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Ole Lund

Publications and source records attributed to Ole Lund.

13 recordsLinked to original sources
PubMed2005

The design and implementation of the immune epitope database and analysis resource.

Epitopes are defined as parts of antigens interacting with receptors of the immune system. Knowledge about their intrinsic structure and how they affect the immune response is required to continue development of techniques that detect, monitor, and fight diseases. Their scientific importance is reflected in the vast amount of epitope-related information gathered, ranging from interactions between epitopes and major histocompatibility complex molecules determined by X-ray crystallography to clinical studies analyzing correlates of protection for epitope based vaccines. Our goal is to provide a central resource capable of capturing this information, allowing users to access and connect realms of knowledge that are currently separated and difficult to access. Here, we portray a new initiative, "The Immune Epitope Database and Analysis Resource." We describe how we plan to capture, structure, and store this information, what query interfaces we will make available to the public, and what additional predictive and analytical tools we will provide.

Animals↗
PubMed2005

The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage.

Cytotoxic T cells (CTLs) perceive the world through small peptides that are eight to ten amino acids long. These peptides (epitopes) are initially generated by the proteasome, a multi-subunit protease that is responsible for the majority of intra-cellular protein degradation. The proteasome generates the exact C-terminal of CTL epitopes, and the N-terminal with a possible extension. CTL responses may diminish if the epitopes are destroyed by the proteasomes. Therefore, the prediction of the proteasome cleavage sites is important to identify potential immunogenic regions in the proteomes of pathogenic microorganisms (or humans). We have recently shown that NetChop, a neural network-based prediction method, is the best method available at the moment to do such predictions; however, its performance is still lower than desired. Here, we use novel sequence encoding methods and show that the new version of NetChop predicts approximately 10% more of the cleavage sites correctly while lowering the number of false positives with close to 15%. With this more reliable prediction tool, we study two important questions concerning the function of the proteasome. First, we estimate the N-terminal extension of epitopes after proteasomal cleavage and find that the average extension is relatively short. However, more than 30% of the peptides have N-terminal extensions of three amino acids or more, and thus, N-terminal trimming might play an important role in the presentation of a substantial fraction of the epitopes. Second, we show that good TAP ligands have an increased chance of being cleaved by the proteasome, i.e., the specificity of TAP has evolved to fit the specificity of the proteasome. This evolutionary relationship allows for a more efficient antigen presentation.

Animals↗
PubMed2005

An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions.

Reverse immunogenetic approaches attempt to optimize the selection of candidate epitopes, and thus minimize the experimental effort needed to identify new epitopes. When predicting cytotoxic T cell epitopes, the main focus has been on the highly specific MHC class I binding event. Methods have also been developed for predicting the antigen-processing steps preceding MHC class I binding, including proteasomal cleavage and transporter associated with antigen processing (TAP) transport efficiency. Here, we use a dataset obtained from the SYFPEITHI database to show that a method integrating predictions of MHC class I binding affinity, TAP transport efficiency, and C-terminal proteasomal cleavage outperforms any of the individual methods. Using an independent evaluation dataset of HIV epitopes from the Los Alamos database, the validity of the integrated method is confirmed. The performance of the integrated method is found to be significantly higher than that of the two publicly available prediction methods BIMAS and SYFPEITHI. To identify 85% of the epitopes in the HIV dataset, 9% and 10% of all possible nonamers in the HIV proteins must be tested when using the BIMAS and SYFPEITHI methods, respectively, for the selection of candidate epitopes. This number is reduced to 7% when using the integrated method. In practical terms, this means that the experimental effort needed to identify an epitope in a hypothetical protein with 85% probability is reduced by 20-30% when using the integrated method. The method is available at http://www.cbs.dtu.dk/services/NetCTL. Supplementary material is available at http://www.cbs.dtu.dk/suppl/immunology/CTL.php.

ATP-Binding Cassette Transporters↗
PubMed2005

Hemodynamic function of the standard St. Jude bileaflet disc valve has no clinical impact 10 years after aortic valve replacement.

