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PubMed2006

TFBScluster web server for the identification of mammalian composite regulatory elements.

Identification of transcriptional regulatory elements represents a critical step in our ability to reconstruct transcriptional regulatory networks from gene expression profiling datasets. To facilitate computational identification of candidate gene regulatory elements from whole genome sequences, we have developed the TFBScluster web server that integrates several tools for the genome-wide identification and subsequent characterization of transcription factor binding site clusters that are conserved in multiple mammalian species. Either the human or mouse genomes can be used as the reference sequence with direct links from the search results to the ENSEMBL and UCSC genome browsers. Moreover, TFBScluster provides seamless integration of transcription factor binding site searches with genome annotation and gene expression profiling data, to allow prioritising computational predictions for subsequent experimental validation. TFBScluster is publicly available at http://hscl.cimr.cam.ac.uk/TFBScluster_genome_portal.html.

Animals↗

PubMed1987

Modeling optical and thermal distributions in tissue during laser irradiation.

The propagation of light energy in tissues is an important problem in phototherapy, especially with the increased use of lasers as light sources. Often a slight difference in delivered energy separates a useless, efficacious, or disastrous treatment. Methods are presented for experimental characterization of the optical properties of a tissue and computational prediction of the distribution of light energy within a tissue. A standard integrating sphere spectrophotometer measured the total transmission, Tt, total reflectance, Rt, and the on-axis transmission, Ta, for incident collimated light that propagated through the dermis of albino mouse skin, over the visible spectrum. The diffusion approximation solution to the one-dimensional (1-D) optical transport equation computed the expected Tt and Rt for different combinations of absorbance, k, scattering, s, and anisotropy, g, and by iterative comparison of the measured and computed Tt and Rt values converged to the intrinsic tissue parameters. For example, mouse dermis presented optical parameters of 2.8 cm-1, 239 cm-1, and 0.74 for k, s, and g, respectively, at 488 nm wavelength. These values were used in the model to simulate the optical propagation of the 488-nm line of an argon laser through mouse skin in vivo. A 1-D Green's function thermal diffusion model computed the temperature distribution within the tissue at different times during laser irradiation. In vitro experiments showed that the threshold temperature range for coagulation was 60 degrees-70 degrees C, and the kinetics were first order, with a temperature-dependent rate constant that obeyed an Arrhenius relation (molar entropy 276 cal/mol-degrees K, molar enthalpy 102 kcal/mol). The model simulation agreed with the corresponding in vivo experiment that a 2-s pulse at 55 W/cm2 irradiance will achieve coagulation of the skin.

Animals↗

PubMed2006

PONGO: a web server for multiple predictions of all-alpha transmembrane proteins.

The annotation efforts of the BIOSAPIENS European Network of Excellence have generated several distributed annotation systems (DAS) with the aim of integrating Bioinformatics resources and annotating metazoan genomes (http://www.biosapiens.info). In this context, the PONGO DAS server (http://pongo.biocomp.unibo.it) provides the annotation on predictive basis for the all-alpha membrane proteins in the human genome, not only through DAS queries, but also directly using a simple web interface. In order to produce a more comprehensive analysis of the sequence at hand, this annotation is carried out with four selected and high scoring predictors: TMHMM2.0, MEMSAT, PRODIV and ENSEMBLE1.0. The stored and pre-computed predictions for the human proteins can be searched and displayed in a graphical view. However the web service allows the prediction of the topology of any kind of putative membrane proteins, regardless of the organism and more importantly with the same sequence profile for a given sequence when required. Here we present a new web server that incorporates the state-of-the-art topology predictors in a single framework, so that putative users can interactively compare and evaluate four predictions simultaneously for a given sequence. Together with the predicted topology, the server also displays a signal peptide prediction determined with SPEP. The PONGO web server is available at http://pongo.biocomp.unibo.it/pongo.

Humans↗

PubMed2004

Potential prodrugs of non-steroidal anti-inflammatory agents for targeted drug delivery to the CNS.

Recently non-steroidal anti-inflammatory drugs (NSAIDs) have been proposed to prevent or to cure Alzheimer's disease. In this respect, we synthesized new potential prodrugs of several NSAIDs in order to increase their access to the brain. The carboxylic group of NSAIDs was attached to the 1,4-dihydro-1-methylpyridine-3-carboxylate moiety, which acts as a carrier, via an amino alcohol bridge, according to the chemical delivery approach developed by Bodor. The lipophilicity of potential prodrugs was evaluated both via traditional experimental parameters, such as partition coefficient and chromatographic R(m) value, and by predictive computational methods. From experimental parameters, all prodrugs were more lipophilic when compared to their corresponding parent compounds and consequently a better blood brain barrier (BBB) penetration is hypothesised. Prodrug lipophilicity was correlated with a calculated log P value according to Kowwin's method. The correlation between experimental Rm0 and calculated log P, determined by PLS analysis, was good for all compounds with the exception of compound 7i. In addition the BBB permeation profile of our synthesized compounds was predicted using the BBB VolSurf model and seven of the synthesized prodrugs resulted in good candidates for BBB penetration.

