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PubMed2005

Multiple target screening method for robust and accurate in silico ligand screening.

We developed a new in silico multiple target screening (MTS) method, based on a multi-receptor versus multi-ligand docking affinity matrixes, and examined its robustness against changes in the scoring system. According to this method, compounds in a database are docked to multiple proteins. The compounds among these proteins that are likely bind to the target protein are selected as the members of the candidate-hit compound group. Then, the compounds in the group are sorted into descending order using the docking score: the first (n-th) compound is expected to be the most (n-th) probable hit compound. This method was applied to the analysis of a set of 142 receptors and 142 compounds using a receptor-ligand docking program, Sievgene [Y. Fukunishi, Y. Mikami, H. Nakamura, Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening, J. Mol. Graphics Modelling, 24 (2005) 34-45], and the results demonstrated that this method achieves a high hit ratio compared to uniform sampling. We prepared two new scores: the DeltaG score, designed to reproduce the protein-ligand binding free energy, and the hit-optimized score, designed to maximize the hit ratio of in silico screening. Using the Sievgene docking score, DeltaG score and hit-optimized score, the MTS method is more robust than the multiple active-site correction scoring method [G.P.A. Vigers, J.P. Rizzi, Multiple active site corrections for docking and virtual screening, J. Med. Chem., 47 (2004) 80-89].

Databases, Factual↗

PubMed2025

Multi-criteria decision making and its application to in silico discovery of vaccine candidates for Toxoplasma gondii.

Vaccine discovery against eukaryotic parasites is not trivial and few exist. Reverse vaccinology is an in silico vaccine discovery approach, designed to identify vaccine candidates from the thousands of protein sequences encoded by a target genome. Previously, we produced the Vacceed bioinformatics pipeline for identification of parasite membrane and excreted/secreted proteins that were likely be exposed to the hosts immune system. More recently, we improved upon machine learning as the final decision-making process to identify parasite proteins that induce a protective response in an animal model. Subsequently, we combined Vacceed with metrics on B and T cell epitope types to produce a new in silico discovery workflow. In this study we extend this in silico workflow to the developability of proteins as vaccines by the incorporation of metrics on the physicochemical properties of proteins. To demonstrate this process, every Toxoplasma gondii protein was ranked in its capacity to provide exposure to the immune system (Vacceed exposure score), presence of epitopes and solubility characteristics by several multicriteria decision making (MCDM) tools (such as TOPSIS, VIKOR and MABAC). A consensus rank was subsequently generated from the results of these tools using a variety of aggregate ranking methods. Levels of uncertainty in the aggregate protein rankings was assessed by conformal interval prediction in association with a machine learning model. Several of the top ranked proteins identified by this approach were novel, uncharacterized membrane transporters or proteins associated with RNA metabolism. In conclusion, MCDM automated the decision making using well known algorithms while conformal prediction intervals varied significantly across the 8000+ proteins of T. gondii. Highly ranked proteins (e.g. the top 100) typically generated low prediction intervals, providing high levels of confidence in their ranks.

Toxoplasma↗

PubMed2002

In silico prediction of ADME and pharmacokinetics. Report of an expert meeting organised by COST B15.

The computational approach is one of the newest and fastest developing techniques in pharmacokinetics, ADME (absorption, distribution, metabolism, excretion) evaluation, drug discovery and toxicity. However, to date, the software packages devoted to ADME prediction, especially of metabolism, have not yet been adequately validated and still require improvements to be effective. Most are 'open' systems, under constant evolution and able to incorporate rapidly, and often easily, new information from user or developer databases. Quantitative in silico predictions are now possible for several pharmacokinetic (PK) parameters, particularly absorption and distribution. The emerging consensus is that the predictions are no worse than those made using in vitro tests, with the decisive advantage that much less investment in technology, resources and time is needed. In addition, and of critical importance, it is possible to screen virtual compounds. Some packages are able to handle thousands of molecules in a few hours. However, common experience shows that, in part at least for essentially irrational reasons, there is currently a lack of confidence in these approaches. An effort should be made by the software producers towards more transparency, in order to improve the confidence of their consumers. It seems highly probable that in silico approaches will evolve rapidly, as did in vitro methods during the last decade. Past experience with the latter should be helpful in avoiding repetition of similar errors and in taking the necessary steps to ensure effective implementation. A general concern is the lack of access to the large amounts of data on compounds no longer in development, but still kept secret by the pharmaceutical industry. Controlled access to these data could be particularly helpful in validating new in silico approaches.

Adsorption↗

PubMed2003

In silico prediction of blood-brain barrier permeation.

