, and eight of these bound the protein in vitro. These nonsteroidal ligands belong to four classes of molecular scaffolds with several available substitution positions that could allow chemical modification to enhance SHBG-binding activity. Interestingly, one of these compounds is structurally similar to a dicyclohexane derivative that binds to rat SHBG and causes azospermia when administered to male rats. Taken together, the in silico strategy we have developed will aid in the discovery of nonsteroidal ligands of SHBG with novel pharmacological properties.

Algorithms↗

PubMed2005

Combining in silico tools and NMR data to validate protein-ligand structural models: application to matrix metalloproteinases.

A combination of in silico tools and experimental NMR data is proposed for relatively fast determination of protein-ligand structural models and demonstrated from known inhibitors of matrix metalloproteinases (MMP). The 15N 1H heteronuclear single quantum coherence (HSQC) spectral assignment and the 3D structure, either X-ray or NMR, are needed. In this method, the HSQC spectrum with or without the ligand is used to determine the interaction region of the ligand. Docking calculations are then performed to obtain a set of structural models. From the latter, the nuclear Overhauser effects (NOEs) between the ligand and the protein can be predicted. Guided by these predictions, a number of NOEs can be detected and assigned through a HSQC NOESY experiment. These data are used as structural restraints to reject/refine the initial structural models through further in silico work. For a test protein (MMP-12, human macrophage metalloelastase), a final structure of a protein-ligand adduct was obtained that matches well with the full structural determination. A number of structural predictions were then made for adducts of a similar protein (MMP-1, human fibroblast collagenase) with the same and different ligands. The quality of the final results depended on the type and number of experimental NOEs, but in all cases, a well-defined ligand conformation in the protein binding site was obtained. This protocol is proposed as a viable alternative to the many approaches described in the literature.

Alanine↗

PubMed2006

In silico human and rat Vss quantitative structure-activity relationship models.

We present herein a QSAR tool enabling an entirely in silico prediction of human and rat steady-state volume of distribution (Vss), to be made prior to chemical synthesis, preceding detailed allometric or mechanistic assessment of Vss. Three different statistical methodologies, Bayesian neural networks (BNN), classification and regression trees (CART), and partial least squares (PLS) were employed to model human (N=199) and rat (N=2086) data sets. The results in prediction of an independent test set show the human model has an r2 of 0.60 and an rms error in prediction of 0.48. The corresponding rat model has an r2 of 0.53 and an rms error in prediction of 0.37, indicating both models could be very useful in the early stages of the drug discovery process. This is the first reported entirely in silico approach to the prediction of rat and human steady-state volume of distribution.

Animals↗

PubMed2007

Plasma protein binding affinity and its relationship to molecular structure: an in-silico analysis.

In-silico plasma protein binding (PPB) models have been generated on human and rat in-house datasets, and on a human dataset sourced from the literature. From the results reported herein, it is apparent that models built on datasets relevant to the chemotypes under investigation in lead optimization programs will perform considerably better in this role than those generated on diverse compounds sourced from the literature. The in-house human and rat partial least-squares regression (PLS) models have cross-validated q2 values of 0.53 and 0.42 on the training sets, respectively. On the independent test and validation sets, they display similar predictive ability, with logK prediction errors of approximately 0.5 log units. This compares to approximately 0.25 log units variability expected for experiment. Given the considerable interspecies PPB differences, the prediction of PPB in one species using measurements in the other is no better than a prediction from an in-silico model generated on that species.

Analysis of Variance↗

PubMed2006

Synthesis of micelle templated silico-aluminas with different alumina contents.

