MHCPEP--a database of MHC-binding peptides: update 1995

Ultrasonic-assisted extraction, fatty acids identification of the seeds oil and isolation of chemical constituent from oil residue of Belamcanda chinensis

Belamcanda chinensis is a common garden herb. The extraction technology of B. chinensis seed oil (BSO) was optimized by ultrasonic-assisted extraction (UAE) method, the composition, relative content of main fatty acids and physicochemical properties of BSO were determined, and the isolation, identification and determination of chemical constituent in BSO residue (BSOR) were also investigated. The optimum process conditions of BSO by UAE were optimized as ultrasound time 14 min, extraction temperature 42℃, the ultrasound power 413 W and the liquid-solid ratio 27:1 mL/g. Under this condition, the extraction yield was 22.32 % with the high contents of linoleic acid and oleic acid in BSO. Ten compounds were isolated and identified from BSOR, and belamcandaoid P (9) was a new compound. The contents of the determined compounds were all at high level in B. chinensis seed. The study provided a certain scientific reference for the comprehensive development and utilization of B. chinensis seeds.

Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution

Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date MHC I binding prediction tools developed over the last decade, thoroughly evaluating feature representation methods, prediction algorithms and model training strategies on a benchmark dataset from Immune Epitope Database. A common limitation was identified during the review that all existing tools can only handle a fixed peptide sequence length. To overcome this limitation, we developed a bilateral and variable long short-term memory (BVLSTM)-based approach, named BVLSTM-MHC. It is the first variable-length MHC class I binding predictor. In comparison to the 10 mainstream prediction tools on an independent validation dataset, BVLSTM-MHC achieved the best performance in six out of eight evaluated metrics. A web server based on the BVLSTM-MHC model was developed to enable accurate and efficient MHC class I binder prediction in human, mouse, macaque and chimpanzee.

Prediction of MHC class I binding peptides by a query learning algorithm based on hidden markov models

A query learning algorithm based on hidden Markov models (HMMs) isdeveloped to design experiments for string analysis and prediction of MHCclass I binding peptides. Query learning is introduced to aim at reducingthe number of peptide binding data for training of HMMs. A multiple numberof HMMs, which will collectively serve as a committee, are trained withbinding data and used for prediction in real-number values. The universeof peptides is randomly sampled and subjected to judgement by the HMMs.Peptides whose prediction is least consistent among committee HMMs aretested by experiment. By iterating the feedback cycle of computationalanalysis and experiment the most wanted information is effectivelyextracted. After 7 rounds of active learning with 181 peptides in all,predictive performance of the algorithm surpassed the so far bestperforming matrix based prediction. Moreover, by combining the bothmethods binder peptides (log Kd < -6) could be predicted with84% accuracy. Parameter distribution of the HMMs that can be inspectedvisually after training further offers a glimpse of dynamic specificity ofthe MHC molecules.

Genome-based bioinformatic prediction of Major Histocompatibility Complex (MHC) epitopes

Over the last 12 years, a large amount of knowledge has been accumulated on various aspects of Major Histocompatibility Complex (MHC) molecules. In conjunction, numerous algorithms and tools have been developed to screen protein molecules for these MHC receptor sites. By combining these computational tools and databases with genomic sequence information that is now widely available for a vast range of organisms, it is possible to screen whole genomes for MHC epitopes. By prescreening these genomes, it allows the researcher to narrow down possible protein targets for further analysis by traditional tools such as gene knockouts and animal efficacy studies.

Molecular cancer vaccines: Tumor therapy using antigen-specific immunizations

Vaccination against tumors promises selective destruction of malignant cells by the host's immune system. Molecular cancer vaccines rely on recently identified tumor antigens as immunogens. Tumor antigens can be applied in many forms, as genes in recombinant vectors, as proteins or peptides representing T cell epitopes.Analysis of various aspects indicates some advantage for peptide-based vaccines over the other modalities. Further refinements and extensively monitored clinical trials are necessary to advance molecular cancer vaccines from concepts into powerful therapy.

