Computational binding assays of antigenic peptides

Computer models can be combined with laboratory experiments for the efficient determination of (i) peptides that bind MHC molecules and (ii) T-cell epitopes. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures. This requires the definition of standards and experimental protocols for model application. We describe the requirements for validation and assessment of computer models. The utility of combining accurate predictions with a limited number of laboratory experiments is illustrated by practical examples. These include the identification of T-cell epitopes from IDDM-, melanoma- and malaria-related antigens by combining computational and conventional laboratory assays. The success rate in determining antigenic peptides, each in the context of a specific HLA molecule, ranged from 27 to 71%, while the natural prevalence of MHC-binding peptides is 0.1-5%.

Prediction of promiscuous peptides that bind HLA class I molecules

Promiscuous T-cell epitopes make ideal targets for vaccine development. We report here a computational system, multipred, for the prediction of peptide binding to the HLA-A2 supertype. It combines a novel representation of peptide/MHC interactions with a hidden Markov model as the prediction algorithm. multipred is both sensitive and specific, and demonstrates high accuracy of peptide-binding predictions for HLA-A*0201, *0204, and *0205 alleles, good accuracy for *0206 allele, and marginal accuracy for *0203 allele. multipred replaces earlier requirements for individual prediction models for each HLA allelic variant and simplifies computational aspects of peptide-binding prediction. Preliminary testing indicates that multipred can predict peptide binding to HLA-A2 supertype molecules with high accuracy, including those allelic variants for which no experimental binding data are currently available.

Prediction of carbon monoxide in fires by conditional moment closure

Carbon monoxide is the chief killer in fires. Dangerous levels of CO can occur when reacting combustion gases are quenched by heat transfer, or by mixing of the fire plume in a cooled under- or overventilated upper layer. In this paper, carbon monoxide predictions for enclosure fires are modeled by the conditional moment closure (CMC) method and are compared with laboratory data. The modeled fire situation is a buoyant, turbulent, diffusion flame burning under a hood. The fire plume entrains fresh air, and the postflame gases are cooled considerably under the hood by conduction and radiation, emulating conditions which occur in enclosure fires and lead to the freezing of CO burnout. Predictions of CO in the cooled layer are presented in the context of a complete computational fluid dynamics solution of velocity, temperature, and major species concentrations. A range of underhood equivalence ratios, from rich to lean, are investigated. The CMC method predicts CO in very good agreement with data. In particular, CMC is able to correctly predict CO concentrations in lean cooled gases, showing its capability in conditions where reaction rates change considerably.

Computational tools for the study of allergens

Allergy is a major cause of morbidity worldwide. The number of characterized allergens and related information is increasing rapidly creating demands for advanced information storage, retrieval and analysis. Bioinformatics provides useful tools for analysing allergens and these are complementary to traditional laboratory techniques for the study of allergens. Specific applications include structural analysis of allergens, identification of B- and T-cell epitopes, assessment of allergenicity and cross-reactivity, and genome analysis. In this paper, the most important bioinformatic tools and methods with relevance to the study of allergy have been reviewed.

PREDBALB/c: a system for the prediction of peptide binding to H2(d) molecules, a haplotype of the BALB/c mouse

PREDBALB/c is a computational system that predicts peptides binding to the major histocompatibility complex-2 (H2(d)) of the BALB/c mouse, an important laboratory model organism. The predictions include the complete set of H2(d) class I ( H2-K-d, H2-L-d and H2-D-d) and class II (I-E-d and I-A(d)) molecules. The prediction system utilizes quantitative matrices, which were rigorously validated using experimentally determined binders and non-binders and also by in vivo studies using viral proteins. The prediction performance of PREDBALB/c is of very high accuracy. To our knowledge, this is the first online server for the prediction of peptides binding to a complete set of major histocompatibility complex molecules in a model organism (H2(d) haplotype). PREDBALB/c is available at http://antigen.i2r.a-star.edu.sg/predBalbc/.