This review examines the progress that is being made towards the in silico prediction of brain permeation. Following a brief introduction to the blood-brain barrier, the datasets currently available for in silico modeling are discussed. Recent developments in in silico models of brain permeation are summarized in the context of the current state of the art in prediction accuracy. An analysis of recent models is presented, focusing on what such models reveal about the molecular properties that determine brain permeation. The review concludes by presenting the current key issues in this area of research, noting in particular, the paucity of brain permeation data available for modeling. Finally, possible future directions are suggested.

Algorithms↗

PubMed2003

In silico multicellular systems biology and minimal genomes.

The in vivo and in silico understanding of genomes and networks in cellular and multicellular systems is essential for drug discovery for multicellular diseases. In silico methodologies, when integrated with in vivo engineering methods, lay the groundwork for understanding multicellular organisms and their genomes. The quest to construct a minimal cell can be followed by designed, minimal multicellular organisms. In silico multicellular systems biology will be essential in the design and construction of minimal genomes for minimal multicellular organisms. Advanced methodologies come to light that can aid drug discovery. These novel approaches include multicellular pharmacodynamics and networked multicellular pharmacodynamics.

Animals↗

PubMedDate not supplied

Probing fragment complementation by rigid-body docking: in silico reconstitution of calbindin D9k.

Fragment complementation is gaining an increasing impact as a nonperturbing method to probe noncovalent interactions within protein supersecondary structures. In this study, the fast Fourier transform rigid-body docking algorithm ZDOCK has been employed for in silico reconstitution of the calcium binding protein calbindin D9k, from its two EF-hands subdomains, namely, EF1 (residues 1-43) and EF2 (residues 44-75). The EF1 fragment has been used both in its wild type and in nine mutant forms, in line with in vitro experiments. Consistent with in vitro data, ZDOCK reconstituted the proper fold of wild-type and mutated calbindin, locating the nativelike structures (i.e., holding a root-mean-square deviation < 1 A with respect to the X-ray structure) among the first 10 top-scored solutions out of 4000. Moreover, the three independent in silico reconstitutions of wild-type calbindin ranked a nativelike structure at the top of the output list, that is, the best scored one. The algorithm has been also successfully challenged in reconstituting the EF2 homodimer from two identical copies of the monomer. Furthermore, quantitative models consisting of linear correlations between thermodynamic data and ZDOCK scores were built, providing a tested tool for very fast in silico predictions of the free energy of association of protein-protein complexes solved at the atomic level and known to not undergo significant conformational changes upon binding.

Calbindins↗

PubMedDate not supplied

In silico techniques for the study and prediction of xenobiotic metabolism: a review.

Knowledge about metabolism is very important to understand the health risks posed by chemicals. The biochemical process of metabolism causes activation, inactivation, toxification, detoxification as well as changes in the physicochemical properties of a chemical. The long time consumption and high costs associated with animal tests and the challenges faced by traditional quantitative structure-activity relationship (QSAR) models in dealing with situations wherein parent chemical structures are less relevant to the ultimate effects have led to the development of in silico techniques for the prediction of xenobiotic metabolism. The strengths and limitations of some of the most commonly used in silico expert systems, and their application in studying metabolism of xenobiotic chemicals, have been reviewed. The in silico metabolism simulators possessed several distinguishing features imparted in part by the nature of knowledge rules (algorithms) encoded within them and in part by the integration of QSAR libraries and computational engines.

Algorithms↗

PubMed2025

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we have integrated Carafe into the widely used Skyline tool.

Journal Article↗

PubMed2025

In vivo and in silico models of Drosophila for Parkinson's disease.

The fruit fly Drosophila melanogaster has emerged as an important model organism to shed light on neurodegeneration. Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, the cause of which is still mostly unclear. The long-term use of available PD drugs may have major side effects, and they only target the symptoms without providing any effective cure for the disease. Therefore, in vivo and in silico approaches are extensively used to model PD-like phenotypes in Drosophila and investigate cellular alterations underlying PD pathogenesis. In vivo models are particularly crucial to provide insight into the PD-related molecular processes. It has been a preferred approach to investigate these models by collecting omics datasets, which can be further analysed using in silico modeling such as genome-scale metabolic models and artificial intelligence applications. This review aims to summarise in vivo and in silico modeling studies in the literature to illustrate the potential of the Drosophila in the characterisation of PD-related biological mechanisms towards providing early biomarkers and novel treatment options for PD.

Humans↗

PubMed1994

A comparison of two silicoating techniques.