Anti-Inflammatory Agents, Non-Steroidal↗

PubMed2026

Comparative characterization of six teleost piscidins reveals distinct antimicrobial, antibiofilm and stability profiles.

Piscidins are cationic α-helical antimicrobial peptides (AMPs) that constitute a key component of the innate immune defense of teleost fish, yet the relationship between their genomic organization, structural properties, and functional specialization remains incompletely understood. In this study, six piscidin peptides from Epinephelus akaara, Seriola dumerili, Thunnus maccoyii, Argyrosomus regius, Dicentrarchus labrax, and Epinephelus coioides were characterized through an integrated sequence-to-function approach combining comparative genomics, structural modeling, physicochemical analysis, and in vitro validation, with the aim of identifying candidates with potential for biomedical and biotechnological applications. All genes studied exhibited the conserved four-exon, three-intron architecture characteristic of teleost piscidins. Structural modeling and circular dichroism confirmed α-helical conformations under membrane-mimetic conditions, despite measurable differences in hydrophobicity, charge distribution, and predicted membrane insertion parameters. Antimicrobial assays revealed distinct functional profiles: Sd_FI25 and Epinecidin_1 displayed broad antibacterial activity against Gram-positive and Gram-negative pathogens, whereas Dl_FI22 showed selective activity with reduced temporal persistence associated with lower peptide stability. Ea_FF25 exhibited comparatively weak antibacterial potency. Antibiofilm activity varied among peptides and did not uniformly parallel planktonic MIC values. Computational predictions further suggested antiviral and antitumoral potential for several sequences, extending their prospective relevance beyond classical antibacterial roles. Conserved genomic architecture and α-helical structure coexist with pronounced functional diversification among teleost piscidins. These findings demonstrate that integrating structural prediction with experimental validation is an effective strategy for identifying fish-derived innate immune peptides as candidates for biomedical applications.

Antimicrobial activity↗

PubMed2004

The applicability of a computer model for predicting head injury incurred during actual motor vehicle collisions.

BACKGROUND: Head injury is a significant cause of both morbidity and mortality. Motor vehicle collisions (MVCs) are the most common source of head injury in the United States. No studies have conclusively determined the applicability of computer models for accurate prediction of head injuries sustained in actual MVCs. This study sought to determine the applicability of such models for predicting head injuries sustained by MVC occupants. METHODS: The Crash Injury Research and Engineering Network (CIREN) database was queried for restrained drivers who sustained a head injury. These collisions were modeled using occupant dynamic modeling (MADYMO) software, and head injury scores were generated. The computer-generated head injury scores then were evaluated with respect to the actual head injuries sustained by the occupants to determine the applicability of MADYMO computer modeling for predicting head injury. RESULTS: Five occupants meeting the selection criteria for the study were selected from the CIREN database. The head injury scores generated by MADYMO were lower than expected given the actual injuries sustained. In only one case did the computer analysis predict a head injury of a severity similar to that actually sustained by the occupant. CONCLUSION: Although computer modeling accurately simulates experimental crash tests, it may not be applicable for predicting head injury in actual MVCs. Many complicating factors surrounding actual MVCs make accurate computer modeling difficult. Future modeling efforts should consider variables such as age of the occupant and should account for a wider variety of crash scenarios.

Accidents, Traffic↗

PubMed2005

An all atom energy based computational protocol for predicting binding affinities of protein-ligand complexes.

We report here a computationally fast protocol for predicting binding affinities of non-metallo protein-ligand complexes. The protocol builds in an all atom energy based empirical scoring function comprising electrostatics, van der Waals, hydrophobicity and loss of conformational entropy of protein side chains upon ligand binding. The method is designed to ensure transferability across diverse systems and has been validated on a heterogenous dataset of 161 complexes consisting of 55 unique protein targets. The scoring function trained on a dataset of 61 complexes yielded a correlation of r=0.92 for the predicted binding free energies against the experimental binding affinities. Model validation and parameter analysis studies ensure the predictive ability of the scoring function. When tested on the remaining 100 protein-ligand complexes a correlation of r=0.92 was recovered. The high correlation obtained underscores the potential applicability of the methodology in drug design endeavors. The scoring function has been web enabled at as binding affinity prediction of protein-ligand (BAPPL) server.