Prediction of Golgi Type II membrane proteins based on their transmembrane domains

Motivation: A major issue in cell biology today is how distinct intracellular regions of the cell, like the Golgi Apparatus, maintain their unique composition of proteins and lipids. The cell differentially separates Golgi resident proteins from proteins that move through the organelle to other subcellular destinations. We set out to determine if we could distinguish these two types of transmembrane proteins using computational approaches. Results: A new method has been developed to predict Golgi membrane proteins based on their transmembrane domains. To establish the prediction procedure, we took the hydrophobicity values and frequencies of different residues within the transmembrane domains into consideration. A simple linear discriminant function was developed with a small number of parameters derived from a dataset of Type II transmembrane proteins of known localization. This can discriminate between proteins destined for Golgi apparatus or other locations (post-Golgi) with a success rate of 89.3% or 85.2%, respectively on our redundancy-reduced data sets.

Computational differentiation of N-terminal signal peptides and transmembrane helices

Signal peptides and transmembrane helices both contain a stretch of hydrophobic amino acids. This common feature makes it difficult for signal peptide and transmembrane helix predictors to correctly assign identity to stretches of hydrophobic residues near the N-terminal methionine of a protein sequence. The inability to reliably distinguish between N-terminal transmembrane helix and signal peptide is an error with serious consequences for the prediction of protein secretory status or transmembrane topology. In this study, we report a new method for differentiating protein N-terminal signal peptides and transmembrane helices. Based on the sequence features extracted from hydrophobic regions (amino acid frequency, hydrophobicity, and the start position), we set up discriminant functions and examined them on non-redundant datasets with jackknife tests. This method can incorporate other signal peptide prediction methods and achieve higher prediction accuracy. For Gram-negative bacterial proteins, 95.7% of N-terminal signal peptides and transmembrane helices can be correctly predicted (coefficient 0.90). Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 99% (coefficient 0.92). For eukaryotic proteins, 94.2% of N-terminal signal peptides and transmembrane helices can be correctly predicted with coefficient 0.83. Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 87% (coefficient 0.85). The method can be used to complement current transmembrane protein prediction and signal peptide prediction methods to improve their prediction accuracies. (C) 2003 Elsevier Inc. All rights reserved.

Algorithms and computational tools for metabolic flux analysis

PhD thesis in Biomedical Engineering

In silico prediction of human carboxylesterase-1 (hCES1) metabolism combining docking analyses and MD simulations.

Metabolic problems lead to numerous failures during clinical trials, and much effort is now devoted in developing in silico models predicting metabolic stability and metabolites. Such models are well known for cytochromes P450 and some transferases, whereas little has been done to predict the hydrolytic activity of human hydrolases. The present study was undertaken to develop a computational approach able to predict the hydrolysis of novel esters by human carboxylesterase hCES1. The study involves both docking analyses of known substrates to develop predictive models, and molecular dynamics (MD) simulations to reveal the in situ behavior of substrates and products, with particular attention being paid to the influence of their ionization state. The results emphasize some crucial properties of the hCES1 catalytic cavity, confirming that as a trend with several exceptions, hCES1 prefers substrates with relatively smaller and somewhat polar alkyl/aryl groups and larger hydrophobic acyl moieties. The docking results underline the usefulness of the hydrophobic interaction score proposed here, which allows a robust prediction of hCES1 catalysis, while the MD simulations show the different behavior of substrates and products in the enzyme cavity, suggesting in particular that basic substrates interact with the enzyme in their unprotonated form.

Homology modeling and metabolism prediction of human carboxylesterase-2 using docking analyses by GriDock: a parallelized tool based on AutoDock 4.0.

