Immunoinformatics and the in silico prediction of immunogenicity:an introduction

Immunoinformatics is the application of informatics techniques to molecules of the immune system. One of its principal goals is the effective prediction of immunogenicity, be that at the level of epitope, subunit vaccine, or attenuated pathogen. Immunogenicity is the ability of a pathogen or component thereof to induce a specific immune response when first exposed to surveillance by the immune system, whereas antigenicity is the capacity for recognition by the extant machinery of the adaptive immune response in a recall response. In thisbook, we introduce these subjects and explore the current state of play in immunoinformatics and the in silico prediction of immunogenicity.

Quantitative structure-activity relationships and the prediction of MHC supermotifs

The underlying assumption in quantitative structure–activity relationship (QSAR) methodology is that related chemical structures exhibit related biological activities. We review here two QSAR methods in terms of their applicability for human MHC supermotif definition. Supermotifs are motifs that characterise binding to more than one allele. Supermotif definition is the initial in silico step of epitope-based vaccine design. The first QSAR method we review here—the additive method—is based on the assumption that the binding affinity of a peptide depends on contributions from both amino acids and the interactions between them. The second method is a 3D-QSAR method: comparative molecular similarity indices analysis (CoMSIA). Both methods were applied to 771 peptides binding to 9 HLA alleles. Five of the alleles (A*0201, A* 0202, A*0203, A*0206 and A*6802) belong to the HLA-A2 superfamily and the other four (A*0301, A*1101, A*3101 and A*6801) to the HLA-A3 superfamily. For each superfamily, supermotifs defined by the two QSAR methods agree closely and are supported by many experimental data.

Coupling in silico and in vitro analysis of peptide-MHC binding:a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.

EpiDOCK:a molecular docking-based tool for MHC class II binding prediction

Cellular peptide vaccines contain T-cell epitopes. The main prerequisite for a peptide to act as a T-cell epitope is that it binds to a major histocompatibility complex (MHC) protein. Peptide MHC binder identification is an extremely costly experimental challenge since human MHCs, named human leukocyte antigen, are highly polymorphic and polygenic. Here we present EpiDOCK, the first structure-based server for MHC class II binding prediction. EpiDOCK predicts binding to the 23 most frequent human, MHC class II proteins. It identifies 90% of true binders and 76% of true non-binders, with an overall accuracy of 83%. EpiDOCK is freely accessible at http://epidock.ddg-pharmfac. net. © The Author 2013. Published by Oxford University Press. All rights reserved.

Exploring Iris Colour Prediction And Ancestry Inference In Admixed Populations Of South America.

New DNA-based predictive tests for physical characteristics and inference of ancestry are highly informative tools that are being increasingly used in forensic genetic analysis. Two eye colour prediction models: a Bayesian classifier - Snipper and a multinomial logistic regression (MLR) system for the Irisplex assay, have been described for the analysis of unadmixed European populations. Since multiple SNPs in combination contribute in varying degrees to eye colour predictability in Europeans, it is likely that these predictive tests will perform in different ways amongst admixed populations that have European co-ancestry, compared to unadmixed Europeans. In this study we examined 99 individuals from two admixed South American populations comparing eye colour versus ancestry in order to reveal a direct correlation of light eye colour phenotypes with European co-ancestry in admixed individuals. Additionally, eye colour prediction following six prediction models, using varying numbers of SNPs and based on Snipper and MLR, were applied to the study populations. Furthermore, patterns of eye colour prediction have been inferred for a set of publicly available admixed and globally distributed populations from the HGDP-CEPH panel and 1000 Genomes databases with a special emphasis on admixed American populations similar to those of the study samples.

Petroleomics By Electrospray Ionization Ft-icr Mass Spectrometry Coupled To Partial Least Squares With Variable Selection Methods: Prediction Of The Total Acid Number Of Crude Oils.

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.

Neck Circumference As A New Anthropometric Indicator For Prediction Of Insulin Resistance And Components Of Metabolic Syndrome In Adolescents: Brazilian Metabolic Syndrome Study.

