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Results for “In silico modeling”

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PubMed2003

Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices.

MDL QSAR (formerly SciVision QSAR IS) software is one of the several software systems under evaluation by the Informatics and Computational Safety Analysis Staff (ICSAS) of the FDA Center for Drug Evaluation and Research for regulatory and scientific decision support applications. MDL QSAR software contains an integrated set of tools for similarity searching, compound clustering, and modeling molecular structure related parameters that includes 240 electrotopological E-state, connectivity, and other descriptors. These molecular descriptors can be statistically correlated with toxicological or biological endpoints. The goal of this research was to evaluate the feasibility of using MDL QSAR software to develop structure-activity relationship (SAR) models that can be used to predict the carcinogenic potential of pharmaceuticals and organic chemicals. A validation study of 108 compounds that include 86 pharmaceuticals and 22 chemicals that were not present in a control rodent carcinogenicity data set of 1275 compounds demonstrated that MDL QSAR models had excellent coverage (93%) and good sensitivity (72%) and specificity (72%) for rodent carcinogenicity. The software correctly predicted 72% of non-carcinogenic compounds and compounds with carcinogenic findings. E-state descriptors contributed to more than half of the SAR models used to predict carcinogenic activity. We believe that electrotopological E-state descriptors and QSAR IS (MDL QSAR) software are promising new in silico approaches for modeling and predicting rodent carcinogenicity and may have application for other toxicological endpoints.

Algorithms↗

PubMed2002

A homology-based model of the human 5-HT2A receptor derived from an in silico activated G-protein coupled receptor.

A homology-based model of the 5-HT2A receptor was produced utilizing an activated form of the bovine rhodopsin (Rh) crystal structure. In silico activation of the Rh structure was accomplished by isomerization of the 11-cis-retinal (1) chromophore, followed by constrained molecular dynamics to relax the resultant high energy structure. The activated form of Rh was then used as a structural template for development of a human 5-HT2A receptor model. Both the 5-HT2A receptor and Rh are members of the G-protein coupled receptor (GPCR) super-family. The resulting homology model of the receptor was then used for docking studies of compounds representing a cross-section of structural classes that activate the 5-HT2A receptor, including ergolines, tryptamines, and amphetamines. The ligand/receptor complexes that ensued were refined and the final binding orientations were observed to be compatible with much of the data acquired through both diversified ligand design and site directed mutagenesis.

GTP-Binding Proteins↗

PubMed2026

ChromBERT-tools: a versatile toolkit for context-specific regulatory representations of transcription regulators across different cell types.

SUMMARY: Representations that encode the genome-wide regulatory behavior of transcription regulators provide a foundation for flexible transcription modeling and in silico regulatory analysis. Existing regulator representations are commonly derived from gene co-expression, motif annotations, or static protein features, which capture useful but limited aspects of regulator identity but do not directly model how regulators participate in region-specific regulatory programs across the genome. ChromBERT addresses this gap by learning context-aware regulatory representations from large-scale ChIP-seq data. However, routine bioinformatics applications require lightweight, accessible, and modular tools for generating, adapting, and interpreting these representations in user-defined biological contexts. Here, we present ChromBERT-tools, a user-oriented toolkit built upon ChromBERT that converts its regulatory representation framework into practical workflows for customizable analysis across cellular contexts. ChromBERT-tools provides command-line interfaces and Python APIs organized into three functional layers: representation generation, predictive modeling, and regulatory interpretation. The representation generation layer produces representations of genomic regions and transcription regulators. The predictive modeling layer fine-tunes ChromBERT for genome-wide regulatory activity prediction through classification or regression tasks, with optimized implementation to reduce running time and computational resource requirements. The regulatory interpretation layer supports inference of the context-specific roles of cis-regulatory elements and transcription regulators. These modules can be used independently or integrated into end-to-end workflows, enabling flexible analyses across diverse datasets. ChromBERT-tools lowers the barrier to applying context-specific regulatory representations in routine genomic analyses. AVAILABILITY AND IMPLEMENTATION: ChromBERT-tools is freely available at https://github.com/TongjiZhanglab/ChromBERT-tools, with documentation at https://chrombert-tools.readthedocs.io/en/latest/. A frozen archival snapshot is available on Zenodo under DOI: 10.5281/zenodo.20094206.

Software↗

PubMed2005

Greater than the sum of its parts: combining models for useful ADMET prediction.