Drug Design↗

PubMed2004

Coevolution of gene expression among interacting proteins.

Physically interacting proteins or parts of proteins are expected to evolve in a coordinated manner that preserves proper interactions. Such coevolution at the amino acid-sequence level is well documented and has been used to predict interacting proteins, domains, and amino acids. Interacting proteins are also often precisely coexpressed with one another, presumably to maintain proper stoichiometry among interacting components. Here, we show that the expression levels of physically interacting proteins coevolve. We estimate average expression levels of genes from four closely related fungi of the genus Saccharomyces using the codon adaptation index and show that expression levels of interacting proteins exhibit coordinated changes in these different species. We find that this coevolution of expression is a more powerful predictor of physical interaction than is coevolution of amino acid sequence. These results demonstrate that gene expression levels can coevolve, adding another dimension to the study of the coevolution of interacting proteins and underscoring the importance of maintaining coexpression of interacting proteins over evolutionary time. Our results also suggest that expression coevolution can be used for computational prediction of protein-protein interactions.

Amino Acid Sequence↗

PubMed2001

Genomic sequence and expression analyses of human chromatin assembly factor 1 p150 gene.

Chromatin assembly factor-1 (CAF-1) plays essential roles in eukaryotic chromatin assembly during DNA replication (Smith and Stillman, 1989. Cell 58, 15-25), (Krude, 1999. Eur. J. Biochem. 263, 1-5). Its p150 subunit, involved in interaction with histone H3 and H4, is critical to the CAF-1 nucleosome assembly activity. In this study, we sequenced a 96-kb genomic DNA region that includes a 42.8-kb CAF-1 p150 subunit gene (CHAF1A), and a 41.1-kb EEN gene. A scripted bioinformatics analysis pipeline (research agent) has been set up to annotate the BAC sequence with a set of integrated algorithms. The CAF-1 p150 subunit gene contains 15 exons and 14 introns. The promoter region is characterized by deletional analyses, revealing a potential repressor. Tissue-correlated alternative splicing forms of the transcript was initially identified by EST clustering analysis, then confirmed by RT-PCR which resulted more splicing forms than computational prediction.

3T3 Cells↗

PubMed2005

Computational methods for transcriptional regulation.

How is the information from a thousand gene-expression arrays, the location of more than two hundred regulatory factors, and nine sequenced genomes to be integrated into a global view of the regulatory network in budding yeast? Computational methods that fit incomplete noisy data provide the outlines of regulatory pathways, but the errors are not quantified. In the fly, embryonic patterning has proved amenable to computational prediction, but only when the DNA-binding preferences of the relevant factors are taken into account. In both these model organisms, simply restricting attention to regulatory sequences that align with related species (i.e. "conserved") discards much information regarding what is functional.

Animals↗

PubMedDate not supplied

Computational and conformational evaluation of FTase alternative substrates: insight into a novel enzyme binding pocket.

Protein farnesyltransferase (FTase) is an important anticancer drug target. In an effort to develop isoprenoid diphosphate-based FTase inhibitors, striking variations have been observed in the ability of conservatively modified analogues to bind to the enzyme. For example, 2Z-GGPP is an alternative substrate with high binding affinity, while GGPP is not an alternative substrate. Using the availability of high-resolution FTase crystal structures, we have used pharmacophore and docking studies to elucidate a new binding pocket for isoprenoid analogues. The unique conformations between the first two isoprene units of 2Z-GGPP, but not GGPP, allows 2Z-GGPP to exploit this new binding pocket. The discovered conformation allows the molecule to adopt a reactive conformation while placing hydrophobic groups within the predominately hydrophobic binding pocket. This computational finding is supported by NMR studies on (13)C-labeled 2Z-farnesol, which confirm that the computationally predicted conformation is also favored in solution. These discoveries suggest that ligand conformational flexibility may be an important design consideration for the development of both inhibitors and alternative substrates of FTase.

Binding Sites↗

PubMed2004

Discovery of estrogen receptor alpha target genes and response elements in breast tumor cells.

