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PubMed1994

Computer prediction of general PCR products based on dynamical solution structures of DNA.

A computer program which can predict general PCR products was developed and experimentally verified to be useful. This means that the approximation of solution structures of DNA based solely on Watson-Crick base pairing is effective. Intramolecular higher structures were shown to be responsible for the discrepancies between the predictions and the experimental results. This study provides a theoretical background to arbitrarily primed (or random) PCR. Other basic issues relating to general PCR are also discussed.

Base Sequence↗
PubMed1987

Computer prediction of potential immunogenic determinants from protein amino acid sequence.

In proteins, immunogenic determinants that can induce protein-reactive antipeptide antibodies reside mostly in those parts of the molecule that have a high tendency to form beta-turns. A program for an IBM personal computer which predicts protein immunogenic determinants is described. The program predicts potential immunogenic determinants from protein amino acid sequences according to a Chou-Fasman-based probability of a beta-turn occurrence, p greater than 1.5 X 10(-4)(P. Y. Chou and G. D. Fasman, 1978, Adv. Enzymol. 47, 46-148). Oncopeptides (whose efficacy in generating protein-reactive antipeptide antibodies has been described) with a beta-turn probability of p greater than 1.5 X 10(-4) elicited antipeptide antibodies that reacted with the parent oncoprotein at a rate of 96%, thus showing a surprisingly good correlation between the tendency to form a beta-turn and the protein reactivity of antipeptide antibodies. Potential immunogenic determinants were predicted on myohemerythrin and myoglobin.

Amino Acid Sequence↗
PubMed1985

Computer prediction of serum theophylline concentrations in ambulatory patients.

This study assessed the ability of a computer program Simulated Kinetics (SIMKIN) to predict serum theophylline concentrations in ambulatory patients receiving oral theophylline. Data were collected by retrospective review of prospectively obtained data. A total of only 20 measured serum theophylline concentrations could be included in the study, although records of 195 patients were reviewed. An estimated patient compliance of 90-110% was required and was computed using prescription refill information. Predicted serum theophylline concentrations were generated for each patient by entering into the SIMKIN program the characteristics pertinent to theophylline disposition and the patient's theophylline dosing regimen. Actual and SIMKIN-predicted theophylline concentrations were compared by using simple linear regression and by constructing a 95% confidence interval around the mean prediction error and root mean squared error. The ability of SIMKIN to predict therapeutic category, i.e., subtherapeutic, therapeutic, or toxic, was assessed using Fisher's exact test. SIMKIN predictions of individual theophylline concentration were insufficiently accurate to replace confirmatory followup monitoring of actual levels. However, SIMKIN was able to predict the therapeutic category with 70% accuracy. We conclude that SIMKIN may be useful for categorizing a dose regimen of theophylline as therapeutic, but that it is of little use in predicting individual concentrations in outpatients when literature-averaged pharmacokinetic parameters are the sole criteria for prediction, and compliance cannot be accurately assessed.

Adolescent↗
PubMed2004

Computational prediction of protein-protein interactions.

Eukaryotic proteins typically contain one or more modular domains such as kinases, phosphatases, and phoshopeptide-binding domains, as well as characteristic sequence motifs that direct post-translational modifications such as phosphorylation, or mediate binding to specific modular domains. A computational approach to predict protein interactions on a proteome-wide basis would therefore consist of identifying modular domains and sequence motifs from protein primary sequence data, creating sequence specificity-based algorithms to connect a domain in one protein with a motif in another in "interaction space," and then graphically constructing possible interaction networks. Computational methods for predicting modular domains in proteins have been quite successful, but identifying the short sequence motifs these domains recognize has been more difficult. We are developing improved methods to identify these motifs by combining experimental and computational techniques with databases of sequences and binding information. Scansite is a web-accessible program that predicts interactions between proteins using experimental binding data from peptide library and phage display experiments. This program focuses on domains important in cell signaling, but it can, in principle, be used for other interactions if the domains and binding motifs are known. This chapter describes in detail how to use Scansite to predict the binding partners of an input protein, and how to find all proteins that contain a given sequence motif.