In silico ADMET (absorption, distribution, metabolism, excretion, and toxicity) models are important tools in combating late-stage attrition in the drug discovery process. This work shows how ADMET models can be combined to tailor predictions depending on one's needs. We demonstrate how the judicious use of data and considered combination of predictions can produce models that provide truly useful answers. This approach is illustrated with the prediction of hERG channel blocking and cytochrome P450 2D6 inhibition, where combination of two predictive models (with >80% of compounds correctly predicted) resulted in models with even better predictive values (with >90% of compounds correctly predicted for those classes of interest).

Bayes Theorem↗

PubMed2006

Hydrophobic collapse in (in silico) protein folding.

A model of hydrophobic collapse, which is treated as the driving force for protein folding, is presented. This model is the superposition of three models commonly used in protein structure prediction: (1) 'oil-drop' model introduced by Kauzmann, (2) a lattice model introduced to decrease the number of degrees of freedom for structural changes and (3) a model of the formation of hydrophobic core as a key feature in driving the folding of proteins. These three models together helped to develop the idea of a fuzzy-oil-drop as a model for an external force field of hydrophobic character mimicking the hydrophobicity-differentiated environment for hydrophobic collapse. All amino acids in the polypeptide interact pair-wise during the folding process (energy minimization procedure) and interact with the external hydrophobic force field defined by a three-dimensional Gaussian function. The value of the Gaussian function usually interpreted as a probability distribution is treated as a normalized hydrophobicity distribution, with its maximum in the center of the ellipsoid and decreasing proportionally with the distance versus the center. The fuzzy-oil-drop is elastic and changes its shape and size during the simulated folding procedure.

Amino Acids↗

PubMed2006

Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening.

A pharmacophoric model was developed for human protein tyrosine phosphatase 1B (h-PTP 1B) inhibitors utilizing the HipHop-REFINE module of CATALYST software. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of physicochemical descriptors and pharmacophore hypothesis that yield consistent QSAR equation of good predictive potential (r = 0.87,F-statistic = 69.13,r(BS)2 = 0.76,r(LOO)2 = 0.68). The validity of the QSAR equation and the associated pharmacophoric hypothesis was experimentally established by the identification of five new h-PTP 1B inhibitors retrieved from the National Cancer Institute (NCI) database.

Algorithms↗

PubMed2026

MIF Promoter Variant rs755622 (-173G/C) in Younger and Older Turkish Adults: An Exploratory Cross-Sectional Genetic and in Silico Analysis.

Age-associated immune-inflammatory remodeling may be influenced by regulatory variation in the macrophage migration inhibitory factor gene (MIF). We conducted an exploratory cross-sectional comparison to assess whether MIF rs755622 (-173G/C) genotype distributions differ between predefined younger and older age groups in a Turkish population and to characterize the observed pattern using genetic-model and in silico analyses. We evaluated 368 individuals: 245 older adults aged 65-102 years and 123 younger controls aged 20-46 years. None of the 26 main association tests remained statistically significant after global multiplicity correction (minimum FDR q = 0.062; minimum Bonferroni-adjusted p = 0.108). Before correction, GC frequency was higher in the older group and increased across the ordered age categories, and sex-adjusted analyses yielded concordant nominal estimates. However, these nominal patterns were sensitive to younger-control genotype reclassification and were not supported by an allele-level or additive association. Younger controls showed Hardy-Weinberg disequilibrium (p < 0.001), without sequencing confirmation, and deterministic and scenario-based Monte Carlo genotype-reclassification analyses indicated sensitivity of the nominal signal to uncertainty in control genotype classification. GTEx data provide C-allele-oriented expression context but do not validate function in this cohort. These preliminary findings warrant independent genotype verification, ancestry-matched replication, and direct functional investigation in future studies.

Humans↗

PubMed2003

Role of ADME characteristics in drug discovery and their in silico evaluation: in silico screening of chemicals for their metabolic stability.

