Computational prediction of cleavage using proteasomal in vitro digestion and MHC I ligand data

Computational prediction of cleavage using proteasomal in vitro digestion and MHC I ligand data 1 first-author 1 corresp ‡Corresponding Author ‡Corresponding Author †E-mail:shenghao@mail.dlut.edu.cn †E-mail:shenghao@mail.dlut.edu.cn shenghao@mail.dlut.edu.cn 2 3 School of Mathematical Sciences, Dalian University of Technology, Dalian 116023, China School of Mathematical Sciences, Dalian University of Technology, Dalian 116023, China College of Science, Hebei University of Science and Technology, Shijiazhuang 050018, China College of Science, Hebei University of Science and Technology, Shijiazhuang 050018, China School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China Proteasomes are responsible for the production of the majority of cytotoxic T lymphocyte (CTL) epitopes. Hence, it is important to identify correctly which peptides will be generated by proteasomes from an unknown protein. However, the pool of proteasome cleavage data used in the prediction algorithms, whether from major histocompatibility complex (MHC) I ligand or in vitro digestion data, is not identical to in vivo proteasomal digestion products. Therefore, the accuracy and reliability of these models still need to be improved. In this paper, three types of proteasomal cleavage data, constitutive proteasome (cCP), immunoproteasome (iCP) in vitro cleavage, and MHC I ligand data, were used for training cleave-site predictive methods based on the kernel-function stabilized matrix method (KSMM). The predictive accuracies of the KSMM+pair coefficients were 75.0%, 72.3%, and 83.1% for cCP, iCP, and MHC I ligand data, respectively, which were comparable to the results from support vector machine (SVM). The three proteasomal cleavage methods were combined in turn with MHC I-peptide binding predictions to model MHC I-peptide processing and the presentation pathway. These integrations markedly improved MHC I peptide identification, increasing area under the receiver operator characteristics (ROC) curve (AUC) values from 0.82 to 0.91. The results suggested that both MHC I ligand and proteasomal in vitro degradation data can give an exact simulation of in vivo processed digestion. The information extracted from cCP and iCP in vitro cleavage data demonstrated that both cCP and iCP are selective in their usage of peptide bonds for cleavage. Cytotoxic T lymphocyte epitopes Kernel function Proteasome Stabilized matrix method Introduction 1 The proteasome plays an important role in the preservation of protein homeostasis as its proteolytic activity can efficiently clear unneeded foreign viral proteins or damaged intracellular self-proteins. The proteasome has also been associated with the supply of the majority of cytotoxic T lymphocyte (CTL) epitopes for antigen presentation. There are three classes of vertebrate proteasomes: constitutive proteasomes (cCPs), immunoproteasomes (iCPs), and thymoproteasomes (tCPs). cCPs are expressed in most tissues (e.g., hematopoietic cells, lymphocytes, and monocytes). iCPs are developed from cCPs: with the stimulation of interferon (IFN)- γ , the three catalytic subunits β 1 , β 2 , and β 5 of cCPs are replaced by their homologous counterparts β 1i , β 2i , and β 5i to form iCPs. tCPs are found in thymic epithelial cells and participate in T cell positive selection (Huber et al., 2012 2012 ). Relative to cCPs, iCPs exhibit altered cleavage preferences, resulting in increased peptide supply for epitope presentation. Several methods for computational recognition of proteasomal cleavage sites have been presented, e.g., PAProC (Nussbaum et al., 2001 2001 ), support vector machines (SVMs) (Bhasin and Raghava, 2005 2005 ), kinetic models (Holzhütter and Kloetzel, 2000 2000 ), and artificial neural networks (Keşmir et al., 2002 2002 ). There is also a method for predicting the production probability of an entire peptide using a linear score function (Ginodi et al., 2008 2008 ). All of these methods are based on experimental proteasomal cleavage sites and statistical analysis of the flanking regions of such sites. Kernel-function stabilized