Metabolic problems lead to numerous failures during clinical trials, and much effort is now devoted to developing in silico models predicting metabolic stability and metabolites. Such models are well known for cytochromes P450 and some transferases, whereas less has been done to predict the activity of human hydrolases. The present study was undertaken to develop a computational approach able to predict the hydrolysis of novel esters by human carboxylesterase hCES2. The study involved first a homology modeling of the hCES2 protein based on the model of hCES1 since the two proteins share a high degree of homology (congruent with 73%). A set of 40 known substrates of hCES2 was taken from the literature; the ligands were docked in both their neutral and ionized forms using GriDock, a parallel tool based on the AutoDock4.0 engine which can perform efficient and easy virtual screening analyses of large molecular databases exploiting multi-core architectures. Useful statistical models (e.g., r (2) = 0.91 for substrates in their unprotonated state) were calculated by correlating experimental pK(m) values with distance between the carbon atom of the substrate's ester group and the hydroxy function of Ser228. Additional parameters in the equations accounted for hydrophobic and electrostatic interactions between substrates and contributing residues. The negatively charged residues in the hCES2 cavity explained the preference of the enzyme for neutral substrates and, more generally, suggested that ligands which interact too strongly by ionic bonds (e.g., ACE inhibitors) cannot be good CES2 substrates because they are trapped in the cavity in unproductive modes and behave as inhibitors. The effects of protonation on substrate recognition and the contrasting behavior of substrates and products were finally investigated by MD simulations of some CES2 complexes.

Computational drug design strategies applied to the modelling of human immunodeficiency virus-1 reverse transcriptase inhibitors

Reverse transcriptase (RT) is a multifunctional enzyme in the human immunodeficiency virus (HIV)-1 life cycle and represents a primary target for drug discovery efforts against HIV-1 infection. Two classes of RT inhibitors, the nucleoside RT inhibitors (NRTIs) and the nonnucleoside transcriptase inhibitors are prominently used in the highly active antiretroviral therapy in combination with other anti-HIV drugs. However, the rapid emergence of drug-resistant viral strains has limited the successful rate of the anti-HIV agents. Computational methods are a significant part of the drug design process and indispensable to study drug resistance. In this review, recent advances in computer-aided drug design for the rational design of new compounds against HIV-1 RT using methods such as molecular docking, molecular dynamics, free energy calculations, quantitative structure-activity relationships, pharmacophore modelling and absorption, distribution, metabolism, excretion and toxicity prediction are discussed. Successful applications of these methodologies are also highlighted.

An assessment of gene prediction accuracy in large DNA sequences

One of the first useful products from the human genome will be a set of predicted genes. Besides its intrinsic scientific interest, the accuracy and completeness of this data set is of considerable importance for human health and medicine. Though progress has been made on computational gene identification in terms of both methods and accuracy evaluation measures, most of the sequence sets in which the programs are tested are short genomic sequences, and there is concern that these accuracy measures may not extrapolate well to larger, more challenging data sets. Given the absence of experimentally verified large genomic data sets, we constructed a semiartificial test set comprising a number of short single-gene genomic sequences with randomly generated intergenic regions. This test set, which should still present an easier problem than real human genomic sequence, mimics the approximately 200kb long BACs being sequenced. In our experiments with these longer genomic sequences, the accuracy of GENSCAN, one of the most accurate ab initio gene prediction programs, dropped significantly, although its sensitivity remained high. Conversely, the accuracy of similarity-based programs, such as GENEWISE, PROCRUSTES, and BLASTX was not affected significantly by the presence of random intergenic sequence, but depended on the strength of the similarity to the protein homolog. As expected, the accuracy dropped if the models were built using more distant homologs, and we were able to quantitatively estimate this decline. However, the specificities of these techniques are still rather good even when the similarity is weak, which is a desirable characteristic for driving expensive follow-up experiments. Our experiments suggest that though gene prediction will improve with every new protein that is discovered and through improvements in the current set of tools, we still have a long way to go before we can decipher the precise exonic structure of every gene in the human genome using purely computational methodology.