BACKGROUND: Estrogens and their receptors are important in human development, physiology and disease. In this study, we utilized an integrated genome-wide molecular and computational approach to characterize the interaction between the activated estrogen receptor (ER) and the regulatory elements of candidate target genes. RESULTS: Of around 19,000 genes surveyed in this study, we observed 137 ER-regulated genes in T-47D cells, of which only 89 were direct target genes. Meta-analysis of heterogeneous in vitro and in vivo datasets showed that the expression profiles in T-47D and MCF-7 cells are remarkably similar and overlap with genes differentially expressed between ER-positive and ER-negative tumors. Computational analysis revealed a significant enrichment of putative estrogen response elements (EREs) in the cis-regulatory regions of direct target genes. Chromatin immunoprecipitation confirmed ligand-dependent ER binding at the computationally predicted EREs in our highest ranked ER direct target genes, NRIP1, GREB1 and ABCA3. Wider examination of the cis-regulatory regions flanking the transcriptional start sites showed species conservation in mouse-human comparisons in only 6% of predicted EREs. CONCLUSIONS: Only a small core set of human genes, validated across experimental systems and closely associated with ER status in breast tumors, appear to be sufficient to induce ER effects in breast cancer cells. That cis-regulatory regions of these core ER target genes are poorly conserved suggests that different evolutionary mechanisms are operative at transcriptional control elements than at coding regions. These results predict that certain biological effects of estrogen signaling will differ between mouse and human to a larger extent than previously thought.

Binding Sites↗

PubMed1993

PROBE: a computer program employing an integrated neural network approach to protein structure prediction.

A computer program, PROBE, has been designed for the prediction of protein structural features from amino acid sequence. This program integrates a variety of computer-simulated neural networks, each predicting an aspect of protein structure, into a single, easy-to-use package. The surface accessibility of each residue, the presence of disulfide bonds, the overall secondary structure composition and the residue secondary structures, including beta-turn type, are predicted. In addition, the overall amino acid composition and relative hydrophobicity are used to determine whether a protein belongs to one of four common folding motifs. PROBE is able to compare and synergistically improve the predictions by allowing communication between the different networks.

Amino Acid Sequence↗

PubMed2025

Missense variants pathogenicity annotation from homologous proteins.

MOTIVATION: High-throughput DNA sequencing has revealed millions of single nucleotide variants (SNVs) in the human genome, with a small fraction linked to disease. The effect of missense variants, which alter the protein sequence, is particularly challenging to interpret due to the scarcity of clinical annotations and experimental information. While using conservation and structural information, current prediction tools still struggle to predict variant pathogenicity. In this study, we explored the pathogenicity of homologous missense variants-variants in equivalent positions across homologous proteins-focusing on proteins involved in autosomal dominant diseases. RESULTS: Our analysis of 2976 pathogenic and 17 555 non-pathogenic homologous variants demonstrated that pathogenicity can be extrapolated with 95% accuracy within a family, or up to 98% for closer homologs. Remarkably, the evaluation of 27 commonly used mutation predictor methods revealed that they were not fully capturing this biological feature. To facilitate the exploration of homologous variants, we created HomolVar, a web server that computationally predicts the pathogenesis of missense variants using annotations from homologous variants, freely available at https://rarevariants.org/HomolVar. Overall, these findings and the accompanying tool offer a robust method for predicting the pathogenicity of unannotated variants, enhancing genotype-phenotype correlations, and contributing to diagnosing rare genetic disorders. AVAILABILITY AND IMPLEMENTATION: HomolVar is freely available at https://rarevariants.org/HomolVar.

Mutation, Missense↗

PubMedDate not supplied

Comparison of two-dimensional numerical approximation and measurement of SAR in a muscle equivalent phantom exposed to a 915 MHz slab-loaded waveguide.

Computer predictions of the specific absorption rate (SAR) distribution in a uniform muscle-equivalent phantom with an overlying bolus have been compared to those measured experimentally. The microwave source was a 10 cm x 10 cm slab-loaded waveguide applicator operating at 915 MHz. The modelling technique (theory) combines the equivalence principle and a two-dimensional finite element technique to determine the incident and the scattered electric fields separately. The E-field was measured using a small dipole device oriented parallel to the polarized field of the waveguide source. Comparisons of the predicted and measured SAR were made for various bolus properties, and reasonable agreement with theory was found in each case. The results demonstrate the usefulness of numerical modelling in characterizing the fields from microwave applicators used in clinical hyperthermia.

