Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Generation and phenotypic characterization of new human ovarian cancer cell lines with the identification of antigens potentially recognizable by HLA-restricted cytotoxic T cells

This study describes a simple method for long-term establishment of human ovarian tumor lines and prediction of T-cell epitopes that could be potentially useful in the generation of tumor-specific cytotoxic T lymphocytes (CTLs), Nine ovarian tumor lines (INT.Ov) were generated from solid primary or metastatic tumors as well as from ascitic fluid, Notably all lines expressed HLA class I, intercellular adhesion molecule-1 (ICAM-1), polymorphic epithelial mucin (PEM) and cytokeratin (CK), but not HLA class II, B7.1 (CD80) or BAGE, While of the 9 lines tested 4 (INT.Ov1, 2, 5 and 6) expressed the folate receptor (FR-alpha) and 6 (INT.Ov1, 2, 5, 6, 7 and 9) expressed the epidermal growth factor receptor (EGFR); MAGE-1 and p185(HER-2/neu) were only found in 2 lines (INT.Ov1 and 2) and GAGE-1 expression in 1 line (INT.Ov2). The identification of class I MHC ligands and T-cell epitopes within protein antigens was achieved by applying several theoretical methods including: 1) similarity or homology searches to MHCPEP; 2) BIMAS and 3) artificial neural network-based predictions of proteins MACE, GAGE, EGFR, p185(HER-2/neu) and FR-alpha expressed in INT.Ov lines, Because of the high frequency of expression of some of these proteins in ovarian cancer and the ability to determine HLA binding peptides efficiently, it is expected that after appropriate screening, a large cohort of ovarian cancer patients may become candidates to receive peptide based vaccines. (C) 1997 Wiley-Liss, Inc.

Typing mineral deposits using their grades and tonnages in an artificial neural network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

Identification of a Golgi-localised GRIP domain protein from Arabidopsis thaliana

A family of Golgi-localised molecules was recently described in animals and fungi possessing extensive coiled regions and a short (similar to40 residues) conserved C-terminal domain, called the GRIP domain, which is responsible for their location to this organelle. Using the model plant Arabidopsis thaliana, we identified a gene (AtGRIP) encoding a putative GRIP protein. We demonstrated that the C-terminal domain from AtGRIP functions as a Golgi-targeting sequence in plant cells. Localisation studies in living cells expressing the AtGRIP fused to a DsRed2 fluorescent probe, showed extensive co-location with the Golgi marker alpha-mannosidase I in transformed tobacco protoplasts. GRIP-like sequences were also found in genomic databases of rice, maize, wheat and alfalfa, suggesting that this domain may be a useful Golgi marker for immunolocalisation studies. Despite low sequence identity amongst GRIP domains, the plant GRIP sequence was able to target to the Golgi of mammalian cells. Taken together, these data indicate that GRIP domain proteins might be implicated in a targeting mechanism that is conserved amongst eukaryotes.

Ultrastructural identification of uncoated caveolin-independent early endocytic vehicles

Using quantitative light microscopy and a modified immunoelectron microscopic technique, we have characterized the entry pathway of the cholera toxin binding subunit (CTB) in primary embryonic fibroblasts. CTB trafficking to the Golgi complex was identical in caveolin-1 null (Cav1 -/-) mouse embryonic fibroblasts (MEFs) and wild-type (WT) MEFs. CTB entry in the Cav1 -/- MEFs was predominantly clathrin and dynamin independent but relatively cholesterol dependent. Immunoelectron microscopy was used to quantify budded and surface-connected caveoloe and to identify noncaveolar endocytic vehicles. In WT MEFs a small fraction of the total Cav1-positive structures were shown to bud from the plasma membrane (2 % per minute), and budding increased upon okadaic acid or lactosyl ceramide treatment. However, the major carriers involved in initial entry of CTB were identified as uncoated tubular or ring-shaped structures. These carriers contained GPI-anchored proteins and fluid phase markers and represented the major vehicles mediating CTB uptake in both WT and caveolae-null cells.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background: The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results: We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

Spectral properties of the tandem Jackson network, seen as a quasi-birth-and-death process