PURPOSE: A major challenge of composite-coated metal restorations is creating a strong bond between the two materials. This study was conducted to compare the bond strengths between composite and metal alloys using two silicoating treatments; the Silicoater Classic and the Silicoater MD (Kulzer Inc, Irvine, CA). MATERIALS AND METHODS: The Classic uses a pyrolytically applied silica glass and the MD process is an oven sintering of a metal oxide silicate layer. Two types of specimens of three different alloys were cast (Deva4: 51Au38Pd, G-Cast: 50Au32Ag12Cu [Degussa Corp. South Plainfield, NJ]; Ticonium: 70Ni15Cr [CMP Industries, Inc, Albany NY]). The specimens were silicoated with either the Classic or the MD process and opaque resin and composite (Dentacolor, Kulzer Inc) were bonded to each surface. A total of five control and 10 thermocycled (5,040 cycles 0 degree C to 67 degrees C; dwell time, 1 minute) specimens were fabricated for each alloy and specimen type. Two bond strength tests were used; a three-point flexure test and a shear bond strength test. Data were analyzed using a multivariate analysis of variance (MANOVA) and a Tukey's multiple comparison test. RESULTS: The standard deviations of the shear bond strength values ranged from +/- 2.30 to +/- 17.82.kg; thus, there was no significant difference in these results. Bond strength ratios were calculated from the flexure strength data. After thermocycling, the Silicoater MD produced significantly higher (P < 0.05) bond strength ratio values compared with the Silicoater Classic for Deva-4 (MD, 9.6 +/- 1.5 v Classic, 4.7 +/- 0.9) and G-Cast (MD, 11.6 +/- 1.2 v Classic, 3.9 +/- 0.5) but lower values for Ticonium (MD, 4.5 +/- 0.4 v Classic, 6.9 +/- 0.9). CONCLUSION: The Silicoater MD produced higher bond strength ratio values with noble alloys, and the Silicoater Classic produced higher bond strength ratio results when base metal alloys were used, suggesting a possible correlation between the effectiveness of the coating processes and the compositions of the alloys. These findings suggest that, for optimum results, selection of the type of silicoating process should be based on the components of the alloy.

Composite Resins↗

PubMed1988

Silicoating: evaluation of a new method of bonding composite resin to metal.

Silicoating is a new type of pretreatment of the metal in the resin-bonded bridge technique. The size of bond strength between resin cement and silicoated metal was investigated. Different resin cements, types of metal, and grain sizes of sand used for roughening the metal surface were tested. Furthermore, the effect on the bond strength of contamination with saliva and of thermocycling was measured. There was no difference of importance between the two resin cements tested. Bond strengths obtained with Wirobond were the highest and insensible to changes in resin cement, grain size of sand, and to thermocycling. It was found that sand of the smallest grain size used brought about the highest bond strengths. Contamination with saliva did not significantly reduce the size of the bond strengths. Rinsing the metal surfaces with water or ethanol after contamination restored the bond strengths to their original magnitude. By silicoating, a greater resistance to thermocycling was obtained. Nevertheless, except for Wirobond, a fall in bond strength was measured after thermocycling. Bond strengths of the size of those between etched enamel and composite resin were measured, and the Silicoater method seems a reliable pretreatment of the metal.

Composite Resins↗

PubMed2006

The challenge of predicting drug toxicity in silico.

Poor pharmacokinetics, side effects and compound toxicity are frequent causes of late-stage failures in drug development. A safe in silico identification of adverse effects triggered by drugs and chemicals would be highly desirable as it not only bears economical potential but also spawns a variety of ecological benefits: sustainable resource management, reduction of animal models and possibly less risky clinical trials. In computer-aided drug discovery, both existing and hypothetical compounds may be studied; the methods are fast, reproducible, and typically based on human bioregulators, making the question of transferability obsolete. In the recent past, our laboratory contributed towards the development of in silico concepts (--> multi-dimensional QSAR) and validated a series of "virtual test kits" based on the oestrogen, androgen, thyroid, and aryl hydrocarbon receptor (endocrine disruption, receptor-mediated toxicity) as well as on the enzyme cytochrome P450 3A4 (metabolic transformations, drug-drug interactions). The test kits are based on the three-dimensional structure of their target protein (i.e. ER(alphabeta), AR, TR(alphabeta), CYP450) or a surrogate thereof (AhR) and were trained using a representative selection of 362 substances. Subsequent evaluation of 107 compounds different therefrom showed that binding affinities are predicted close to experimental uncertainty. These results suggest that our approach is suited for the in silico identification of adverse effects triggered by drugs and chemicals and encouraged us to compile an Internet Database for the virtual screening of drugs and chemicals for toxic effects.