To evaluate the correlation between neck circumference and insulin resistance and components of metabolic syndrome in adolescents with different adiposity levels and pubertal stages, as well as to determine the usefulness of neck circumference to predict insulin resistance in adolescents. Cross-sectional study with 388 adolescents of both genders from ten to 19 years old. The adolescents underwent anthropometric and body composition assessment, including neck and waist circumferences, and biochemical evaluation. The pubertal stage was obtained by self-assessment, and the blood pressure, by auscultation. Insulin resistance was evaluated by the Homeostasis Model Assessment-Insulin Resistance. The correlation between two variables was evaluated by partial correlation coefficient adjusted for the percentage of body fat and pubertal stage. The performance of neck circumference to identify insulin resistance was tested by Receiver Operating Characteristic Curve. After the adjustment for percentage body fat and pubertal stage, neck circumference correlated with waist circumference, blood pressure, triglycerides and markers of insulin resistance in both genders. The results showed that the neck circumference is a useful tool for the detection of insulin resistance and changes in the indicators of metabolic syndrome in adolescents. The easiness of application and low cost of this measure may allow its use in Public Health services.

Synthesis, screening for antiacetylcholinesterase activity and binding mode prediction of a new series of [3-(disubstituted-phosphate)-4,4,4-trifluoro-butyl]-carbamic acid ethyl esters

A series of nine new [3-(disubstituted-phosphate)-4,4,4-trifluoro-butyl]-carbamic acid ethyl esters (phosphate-carbamate compounds) was obtained through the reaction of (4,4,4-trifluoro-3-hydroxybut-1-yl)-carbamic acid ethyl esters with phosphorus oxychloride followed by the addition of alcohols. The products were characterized by ¹H, 13C, 31P, and 19F NMR spectroscopy, GC-MS, and elemental analysis. All the synthesized compounds were screened for acetylcholinesterase (AChE) inhibitory activity using the Ellman method. All compounds containing phosphate and carbamate pharmacophores in their structures showed enzyme inhibition, being the compound bearing the diethoxy phosphate group (2b) the most active compound. Molecular modeling studies were performed to investigate the detailed interactions between AChE active site and small-molecule inhibitor candidates, providing valuable structural insights into AChE inhibition.

Keratoconus prediction using a finite element model of the cornea with local biomechanical properties

PURPOSE: The ability to predict and understand which biomechanical properties of the cornea are responsible for the stability or progression of keratoconus may be an important clinical and surgical tool for the eye-care professional. We have developed a finite element model of the cornea, that tries to predicts keratoconus-like behavior and its evolution based on material properties of the corneal tissue. METHODS: Corneal material properties were modeled using bibliographic data and corneal topography was based on literature values from a schematic eye model. Commercial software was used to simulate mechanical and surface properties when the cornea was subject to different local parameters, such as elasticity. RESULTS: The simulation has shown that, depending on the corneal initial surface shape, changes in local material properties and also different intraocular pressures values induce a localized protuberance and increase in curvature when compared to the remaining portion of the cornea. CONCLUSIONS: This technique provides a quantitative and accurate approach to the problem of understanding the biomechanical nature of keratoconus. The implemented model has shown that changes in local material properties of the cornea and intraocular pressure are intrinsically related to keratoconus pathology and its shape/curvature.

Prediction of good glass formers in the Al-Ni-La and Al-Ni-Gd systems using topological instability and electronegativity

A new criterion has been recently proposed combining the topological instability (lambda criterion) and the average electronegativity difference (Delta e) among the elements of an alloy to predict and select new glass-forming compositions. In the present work, this criterion (lambda.Delta e) is applied to the Al-Ni-La and Al-Ni-Gd ternary systems and its predictability is validated using literature data for both systems and additionally, using own experimental data for the Al-La-Ni system. The compositions with a high lambda.Delta e value found in each ternary system exhibit a very good correlation with the glass-forming ability of different alloys as indicated by their supercooled liquid regions (Delta T(x)) and their critical casting thicknesses. In the case of the Al-La-Ni system, the alloy with the largest lambda.Delta e value, La(56)Al(26.5)Ni(17.5), exhibits the highest glass-forming ability verified for this system. Therefore, the combined lambda.Delta e criterion is a simple and efficient tool to select new glass-forming compositions in Al-Ni-RE systems. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3563099]

System Identification and Prediction of Dengue Fever Incidence in Rio de Janeiro

Identification, prediction, and control of a system are engineering subjects, regardless of the nature of the system. Here, the temporal evolution of the number of individuals with dengue fever weekly recorded in the city of Rio de Janeiro, Brazil, during 2007, is used to identify SIS (susceptible-infective-susceptible) and SIR (susceptible-infective-removed) models formulated in terms of cellular automaton (CA). In the identification process, a genetic algorithm (GA) is utilized to find the probabilities of the state transition S -> I able of reproducing in the CA lattice the historical series of 2007. These probabilities depend on the number of infective neighbors. Time-varying and non-time-varying probabilities, three different sizes of lattices, and two kinds of coupling topology among the cells are taken into consideration. Then, these epidemiological models built by combining CA and GA are employed for predicting the cases of sick persons in 2008. Such models can be useful for forecasting and controlling the spreading of this infectious disease.