PREDICTING PEPTIDE INTERACTIONS WITH MODEL CLASS II MHC STRUCTURES

An automated method for constructing 3D models of class II MHC structures that uses constraint logic programming to select side-chain conformations is described. This method follows a comparative modeling approach in basing the model structures on experimentally determined MHC-peptide structures, but it uses constraints to ease open the peptide binding groove so that the modeled MHC structure is a less specific fit for the co-crystallized peptide in the starting structure. The resulting models are used by a "peptide threading" program that attempts to predict peptides from a protein sequence that will bind strongly to particular MHC alleles. Our results indicate that MHC models that have been constructed in this way enable the peptide threading program to make binding predictions that are comparable with those obtained when using experimentally determined MHC structures, suggesting that a combined modeling and peptide threading approach is worth pursuing for MHC molecules for which experimentally determined structures are not available.

Prediction of functional class of proteins and peptides irrespective of sequence homology by support vector machines

Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clustering-based, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented.

Empirical evaluation of a dynamic experiment design method for prediction of MHC class I-binding peptides

The ability to predict MHC-binding peptides remains limited despite ever expanding demands for specific immunotherapy against cancers, infectious diseases, and autoimmune disorders. Previous analyses revealed position-specific preference of amino acids but failed to detect sequence patterns. Efforts to use computational analysis to identify sequence patterns have been hampered by the insufficiency of the number/quality of the peptide binding data. We propose here a dynamic experiment design to search for sequence patterns that are common to the MHC class I-binding peptides. The method is based on a committee-based framework of query learning using hidden Markov models as its component algorithm. It enables a comprehensive search of a large variety (20(9)) of peptides with a small number of experiments. The learning was conducted in seven rounds of feedback loops, in which our computational method was used to determine the next set of peptides to be analyzed based on the results of the earlier iterations. After these training cycles, the algorithm enabled a real number prediction of MHC binding peptides with an accuracy surpassing that of the hitherto best performing positional scanning method.

Human B cells secrete migration inhibition factor (MIF) and present a naturally processed MIF peptide on HLA-DRB1*0405 by a FXXL motif

A better knowledge of peptide structures interacting with major histocompatibility complex (MHC) molecules is of great interest for better understanding of the molecular basis of immune recognition. We have isolated naturally processed peptides from a continuously growing antigen-presenting Epstein-Barr virus-transformed human B-cell line. HLA-DR complexes were purified by specific affinity chromatography and complexed peptides were released by acid treatment. The isolated peptides were separated by reversed phase chromatography and fractions were analysed by Edman degradation at picomolar ranges. From 30 fractions that were examined seven peptides bound to the HLA-DRB1*0405 and two peptides from the human leucocyte antigen (HLA) class II associated invariant chain bound to HLA-DRB1*1302. In addition, a N-terminal beta-chain peptide of the 0405 allele was identified. Evaluation of amino acid sequences revealed a refined FXXL motif for the 0405 allele, in which F (phenylalanine) stands for any aromatic amino acid and L (leucine) can be exchanged by either I (isoleucine) or V (valine). In total, three fractions contained a peptide derived from the human migration inhibition factor (MIF), a pro-inflammatory cytokine that is normally produced by activated T lymphocytes and monocytes/macrophages. Indeed, cytokine analysis revealed high amounts of MIF secreted by the B-cell line, confirming that MHC class II expressing cells can present any intrinsic peptide that contains the distinct motif for HLA-binding. For MIF, the amino acid sequence Y36IAV39 represents the required binding motif for HLA-DRB1*0405. Nevertheless, it is the first time that cytokine fragments were found to bind to HLA molecules on human B cells.