matrix method (KSMM) used in this paper is a modified version of the stabilized matrix method (SMM) (Peters et al., 2003 2003 ). SMM is a linear method which has been successfully applied to the prediction of MHC-peptide binding affinities, antigenic peptides presented by transporters associated with antigen processing (TAPs) and proteasomal cleavage sites (Peters et al., 2003 2003 ; Peters and Sette, 2005 2005 ; Nielsen et al., 2007 2007 ). Its online software can be found at http://70.167.3.42/smm/ http://70.167.3.42/smm/ . However, some studies found that the contributions of amino acids (AAs) to the predictive values (like binding affinities) are not linearly related (Jacob and Vert, 2008 2008 ). This led to a lower prediction accuracy of SMM (Nielsen et al., 2007 2007 ). Thus, it is reasonable to develop a nonlinear method KSMM by integrating kernel functions into SMM. In addition, the kernel functions are incorporated into pair-coefficients estimation between AAs in different peptide positions. The benchmark for cleavage data shows that KSMM is consistently better than SMM and that KSMM+pair coefficients are comparable to SVMs. There are usually two types of data used in these models, proteasomal in vitro degradation data and MHC I ligand data. However, these two types of proteasomal digestion data do not necessarily bear enough resemblance to natural proteasomal cleavage. The proteasomal in vitro degradation data are generated by activated 20S proteasomes, but the bulk of cellular proteins (80%–90%) are degraded by 26S proteasomes (Sorokin et al., 2009 2009 ). The 20S proteasome forms the catalytic core of the 26S proteasome, and generates a different pool of products from 26S proteasomes (Emmerich et al., 2000 2000 ). As the C-termini of MHC I ligands are mostly cleaved by proteasomes (Heinemeyer et al., 2004 2004 ), MHC I ligands are also used for studying the specificity of the proteasome. However, MHC I ligands represent only a subset of in vivo degradation products: in view of the length distribution of degradation products, possible CTL epitopes must be 8–15 AAs long, accounting for 15% of the cleavage products (Keşmir et al., 2003 2003 ). As 1%–2% of them are MHC I epitopes (Kosmrlj et al., 2010 2010 ), MHC I ligands account for at most 0.3% of the cleavage products. Therefore, the accuracy and reliability of these models still need to be improved (Diez-Rivero et al., 2010 2010 ). In this study, the two types of data, from experimentally identified cCP and iCP digestion products and naturally processed MHC I ligands, both of which are mainly from viruses, were used for training and evaluating KSMM. These three cleavage models, singly or combined with peptide binding prediction, were tested for CTL epitope identification. cCP and iCP cleavage specificities were predicted computationally through reduction of the noise from the experimental data, and the profiles of the cCP and iCP interacting with their substrate were analyzed from our research results. This approach might lead to a better understanding of the processing of MHC I antigens and to the design of peptide vaccines with longer half-life. Materials and methods 1 Data 2 cCP and iCP in vitro cleavage training data 3 Firstly, we trained the cCP predictive model. For the purpose of extracting comprehensive experimental information and reducing the risk of over-training the model with a limited sample size, proteasomal digestion products of both whole proteins and short peptides were included in the cCP and iCP training sets. We made the assumption that there was no difference in the proteasomal proteolysis degradation of whole proteins and peptides. We collected four fully quantified cCP in vitro digests of whole proteins (Emmerich et al., 2000 2000 ; Toes et al., 2001 2001 ; Lucchiari-Hartz et al., 2003 2003 ; Tenzer et al., 2004 2004 ): yeast enolase, bovine casein, prion protein, and Nef protein, as well as cCP in vitro digests of 32 peptides 10–56 AAs in length for training our cCP predictive model. Twenty-five of the 32 peptide digest datasets were extracted from cCP in vitro studies (Rivett, 1985 1985 ; Dick L.R. et al., 1994 1994 ; Leibovitz et al., 