Estimation and prediction of parameters and breeding values in soybean using REML/BLUP and Least Squares

The aim of this study was to compare REML/BLUP and Least Square procedures in the prediction and estimation of genetic parameters and breeding values in soybean progenies. F(2:3) and F(4:5) progenies were evaluated in the 2005/06 growing season and the F(2:4) and F(4:6) generations derived thereof were evaluated in 2006/07. These progenies were originated from two semi-early, experimental lines that differ in grain yield. The experiments were conducted in a lattice design and plots consisted of a 2 m row, spaced 0.5 m apart. The trait grain yield per plot was evaluated. It was observed that early selection is more efficient for the discrimination of the best lines from the F(4) generation onwards. No practical differences were observed between the least square and REML/BLUP procedures in the case of the models and simplifications for REML/BLUP used here.

Shallow landslide prediction in the Serra do Mar, Sao Paulo, Brazil

Various methods are currently used in order to predict shallow landslides within the catchment scale. Among them, physically based models present advantages associated with the physical description of processes by means of mathematical equations. The main objective of this research is the prediction of shallow landslides using TRIGRS model, in a pilot catchment located at Serra do Mar mountain range, Sao Paulo State, southeastern Brazil. Susceptibility scenarios have been simulated taking into account different mechanical and hydrological values. These scenarios were analysed based on a landslide scars map from the January 1985 event, upon which two indexes were applied: Scars Concentration (SC - ratio between the number of cells with scars, in each class, and the total number of cells with scars within the catchment) and Landslide Potential (LP - ratio between the number of cells with scars, in each class, and the total number of cells in that same class). The results showed a significant agreement between the simulated scenarios and the scar's map. In unstable areas (SF <= 1), the SC values exceeded 50% in all scenarios. Based on the results, the use of this model should be considered an important tool for shallow landslide prediction, especially in areas where mechanical and hydrological properties of the materials are not well known.

Biologically Plausible Connectionist Prediction of Natural Language Thematic Relations

In Natural Language Processing (NLP) symbolic systems, several linguistic phenomena, for instance, the thematic role relationships between sentence constituents, such as AGENT, PATIENT, and LOCATION, can be accounted for by the employment of a rule-based grammar. Another approach to NLP concerns the use of the connectionist model, which has the benefits of learning, generalization and fault tolerance, among others. A third option merges the two previous approaches into a hybrid one: a symbolic thematic theory is used to supply the connectionist network with initial knowledge. Inspired on neuroscience, it is proposed a symbolic-connectionist hybrid system called BIO theta PRED (BIOlogically plausible thematic (theta) symbolic-connectionist PREDictor), designed to reveal the thematic grid assigned to a sentence. Its connectionist architecture comprises, as input, a featural representation of the words (based on the verb/noun WordNet classification and on the classical semantic microfeature representation), and, as output, the thematic grid assigned to the sentence. BIO theta PRED is designed to ""predict"" thematic (semantic) roles assigned to words in a sentence context, employing biologically inspired training algorithm and architecture, and adopting a psycholinguistic view of thematic theory.

Accurate prediction of the Si/SiO(2) interface band offset using the self-consistent ab initio DFT/LDA-1/2 method

We use the density functional theory/local-density approximation (DFT/LDA)-1/2 method [L. G. Ferreira , Phys. Rev. B 78, 125116 (2008)], which attempts to fix the electron self-energy deficiency of DFT/LDA by half-ionizing the whole Bloch band of the crystal, to calculate the band offsets of two Si/SiO(2) interface models. Our results are similar to those obtained with a ""state-of-the-art"" GW approach [R. Shaltaf , Phys. Rev. Lett. 100, 186401 (2008)], with the advantage of being as computationally inexpensive as the usual DFT/LDA. Our band gap and band offset predictions are in excellent agreement with experiments.