The computer aided analysis of the EPR spectra of radical surfactant probes inserted in cetyltrimethylammonium bromide micelles provided information on the kinetics of formation of micelle templated silico-aluminas (MTSA) at 343 K, obtained by means of silica and alumina alkaline solutions at different Si/Al ratios (from infinity to 4). Mainly two spectral components were analyzed and relatively quantified in the EPR spectra: (1) the micellar component, due to probes inserted in the surfactant aggregates, whose mobility decreases over the synthesis time, thus reporting on the progressive modification of the micelle structure and the solid condensation; (2) the interacting component, mainly arising from the electrostatic interactions between the surfactant heads and the charged surface sites. This last component increases its relative intensity over the synthesis time, informing about condensation and structuration of the silico-alumina at the micelle surface. X-ray diffraction (XRD), nitrogen sorption isotherms at 77 K, thermogravimetric analysis, TEM and chemical analysis were performed to characterize both as-synthesized and calcined MTSA materials. Nitrogen sorption isotherms allowed us to evaluate the pore diameter, the specific surface area and the pore volume. At Si/Al<15 a decrease in pore volume and specific surface area was interpreted as due to the contemporaneous presence of a hexagonal MTSA and an amorphous material, which was ascertained by means of XRD as the only present at Si/Al=4. The amorphous structure at Si/Al<15 used Na+ as contraions, whereas the surfactants are no more needed to neutralize the negatively charged groups at the solid surface. The hypothesis of a "break" at Si/Al=15 was supported by EPR: the interactions between the surfactant probe heads and the negatively charged surface groups are drastically reduced at Si/Al<15. On the contrary, at Si/Al>15, increasing amounts of alumina slow the kinetics of the synthesis but enhance electrostatic interactions between the surfactant heads and the negatively charged surface groups. Dilution of the synthesis mixture decreased the extent of the interactions, due to partial protonation of the silanol groups, and slowed the synthesis process.

Journal Article↗

PubMedDate not supplied

Definition and characterization of a "trypsinosome" from specific peptide characteristics by nano-HPLC-MS/MS and in silico analysis of complex protein mixtures.

Although HPLC-ESI-MS/MS is rapidly becoming an indispensable tool for the analysis of peptides in complex mixtures, the sequence coverage it affords is often quite poor. Low protein expression resulting in peptide signal intensities that fall below the limit of detection of the MS system in combination with differences in peptide ionization efficiency plays a significant role in this. A second important factor stems from differences in physicochemical properties of each peptide and how these properties relate to chromatographic retention and ultimate detection. To identify and understand those properties, we compared data from experimentally identified peptides with data from peptides predicted by in silico digest of all corresponding proteins in the experimental set. Three different complex protein mixtures extracted were used to define a training set to evaluate the amino acid retention coefficients based on linear regression analysis. The retention coefficients were also compared with other previous hydrophobic and retention scale. From this, we have constructed an empirical model that can be readily used to predict peptides that are likely to be observed on our HPLC-ESI-MS/MS system based on their physicochemical properties. Finally, we demonstrated that in silico prediction of peptides and their retention coefficients can be used to generate an inclusion list for a targeted mass spectrometric identification of low abundance proteins in complex protein samples. This approach is based on experimentally derived data to calibrate the method and therefore may theoretically be applied to any HPLC-MS/MS system on which data are being generated.

Animals↗

PubMed2003

In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values.

We have investigated whether three important ADME (absorption, distribution, metabolism, excretion) related properties (aqueous solubility, human plasma protein binding, and human volume of distribution at steady-state) can be predicted from chemical structure alone if only the predicted predominant ionisation state and lipophilicity (calculated logP [P = octanol-water partition coefficient]) are considered. A simple, fast method for the in silico prediction of aqueous solubility of predominantly uncharged compounds has been developed, while some potential is shown for the prediction of predominantly charged or zwitterionic compounds. Ten other known in silico prediction methods for aqueous solubility have also been evaluated. It has furthermore been demonstrated that the molecular weight (MW) profile of training sets for the development of aqueous solubility prediction methods can influence their predictive performance with regard to test sets of either matching or diverging profiles. The same property descriptors which have been found most relevant for the prediction of aqueous solubility have also proved useful for the prediction of human plasma protein binding and human volume of distribution at steady-state.

Blood Proteins↗

PubMed2026

Multitargeted comparative evaluation suggests 2-Aoeobenoxmide shows favourable in silico binding compared to Tucatinib against ERα, HER2, AKT1, EGFR, and PIK3CA in breast cancer.