Drug discovery is a long, arduous process broadly grouped into disease target identification, target validation, high-throughput identification of "hits" and "leads", lead optimization, and pre-clinical and clinical evaluation. Each area is a vast discipline in itself. However, all but the first two stages involve, to varying degrees, the characterization of absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of the molecules being pursued as potential drug candidates. Clinical failures of about 50% of the Investigational New Drug (IND) filings are attributed to their inadequate ADMET attributes. It is, therefore, no surprise that, in the current climate of social and regulatory pressure on healthcare costs, the pharmaceutical industry is searching for any means to minimize this attrition. Building mathematical models, called in silico screens, to reliably predict ADMET attributes solely from molecular structure is at the heart of this effort in reducing costs as well as development cycle times. This article reviews the emerging field of in silico evaluation of ADME characteristics. For different approaches that have been employed in this area, a critique of the scope and limitations of their descriptors, statistical methods, and reliability are presented. For instance, are geometry-based descriptors absolutely essential or is lower-level structure quantification equally good? What advantages, if any, do we have for methods like artificial neural networks over the least squares optimization methods with rigorous statistical diagnostics? Is any in silico screen worth application, let alone interpretation, if it is not adequately validated? Once deemed acceptable, what good is an in silico screen if it cannot be made available at the workbench of drug discovery teams distributed across the globe throughout multi-national pharmaceutical companies? These are not mere discussion points, rather this article embarks on the stepwise mechanics of developing a successful in silico screen. The process is exemplified by our efforts in developing one such screen for predicting metabolic stability of chemicals in a human S9 liver homogenate assay. A real-life use of this in silico screen in a variety of discovery projects at GlaxoSmithKline is presented, highlighting successes and limitations of such applications. Finally, we project some capabilities of in silico ADME tools for greater impact and contribution to successful, efficient drug discovery.

Computer-Aided Design↗

PubMed2005

In silico phenotypic screening method of mutants based on statistical modeling of genetically mixed samples.

In comprehensive functional genomics projects, systematic analysis of phenotypes is vital. However, conventional phenotypic screening is done mainly by imprecise visual observation of qualitative traits, and, therefore, in silico screening techniques for quantitative traits are required. In this report, we propose in silico phenotypic screening method that utilizes a Gaussian mixture model for the trait distribution in the offspring of a mutagenized line and the likelihood ratio test between the estimated Gaussian mixture model and the wild-type single Gaussian model. In order to evaluate the proposed method, we performed a screening experiment using real trait data of Arabidopsis. In this experiment, the proposed screening method properly distinguished the mutant line from the wild-type line. Furthermore, we conducted power analysis of the proposed method and two conventional methods under various simulated conditions of sample size and distribution of trait frequency. The result of the power analysis confirmed the effectiveness of the proposed method compared to the conventional methods.

Algorithms↗

PubMed2002

Analysis of the C-terminal membrane anchor domains of hepatitis C virus glycoproteins E1 and E2: toward a topological model.

The hepatitis C virus (HCV) glycoproteins E1 and E2 should be anchored in the viral membrane by their C-terminal domains. During synthesis, they are translocated to the endoplasmic reticulum (ER) lumen where they remain. The 31 C-terminal residues of the E1 protein and the 29 C-terminal residues of the E2 protein are implicated in the ER retention. Moreover, the E1 and E2 C termini are implicated in E1-E2 heterodimerization. We studied the E1 and E2 C-terminal sequences of 25 HCV strains in silico using molecular modeling techniques. We conclude that both C-terminal domains should adopt a similar and peculiar configuration: one amphipathic alpha-helix followed by a pair of transmembrane beta-strands. Several three-dimensional (3-D) models were generated. After energy minimization, their ability to interact with membranes was studied using the molecular hydrophobicity potentials calculation and the IMPALA procedure. The latter simulates interactions with a membrane by a Monte Carlo minimization of energy. These methods suggest that the beta-hairpins could anchor the glycoproteins in the ER membrane at least transiently. Anchoring could be stabilized by the adsorption of the nearby amphipathic alpha-helices at the membrane surface. The 3-D models correlate with experimental results which indicate that the E1-E2 transmembrane domains are involved in the heterodimerization and have ER retention properties.

Amino Acid Sequence↗

PubMed2004

Topology scanning and putative three-dimensional structure of the extracellular binding domains of the apical sodium-dependent bile acid transporter (SLC10A2).

The apical sodium-dependent bile acid transporter (ASBT, SLC10A2) facilitates the enterohepatic circulation of bile salts and plays a key role in cholesterol metabolism. The membrane topology of ASBT was initially scanned using a consensus topography analysis that predominantly predicts a seven transmembrane (TM) domain configuration adhering to the "positive inside" rule. Membrane topology was further evaluated and confirmed by N-glycosylation-scanning mutagenesis, as reporter sites inserted in the putative extracellular loops 1 and 3 were glycosylated. On the basis of a 7TM topology, we built a three-dimensional model of ASBT using an approach of homology-modeling and remote-threading techniques for the extramembranous domains using bacteriorhodopsin as a scaffold for membrane attachment points; the model was refined using energy minimizations and molecular dynamics simulations. Ramachandran scores and other geometric indicators show that the model is comparable in quality to the crystal structures of similar proteins. Simulated annealing and docking of cholic acid, a natural substrate, onto the protein surface revealed four distinct binding sites. Subsequent site-directed mutagenesis of the predicted binding domain further validated the model. This model agrees further with available data for a pathological mutation (P290S) because the mutant model after in silico mutagenesis loses the ability to bind bile acids.