Amino Acid Motifs↗
PubMed1990

Comparison of the X-ray structure of baboon alpha-lactalbumin and the tertiary predicted computer models of human alpha-lactalbumin.

The previously predicted structures of human alpha-lactalbumin by homology with hen egg white lysozyme by an automatic method, after the alignment stage, are compared to the X-ray determined structure of baboon alpha-lactalbumin. The root mean square by rotation method (RMSR) deviations for 122 C-alpha atoms between the two models and the X-ray structure are 2.0 A and 2.3 A. The RMSR deviations for all atoms, except for differences in human and baboon sequences, are 2.8 A and 3.1 A. If the flexible C-terminus (residues 112-122) are removed then these RMSR deviations are reduced to 2.4 A and 2.3 A respectively. These results are consistent with the fact that the RMSR deviation between the human and baboon X-ray structures increases from residue 112 onwards and is conformationally flexible.

Amino Acid Sequence↗
PubMed2006

Computational prediction of genomic functional cores specific to different microbes.

Computational and experimental attempts tried to characterize a universal core of genes representing the minimal set of functional needs for an organism. Based on the increasing number of available complete genomes, comparative genomics has concluded that the universal core contains < 50 genes. In contrast, experiments suggest a much larger set of essential genes (certainly more than several hundreds, even under the most restrictive hypotheses) that is dependent on the biological complexity and environmental specificity of the organism. Highly biased genes, which are generally also the most expressed in translationally biased organisms, tend to be over represented in the class of genes deemed to be essential for any given bacterial species. This association is far from perfect; nevertheless, it allows us to propose a new computational method to detect, to a certain extent, ubiquitous genes, nonorthologous genes, environment-specific genes, genes involved in the stress response, and genes with no identified function but highly likely to be essential for the cell. Most of these groups of genes cannot be identified with previously attempted computational and experimental approaches. The large variety of life-styles and the unusually detectable functional signals characterizing translationally biased organisms suggest using them as reference organisms to infer essentiality in other microbial species. The case of small parasitic genomes is discussed. Data issued by the analysis are compared with previous computational and experimental studies. Results are discussed both on methodological and biological grounds.

Bacillus subtilis↗
PubMed2005

Computational predictions of pulmonary blood flow gradients: gravity versus structure.

A computational model of blood flow through the human pulmonary arterial tree has been developed to investigate the mechanisms contributing to regional pulmonary perfusion in the isolated network when the lung is in different orientations. The arterial geometric model was constructed using a combination of computed tomography and a volume-filling branching algorithm. Equations governing conservation of mass, momentum, and vessel distension, incorporating gravity, were solved to predict pressure, flow, and vessel radius. Analysis of results in the upright posture, with and without gravity, and in the inverted, prone, and supine postures reveals significant flow heterogeneity and a persistent decrease in flow in the cranial and caudal regions for all postures suggesting that vascular geometry makes a major contribution to regional flow with gravity having a lesser role. Results in the isolated arterial tree demonstrate that the vascular path lengths and therefore the positioning of the pulmonary trunk relative to the rest of the network play a significant role in the determination of flow.

Algorithms↗
PubMed2006

Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties.

BACKGROUND: Computational methods have been developed for predicting allergen proteins from sequence segments that show identity, homology, or motif match to a known allergen. These methods achieve good prediction accuracies, but are less effective for novel proteins with no similarity to any known allergen. METHODS: This work tests the feasibility of using a statistical learning method, support vector machines, as such a method. The prediction system is trained and tested by using 1005 allergen proteins from the Allergome database and 22,469 non-allergen proteins from 7871 Pfam families. RESULTS: Testing results by an independent set of 229 allergen and 6717 non-allergen proteins from 7871 Pfam families show that 93.0% and 99.9% of these are correctly predicted, which are comparable to the best results of other methods. Of the 18 novel allergen proteins non-homologous to any other proteins in the Swissprot database, 88.9% is correctly predicted. A further screening of 168,128 proteins in the Swissprot database finds that 2.9% of the proteins are predicted as allergen proteins, which is consistent with the estimated numbers from motif-based methods. CONCLUSIONS: Our study suggests that SVM is a potentially useful method for predicting allergen proteins and it has certain capability for predicting novel allergen proteins. Our software can be accessed at .

