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PubMed1982

Computer predictions of New Zealand doctor numbers.

Considerable debate has been generated about "the coming crisis in medical manpower" with most current analyses predicting a large surplus of doctors in the late 1980s. However, predictions can be complex because of potential fluctuations in the input variables due to political and social factors. Variables affecting active doctor numbers include the numbers of medical students graduating each year, immigration, emigration, the proportions of males and females, age at retirement, and extent of part-time work. A computer simulation is described which enables the relative effects of these variables to be compared. It is found that adjustments in the medical school intake have a relatively small short-term effect compared with the effects of adjustments in several of the other variables, particularly immigration numbers.

Computers↗
PubMed2006

Recent advances in computational prediction of drug absorption and permeability in drug discovery.

Approximately 40%-60% of developing drugs failed during the clinical trials because of ADME/Tox deficiencies. Virtual screening should not be restricted to optimize binding affinity and improve selectivity; and the pharmacokinetic properties should also be included as important filters in virtual screening. Here, the current development in theoretical models to predict drug absorption-related properties, such as intestinal absorption, Caco-2 permeability, and blood-brain partitioning are reviewed. The important physicochemical properties used in the prediction of drug absorption, and the relevance of predictive models in the evaluation of passive drug absorption are discussed. Recent developments in the prediction of drug absorption, especially with the application of new machine learning methods and newly developed software are also discussed. Future directions for research are outlined.

Computer Simulation↗
PubMedDate not supplied

A comparison of neural networks for computing predicted probability of survival for trauma victims.

TRISS is a statistical method for predicting the probability of survival of trauma victims. Analysis of data from the Trauma Registry at Charleston Area Medical Center showed that only 48% of the trauma fatalities in the 5-year period 1992-1996 were correctly predicted by TRISS. Trauma practitioners from other Trauma Centers report similar problems with TRISS. Researchers have suggested improvements that range from simply changing the input variables and/or regression coefficients in TRISS to using an entirely different model. In this study we describe a method of calculating survival probabilities using Artificial Neural Networks (ANN). This method was chosen because of the similarity of the ANN output function to the function that produces the TRISS probability of survival. Additional variables were added based on the results of other research efforts as well as analysis of the CAMC Trauma Registry. A comparison was made between the abilities of TRISS to predict fatalities and to approximate probability of survival. The ANN outperformed TRISS in predicting fatalities in a training set (68.1% correct vs. 47.9% correct) and in a testing set (61.3% correct vs. 51.3% correct). More importantly, the ANN produced better estimates of predicted deaths. Using a data set that included 119 deaths, the ANN model predicted 125 deaths for a 5% relative error. The predicted number using TRISS was 86 for a relative error of 27.7%. Since effective quality improvement for trauma care depends on accurately identifying cases that fall outside the expected results, a more accurate predictive tool allows a more focused review of those significant cases, thus conserving resources without compromising quality. Neural Networks appear to be a predictive tool that can provide probability of survival estimates that are more accurate than TRISS.

Humans↗
PubMed1998

Analysis of RNA-dependent RNA polymerase structure and function as guided by known polymerase structures and computer predictions of secondary structure.

RNA-dependent RNA polymerases (RdRps) function as the catalytic subunit of the viral replicase required for the replication of all positive strand RNA viruses. The vast majority of RdRps have been identified solely on the basis of sequence similarity. Structural studies of RdRps have lagged behind those of the DNA-dependent DNA polymerases, DNA-dependent RNA polymerases, and reverse transcriptases until the recent report of the partial crystal structure of the poliovirus RdRp, 3Dpol [Hansen, J. L., et al. (1997). Structure 5, 1109-1122]. We seek to address whether all RdRps will have structures similar to those found in the poliovirus polymerase structure. Therefore, the PHD method of Rost and Sander [Rost, B., and Sander, C. (1993a). J. Mol. Biol. 232, 584-599; Rost, B., and Sander, C. (1994). Protein 19, 55-77] was used to predict the secondary structure of the RdRps from six different viral families: bromoviruses, tobamoviruses, tombusvirus, leviviruses, hepatitis C-like viruses, and picornaviruses. These predictions were compared with the known crystal structure of the poliovirus polymerase. The PHD method was also used to predict picornavirus structures in places in which the poliovirus crystal structure was disordered. All five families and the picornaviruses share a similar order of secondary structure elements present in their polymerase proteins. All except the leviviruses have the unique region observed in the poliovirus 3Dpol that is suggested to be involved in polymerase oligomerization. These structural predictions are used to explain the phenotypes of a collection of mutations that exist in several RNA polymerases. This analysis will help to guide further characterization of RdRps.

