Towards quantitative prediction of proteasomal digestion patterns of proteins

We discuss the problem of proteasomal degradation of proteins. Though proteasomes are important for all aspects of the cellular metabolism, some details of the physical mechanism of the process remain unknown. We introduce a stochastic model of the proteasomal degradation of proteins, which accounts for the protein translocation and the topology of the positioning of cleavage centers of a proteasome from first principles. For this model we develop the mathematical description based on a master-equation and techniques for reconstruction of the cleavage specificity inherent to proteins and the proteasomal translocation rates, which are a property of the proteasome specie, from mass spectroscopy data on digestion patterns. With these properties determined, one can quantitatively predict digestion patterns for new experimental set-ups. Additionally we design an experimental set-up for a synthetic polypeptide with a periodic sequence of amino acids, which enables especially reliable determination of translocation rates.Comment: 14 pages, 4 figures, submitted to J. Stat. Mech. (Special issue for proceedings of 5th Intl. Conf. on Unsolved Problems on Noise and Fluctuations in Physics, Biology & High Technology, Lyon (France), June 2-6, 2008

Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences

This manuscript describes a support vector machine based method for the prediction of constitutive as well as immunoproteasome cleavage sites in antigenic sequences. This method achieved Matthew's correlation coefficents of 0.54 and 0.43 on in vitro and major histocompatibility complex ligand data, respectively. This shows that the performance of our method is comparable to that of the NetChop method, which is currently considered to be the best method for proteasome cleavage site prediction. Based on the method, a web server, Pcleavage, has also been developed. This server accepts protein sequences in any standard format and present results in a user-friendly format. The server is available for free use by all academic users at the URL or

Improved proteasomal cleavage prediction with positive-unlabeled learning

Accurate in silico modeling of the antigen processing pathway is crucial to enable personalized epitope vaccine design for cancer. An important step of such pathway is the degradation of the vaccine into smaller peptides by the proteasome, some of which are going to be presented to T cells by the MHC complex. While predicting MHC-peptide presentation has received a lot of attention recently, proteasomal cleavage prediction remains a relatively unexplored area in light of recent advancesin high-throughput mass spectrometry-based MHC ligandomics. Moreover, as such experimental techniques do not allow to identify regions that cannot be cleaved, the latest predictors generate decoy negative samples and treat them as true negatives when training, even though some of them could actually be positives. In this work, we thus present a new predictor trained with an expanded dataset and the solid theoretical underpinning of positive-unlabeled learning, achieving a new state-of-the-art in proteasomal cleavage prediction. The improved predictive capabilities will in turn enable more precise vaccine development improving the efficacy of epitope-based vaccines. Pretrained models are available on GitHubComment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual, http://www.ml4h.cc, 8 page

From the test tube to the World Wide Web - The cleavage specificity of the proteasome

Diese Dissertation handelt von Proteasomen (von 'Protease' und dem griechischen 'soma' = Protein-schneidender Körper) und ihrer Rolle in der Regulierung von Immunantworten. Proteasomen sind fassförmige, molekulare Maschinen (Enzyme), die in jeder Körperzelle zu finden sind. Ihre Aufgabe ist es, Proteine klein zu hacken, so ähnlich wie eine Häckselmaschine, die Äste und Zweige in kleine Stücke schneidet. Die kleinen Proteinstücke können zur Zelloberfläche transportiert und dort den zu den weißen Blutkörperchen gehörenden T-Zellen präsentiert werden. Wenn eine Körperzelle 'krank' ist (d.h. sie ist zu einer Tumorzelle geworden oder ist mit Krankheitserregern wie Viren oder Bakterien infiziert), sehen die Proteinfragmente auf der Zelloberfläche anders aus. T-Zellen werden dadurch aktiviert, die 'kranke' Körperzelle zum Wohl des Gesamtorganismus abzutöten. Während der Forschung für meine Diplomarbeit (April-Dez. 1997) und meine Doktorarbeit (Jan. 1998-Dez. 2000) versuchte ich im Detail zu klären, wie Proteine von Proteasomen klein geschnitten werden. Ich hatte Glück und konnte einige Regeln bestimmen, nach denen Proteasomen Proteine zerhäckseln. Diese Regeln wurden als Grundlage für die Vorhersage von Proteasomen-Schnitten herangezogen. Meine Forschungsergebnisse haben großen Nutzen für die Entwicklung von Impfstoffen und die Vorhersage von Immunantworten.This dissertation deals with proteasomes (from 'protease' and Greek 'soma' = protein-chopping body) and their role in the regulation of immune responses. Proteasomes are barrel-shaped molecular machines (enzymes) that are found in every cell of the body. Their job is to chop up proteins, much like a garden shredder that cuts twigs and branches into small pieces. The small protein pieces can be transported to the cell surface to be presented to T-cells, immune cells that constitute a part of the white blood cells. If a body cell is 'sick' (i.e. it has turned into a tumor cell or is infected by pathogens such as viruses or bacteria), the protein fragments on the cell surface look different. They therefore can activate T-cells to kill the diseased cell for the good of the whole organism. During the research for my Diploma thesis (April-Dec. 1997) and my Ph.D. thesis (Jan. 1998-Dec. 2000) I tried to find out more about how exactly proteins are cleaved by proteasomes. I was lucky and could determine some of the rules that proteasomes follow to chop up proteins. These rules were used as a basis for the prediction of proteasome cleavages. My results have important implications for vaccine development and the prediction of immune responses

Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction

<p>Abstract</p> <p>Background</p> <p>Reliable predictions of Cytotoxic T lymphocyte (CTL) epitopes are essential for rational vaccine design. Most importantly, they can minimize the experimental effort needed to identify epitopes. NetCTL is a web-based tool designed for predicting human CTL epitopes in any given protein. It does so by integrating predictions of proteasomal cleavage, TAP transport efficiency, and MHC class I affinity. At least four other methods have been developed recently that likewise attempt to predict CTL epitopes: EpiJen, MAPPP, MHC-pathway, and WAPP. In order to compare the performance of prediction methods, objective benchmarks and standardized performance measures are needed. Here, we develop such large-scale benchmark and corresponding performance measures and report the performance of an updated version 1.2 of NetCTL in comparison with the four other methods.</p> <p>Results</p> <p>We define a number of performance measures that can handle the different types of output data from the five methods. We use two evaluation datasets consisting of known HIV CTL epitopes and their source proteins. The source proteins are split into all possible 9 mers and except for annotated epitopes; all other 9 mers are considered non-epitopes. In the RANK measure, we compare two methods at a time and count how often each of the methods rank the epitope highest. In another measure, we find the specificity of the methods at three predefined sensitivity values. Lastly, for each method, we calculate the percentage of known epitopes that rank within the 5% peptides with the highest predicted score.</p> <p>Conclusion</p> <p>NetCTL-1.2 is demonstrated to have a higher predictive performance than EpiJen, MAPPP, MHC-pathway, and WAPP on all performance measures. The higher performance of NetCTL-1.2 as compared to EpiJen and MHC-pathway is, however, not statistically significant on all measures. In the large-scale benchmark calculation consisting of 216 known HIV epitopes covering all 12 recognized HLA supertypes, the NetCTL-1.2 method was shown to have a sensitivity among the 5% top-scoring peptides above 0.72. On this dataset, the best of the other methods achieved a sensitivity of 0.64. The NetCTL-1.2 method is available at <url>http://www.cbs.dtu.dk/services/NetCTL</url>.</p> <p>All used datasets are available at <url>http://www.cbs.dtu.dk/suppl/immunology/CTL-1.2.php</url>.</p

State of the art and challenges in sequence based T-cell epitope prediction

Sequence based T-cell epitope predictions have improved immensely in the last decade. From predictions of peptide binding to major histocompatibility complex molecules with moderate accuracy, limited allele coverage, and no good estimates of the other events in the antigen-processing pathway, the field has evolved significantly. Methods have now been developed that produce highly accurate binding predictions for many alleles and integrate both proteasomal cleavage and transport events. Moreover have so-called pan-specific methods been developed, which allow for prediction of peptide binding to MHC alleles characterized by limited or no peptide binding data. Most of the developed methods are publicly available, and have proven to be very useful as a shortcut in epitope discovery. Here, we will go through some of the history of sequence-based predictions of helper as well as cytotoxic T cell epitopes. We will focus on some of the most accurate methods and their basic background

ProPred1: prediction of promiscuous MHC class-I binding sites

ProPred1 is an on-line web tool for the prediction of peptide binding to MHC class-I alleles. This is a matrix-based method that allows the prediction of MHC binding sites in an antigenic sequence for 47 MHC class-I alleles. The server represents MHC binding regions within an antigenic sequence in user-friendly formats. These formats assist user in the identification of promiscuous MHC binders in an antigen sequence that can bind to large number of alleles. ProPred1 also allows the prediction of the standard proteasome and immunoproteasome cleavage sites in an antigenic sequence. This server allows identification of MHC binders, who have the cleavage site at the C terminus. The simultaneous prediction of MHC binders and proteasome cleavage sites in an antigenic sequence leads to the identification of potential T-cell epitopes. Availability: Server is available at http://www.imtech.res. in/raghava/propred1/. Mirror site of this server is available at http://bioinformatics.uams.edu/mirror/propred1/

Computational analysis and modeling of cleavage by the immunoproteasome and the constitutive proteasome

Proteasomes play a central role in the major histocompatibility class I (MHCI) antigen processing pathway. They conduct the proteolytic degradation of proteins in the cytosol, generating the C-terminus of CD8 T cell epitopes and MHCI-peptide ligands (P1 residue of cleavage site). There are two types of proteasomes, the constitutive form, expressed in most cell types, and the immunoproteasome, which is constitutively expressed in mature dendritic cells. Protective CD8 T cell epitopes are likely generated by the immunoproteasome and the constitutive proteasome, and here we have modeled and analyzed the cleavage by these two proteases. RESULTS: We have modeled the immunoproteasome and proteasome cleavage sites upon two non-overlapping sets of peptides consisting of 553 CD8 T cell epitopes, naturally processed and restricted by human MHCI molecules, and 382 peptides eluted from human MHCI molecules, respectively, using N-grams. Cleavage models were generated considering different epitope and MHCI-eluted fragment lengths and the same number of C-terminal flanking residues. Models were evaluated in 5-fold cross-validation. Judging by the Mathew's Correlation Coefficient (MCC), optimal cleavage models for the proteasome (MCC = 0.43 +/- 0.07) and the immunoproteasome (MCC = 0.36 +/- 0.06) were obtained from 12-residue peptide fragments. Using an independent dataset consisting of 137 HIV1-specific CD8 T cell epitopes, the immunoproteasome and proteasome cleavage models achieved MCC values of 0.30 and 0.18, respectively, comparatively better than those achieved by related methods. Using ROC analyses, we have also shown that, combined with MHCI-peptide binding predictions, cleavage predictions by the immunoproteasome and proteasome models significantly increase the discovery rate of CD8 T cell epitopes restricted by different MHCI molecules, including A*0201, A*0301, A*2402, B*0702, B*2705. CONCLUSIONS: We have developed models that are specific to predict cleavage by the proteasome and the immunoproteasome. These models ought to be instrumental to identify protective CD8 T cell epitopes and are readily available for free public use at http://imed.med.ucm.es/Tools/PCPS