Quasi-birth-and-death (QBD) processes with infinite “phase spaces” can exhibit unusual and interesting behavior. One of the simplest examples of such a process is the two-node tandem Jackson network, with the “phase” giving the state of the first queue and the “level” giving the state of the second queue. In this paper, we undertake an extensive analysis of the properties of this QBD. In particular, we investigate the spectral properties of Neuts’s R-matrix and show that the decay rate of the stationary distribution of the “level” process is not always equal to the convergence norm of R. In fact, we show that we can obtain any decay rate from a certain range by controlling only the transition structure at level zero, which is independent of R. We also consider the sequence of tandem queues that is constructed by restricting the waiting room of the first queue to some finite capacity, and then allowing this capacity to increase to infinity. We show that the decay rates for the finite truncations converge to a value, which is not necessarily the decay rate in the infinite waiting room case. Finally, we show that the probability that the process hits level n before level 0 given that it starts in level 1 decays at a rate which is not necessarily the same as the decay rate for the stationary distribution.

Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Identification of two novel mutations in the hydroxymethylbilane synthase gene in three patients from two unrelated families with acute intermittent porphyria

We have screened the hydroxymethylbilane synthase cDNAs of 3 patients from 2 families suffering from acute intermittent porphyria (AIP) from Scotland and South Africa using heteroduplex and chemical cleavage of mismatch analyses, Direct sequencing was used to characterise the mutations, The two novel mutations identified were a missense mutation at nucleotide position 64 in exon 3 (R22C) and a single base-pair deletion in exon 15, These mutations are predicted to affect the normal function of the enzyme and, therefore, are expected to be the primary cause of disease in these patients.

Capillary network models for transport in packed beds: Considerations of pore aspect ratio

The conventional analysis for the estimation of the tortuosity factor for transport in porous media is modified here to account for the effect of pore aspect ratio. Structural models of the porous medium are also constructed for calculating the aspect ratio as a function of porosity. Comparison of the model predictions with the extensive data of Currie (1960) for the effective diffusivity of hydrogen in packed beds shows good agreement with a network model of randomly oriented intersecting pores for porosities upto about 50 percent, which is the region of practical interest. The predictions based on this network model are also found to be in better agreement with the data of Currie than earlier expressions developed for unconsolidated and grainy media.

Spectroscopic identification of a dinuclear metal centre in manganese(II)-activated aminopeptidase P from Escherichia coli: implications for human prolidase

Electron paramagnetic resonance (EPR) spectra and X-ray absorption (EXAFS and XANES) data have been recorded for the manganese enzyme aminopeptidase P (AMPP, PepP protein) from Escherichia coli. The biological function of the protein, a tetramer of 50-kDa subunits, is the hydrolysis of N-terminal Xaa-Pro peptide bonds. Activity assays confirm that the enzyme is activated by treatment with Mn2+. The EPR spectrum of Mn2+-activated AMPP at liquid-He temperature is characteristic of an exchange-coupled dinuclear Mn(II) site, the Mn-Mn separation calculated from the zero-field splitting D of the quintet state being 3.5 (+/- 0.1) Angstrom. In the X-ray absorption spectrum of Mn2+-activated AMPP at the Mn K edge, the near-edge features are consistent with octahedrally coordinated Mn atoms in oxidation state +2. EXAFS data, limited to k less than or equal to 12 Angstrom(-1) by traces of Fe in the protein, are consistent with a single coordination shell occupied predominantly by O donor atoms at an average Mn-ligand distance of 2.15 Angstrom, but the possibility of a mixture of O and N donor atoms is not excluded. The Mn-Mn interaction at 3.5 Angstrom, is not detected in the EXAFS, probably due to destructive interference from light outer-shell atoms. The biological function, amino acid sequence and metal-ion dependence of E. coli AMPP are closely related to those of human prolidase, an enzyme that specifically cleaves Xaa-Pro dipeptides. Mutations that lead to human prolidase deficiency and clinical symptoms have been identified. Several known inhibitors of prolidase also inhibit AMPP. When these inhibitors are added to Mn2+-activated AMPP, the EPR spectrum and EXAFS remain unchanged. It can be inferred that the inhibitors either do not bind directly to the Mn centres, or substitute for existing Mn ligands without a significant change in donor atoms or coordination geometry. The conclusions from the spectroscopic measurements on AMPP have been verified by, and complement, a recent crystal structure analysis.