OBJECTIVES: Size mismatch and impaired left ventricular function have been shown to determine the hemodynamic function of the standard St. Jude bileaflet disc valve early after aortic valve replacement (AVR). We aimed to analyse St. Jude valve hemodynamic function and its clinical impact in the survivors of a prospective series 10 years after AVR for aortic stenosis. DESIGN: Forty-three survivors aged 32-90 years from a prospective series attended a follow-up study with Doppler echo and radionuclide cardiography 10 years after AVR for aortic stenosis. Six patients with significant left sided valve regurgitation were excluded from further analysis: they had significantly lower St. Jude valve gradient and left ventricular ejection fraction (LVEF) and larger mass index (LVMi) than 37 without. RESULTS: In the 37 patients without left sided valve regurgitation peak and mean gradients were inversely related to St. Jude valve geometric orifice area (GOA) indexed for either body surface area or left ventricular end-diastolic dimension (LVEDD). The gradients correlated directly with LVEDD but not with LVEF or LVMi. Eleven patients with hypertension had higher peak gradients (31+/-13 versus 22+/-8 mmHg, p<0.05), lower LVEF, and higher LVEDD and LVMi than 26 without. Peak gradient was greater than 35 mmHg in five hypertensive patients with normal LVEF but lesser than 30 mmHg in six with impaired LVEF. Supranormal LVEF and severe size mismatch identified the remaining patients (N=3) with peak gradient above 35 mmHg. In a multilinear regression analysis GOA indexed for LVEDD, hypertension, and LVEF were independently related to peak gradient. CONCLUSION: High gradients of the standard St. Jude bileaflet disc valve 10 years after AVR was primarily related to systemic hypertension and mismatch between valve and left ventricular cavity size. Hypertension and left sided valve regurgitation, but not St. Jude valve gradient or size mismatch, were the dominant determinants of left ventricular hypertrophy and impaired function.

Adult↗
PubMed2005

Selection of proteins for human MHC class II presentation.

We investigated the predicted function of proteins eluded from human MHC class II molecules. Peptides that are presented by MHC class II were obtained from the SYFPEITHI database and the corresponding proteins were found in the SWISSPROT database. The functions of these proteins were predicted using the protfun server. Our analysis showed that human proteins presented by MHC class II molecules are likely to be in the cell envelope, be a receptor or involved in immune responses. Presented proteins from bacteria and virus, on the other hand, are more likely to be involved in regulatory functions, translation, transcription as well as replication. These results can lead to better understanding the autoimmunity and the response to infections.

Animals↗
PubMed2004

Coronavirus 3CLpro proteinase cleavage sites: possible relevance to SARS virus pathology.

BACKGROUND: Despite the passing of more than a year since the first outbreak of Severe Acute Respiratory Syndrome (SARS), efficient counter-measures are still few and many believe that reappearance of SARS, or a similar disease caused by a coronavirus, is not unlikely. For other virus families like the picornaviruses it is known that pathology is related to proteolytic cleavage of host proteins by viral proteinases. Furthermore, several studies indicate that virus proliferation can be arrested using specific proteinase inhibitors supporting the belief that proteinases are indeed important during infection. Prompted by this, we set out to analyse and predict cleavage by the coronavirus main proteinase using computational methods. RESULTS: We retrieved sequence data on seven fully sequenced coronaviruses and identified the main 3CL proteinase cleavage sites in polyproteins using alignments. A neural network was trained to recognise the cleavage sites in the genomes obtaining a sensitivity of 87.0% and a specificity of 99.0%. Several proteins known to be cleaved by other viruses were submitted to prediction as well as proteins suspected relevant in coronavirus pathology. Cleavage sites were predicted in proteins such as the cystic fibrosis transmembrane conductance regulator (CFTR), transcription factors CREB-RP and OCT-1, and components of the ubiquitin pathway. CONCLUSIONS: Our prediction method NetCorona predicts coronavirus cleavage sites with high specificity and several potential cleavage candidates were identified which might be important to elucidate coronavirus pathology. Furthermore, the method might assist in design of proteinase inhibitors for treatment of SARS and possible future diseases caused by coronaviruses. It is made available for public use at our website: http://www.cbs.dtu.dk/services/NetCorona/.

Artificial Intelligence↗
PubMed2004

Definition of supertypes for HLA molecules using clustering of specificity matrices.

Major histocompatibility complex (MHC) proteins are encoded by extremely polymorphic genes and play a crucial role in immunity. However, not all genetically different MHC molecules are functionally different. Sette and Sidney (1999) have defined nine HLA class I supertypes and showed that with only nine main functional binding specificities it is possible to cover the binding properties of almost all known HLA class I molecules. Here we present a comprehensive study of the functional relationship between all HLA molecules with known specificities in a uniform and automated way. We have developed a novel method for clustering sequence motifs. We construct hidden Markov models for HLA class I molecules using a Gibbs sampling procedure and use the similarities among these to define clusters of specificities. These clusters are extensions of the previously suggested ones. We suggest splitting some of the alleles in the A1 supertype into a new A26 supertype, and some of the alleles in the B27 supertype into a new B39 supertype. Furthermore the B8 alleles may define their own supertype. We also use the published specificities for a number of HLA-DR types to define clusters with similar specificities. We report that the previously observed specificities of these class II molecules can be clustered into nine classes, which only partly correspond to the serological classification. We show that classification of HLA molecules may be done in a uniform and automated way. The definition of clusters allows for selection of representative HLA molecules that can cover the HLA specificity space better. This makes it possible to target most of the known HLA alleles with known specificities using only a few peptides, and may be used in construction of vaccines. Supplementary material is available at http://www.cbs.dtu.dk/researchgroups/immunology/supertypes.html.