Amino Acid Sequence↗
PubMed2006

Quantitative structure activity relationship for the computational prediction of nitrocompounds carcinogenicity.

Several nitrocompounds have been screened for carcinogenicity in rodents, but this is a lengthy and expensive process, taking two years and typically costing 2.5 million dollars, and uses large numbers of animals. There is, therefore, much impetus to develop suitable alternative methods. One possible way of predicting carcinogenicity is to use quantitative structure-activity relationships (QSARs). QSARs have been widely utilized for toxicity testing, thereby contributing to a reduction in the need for experimental animals. This paper describes the results of applying a TOPological substructural molecular design (TOPS-MODE) approach for predicting the rodent carcinogenicity of nitrocompounds. The model described 79.10% of the experimental variance, with a standard deviation of 0.424. The predictive power of the model was validated by leave-one-out validation, with a determination coefficient of 0.666. In addition, this approach enabled the contribution of different fragments to carcinogenic potency to be assessed, thereby making the relationships between structure and carcinogenicity to be transparent. It was found that the carcinogenic activity of the chemicals analysed was increased by the presence of a primary amine group bonded to the aromatic ring, a manner that was proportional to the ring aromaticity. The nitro group bonded to an aromatic carbon atom is a more important determinant of carcinogenicity than the nitro group bonded to an aliphatic carbon. Finally, the TOPS-MODE approach was compared with four other predictive models, but none of these could explain more than 66% of the variance in the carcinogenic potency with the same number of variables.

Animals↗
PubMed1997

Computational predictive programs (expert systems) in toxicology.

The increasing number of pollutants in the environment raises the problem of the toxicological risk evaluation of these chemicals. Several so called expert systems (ES) have been claimed to be able to predict toxicity of certain chemical structures. Different approaches are currently used for these ES, based on explicit rules derived from the knowledge of human experts that compiled lists of toxic moieties for instance in the case of programs called HazardExpert and DEREK or relying on statistical approaches, as in the CASE and TOPKAT programs. Here we describe and compare these and other intelligent computer programs because of their utility in obtaining at least a first rough indication of the potential toxic activity of chemicals.

Animals↗
PubMed1997

Cyclic HIV-1 protease inhibitors derived from mannitol: synthesis, inhibitory potencies, and computational predictions of binding affinities.

Ten C2-symmetric cyclic urea and sulfamide derivatives have been synthesized from L-mannonic gamma-lactone and D-mannitol. The results of experimental measurement of their inhibitory potencies against HIV-1 protease were compared to calculated free energies of binding derived from molecular dynamics (MD) simulations. The compounds were selected, firstly, to enable elucidation of the role of stereochemistry for binding affinity (1a-d) and, secondly, to allow evaluation of the effects of variation in the link to the P1 and P1' phenyl groups on affinity (1a and 2-5). Thirdly, compounds with hydrogen bond-accepting or-donating groups attached to the phenyl groups in the P2 and P2' side chains (6 and 7) were selected. Binding free energies were estimated by a linear response method, whose predictive power for estimating binding affinities from MD simulations was demonstrated.

Anti-HIV Agents↗
PubMed2005

Computational prediction of native protein ligand-binding and enzyme active site sequences.