1994 1994 ; Niedermann et al., 1995 1995 ; 1996 1996 ; 1997 1997 ; Ossendorp et al., 1996 1996 ; Shimbara et al., 1997 1997 ; Dick T.P. et al., 1998 1998 ; Alvarez et al., 2001 2001 ; Cascio et al., 2001 2001 ; Peters et al., 2002 2002 ; Sun et al., 2002 2002 ; Vigneron et al., 2004 2004 ; Warren et al., 2006 2006 ; Chapiro et al., 2006 2006 ; Goldobin and Zaikin, 2009 2009 ; Ma et al., 2011 2011 ). The 7 remaining samples were downloaded from Tenzer-Suppl-Table1.xls at http://70.167.3.42/supplement/ http://70.167.3.42/supplement/ (Jan. 11, 2006). The final cCP training set comprised 650 cleavage sites and 1 184 internal non-cleavage sites. Secondly, we trained iCP predictive models. The iCP degradation training data consisted of results of three whole proteins (Toes et al., 2001 2001 ; Lucchiari-Hartz et al., 2003 2003 ; Tenzer et al., 2004 2004 ): yeast enolase, prion protein and Nef protein, and iCP in vitro digests of 27 peptides 18–56 AAs in length, including 21 peptide products from iCP in vitro digestion studies (Ehring et al., 1996 1996 ; Beekman et al., 2000 2000 ; Morel et al., 2000 2000 ; Alvarez et al., 2001 2001 ; Mommaas et al., 2002 2002 ; Peters et al., 2002 2002 ; Schultz et al., 2002 2002 ; Sun et al., 2002 2002 ; Chapiro et al., 2006 2006 ; Ma et al., 2011 2011 ) and 6 peptide products from Tenzer-Suppl-Table1.xls at http://70.167.3.42/supplement/ http://70.167.3.42/supplement/ (Jan. 11, 2006). This gave a total of 537 internal cleavage sites and 961 internal non-cleavage sites. CTL epitope data 3 The third method was trained with CTL epitope data. Proteasomes generate most MHC I peptides, but other proteases have also been found to generate some MHC I peptides (Cascio et al., 2001 2001 ). The use of MHC I peptides for training proteasomal cleavage models is based on the hypothesis that MHC I peptides are all generated by proteasomes. A total of 6 277 naturally processed MHC I epitopes were extracted from the Antigen database (Blythe et al., 2002 2002 ). Because the C-terminus of ligands presented by human leukocyte antigen (HLA)-A * 03 is very unlikely to be generated by the proteasome (Tenzer et al., 2005 2005 ), HLA-A3 restricted epitopes were excluded from the training set. The C-termini of these epitopes were assigned as cleavage samples, and the internal positions within epitopes were assigned as non-cleavage samples. Non-cleavage samples that showed strong cleavage characteristics (e.g., those duplicated as cleavage samples) were discarded. The final CTL epitope training data contained 6 872 non-cleavage and 1 362 cleavage sites. The training set was referred to as ‘Antigen-human’. In these cleavage training sets, peptides duplicated or included in the test set of Saxová et al. ( 2003 2003 ) were discarded. MHC I binding affinity data 3 Datasets for binding affinity predictive models consisted of a training set and a test set extracted from the MHCBN database (Bhasin et al., 2003 2003 ). Three hundred and sixteen peptides with measured half maximal inhibitory concentration (IC 50 ) constituted the training set, which is termed ‘MHCBN-train’. Binding affinity in practical calculation is defined as −lg 50 000 (IC 50 in nmol/L) (Nielsen et al., 2007 2007 ). Nine hundred and seventy-five peptides that had interacted with HLA-A * 0201 constituted the test set. Every peptide could be mapped back to either a human protein or a protein from a human pathogen in the SwissProt database (Bairoch and Apweiler, 2000 2000 ). Peptides that did not have an exact matched source protein or a duplicate in the proteasomal in vitro digestion data and the Antigen-human dataset were discarded. The resulting test set contained 754 peptides, including 621 binders and 133 non-binders. The test set was referred to as ‘MHCBN-test’. Sequence encoding 2 A cleavage within the peptide was mapped back to its source protein to flank the cleavage site region (PL … P2 P1 | P1′ … P2′ PL′). The cleavage site is signified by ‘|’ and the N-terminus is on the left. Following the suggestions of Keşmir et al. ( 2003 2003 ) and Tenzer et al. ( 2005 2005 ), a window size of 8 was used for training the three cleavage models. Through the AA