Allergens↗
PubMed2006

Computational prediction of the chromosome-damaging potential of chemicals.

We report on the generation of computer-based models for the prediction of the chromosome-damaging potential of chemicals as assessed in the in vitro chromosome aberration (CA) test. On the basis of publicly available CA-test results of more than 650 chemical substances, half of which are drug-like compounds, we generated two different computational models. The first model was realized using the (Q)SAR tool MCASE. Results obtained with this model indicate a limited performance (53%) for the assessment of a chromosome-damaging potential (sensitivity), whereas CA-test negative compounds were correctly predicted with a specificity of 75%. The low sensitivity of this model might be explained by the fact that the underlying 2D-structural descriptors only describe part of the molecular mechanism leading to the induction of chromosome aberrations, that is, direct drug-DNA interactions. The second model was constructed with a more sophisticated machine learning approach and generated a classification model based on 14 molecular descriptors, which were obtained after feature selection. The performance of this model was superior to the MCASE model, primarily because of an improved sensitivity, suggesting that the more complex molecular descriptors in combination with statistical learning approaches are better suited to model the complex nature of mechanisms leading to a positive effect in the CA-test. An analysis of misclassified pharmaceuticals by this model showed that a large part of the false-negative predicted compounds were uniquely positive in the CA-test but lacked a genotoxic potential in other mutagenicity tests of the regulatory testing battery, suggesting that biologically nonsignificant mechanisms could be responsible for the observed positive CA-test result. Since such mechanisms are not amenable to modeling approaches it is suggested that a positive prediction made by the model reflects a biologically significant genotoxic potential. An integration of the machine-learning model as a screening tool in early discovery phases of drug development is proposed.

Chromosomes↗
PubMed1995

Computational prediction of hemolysis in a centrifugal ventricular assist device.

This paper describes the use of computational fluid dynamics (CFD) to predict numerically the hemolysis in centrifugal pumps. A numerical hydrodynamical model, based on the full Navier-Stokes equation, was used to obtain the flow in a vaneless centrifugal pump (of corotating disks type). After proper postprocessing, critical zones in the channel were identified by means of two-dimensional color-coded maps of %Hb release. Simulation of different conditions revealed that flow behavior at the entrance region of the channel is the main cause of blood trauma in such devices. A useful feature resulting from the CFD simulation is the visualization of critical flow zones that are impossible to determine experimentally with in vitro hemolysis tests.

Heart-Assist Devices↗
PubMed2006

Computational prediction and experimental evaluation of a photoinduced electron-transfer sensor.

An approach is presented for the design of photoinduced electron-transfer-based sensors. The approach relies on the computational and theoretical prediction of electron-transfer kinetics based on Rehm-Weller and Marcus theories. The approach allows evaluation of the photophysical behavior of a prototype fluorescent probe/sensor prior to the synthesis of the molecule. As a proof of concept, a prototype sensor for divalent metal ions is evaluated computationally, synthesized, and then analyzed spectroscopically for its fluorescence response to zinc. Calculations predicted that the system would show a competition between electron transfer and fluorescence in the free state. In the zinc-bound state, the compound was predicted to be more highly fluorescent, due to the inhibition of electron transfer. Both predictions were confirmed experimentally. A nonzero fluorescence signal was observed in the absence of zinc and an enhancement was observed in the presence of zinc. Specifically, a 56-fold enhancement was observed over a 10-fold increase in zinc concentration.

Journal Article↗
PubMed1993

Targeting of antisense DNA: comparison of activity of anti-rabbit beta-globin oligodeoxyribonucleoside phosphorothioates with computer predictions of mRNA folding.