Amino Acid Motifs↗
PubMed2004

Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach.

MOTIVATION: Prediction of which peptides will bind a specific major histocompatibility complex (MHC) constitutes an important step in identifying potential T-cell epitopes suitable as vaccine candidates. MHC class II binding peptides have a broad length distribution complicating such predictions. Thus, identifying the correct alignment is a crucial part of identifying the core of an MHC class II binding motif. In this context, we wish to describe a novel Gibbs motif sampler method ideally suited for recognizing such weak sequence motifs. The method is based on the Gibbs sampling method, and it incorporates novel features optimized for the task of recognizing the binding motif of MHC classes I and II. The method locates the binding motif in a set of sequences and characterizes the motif in terms of a weight-matrix. Subsequently, the weight-matrix can be applied to identifying effectively potential MHC binding peptides and to guiding the process of rational vaccine design. RESULTS: We apply the motif sampler method to the complex problem of MHC class II binding. The input to the method is amino acid peptide sequences extracted from the public databases of SYFPEITHI and MHCPEP and known to bind to the MHC class II complex HLA-DR4(B1*0401). Prior identification of information-rich (anchor) positions in the binding motif is shown to improve the predictive performance of the Gibbs sampler. Similarly, a consensus solution obtained from an ensemble average over suboptimal solutions is shown to outperform the use of a single optimal solution. In a large-scale benchmark calculation, the performance is quantified using relative operating characteristics curve (ROC) plots and we make a detailed comparison of the performance with that of both the TEPITOPE method and a weight-matrix derived using the conventional alignment algorithm of ClustalW. The calculation demonstrates that the predictive performance of the Gibbs sampler is higher than that of ClustalW and in most cases also higher than that of the TEPITOPE method.

Algorithms↗
PubMed2004

Predictable changes in left ventricular mass and function during ten years after valve replacement for aortic stenosis.

BACKGROUND AND AIM OF THE STUDY: Left ventricular (LV) hypertrophy is the underlying basis for longevity after aortic valve replacement (AVR) for aortic stenosis (AS). However, a detailed account of changes in LV mass and function in the long term after AVR and identification of the determinants of such changes have not yet been presented. METHODS: Ninety-one unselected consecutive adult patients with AS underwent AVR and were followed up to 10 years, at which time 41 survivors without new mitral disease underwent repeat measurement of LV mass index (LVMi), ejection fraction (LVEF), fast filling fraction (LVFFF), and end-diastolic volume index (LVEDVi). A subgroup comprising 49 patients was also assessed at eight days, three months, and 1.5 years postoperatively. All measurements were analyzed in a longitudinal regression model for repeated measures. RESULTS: LVMi fell from 202 +/- 58 g/m2 (n = 91) via 150 +/- 45 g/m2 (n = 39) at 1.5 years to 139 +/- 40 g/m2 (n = 41) at 10 years in all patients, and to 124 +/- 31 g/m2 (n = 29) in non-hypertensive patients. The LVMi falls were paralleled by improvements in LVEF and LVEDVi. LVFFF was not correlated to LVMi before the 10-year study. The longitudinal model indicated progressive reduction of LVMi to 1.5 years, but no change thereafter. The predictor variables were preoperative LVMi and end-systolic dimension index (high values of both related to high postoperative LVMi), hypertension, and male gender. The model for LVEF indicated a rapid increase to three months, followed by a slight decrease to 1.5 years and further to 10 years, predicted by preoperative LVEF and LVFFF. LVFFF fell sharply by three months, had recovered somewhat at 1.5 years and fully at 10 years, positively related to preoperative LVFFF and inversely to end-systolic chamber radius:wall thickness ration and small-sized prosthetic valves. LVEDVi converged from extreme values over time predicted by preoperative LVEF, but rose with hypertension and coronary artery disease. Hemodynamic function of the prosthetic aortic valve at any of the measurement times had no impact. CONCLUSION: Changes in LV mass and function up to 10 years after AVR for AS were highly predictable. Poorer outcomes were related to preoperative excessive hypertrophy and indices of underlying irreversible myocardial disease and further compromised by hypertension and, to a lesser extent, coronary artery disease. The hemodynamic function of the aortic prosthetic valve did not seem to play a role.