Recent studies reveal that the core sequences of many proteins were nearly optimized for stability by natural evolution. Surface residues, by contrast, are not so optimized, presumably because protein function is mediated through surface interactions with other molecules. Here, we sought to determine the extent to which the sequences of protein ligand-binding and enzyme active sites could be predicted by optimization of scoring functions based on protein ligand-binding affinity rather than structural stability. Optimization of binding affinity under constraints on the folding free energy correctly predicted 83% of amino acid residues (94% similar) in the binding sites of two model receptor-ligand complexes, streptavidin-biotin and glucose-binding protein. To explore the applicability of this methodology to enzymes, we applied an identical algorithm to the active sites of diverse enzymes from the peptidase, beta-gal, and nucleotide synthase families. Although simple optimization of binding affinity reproduced the sequences of some enzyme active sites with high precision, imposition of additional, geometric constraints on side-chain conformations based on the catalytic mechanism was required in other cases. With these modifications, our sequence optimization algorithm correctly predicted 78% of residues from all of the enzymes, with 83% similar to native (90% correct, with 95% similar, excluding residues with high variability in multiple sequence alignments). Furthermore, the conformations of the selected side chains were often correctly predicted within crystallographic error. These findings suggest that simple selection pressures may have played a predominant role in determining the sequences of ligand-binding and active sites in proteins.

Algorithms↗
PubMed2007

RUN-CBFbeta interaction in C. elegans: computational prediction and experimental verification.

The Runt domain proteins are eukaryotic transcription factors that regulate major developmental pathways. All members of this family contain a highly-conserved sequence-specific DNA binding domain: the Runt domain (RD). Structural and biochemical studies have shown that the Runt domain undergoes a conformational transition upon binding to DNA and that this process is regulated by an unrelated partner protein CBFbeta that enhances the DNA binding affinity of RD. Most of the reported studies on the Runt domain transcription factors were performed on proteins from mammals and Drosophila whereas very little has been known about the C. elegans RD protein, RUN, which provides the simplest model system for understanding the function of this class of transcription factors. We performed computational studies on RD domains from various species including C. elegans, Drosophila, and human, using the atom-atom contact surface area scoring method. The scoring analysis indicates that the DNA binding regulation of the C. elegans RD protein (CeRD) occurs via its interaction with a CBFbeta-like partner, as found for the human proteins, whereas a different mode of regulation may occur in the Drosophila system. Sequence, secondary structure and fold analyses of a putative CBFbeta protein identified in the C. elegans genome, CeCBFbeta, sharing a 22% identity with the human protein, predict a similar structure of this protein to that of the human CBFbeta protein. We produced the C. elegans proteins CeRD and CeCBFbeta in bacteria and confirmed their physical interaction as well as cross interactions with the corresponding human proteins. We also confirmed the structural similarity of CBFbeta and CeCBFbeta by circular dichroism analysis. The combined results suggest that a similar mechanism of regulation operates for the human and the C. elegans RD proteins despite the low sequence identity between their CBFbeta proteins and the evolutionary distance between the two systems.

Amino Acid Sequence↗
PubMed2006

Computational prediction and identification of HLA-A2.1-specific Ebola virus CTL epitopes.

Ebola virus (EBOV) is known to cause a severe hemorrhagic fever resulting in high mortality. Although the precise host defense mechanism(s) that afford protection against EBOV is not completely understood, T cell-mediated immune responses is believed to play a pivotal role in controlling virus replication and EBOV infection. There have been no reports on mapping of MHC Class I-binding CTL epitopes for EBOV till to date. In this study, we identified five HLA-A2-binding 9-mer peptides of EBOV nucleoprotein (NP) using computer-assisted algorithm. The peptides were synthesized and examined for their ability to bind to MHC class I molecules using a flow cytometry based MHC stabilization assay. Three of the EBOV-NP peptides tested (FLSFASLFL, RLMRTNFLI and KLTEAITAA) stabilized HLA-A2. The ability of the HLA-A2-binding EBOV-NP peptides to generate peptide-specific CTLs was evaluated in HLA-A2.1 transgenic mice. Epitope-specific CTL responses were confirmed by cytotoxic assays against peptide-pulsed target cells and interferon-gamma ELISPOT assay. Each of the EBOV-NP peptides induced CTL responses in HLA-A2-transgenic mice. Interestingly, all the three peptides were conserved in three different strains of Ebola (Zaire and Reston and Sudan). Taken together, these findings provide direct evidence for the existence of EBOV-derived NP epitopes that may be useful in the development of protective immunogens for this hemorrhagic virus.