Computational Biology↗

PubMed2005

In silico predictions of blood-brain barrier penetration: considerations to "keep in mind".

Within drug discovery, it is desirable to determine whether a compound will penetrate and distribute within the central nervous system (CNS) with the requisite pharmacokinetic and pharmacodynamic performance required for a CNS target or if it will be excluded from the CNS, wherein potential toxicities would mitigate its applicability. A variety of in vivo and in vitro methods for assessing CNS penetration have therefore been developed and applied to advancing drug candidates with the desired properties. In silico methods to predict CNS penetration from chemical structures have been developed to address virtual screening and prospective design. In silico predictive methods are impacted by the quality, quantity, sources, and generation of the measured data available for model development. Key considerations for predictions of CNS penetration include the comparison of local (in chemistry space) versus global (more structurally diverse) models and where in the drug discovery process such models may be best deployed. Preference should also be given to in vitro and in vivo measurements of greater mechanistic clarity that better support the development of structure-property relationships. Although there are numerous statistical methods that have been brought to bear on the prediction of CNS penetration, a greater concern is that such models are appropriate for the quality of measured data available and are statistically validated. In addition, the assessment of prediction uncertainty and relevance of predictive models to structures of interest are critical. This article will address these key considerations for the development and application of in silico methods in drug discovery.

Animals↗

PubMed2005

Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation.

Comparative analysis of the genomes of mixed-acid-fermenting Escherichia coli and succinic acid-overproducing Mannheimia succiniciproducens was carried out to identify candidate genes to be manipulated for overproducing succinic acid in E. coli. This resulted in the identification of five genes or operons, including ptsG, pykF, sdhA, mqo, and aceBA, which may drive metabolic fluxes away from succinic acid formation in the central metabolic pathway of E. coli. However, combinatorial disruption of these rationally selected genes did not allow enhanced succinic acid production in E. coli. Therefore, in silico metabolic analysis based on linear programming was carried out to evaluate the correlation between the maximum biomass and succinic acid production for various combinatorial knockout strains. This in silico analysis predicted that disrupting the genes for three pyruvate forming enzymes, ptsG, pykF, and pykA, allows enhanced succinic acid production. Indeed, this triple mutation increased the succinic acid production by more than sevenfold and the ratio of succinic acid to fermentation products by ninefold. It could be concluded that reducing the metabolic flux to pyruvate is crucial to achieve efficient succinic acid production in E. coli. These results suggest that the comparative genome analysis combined with in silico metabolic analysis can be an efficient way of developing strategies for strain improvement.

Base Sequence↗

PubMed2003

In silico quantitative trait locus map for atherosclerosis susceptibility in apolipoprotein E-deficient mice.

OBJECTIVE: Atherosclerosis susceptibility is a genetic trait that varies between mouse strains. The goal of this study was to use a public mouse single nucleotide polymorphism (SNP) database to define the genetic loci that are associated with this trait, without the need to perform strain intercrosses that are normally required to obtain these loci. METHODS AND RESULTS: Apolipoprotein E (apoE)-deficient mice on 6 inbred genetic backgrounds were compared for atherosclerosis lesion size in the aortic root in 2 independent studies. After normalization to the C57BL/6 strain that was used in both studies, lesion areas were found in the following rank order: DBA/2J>C57BL/6>129/SV-ter>AKR/J approximately BALB/cByJ approximately C3H/HeJ. The log lesion difference in phenotypes between each of the 15 heterologous strain pairs was determined. A mouse SNP database was then used to calculate the genetic differences between the 15 strain pairs in partially overlapping 30-cM bins across the mouse genome. Correlation analyses were preformed to analyze the genetic and phenotypic differences among the strain pairs for each genetic region. The genetic regions with the highest correlations define the in silico quantitative trait loci (QTL) associated with the atherosclerosis phenotype. Five in silico atherosclerosis QTL were identified on chromosomes 1, 10, 14, 15, and 18. The loci on chromosomes 1, 10, 14, and 18 overlap with suggestive atherosclerosis QTL identified through analyses of an F(2) cohort derived from apoE-deficient mice on the C57BL/6 and FVB/N strains. CONCLUSIONS: The 5 identified in silico QTL are candidates for further study to confirm the presence and identity of atherosclerosis susceptibility genes within these loci.

Animals↗

PubMed2004

IdentiCS--identification of coding sequence and in silico reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence.