Adult↗
PubMed2003

Regression of left ventricular hypertrophy during 10 years after valve replacement for aortic stenosis is related to the preoperative risk profile.

BACKGROUND: Previous studies have suggested that regression of hypertrophy may be the underlying determinant of longevity and left ventricular function after valve replacement (AVR) for aortic stenosis (AS). The potential for hypertrophy regression could therefore be related to the preoperative risk profile. METHODS: Ninety-one consecutive patients with AS had a "project" Doppler-echo and radionuclide ventriculography in addition to the standard investigation programme prior to AVR with a disc valve (19-29mm, n=82), a caged ball valve (26-29mm, n=8), or a stented porcine valve (26mm, n=1); 49 (group A) were selected for a serial follow-up study while 42 served as controls (group B). Forty-two group A patients took part in a 1.5-year examination while 47 (26 group A, 21 group B) patients were studied at 10 years. RESULTS: Groups A and B were comparable as regards all pre- and intra-operative data including left ventricular mass index (LVMi). A previously developed preoperative prognostic index (PI) separated the patients into groups with low (n=23), intermediary (n=19) and high risk (n=49) with 10-year survivals of 87%, 58% and 43% (P<0.01). LVMi dropped from 202+/-58g/m(2)preoperatively to 152+/-45g/m(2)(P<0.0001) at 1.5 years, and 139+/-40g/m(2)(P<0.0001) at 10 years (three and six patients, respectively, with paravalvular leak or mitral regurgitation excluded). PI correlated with preoperative (r=0.51, P<0.001), 1.5-year (r=0.46, P<0.01), and 10-year LVMi (r=0.41, P<0.01). Also preoperative left ventricular ejection fraction correlated with the three LVMi measurements. Patients with systemic hypertension had higher LVMi at 1.5 years (193+/-42, n=6 vs 144+/-42, n=33, P<0.05) and 10 years (175+/-39, n=12 vs 124+/-31g/m(2), n=29, P<0.001). Patients with low, intermediary or high PI, excluding those with hypertension, had 1.5-year LVMi of 110+/-35 (n=8), 134+/-43 (n=9) and 164+/-33g/m(2)(n=16; P<0.01), respectively, and 10-year LVMi of 116+/-25 (n=17), 126+/-27 (n=6), and 146+/-41g/m(2)(n=6; P<0.05), respectively. There was no relation between LVMi at 1.5 or 10 years and peak or mean Doppler gradient, prosthetic valve size, or valve size index. CONCLUSIONS: Left ventricular hypertrophy regression for patients who survived up to 10 years after AVR for AS is dependent on the preoperative risk profile indicating that irreversible myocardial disease is the underlying factor. Systemic hypertension is an important factor in its own right.

Aged↗
PubMed2003

Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.

Amino Acid Sequence↗
PubMed2003

Selecting informative data for developing peptide-MHC binding predictors using a query by committee approach.

Strategies for selecting informative data points for training prediction algorithms are important, particularly when data points are difficult and costly to obtain. A Query by Committee (QBC) training strategy for selecting new data points uses the disagreement between a committee of different algorithms to suggest new data points, which most rationally complement existing data, that is, they are the most informative data points. In order to evaluate this QBC approach on a real-world problem, we compared strategies for selecting new data points. We trained neural network algorithms to obtain methods to predict the binding affinity of peptides binding to the MHC class I molecule, HLA-A2. We show that the QBC strategy leads to a higher performance than a baseline strategy where new data points are selected at random from a pool of available data. Most peptides bind HLA-A2 with a low affinity, and as expected using a strategy of selecting peptides that are predicted to have high binding affinities also lead to more accurate predictors than the base line strategy. The QBC value is shown to correlate with the measured binding affinity. This demonstrates that the different predictors can easily learn if a peptide will fail to bind, but often conflict in predicting if a peptide binds. Using a carefully constructed computational setup, we demonstrate that selecting peptides with a high QBC performs better than low QBC peptides independently from binding affinity. When predictors are trained on a very limited set of data they cannot be expected to disagree in a meaningful way and we find a data limit below which the QBC strategy fails. Finally, it should be noted that data selection strategies similar to those used here might be of use in other settings in which generation of more data is a costly process.

Algorithms↗