To assess the usefulness of computer-assisted modeling of mRNA as an aid in design of antisense DNA, the efficiency of inhibition of translation of rabbit beta-globin mRNA by various antisense sequences was compared with calculated structures of the mRNA. The model obtained by consideration of 30 lowest-energy computer-simulated structures is consistent with the high accessibility of the AUG initiation codon region known from digestion with nucleases and with previous antisense inhibition studies reported in the literature. Additional antisense inhibition data were obtained with 20-mer phosphorothioate oligonucleotides, targeted to regions of beta-globin mRNA differing moderately in their degree of participation in intramolecular folding. The efficiency of translation arrest by the oligonucleotides in cell-free expression systems (wheat germ extract and rabbit reticulocyte lysate) was obtained by measuring incorporation of [35S]methionine into total protein, and corrected for sequence-nonspecific inhibition using brome mosaic virus mRNA. In the presence of RNase H (wheat germ system), the inhibitory activity of the oligonucleotides showed correlation with the calculated secondary structure of mRNA, in particular at low oligonucleotide-to-mRNA ratios (correlation coefficient, 0.95). No correlation was observed in the reticulocyte lysate system, in which the inhibition is mediated by translational arrest.

Animals↗
PubMed1985

Variability of fentanyl pharmacokinetics in man. Computer predicted plasma concentrations for three intravenous dosage regimens.

The derived pharmacokinetic data for the intravenous administration of fentanyl obtained from seven previous studies were compared using computer simulation of predicted plasma concentrations following three intravenous dosage regimens. There was wide discrepancy between the reported calculated pharmacokinetic constants from the various studies, such that the volume of distribution ranged from 4.4 to 59.7 litres, estimates of terminal elimination half-life ranged from 141 to 853 minutes while total body clearance values ranged from 160 to 1530 ml/minute. The differences in predicted plasma concentrations were marked. The peak concentration following a bolus of 500 micrograms ranged from 8.4-113.6 ng/ml and took from 2.9 to 18.9 hours to fall to 0.5 ng/ml. The steady state plasma concentration reached with an infusion of 0.3 microgram/kg/minute varied from 12.2-119.9 ng/ml and the plateau level attained with a two-rate infusion (2.7 micrograms/kg/minute for 20 minutes then 0.3 micrograms/kg/minute) ranged from 10.6-50.8 ng/ml. The aim of descriptive pharmacokinetics is to allow the clinician to predict the plasma concentration achieved by a given dose and to facilitate dosage choice and adjustment. Recent interest has centred on the use of pharmacokinetics to calculate continuous intravenous infusion dosage regimens. The clinical application of current pharmacokinetic data for fentanyl is questionable.

Adult↗
PubMed1986

Computer prediction of peptide maps: assignment of polypeptides to human and mouse mitochondrial DNA genes by analysis of two-dimensional-proteolytic digest gels.

We have prepared a computer program that predicts complete and partial peptide maps from amino acid sequences. The program fragments amino acid sequences at designated cleavage sites and calculates the molecular weight and relative labeling of each peptide. These data are graphed as log molecular weight of the original protein (X-axis) vs. log molecular weight of the component peptides (Y-axis). The program is interactive, permitting adjustment of a number of graphic parameters and alteration of the position of proteins in the first dimension to accommodate aberrations in protein mobility. The program has been used to predict the V8 protease peptide maps of the 13 open reading frames (ORFs) identified in the human and the mouse mitochondrial DNA (mtDNA) sequences. The results were compared to the V8 protease peptide maps obtained for mouse and human mitochondrially synthesized proteins by two-dimensional proteolytic digest gels. A high correlation was observed between the predicted and observed peptide maps. These results suggest the assignment of several proteins to mtDNA genes.

Amino Acid Sequence↗
PubMed2005

Computational prediction of genotoxicity: room for improvement.

Decades of mutagenesis and clastogenesis studies have yielded enough structure-activity-relationship (SAR) information to make feasible the construction of computational models for prediction of endpoints based on molecular structure and reactivity. Although there is cause for optimism that these approaches might someday reduce or eliminate the need for actual genotoxicity testing, we are in fact a long way from this. We provide an overview of the state of the art of such approaches, dissecting out how these models are suboptimal. It is clear that current programs still have limited predictive capabilities. We propose that one of the major contributing factors for the inherent lack of sensitivity (typically 50-60%) is inadequate coverage of non-covalent DNA interactions. Suboptimal specificity can be partly attributed to chemical space considerations with associated non-causal activity correlations.