Amino Acid Sequence↗

PubMed2025

Function-based selection of synthetic communities enables mechanistic microbiome studies.

Understanding the complex interactions between microbes and their environment requires robust model systems such as synthetic communities (SynComs). We developed a functionally directed approach to generate SynComs by selecting strains that encode key functions identified in metagenomes. This approach enables the rapid construction of SynComs tailored to any ecosystem. To optimize community design, we implemented genome-scale metabolic models, providing in silico evidence for cooperative strain coexistence prior to experimental validation. Using this strategy, we designed multiple host-specific SynComs, including those for the rumen, mouse, and human microbiomes. By weighting functions differentially enriched in diseased versus healthy individuals, we constructed SynComs that capture complex host-microbe interactions. We designed an inflammatory bowel disease SynCom of 10 members that successfully induced colitis in gnotobiotic IL10-/- mice, demonstrating the potential of this method to model disease-associated microbiomes. Our study establishes a framework for designing functionally representative SynComs of any microbial ecosystem, facilitating mechanistic study.

Animals↗

PubMed2006

Hydrophobic collapse in late-stage folding (in silico) of bovine pancreatic trypsin inhibitor.

Hydrophobic collapse is commonly considered as a process of significance for protein folding. Many models have been applied for description of this event. A model introducing an external force field mimicking the hydrophobic environment and simultaneously the driving force for the folding process is presented in this paper. Bovine pancreatic trypsin inhibitor (BPTI) was taken as a test protein. An early-stage folding (in silico) model presented elsewhere was used to create the starting structure for hydrophobic collapse. The resulting structure was energy-refined using molecular dynamics simulation in an explicit solvent. The similarity versus the crystal structure of BPTI is estimated using visual analysis, residue-residue contacts, phi, psi angle distributions, RMSD, accessible solvent area, radii of gyration and hydrodynamic radii. A program allowing creation of early-stage folding structural forms to be created for any protein is available from http://bioinformatics.cm-uj.krakow.pl/earlystage. The program for late-stage folding simulation is available on request.

Animals↗

PubMed2005

Highly active mutants of carbonyl reductase S1 with inverted coenzyme specificity and production of optically active alcohols.

A wild type NADPH-dependent carbonyl reductase from Candida magnoliae (reductase S1) has been found not to utilize NADH as a coenzyme. A mutation to exchange the coenzyme specificity in reductase S1 has been designed by computer-aided methods, including three-dimensional structure modeling and in silico screening of enzyme mutants. Site-directed mutagenesis has been used to introduce systematic substitutions of seven or eight amino acid residues onto the adenosine-binding pocket of the enzyme according to rational computational design. The resulting S1 mutants show NADH-dependency and have lost their ability to utilize NADPH as a coenzyme, but retain those catalytic activities. Kinetic parameter V(max) and K(m) values of those mutants for NADH are 1/3- to 1/10-fold those of the wild type enzyme for NADPH. As a model system for industrial production of optically active alcohols, the S1 mutants can be applied to an asymmetric reduction of ketones, cooperating with a coenzyme-regeneration system that uses an NAD-dependent formate dehydrogenase.

Alcohol Oxidoreductases↗

PubMed2005

Inhibition of human liver catechol-O-methyltransferase by tea catechins and their metabolites: structure-activity relationship and molecular-modeling studies.