descriptors, peptides can be represented by numerical vectors. For the MHC I binding affinity models, we used 5 z -scale descriptors (Sandberg et al., 1998 1998 ); and for the cleavage prediction models, we used sparse binary descriptors (Nussbaum et al., 2001 2001 ). Every peptide corresponds to an attribute value. An MHC I epitope has a binding affinity of [0, 1], and in vitro cleavage samples and CTL epitope cleavage samples have a cleavage value which is either 1 (positive sample) or −1 (negative sample). So every training set and test set can be denoted by $$T = \{ ({x_1},{y_1}),\;({x_2},{y_2}),\;...,\;({x_n},{y_n})\}$$ , where $${x_i} = {[{x_{i1}},{x_{i2}}, \cdots ,{x_{im}}]^{\text{T}}} \in {R^m}$$ is the peptide-sequence encoding vector, and y i is its attribute value. Kernel-function stabilized matrix method 2 The key idea of SMM is to seek a vector w , which can reflect the corresponding relationship of vector x i and value y i . Given an input x , the model can predict the reasonable output $${x^{\text{T}}}w + b$$ , which best approximates the unknown y . The corresponding minimized energy function is $$E = \sum\limits_i {|\sum\limits_k {{x_{ik}} \cdot {w_{{a_i},k}}} + b - {y_i}|} + \lambda \cdot \sum\limits_{a, k} {w_{a,k}^2}$$ , where b is a constant offset, and λ is the penalty parameter controlling the trade-off between the margin maximization and the degree of misclassification. If the pair coefficient $${w'_{a,i,a',i'}}$$ quantifies the effect of interactions between an AA a at position i and an AA a′ at position i′ on the binding or cleavage, then the minimized energy function for the combined SMM+pair coefficient algorithm (Peters et al., 2003 2003 ) is $$E = \sum\limits_i {|\sum\limits_k {{x_{ik}} \cdot {w_{{a_i},k}}} + \sum\limits_k {\sum\limits_{k'} {{{w'}_{{a_i},k,{{a'}_i},k'}}} } + b - {y_i}|} + \lambda \sum\limits_{a,k} {w_{a,k}^2} + \lambda '\sum\limits_{a,k,a',k'} {{{w'}^2}_{a,k,a',k'}}$$ . We then set out to introduce KSMM by incorporation of kernel functions into SMM. For different problems, the most suitable kernel functions are different. We compared three kernel functions: linear, polynomial, and radial-based (Eqs. ( 3 3 )–( 5 5 )). KSMM with a linear kernel is the same as SMM. Linear kernel function: $$K({x_i}, w) = \textless {x_i}, w \textgreater$$ . Polynomial kernel function: $$K({x_i},w) = {[ \textless {x_i},w \textgreater + 1]^q}$$ . Radial-based kernel function: $$K({x_i},w) = {{\text{e}}^{ - |{x_i} - w{|^2}/{\sigma ^2}}}$$ . Thus, the minimized energy function (Eq. ( 1 1 )) is rewritten as $$E = \sum\limits_i {|{c_1}{K_1}({x_i},w) + b - {y_i}|} + \lambda ||w|{|^2}$$ , where c 1 is a constant that scales the predictive attribute range [−1, 1]. The optimal value for λ is determined by minimizing the distance defined by the first term in Eq. ( 6 6 ). It is impossible to estimate all pairs of AAs in the samples with limited data. For 8-mer peptides, the number of different pair coefficients reaches 20×20×28=11 200. We follow the proposal of Peters et al. ( 2003 2003 ) that defines N min as the minimum number of peptides in the training set that we can determine reliably, and discard the pair coefficients which have less than N min peptides. N min was set at 40, 12, and 12 for the Antigen-human, cCP, and iCP training data respectively, and pair coefficients 682, 340, and 212, respectively needed to be identified. The presence or absence of a chosen pair of AAs in the samples was represented by 1/0 in a sparse binary encoding vector $${{\mathbf{x'}}_i}$$ . Using the optimal value for the vector w determined from Eq. ( 6 6 ), we drew the coefficient vector w ′ of peptide position pairs from the systematic difference between the predicted values y pred and the measured values y meas . The optimal value for the pair coefficients vector w ′ was searched by minimizing the energy function equation: $$E = \sum\limits_i {|{c_1}{K_1}({x_i}, w) + {c_{\text{2}}}{K_2}({{x'}_i},w') + b - {y_i}|} + \lambda '||w'|{|^2}$$ , where c 2 is a scale parameter. The optimal value for λ′ was determined by minimizing the distance defined by the first term in Eq. ( 7 7 ). The same kernel function was used for K 1 and K 