T-cell epitopes in type 1 diabetes autoantigen tyrosine phosphatase IA-2: potential for mimicry with rotavirus and other environmental agents

The tyrosine phosphatase IA-2 is a molecular target of pancreatic islet autoimmunity in type 1 diabetes. T-cell epitope peptides in autoantigens have potential diagnostic and therapeutic applications, and they may hold clues to environmental agents with similar sequences that could trigger or exacerbate autoimmune disease. We identified 13 epitope peptides in IA-2 by measuring peripheral blood T-cell proliferation to 68 overlapping, synthetic peptides encompassing the intracytoplasmic domain of IA-2 in six at-risk type 1 diabetes relatives selected for HLA susceptibility haplotypes. The dominant epitope, VIVMLTPLVEDGVKQC (aa 805-820), which elicited the highest T-cell responses in all at-risk relatives, has 56% identity and 100% similarity over 9 amino acids (aa) with a sequence in VP7, a major immunogenic protein of human rotavirus. Both peptides bind to HLA-DR4(*0401) and are deduced to present identical aa to the T-cell receptor. The contiguous sequence of VP7 has 75% identity and 92% similarity over 12 aa with a known T-cell epitope in glutamic acid decarboxylase (GAD), another autoantigen in type 1 diabetes. This dominant IA-2 epitope peptide also has 75-45% identity and 88-64% similarity over 8-14 aa to sequences in Dengue, cytomegalovirus, measles, hepatitis C, and canine distemper viruses, and the bacterium Haemophilus influenzae. Three other IA-2 epitope peptides are 71-100% similar over 7-12 aa to herpes, rhino-, hanta- and flaviviruses. Two others are 80-82% similar over 10-11 aa to sequences in milk, wheat, and bean proteins. Further studies should now be carried out to directly test the hypothesis that T-cell activation by rotavirus and possibly other viruses, and dietary proteins, could trigger or exacerbate beta-cell autoimmunity through molecular mimicry with IA-2 and (for rotavirus) GAD.

Minors held by majors: the H13 minor histocompatibility locus defined as a peptide/MHC class I complex

The products of minor histocompatibility (H) loci are serious barriers to tissue transplantation even among major histocompatibility complex (MHC) identical individuals, frequently causing chronic graft rejection and graft versus host disease. Over 50 minor H loci map to mouse autosomal chromosomes but none are known at the molecular level. By expression cloning, we identified the H13 locus, a classical minor H locus first detected 30 years ago by the trait of graft rejection. The H13a allele is located on chromosome 2 and encodes a novel protein that yields the rare naturally processed nonapeptide SSVVGVWYL (SVL9) for presentation by the Db MHC class I molecule. The SVL9 peptide binds Db MHC despite the absence of the consensus binding motif, and a conservative methyl group substitution (Valine 4 <--> Isoleucine) explains why reciprocal T cell responses are elicited in H13a and H13b congenic strains.

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 p185HER-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 MAGE, GAGE, EGFR, p185HER-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.

MHCPEP, a database of MHC-binding peptides: update 1996

MHCPEP is a curated database comprising over 9000 peptide sequences known to bind MHC molecules. Entries are compiled from published reports as well as from direct submissions of experimental data. Each entry contains the peptide sequence, its MHC specificity and, when available, experimental method, observed activity, binding affinity, source protein, anchor positions and publication references. The present format of the database allows text string matching searches but can easily be converted for use in conjunction with sequence analysis packages. The database can be accessed via Internet using WWW, FTP or Gopher.

Molecular mechanisms of class I major histocompatibility complex antigen processing and presentation

The presentation of antigenic peptides by class I major histocompatibility complex molecules plays a central role in the cellular immune response, since immune surveillance for detection of viral infections or malignant transformations is achieved by CD8+ T lymphocytes which inspect peptides, derived from intracellular proteins, bind to class I molecules on the surface of most cells. The transporter associated with antigen processing selectively translocates cytoplasmically derived peptides of appropriate sequence and length into the lumen of the endoplasmic reticulum where they associate with newly synthesized class I molecules. The translocated peptides are generated by multicatalytic and multisubunit proteasomes which degrade cytoplasmic proteins in a ATP-ubiquitin-dependent manner. This review discusses our current molecular understanding of class I antigen processing and presentation.