(-)-Epigallocatechin-3-gallate (EGCG) is the major polyphenol present in green tea. We previously demonstrated that EGCG was both a substrate and potent inhibitor of human liver cytosolic catechol-O-methyltransferease (COMT). We now report the structure-activity relationship for the inhibition of COMT-catalyzed O-methylation of catecholestrogens in human liver cytosol by tea catechins and some of their metabolites. The most potent inhibitors were catechins with a galloyl-type D-ring, including EGCG (IC(50)=0.07 microM), 4''-O-methyl-EGCG (IC(50)=0.10 microM), 4',4''-di-O-methyl-EGCG (4',4''-DiMeEGCG) (IC(50)=0.15 microM), and (-)-epicatechin-3-gallate (ECG) (IC(50)=0.20 microM). Catechins without the D-ring showed two to three orders of magnitude less inhibitory potency. Enzyme kinetic analyses revealed that EGCG behaved as a mixed inhibitor, whereas 4',4''-di-O-methyl-EGCG exhibited competitive kinetics for the S-adenosylmethionine (SAM), and noncompetitive kinetics for the catechol binding site. These compounds may represent a new type of COMT inhibitor. In silico molecular-modeling studies using a homology model of human COMT were conducted to aid in the understanding the catalytic and inhibitory mechanisms. Either D-ring or B-ring of EGCG could be accommodated to the substrate binding pocket of human COMT. However, the close proximity (2.6A) of 4''-OH to the critical residue Lys144, the higher acidity of the hydroxyl groups of the D-ring, and the hydrophobic interactions between the D-ring and residues in the binding pocket greatly facilitated the interaction of the D-ring with the enzyme, and resulted in increased inhibitory potency. These results provide mechanistic insight into the inhibition of COMT by commonly consumed tea catechins.

Catechin↗

PubMed2005

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network.

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146+/-0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p=0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2005

Differential interactions of G-proteins and adenylyl cyclase with nucleoside 5'-triphosphates, nucleoside 5'-[gamma-thio]triphosphates and nucleoside 5'-[beta,gamma-imido]triphosphates.

The regulatory G-proteins of adenylyl cyclase (AC), G(i) and G(s), are not only activated by GTP and the stable GTP analogs, guanosine 5'-[gamma-thio]triphosphate (GTPgammaS) and guanosine 5'-[beta,gamma-imido]triphosphate (GppNHp), but also by hypoxanthine, xanthine, uracil and cytidine nucleotides. The latter nucleotides were previously used to analyze distinct active G-protein states. Surprisingly, recent studies have shown that inosine 5'-[gamma-thio]triphosphate and uridine 5'-[gamma-thio]triphosphate can also inhibit AC directly. Therefore, we systematically compared the interactions of nucleoside 5'-triphosphates (NTPs), nucleoside 5'-[gamma-thio]triphosphates (NTPgammaSs) and nucleoside 5'-[beta,gamma-imido]triphosphates (NppNHps) with G(i), G(s) and AC. NTPgammaSs exhibited up to 26,000-fold higher affinity for G-proteins than NTPs and NppNHps. NTPgammaSs were up to 150-fold more potent direct AC inhibitors than NTPs and NppNHps. G-proteins exhibited striking preference for guanine nucleotides compared to other purine and pyrimidine nucleotides, whereas base-selectivity of various ACs, particularly the purified catalytic subunits C1.C2, was rather poor. GTP, GTPgammaS and GppNHp exhibited much higher selectivity for G-proteins relative to AC than all other purine and pyrimidine nucleotides. We have energetically characterized the interactions of purine and pyrimidine nucleotides with AC in silico, constructing pharmacophore models that correlate well with experimental affinities and have elucidated specific amino acid residues with greatest influence on nucleotide binding. Collectively, both G-proteins and ACs bind purine and pyrimidine nucleotides, with G-proteins showing much higher base-selectivity than AC. Thus, direct inhibitory effects of nucleotides on AC should be understood and considered when probing distinct active G-protein states with non-guanine nucleotides.

Adenylyl Cyclases↗

PubMed2005

Relationships between structure and high-throughput screening permeability of diverse drugs with artificial membranes: application to prediction of Caco-2 cell permeability.

To evaluate the absorption of drugs with diverse structures across a membrane via the transcellular route, their permeability was measured using the parallel artificial membrane permeation assay (PAMPA). The permeability coefficients obtained by PAMPA were analyzed using a classical quantitative structure-activity relationship (QSAR) approach with simple physicochemical parameters and 3D-QSAR, VolSurf. We formulated correlation equations for diverse drugs similar to the equation obtained for peptide-related compounds in our previous study. The hydrogen-bonding ability of molecules, not only the hydrogen-accepting ability but also the hydrogen-donating ability, in addition to hydrophobicity at a particular pH, was significant in determining variations in PAMPA permeability coefficients. Based on this result, an in silico good prediction model for the passive transcellular permeability of diverse structural compounds was obtained. The artificial lipid-membrane permeability coefficients of the drugs, except salicylic acid, were well correlated with the Caco-2 permeability in a previous report suggesting the importance of absorption by the transcellular mechanism for these drugs.

Caco-2 Cells↗