Animals↗
PubMed2006

Computational prediction of the cross-reactive neutralizing epitope corresponding to the [corrected] monclonal [corrected] antibody b12 specific for HIV-1 gp120.

Backtracking from antibodies to their corresponding epitopes is a rational approach for vaccine design. Here we apply such a reverse immunological strategy for mapping the cross-reactive neutralizing epitope corresponding to the monoclonal antibody (mAb) b12 specific for HIV-1 gp120. b12 was used to screen a combinatorial phage display random peptide library and nineteen 12mer cysteine-looped peptides were affinity purified. These were used as input for analysis with the predictive algorithm Mapitope. Based on the input panel of peptides and the antigen's atomic structure, Mapitope predicts candidate epitopes on the surface of the antigen. Two major clusters were predicted as candidate b12 epitopes. These could be discriminated by a series of experiments, which included point mutagenesis of selected residues and binding assays. Moreover, the prediction of the b12 epitope was further strengthened by comparison with additional predictions for two competing antibodies, b6 and m14. Finally, support of our prediction was obtained in view of the fact that b12, m14, and b6 were found to compete against mAb 17b binding to gp120. The b12 epitope is predicted to consist of four peptide segments of gp120 (residues V254-T257, D368-F376, E381-Y384, and I420-I424), which lie at the periphery of the CD4 binding site.

Algorithms↗
PubMed2006

Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression.

The identification of regulatory regions is one of the most important and challenging problems toward the functional annotation of the human genome. In higher eukaryotes, transcription-factor (TF) binding sites are often organized in clusters called cis-regulatory modules (CRM). While the prediction of individual TF-binding sites is a notoriously difficult problem, CRM prediction has proven to be somewhat more reliable. Starting from a set of predicted binding sites for more than 200 TF families documented in Transfac, we describe an algorithm relying on the principle that CRMs generally contain several phylogenetically conserved binding sites for a few different TFs. The method allows the prediction of more than 118,000 CRMs within the human genome. A subset of these is shown to be bound in vivo by TFs using ChIP-chip. Their analysis reveals, among other things, that CRM density varies widely across the genome, with CRM-rich regions often being located near genes encoding transcription factors involved in development. Predicted CRMs show a surprising enrichment near the 3' end of genes and in regions far from genes. We document the tendency for certain TFs to bind modules located in specific regions with respect to their target genes and identify TFs likely to be involved in tissue-specific regulation. The set of predicted CRMs, which is made available as a public database called PReMod (http://genomequebec.mcgill.ca/PReMod), will help analyze regulatory mechanisms in specific biological systems.

Algorithms↗
PubMed2005

Substrate specificity of protein kinases and computational prediction of substrates.

To ensure signalling fidelity, kinases must act only on a defined subset of cellular targets. Appreciating the basis for this substrate specificity is essential for understanding the role of an individual protein kinase in a particular cellular process. The specificity in the cell is determined by a combination of "peptide specificity" of the kinase (the molecular recognition of the sequence surrounding the phosphorylation site), substrate recruitment and phosphatase activity. Peptide specificity plays a crucial role and depends on the complementarity between the kinase and the substrate and therefore on their three-dimensional structures. Methods for experimental identification of kinase substrates and characterization of specificity are expensive and laborious, therefore, computational approaches are being developed to reduce the amount of experimental work required in substrate identification. We discuss the structural basis of substrate specificity of protein kinases and review the experimental and computational methods used to obtain specificity information.

Binding Sites↗
PubMed1999

[Reversal of drug resistance in bacteria and tumor cells. (Drug research based on computer-predicted activity)].

This paper present a short overview on the new perspectives in drug-design against drug resistance phenomena focusing on the infectious antibiotic resistance of bacteria. The role of plasmids in resistance of bacteria, the mode of transmissions were analyzed in details. The elimination of plasmids--the genetic basis of bacterial resistance located extrachromosomally--was studied systematically with several heterocyclic compounds. Correlations were analyzed between the anti-plasmids++ effects and chemical structure of tricyclic compounds in quantitative structure activity relationship (QSAR) studies. A hypothesis was presented to exploit the analogous mechanisms of multidrug resistance in bacterial and cancer cells by inhibiting the action of drug exporter proteins in the presence of compounds synthetized on the basis of computer aided drug design.