2 in Eq. ( 7 7 ). Table 1 1 lists the ranges and best values (BVs) of different parameters in KSMM. 2 Function type 3 Kernel function parameter 2 Scale parameter 3 Penalty parameter Range BV of K 1 BV of K 2 c 1 c 2 Range BV of λ BV of λ′ Polynomial 2–6 4.00 4.00 2.00 1.00 10 −2 −10 6 10.00 100.00 Radial-based 10 −4 –10 4 2.00 2.00 4.00 0.80 10 −2 –10 6 10.00 10.00 Linear 1.00 1.00 10 −2 –10 6 1.00 5.00 Monte Carlo (MC) method (Metropolis and Ulam, 1949 1949 ) was implemented for optimizing components of vectors w and w ′. Our algorithm has two stages: firstly, to effectively escape from local minima, the MC calculations are repeated 200 times with different initial configurations. For each run, 20 000 MC moves are carried out and the final energy and weight matrix are recorded. In each move, the components of the vector are assigned random values. Secondly, the average of the top 10 records in stage 1 is treated as the initial weight of the vector. In each MC move, two components of the vector are selected at random, and the weights on these two components are updated, keeping the sum of the weights unchanged. The probability of accepting a move in our algorithm is $$P={\text{min}}\left\{ {{\text{1, }}{{\text{e}}^{\frac{{{\text{d}}E}}{T}}}} \right\}$$ , where d E is the difference in energy between the end and start weights, and T is a scalar. Eq. ( 8 8 ) shows that moves that decrease E will always be accepted, since P =1 (d E > 0). On the other hand, moves that increase E will be accepted with P =exp(d E / T ) (d E < 0). T is lowered during the calculation in order to reduce the probability of accepting unfavorable moves. Finally, the classification function of the prediction is $$f(x) = {c_1}{K_1}({x_i},{w^*}) + {c_{\text{2}}}{K_2}({x'_i},{w'^*}) + {b^*}$$ , where w * and $$w{{\text{'}}^*}$$ are the weight vectors of the optimal plane in feature space, and b * is the threshold value of classification. Performance evaluation measures 2 When using in vitro digestion data and MHC binding data, it is easy to make a clear assignment of positive and negative samples. However, for Antigen-human, definite negative samples do not exist. Goldberg et al. ( 2002 2002 ) demonstrated that internal positions within the epitopes are also cleaved. To obtain the most cleavage characteristics, we adopted Saxová’s schema which assumes that positions within an epitope are less likely to be cleaved than C-terminus. Under this requirement, classification using Antigen-human is as follows: TP (true positive): PC>Td, FN (false negative): PC<Td, TN (true negative): for every internal position i within the epitope, Pi < PC or Pi < Td, FP (false positive): at least one internal position i within the epitope, Pi > PC and Pi > Td, where PC is the predictive cleavage score at the C-terminus, Pi is the predictive cleavage score at the position i , and Td is the threshold value for sorting predictive cleavage scores into cleavage and non-cleavage sites. The following measures are introduced, namely sensitivity (SE), specificity (SP), accuracy (AC), and Matthews correlation coefficient (MCC) (Saxová et al., 2003 2003 ): $${\text{SE}}=\frac{{{\text{TP}}}}{{{\text{TP + FN}}}}$$ , $${\text{SP}} = \frac{{{\text{TN}}}}{{{\text{TN + FP}}}}$$ , $${\text{AC}} = \frac{{{\text{TP + TN}}}}{{{\text{TP + FP + TN + FN}}}}$$ , $${\text{MCC}} = \frac{{{\text{TP}} \times {\text{TN}} - {\text{FN}} \times {\text{FP}}}}{{\sqrt {{\text{(TN + FN)(FN + TP)(TP + FP)(FP + TN)}}} }}$$ . The receiver operator characteristics (ROC) curve is a 2D curve in which the true positive rate is plotted on the Y axis and the false positive rate is plotted on the X axis over a continuous range of cut-off values from high to low. The area under the ROC curve (AUC) reflects the ability of a model to distinguish a randomly chosen positive instance from a randomly chosen negative one (Swets, 1988 1988 ). A random model has an AUC of about 0.5, but reasonable models should have AUC values higher than 0.7. In this study, ROC analysis was used to measure the ability of different models to identify the CTL epitopes. For a robust comparison of different methods, we generated 10 sets by randomly