Prediction of enzyme function by combining sequence similarity and protein interactions

Background: A number of studies have used protein interaction data alone for protein function prediction. Here, we introduce a computational approach for annotation of enzymes, based on the observation that similar protein sequences are more likely to perform the same function if they share similar interacting partners. Results: The method has been tested against the PSI-BLAST program using a set of 3,890 protein sequences from which interaction data was available. For protein sequences that align with at least 40% sequence identity to a known enzyme, the specificity of our method in predicting the first three EC digits increased from 80% to 90% at 80% coverage when compared to PSI-BLAST. Conclusion: Our method can also be used in proteins for which homologous sequences with known interacting partners can be detected. Thus, our method could increase 10% the specificity of genome-wide enzyme predictions based on sequence matching by PSI-BLAST alone.

The role of computational fluid dynamics in the management of unruptured intracranial aneurysms: a clinicians' view

Objective: The importance of hemodynamics in the etiopathogenesis of intracranial aneurysms (IAs) is widely accepted.Computational fluid dynamics (CFD) is being used increasingly for hemodynamic predictions. However, alogn with thecontinuing development and validation of these tools, it is imperative to collect the opinion of the clinicians. Methods: A workshopon CFD was conducted during the European Society of Minimally Invasive Neurological Therapy (ESMINT) Teaching Course,Lisbon, Portugal. 36 delegates, mostly clinicians, performed supervised CFD analysis for an IA, using the @neuFuse softwaredeveloped within the European project @neurIST. Feedback on the workshop was collected and analyzed. The performancewas assessed on a scale of 1 to 4 and, compared with experts’ performance. Results: Current dilemmas in the management ofunruptured IAs remained the most important motivating factor to attend the workshop and majority of participants showedinterest in participating in a multicentric trial. The participants achieved an average score of 2.52 (range 0–4) which was 63% (range 0–100%) of an expert user. Conclusions: Although participants showed a manifest interest in CFD, there was a clear lack ofawareness concerning the role of hemodynamics in the etiopathogenesis of IAs and the use of CFD in this context. More effortstherefore are required to enhance understanding of the clinicians in the subject.

Computational fluid dynamics of the right ventricular outflow tract and of the pulmonary artery: a bench model of flow dynamics.

OBJECTIVES: The reconstruction of the right ventricular outflow tract (RVOT) with valved conduits remains a challenge. The reoperation rate at 5 years can be as high as 25% and depends on age, type of conduit, conduit diameter and principal heart malformation. The aim of this study is to provide a bench model with computer fluid dynamics to analyse the haemodynamics of the RVOT, pulmonary artery, its bifurcation, and left and right pulmonary arteries that in the future may serve as a tool for analysis and prediction of outcome following RVOT reconstruction. METHODS: Pressure, flow and diameter at the RVOT, pulmonary artery, bifurcation of the pulmonary artery, and left and right pulmonary arteries were measured in five normal pigs with a mean weight of 24.6 ± 0.89 kg. Data obtained were used for a 3D computer fluid-dynamics simulation of flow conditions, focusing on the pressure, flow and shear stress profile of the pulmonary trunk to the level of the left and right pulmonary arteries. RESULTS: Three inlet steady flow profiles were obtained at 0.2, 0.29 and 0.36 m/s that correspond to the flow rates of 1.5, 2.0 and 2.5 l/min flow at the RVOT. The flow velocity profile was constant at the RVOT down to the bifurcation and decreased at the left and right pulmonary arteries. In all three inlet velocity profiles, low sheer stress and low-velocity areas were detected along the left wall of the pulmonary artery, at the pulmonary artery bifurcation and at the ostia of both pulmonary arteries. CONCLUSIONS: This computed fluid real-time model provides us with a realistic picture of fluid dynamics in the pulmonary tract area. Deep shear stress areas correspond to a turbulent flow profile that is a predictive factor for the development of vessel wall arteriosclerosis. We believe that this bench model may be a useful tool for further evaluation of RVOT pathology following surgical reconstructions.