Animals↗
PubMed2004

Theoretical calculations of the effects of 2-heavier group 14 element and substituents on the singlet-triplet energy gap in cyclopentane-1,3-diyls and computational prediction of the reactivity of singlet 2-silacyclopentane-1,3-diyls.

UDFT and CASSCF calculations with the 6-31G(d) basis set were performed to investigate the heavier group 14 element (M) effect on the ground-state spin multiplicity of cyclopentane-1,3-diyls and their reactivity. The calculations find that 2-metallacyclopentane-1,3-diyls (M = Si, Ge) that possess a variety of substituents (X = H, Me, F, OR, SiH(3)) at M(2) are singlet ground-state molecules. The energies of the 1,3-diphenyl-substituted singlet 2-silacyclopentane-1,3-diyls are calculated to be ca. 5 kcal/mol lower than those of the intramolecular ring-closure products, i.e., 1,4-diphenyl-5-silabicyclo[2.1.0]pentanes, at the B3LYP/6-31G(d) level of theory. The energy barrier for the disrotatory ring closure of singlet 2,2-dimethyl-1,3-diphenyl-2-silacyclopentane-1,3-diyl (lambda(calcd) = 757 nm, f = 1.01 at RCIS/6-31G(d)) to the corresponding 5-silabicyclo[2.1.0]pentane is computed to be 11.6 kcal/mol, which is 13.1 kcal/mol lower in energy than that for the conrotatory ring-opening to a 3-silapenta-1,4-diene. The computational work predicts that singlet 1,3-diaryl-2-silacyclopentane-1,3-diyls are persistent molecules under conditions without trapping agents.

Journal Article↗
PubMed2004

Computational prediction of RNA editing sites.

MOTIVATION: Some organisms edit their messenger RNA resulting in differences between the genomic sequence for a gene and the corresponding messenger RNA sequence. This difference complicates experimental and computational attempts to find and study genes in organisms with RNA editing even if the full genomic sequence is known. Nevertheless, knowledge of these editing sites is crucial for understanding the editing machinery of these organisms. RESULTS: We present a computational technique that predicts the position of editing sites in the genomic sequence. It uses a statistical approach drawing on the protein sequences of related genes and general features of editing sites of the organism. We apply the method to the mitochondrion of the slime mold Physarum polycephalum. It correctly predicts over 90% of the amino acids and over 70% of the editing sites.

Algorithms↗
PubMed2004

Computational prediction of human metabolic pathways from the complete human genome.

BACKGROUND: We present a computational pathway analysis of the human genome that assigns enzymes encoded therein to predicted metabolic pathways. Pathway assignments place genes in their larger biological context, and are a necessary first step toward quantitative modeling of metabolism. RESULTS: Our analysis assigns 2,709 human enzymes to 896 bioreactions; 622 of the enzymes are assigned roles in 135 predicted metabolic pathways. The predicted pathways closely match the known nutritional requirements of humans. This analysis identifies probable omissions in the human genome annotation in the form of 203 pathway holes (missing enzymes within the predicted pathways). We have identified putative genes to fill 25 of these holes. The predicted human metabolic map is described by a Pathway/Genome Database called HumanCyc, which is available at http://HumanCyc.org/. We describe the generation of HumanCyc, and present an analysis of the human metabolic map. For example, we compare the predicted human metabolic pathway complement to the pathways of Escherichia coli and Arabidopsis thaliana and identify 35 pathways that are shared among all three organisms. CONCLUSIONS: Our analysis elucidates a significant portion of the human metabolic map, and also indicates probable unidentified genes in the genome. HumanCyc provides a genome-based view of human nutrition that associates the essential dietary requirements of humans with a set of metabolic pathways whose existence is supported by the human genome. The database places many human genes in a pathway context, thereby facilitating analysis of gene expression, proteomics, and metabolomics datasets through a publicly available online tool called the Omics Viewer.

Arabidopsis↗