drawing n samples from the test set with a constant ratio of positives to negatives, where n is the size of MHCBN-test. We then performed ROC analyses 10 times. A method is considered significantly better than another if the distribution of its AUC values in a paired two-tailed t- test is significantly higher ( P ≤0.05). Results 1 Comparison of KSMM and SVM in proteasomal cleavage benchmarking 2 We have developed a kernel-based prediction method called KSMM. A 5-fold cross-validation technique using three types of proteasome data was used to evaluate the performance of classifiers with different kernels. The results are summarized in Table 2 2 . To identify the quality of KSMM, the SVM method was also implemented using the freely downloadable software LIBSVM (Chang and Lin, 2011 2011 ). SVM is a popular machine learning tool. Due to its superiority in bioinformatics classification problems, SVM has been used in a wide range of protein-peptide interaction computational predictions (Bhasin and Raghava, 2005 2005 ; Liu et al., 2009 2009 ). Polynomial KSMM performed the best when cCP in vitro data were used (Table 2 2 ), achieving an accuracy of 75% and an MCC of 0.482. Using iCP digested data, radial-based SVM had the highest accuracy (72.4%) and MCC (0.461). For the MHC I ligand data, radial-based KSMM was able to recognize the cleavage sites and non-cleavage sites with > 83% accuracy. Taken together, KSMM is a bioinformatics tool that is comparable to the SVM method. The incorporation of kernels into SMM method gave a significant improvement: the accuracy of KSMM with nonlinear kernels was 3% higher than that of SMM (linear kernel). SE: sensitivity; SP: specificity; AC: accuracy; MCC: Matthews correlation coefficient 2 Classifier 4 cCP in vitro data 4 iCP in vitro data 4 MHC I ligand data SE SP AC MCC SE SP AC MCC SE SP AC MCC KSMM  Linear 68.5 68.2 68.3 0.385 67.8 70.3 69.4 0.370 80.0 79.8 79.9 0.603  Radial-based 74.5 74.3 74.3 0.471 72.9 72.0 72.3 0.436 82.9 83.2 83.1 0.661  Polynomial 74.8 75.1 75.0 0.482 71.1 72.0 71.7 0.419 81.6 82.5 82.1 0.640 SVM  Radial-based 82.1 69.7 74.0 0.476 79.5 68.3 72.4 0.461 83.2 81.7 82.5 0.649  Polynomial 80.3 70.1 73.6 0.457 77.0 67.4 70.9 0.428 81.5 81.4 81.5 0.630 Using the MHC I ligand test set and the cCP in vitro test set of Saxová et al. ( 2003 2003 ), we tested the quality of three proteasomal classifiers: polynomial KSMM trained with cCP in vitro data (KSMM-c), radial-based KSMM trained with iCP in vitro data (KSMM-i), and radial-based KSMM trained with MHC I ligands (KSMM-ld). The comparison shows that our classifiers gave the most satisfying performance using both in vitro and MHC I data (Table 3 3 ). The results of PRProC (Nussbaum et al., 2001 2001 ), FragPredict (Holzhütter and Kloetzel, 2000 2000 ), and NetChop (Keşmir et al., 2002 2002 ) were given by Saxová et al. ( 2003 2003 ). An unexpected result is that KSMM-i outperforms KSMM-c using the cCP digestion data. SE: sensitivity; SP: specificity; MCC: Matthews correlation coefficient 2 Method 3 cCP in vitro 3 MHC I ligand SE SP MCC SE SP MCC PAProC 45.6 30.0 −0.25 46.4 64.7 0.11 FragPredict 83.5 16.5 0.00 72.1 41.4 0.12 NetChop 1.0 39.8 16.3 −0.14 34.4 91.4 0.31 NetChop 2.0 73.6 42.4 0.16 57.4 76.4 0.32 KSMM-c 73.8 77.7 0.46 61.5 56.3 0.18 KSMM-i 73.8 79.4 0.49 73.6 60.6 0.35 KSMM-ld 68.9 61.7 0.35 75.8 72.7 0.49 Proteasomal models benchmarked on MHC I epitope prediction 2 Using the MHCBN-test described in Section 2, we measured the suitability of different proteasome models for MHC I ligand identification (Fig. 1 1 ). Before evaluating the combination of proteasome and MHC I binding predictions, we used the thresholds when the sensitivity of cleavage prediction was 0.90. The highest corresponding specificities (true negative rates) of KSMM-c, KSMM-i, and KSMM-ld were 51%, 46%, and 64%, respectively. A sensitivity of 0.90 could significantly diminish false positives at the cost of a slight increase in false negatives. We filtered the MHCBN test by discarding ligands that had a prediction lower than the threshold. Then, we made a binding affinity prediction based on the reduced test set and generated an ROC graph. Comparing