BACKGROUND: A necessary step for a genome level analysis of the cellular metabolism is the in silico reconstruction of the metabolic network from genome sequences. The available methods are mainly based on the annotation of genome sequences including two successive steps, the prediction of coding sequences (CDS) and their function assignment. The annotation process takes time. The available methods often encounter difficulties when dealing with unfinished error-containing genomic sequence. RESULTS: In this work a fast method is proposed to use unannotated genome sequence for predicting CDSs and for an in silico reconstruction of metabolic networks. Instead of using predicted genes or CDSs to query public databases, entries from public DNA or protein databases are used as queries to search a local database of the unannotated genome sequence to predict CDSs. Functions are assigned to the predicted CDSs simultaneously. The well-annotated genome of Salmonella typhimurium LT2 is used as an example to demonstrate the applicability of the method. 97.7% of the CDSs in the original annotation are correctly identified. The use of SWISS-PROT-TrEMBL databases resulted in an identification of 98.9% of CDSs that have EC-numbers in the published annotation. Furthermore, two versions of sequences of the bacterium Klebsiella pneumoniae with different genome coverage (3.9 and 7.9 fold, respectively) are examined. The results suggest that a 3.9-fold coverage of the bacterial genome could be sufficiently used for the in silico reconstruction of the metabolic network. Compared to other gene finding methods such as CRITICA our method is more suitable for exploiting sequences of low genome coverage. Based on the new method, a program called IdentiCS (Identification of Coding Sequences from Unfinished Genome Sequences) is delivered that combines the identification of CDSs with the reconstruction, comparison and visualization of metabolic networks (free to download at http://genome.gbf.de/bioinformatics/index.html). CONCLUSIONS: The reversed querying process and the program IdentiCS allow a fast and adequate prediction protein coding sequences and reconstruction of the potential metabolic network from low coverage genome sequences of bacteria. The new method can accelerate the use of genomic data for studying cellular metabolism.

Base Sequence↗

PubMed2005

Novel curcumin- and emodin-related compounds identified by in silico 2D/3D conformer screening induce apoptosis in tumor cells.

BACKGROUND: Inhibition of the COP9 signalosome (CSN) associated kinases CK2 and PKD by curcumin causes stabilization of the tumor suppressor p53. It has been shown that curcumin induces tumor cell death and apoptosis. Curcumin and emodin block the CSN-directed c-Jun signaling pathway, which results in diminished c-Jun steady state levels in HeLa cells. The aim of this work was to search for new CSN kinase inhibitors analogue to curcumin and emodin by means of an in silico screening method. METHODS: Here we present a novel method to identify efficient inhibitors of CSN-associated kinases. Using curcumin and emodin as lead structures an in silico screening with our in-house database containing more than 10(6) structures was carried out. Thirty-five compounds were identified and further evaluated by the Lipinski's rule-of-five. Two groups of compounds can be clearly discriminated according to their structures: the curcumin-group and the emodin-group. The compounds were evaluated in in vitro kinase assays and in cell culture experiments. RESULTS: The data revealed 3 compounds of the curcumin-group (e.g. piceatannol) and 4 of the emodin-group (e.g. anthrachinone) as potent inhibitors of CSN-associated kinases. Identified agents increased p53 levels and induced apoptosis in tumor cells as determined by annexin V-FITC binding, DNA fragmentation and caspase activity assays. CONCLUSION: Our data demonstrate that the new in silico screening method is highly efficient for identifying potential anti-tumor drugs.

Annexin A5↗

PubMed2004

Significance tests and weighted values for AFLP similarities, based on Arabidopsis in silico AFLP fragment length distributions.

Many AFLP studies include relatively unrelated genotypes that contribute noise to data sets instead of signal. We developed: (1) estimates of expected AFLP similarities between unrelated genotypes, (2) significance tests for AFLP similarities, enabling the detection of unrelated genotypes, and (3) weighted similarity coefficients, including band position information. Detection of unrelated genotypes and use of weighted similarity coefficients will make the analysis of AFLP data sets more informative and more reliable. Test statistics and weighted coefficients were developed for total numbers of shared bands and for Dice, Jaccard, Nei and Li, and simple matching (dis)similarity coefficients. Theoretical and in silico AFLP fragment length distributions (FLDs) were examined as a basis for the tests. The in silico AFLP FLD based on the Arabidopsis thaliana genome sequence was the most appropriate for angiosperms. The G + C content of the selective nucleotides in the in silico AFLP procedure significantly influenced the FLD. Therefore, separate test statistics were calculated for AFLP procedures with high, average, and low G + C contents in the selective nucleotides. The test statistics are generally applicable for angiosperms with a G + C content of approximately 35-40%, but represent conservative estimates for genotypes with higher G + C contents. For the latter, test statistics based on a rice genome sequence are more appropriate.

Arabidopsis↗