Breast cancer is a leading cause of cancer-related morbidity and mortality globally, with the WHO reporting approximately 2.3 million new cases and 685,000 deaths annually. Drug resistance in breast cancer complicates treatment, with mutations in critical proteins contributing to therapy failure. Key oncogenic proteins involved in breast cancer progression-namely ERα (a ligand-activated nuclear receptor; PDB: 1A52) and the kinase domains of HER2 (PDB ID: 3PP0), AKT1 (PDB ID: 4EJN), EGFR (PDB ID: 4I23) and PIK3CA (PDB ID: 7R9V)-are pivotal in tumour progression and resistance mechanisms. Targeting these proteins using multitargeted therapeutic strategies may overcome resistance by disrupting key signalling pathways involved in cell proliferation, survival, and metastasis. Such combinatorial approaches promise to improve treatment efficacy and patient outcomes in cases of resistant breast cancer. In this study, we performed multitarget docking on prepared and validated protein structures against the ZINC natural compound library using HTVS, SP, and XP, with pose validation using MM-GBSA. We identified 2-Aoeobenoxmide (2-[1-(2-amino-2-oxo-ethoxy)-6-oxo-benzo[c]chromen-3-yl]oxyacetamide, ZINC134008) with docking and MM-GBSA scores ranging from -8.162 to -10.327 kcal/mol and from -47.18 to -57.62 kcal/mol, respectively, and compared the results with the FDA-approved drug Tucatinib, which exhibited lower binding affinity scores. We further evaluated pharmacokinetic properties using QikProp and electronic properties using DFT (Jaguar) and compared the descriptors of 2-Aoeobenoxmide with those of Tucatinib and with accepted reference ranges. We also performed the WaterMap for 5 nanoseconds (ns), computed various energies, interactions and hydration sites, and the comparison suggests that 2-Aoeobenoxmide shows more favourable hydration-site displacement and binding interactions than Tucatinib. Additionally, a 100 ns MD Simulation has resulted in far less deviation, fluctuations, and intermolecular interactions than Tucatinib, suggesting stable protein-ligand interactions, while the binding free energy and total complex energy computed across 0-1000 frames of the MD trajectories indicate that 2-Aoeobenoxmide is a promising in silico candidate. Importantly, because the entire study is computational, the findings should be interpreted as in silico hypotheses, and experimental validation through in vitro and in vivo assays is warranted before any clinical translation is considered.

Humans↗

PubMed2003

Putative in silico mapping of DNA sequences to livestock genome maps using SSLP flanking sequences.

In this study, an in silico approach was developed to identify homologies existing between livestock microsatellite flanking sequences and GenBank nucleotide sequences. Initially, 1955 bovine, 1570 porcine and 1121 chicken microsatellites were downloaded and the flanking sequences were compared with the nr and dbEST databases of GenBank. A total of 74 bovine, 44 porcine and 37 chicken microsatellite flanking sequences passed our criteria and had at least one significant match to human genomic sequence, genes/expressed sequence tags (ESTs) or both. GenBank annotation and BLAT searches of the UCSC human genome assembly revealed that 38 bovine, 13 porcine and 17 chicken microsatellite flanking sequences were highly similar to known human genes. Map locations were available for 67 bovine, 44 porcine and 21 chicken microsatellite flanking sequences, providing useful links in the comparative maps of humans and livestock. In support of our approach, 112 alignments with both microsatellite and match mapping information were located in the expected chromosomal regions based on previously reported syntenic relationships. The development of this in silico mapping approach has significantly increased the number of genes and EST sequences anchored to the bovine, porcine and chicken genome maps and the number of links in various human-livestock comparative maps.

Animals↗

PubMed2005

Mapping Dmef2-binding regulatory modules by using a ChIP-enriched in silico targets approach.

Mapping the regulatory modules to which transcription factors bind in vivo is a key step toward understanding of global gene expression programs. We have developed a chromatin immunoprecipitation (ChIP)-chip strategy for identifying factor-specific regulatory regions acting in vivo. This method, called the ChIP-enriched in silico targets (ChEST) approach, combines immunoprecipitation of cross-linked protein-DNA complexes (X-ChIP) with in silico prediction of targets and generation of computed DNA microarrays. We report the use of ChEST in Drosophila to identify several previously unknown targets of myocyte enhancer factor 2 (MEF2), a key regulator of myogenic differentiation. Our approach was validated by demonstrating that the identified sequences act as enhancers in vivo and are able to drive reporter gene expression specifically in MEF2-positive muscle cells. Presented here, the ChEST strategy was originally designed to identify regulatory modules in Drosophila, but it can be adapted for any sequenced and annotated genome.

Animals↗