Antineoplastic Agents↗
PubMed2005

Experimental detection of one case of benzene epoxidation by a peroxy radical and computational prediction of another.

Reaction of the beta-styryl radical with O2 in benzene results in a low yield of benzene oxide, which is shown by isotopic labeling to arise from the solvent. Ab initio and DFT calculations elucidate the mechanism of this reaction, and identify the properties of other radicals that should be more effective promoters of the reaction. The CN radical is found to be one candidate.

Benzene↗
PubMed1989

Clinical decision making and laparoscopy versus computer prediction in the management of the acute abdomen.

A prospective study has been undertaken of 321 patients with acute abdominal pain admitted to hospital under one surgical firm over a period of 21 months. During the first 10 months patients were classified on admission according to the perceived need for operation, with laparoscopy being performed on all those in whom the need for operation was uncertain. In addition, all women with suspected appendicitis underwent laparoscopy because previous studies by us and others have demonstrated a high error rate in this group. During the second 11 months of the study a similar system of classification and procedure was used but the patient's initial assessment was entered on a structured data sheet. After the patient had been discharged home this information was entered into a computer-aided diagnosis program. Hypothetical retrospective computer-aided decisions were then made about patient management. The final management error rate (correct decision to operate or not) was compared with the actual error rate using the clinical system. The final overall error rate in the first 10 months was 11 out of 163 patients and this was improved to 3 out of 158 in the second 11 months of the study by the addition of the structured data sheet to selective laparoscopy. A management policy based entirely on diagnostic probabilities taken from the computer-aided diagnosis program would have produced an error rate of 26 out of 158. We conclude that in the management of the acute abdomen a policy based on clinical decision combined with selective laparoscopy may be superior to one based on diagnostic probabilities alone. Further improvement in results follows the introduction of a structured data sheet for initial data collection.

Abdomen↗
PubMed2005

NMR structural characterization and computational predictions of the major intermediate in oxidative folding of leech carboxypeptidase inhibitor.

The III-A intermediate constitutes the major rate-determining step in the oxidative folding of leech carboxypeptidase inhibitor (LCI). In this work, III-A has been directly purified from the folding reaction and structurally characterized by NMR spectroscopy. This species, containing three native disulfides, displays a highly native-like structure; however, it lacks some secondary structure elements, making it more flexible than native LCI. III-A represents a structurally determined example of a disulfide-insecure intermediate; direct oxidation of this species to the fully native protein seems to be restricted by the burial of its two free cysteine residues inside a native-like structure. We also show that theoretical approaches based on topological constraints predict with good accuracy the presence of this folding intermediate. Overall, the derived results suggest that, as it occurs with non-disulfide bonded proteins, native-like interactions between segments of secondary structure rather than the crosslinking of disulfide bonds direct the folding of LCI.

Animals↗
PubMed2000

Computer prediction of sites associated with various elements of the nuclear matrix.

Attachment regions of the eukaryotic chromosomal DNA to the nuclear scaffold/matrix (S/MARs) participate in various important cellular processes. However, no obvious characteristics common for these nucleotide sequences have been revealed, except that S/MARs are non-coding sites containing putative regulatory elements and binding sites of DNA-topoisomerase II. Heterogeneity among S/MARs can be caused by a variety of biological factors. In this paper, the accuracy of two S/MARs prediction programs, MAR-Finder (Singh, Kramer and Krawetz, 1997) and ChrClass (Glazkov, Rogozin and Glazko, 1998) are compared and it is concluded that both programs can be recommended for analysis of eukaryotic genomes. However, results of their prediction should be interpreted with caution since estimation of prediction accuracy of both programs needs further analysis. Problems of S/MARs prediction are illustrated on several examples of human protein-coding genes, repeated elements and the beta-globin locus from different mammalian species. Results of our analysis suggest that the proportion of missed S/MARs is lower for ChrClass, whereas the proportion of wrong S/MARs is lower for MAR-Finder (a default set of parameters).

Animals↗