Computer Simulation↗

PubMed2026

Gut microbiota-derived metabolites target C5AR1/KDM2A/HCAR3 axis in inflammatory bowel disease: a multi-machine learning algorithms and molecular docking study.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic recurrent disorder. Gut microbiota-derived metabolites regulate intestinal homeostasis, but their molecular mechanisms in IBD remain unclear. Current studies lack systematic "microbiota-metabolite-target" network mining with multi-method validation. This study integrates network pharmacology, three machine learning algorithms, and molecular docking to construct this regulatory network in IBD. METHODS: Transcriptome data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using limma (p < 0.05, |log2FC| > 0.5). Weighted gene co-expression network analysis (WGCNA) with an optimal soft threshold of β = 7 was performed to identify key module genes. Candidate genes were obtained by intersecting DEGs, gut microbiota-associated genes from the gutMGene database, and WGCNA module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of candidate genes. Core genes were identified using three machine learning algorithms (LASSO, Boruta, and SVM-RFE), followed by protein-protein interaction (PPI) network analysis. Molecular docking was performed to assess the binding affinities between hub proteins and gut microbiota-derived metabolites. RESULTS: A total of 885 DEGs were identified between the IBD and control groups, including 463 upregulated and 422 downregulated genes. WGCNA identified 280 key module genes from the purple and yellow modules. The intersection of DEGs, gut microbiota-associated genes, and WGCNA module genes yielded 19 core candidate genes. PPI network analysis combined with three machine learning algorithms jointly identified C5AR1, KDM2A, and HCAR3 as core hub genes. ROC curve analysis demonstrated that all three hub genes achieved AUC values greater than 0.7 in both the training and validation sets, indicating excellent diagnostic performance for IBD. Enrichment analysis revealed significant associations with the TNF, NF-κB, and IL-17 signaling pathways. Molecular docking confirmed stable binding of C5AR1 with 1,3-Diphenylpropan-2-Ol (-7.87 ± 0.83 kcal·mol-¹) and HCAR3 with 3-Indolepropionic Acid (-6.35 ± 0.70 kcal·mol-¹), both below -5.0 kcal·mol-¹. CONCLUSION: This study first constructs a "gut microbiota-metabolite-hub gene" axis in IBD, providing a computational framework for microbiota-targeted precision therapy, and identifying C5AR1/KDM2A/HCAR3 as computationally predicted diagnostic biomarkers and 1,3-Diphenylpropan-2-Ol/3-Indolepropionic Acid as candidate intervention molecules that warrant further experimental validation.

Molecular Docking Simulation↗

PubMedDate not supplied

Bioinformatics-based discovery of a novel factor with apparent specificity to colon cancer.

In a previous study, a data mining tool called Digital Differential Display (DDD) from the Cancer Genome Anatomy Project (CGAP) was used to predict solid tumor- and organ-specific genes from the expressed sequence tag (EST) database. To validate the use of bioinformatics approaches in gene discovery, one of the ESTs, which was predicted to be colon tumor-specific, was chosen for further study. Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) analysis of matched sets of cDNAs from normal and colon tumor tissues indicated that the EST was specifically expressed in the majority of colon tumors. Expression was also detected in early adenomas. Among other normal tissues, EST expression was detected only in the small intestine. The colon tumor specificity of this EST was inferred from the lack of expression in carcinomas of the breast, lung, ovary, pancreas and prostate. To validate the computational prediction of specificity, a full-length cDNA encompassing the entire open reading frame was cloned and, in view of its apparent specificity to the colon tumors, this gene was termed Colon Carcinoma Related Gene (CCRG). CCRG encodes a novel cysteine-rich motif and a putative signal peptide sequence. Supernatant from COS cells transfected with the CCRG expression vector stimulated proliferation of colon cancer cells. Immunoreactive CCRG was also detected in the paraffin sections of colon tumor samples. CCRG belongs to a new class of growth factors and may be important in the diagnosis and treatment of colon cancers. Identification of CCRG using bioinformatics approaches validates gene discovery using computational approaches.

Amino Acid Sequence↗

PubMed2001

Detection of cis-element clusters in higher eukaryotic DNA.

MOTIVATION: Computational prediction and analysis of transcription regulatory regions in DNA sequences has the potential to accelerate greatly our understanding of how cellular processes are controlled. We present a hidden Markov model based method for detecting regulatory regions in DNA sequences, by searching for clusters of cis-elements. RESULTS: When applied to regulatory targets of the transcription factor LSF, this method achieves a sensitivity of 67%, while making one prediction per 33 kb of non-repetitive human genomic sequence. When applied to muscle specific regulatory regions, we obtain a sensitivity and prediction rate that compare favorably with one of the best alternative approaches. Our method, which we call Cister, can be used to predict different varieties of regulatory region by searching for clusters of cis-elements of any type chosen by the user. Cister is simple to use and is available on the web. AVAILABILITY: http://sullivan.bu.edu/~mfrith/cister.shtml. CONTACT: mfrith@bu.edu; zhiping@bu.edu.

Algorithms↗