proteasome models alone (Fig. 1 1 ), KSMM-ld trained with MHC I ligands performed the best, with an AUC value of 0.78. KSMM-i again clearly gave a better prediction than KSMM-c (0.72 vs. 0.66). The MHC binding affinity predictive method was developed by training radial-based KSMM with MHCBN-train, which achieved an AUC value of 0.82. The joint combination of MHC and the cleavage predictions significantly enhanced the AUC values (~0.90). When combined with MHC, the difference between KSMM-i and KSMM-c was statistically significant, whereas the difference between KSMM-c and KSMM-ld was not. The increased AUC values of integral predictions resulted from a decrease in false positives. Compared to MHC I affinity prediction alone, over the entire range of true positive rates in the ROC calculation, the reduction in the false positive rate was 50% when combining KSMM-c, and 55% when combining KSMM-ld. The integration with KSMM-i led to an even larger reduction in the false positive rate (71%). The basis of this reduction is that the proteasome digestion of source protein into MHC I ligands has some additional degree of selectivity. Predictions were made on the MHCBN-test, and proteasome predictions were made by identifying HLA-A * 0201 epitopes in their source proteins. AUC values are given based on the methods shown in the box. The different prediction methods were: KSMM-i/c/ld +MHC, the combined proteasome predictions+MHC I epitope prediction; MHC, radial-based KSMM alone in Eq. ( 6 6 ) trained with MHCBN-train; KSMM-i/c/ld, the constitutive, immunoproteasome, and MHC I ligand cleavage algorithms Weight coefficients of the linear iCP and cCP models 2 Besides those AAs at P1/P1′, AAs nearby also have a great influence on the choice of proteasomal cleavage (Toes et al., 2001 2001 ). This explains why cleavage happens for certain P1/P1′ compositions, while other identical P1/P1′ compositions are ignored. Each score of the optimal plane w * in Eq. ( 9 9 ) corresponds to a kind of AA in a sequence position, and the sum of these scores is the predicted usage of the cleavage site. Hence, the weight coefficient w * can be seen as the impact of each AA in a sequence position on the cleavage site usage. For the 8-residue window of cCP and iCP in vitro data, we calculated w * with a linear function in Eq. ( 6 6 ). The values of w * are shown in Fig. 2 2 . Each symbol column corresponds to a sequence position close to a possible cleavage site between P1 and P1′. The colors of AA symbols represent their physicochemical characteristics: black, neutral and hydrophobic; blue, basic; green, neutral and polar; red, acidic. The upright or upside-down AA symbol represents a positive or negative weight coefficient of w * , respectively. The height of the symbol is proportional to the absolute w * value of the AA (Note: for interpretation of the references to color in this figure legend, the reader is referred to the web version of this article) Discussion 1 The 20S proteasome is shaped like a hollow barrel containing 4 layers of rings, each composed of seven subunits. The 2 inner rings each have 7 β subunits. Among them, the β 1 , β 2 , and β 5 subunits are proteolytically active, and each has an NH 2 -terminus Thr1 playing a critical role in the hydrolysis of peptide bonds. Protein substrate is degraded not only by single active subunits, but also by cooperation between two active subunits (Wenzel et al., 1994 1994 ). The protein substrate within the proteasome is fully unfolded (Sorokin et al., 2009 2009 ). According to the ‘molecular ruler’ of Wenzel et al. ( 1994 1994 ), the length of the degradation products is related to the distance between active sites. We computed the distances between the active sites of mouse 20S cCP and iCP (Table 4 4 ). Distances between active sites of iCP were a little longer (0.04–0.26 nm) than those between active sites of cCP. Toes et al. ( 2001 2001 ) reported that cCP and iCP generated peptides with an average lengths of 7.4 and 8.6 AAs, respectively. The average length of iCP production is about 1 AA longer than that of cCP production. On the basis of the correspondence between the length of unfolded protein sequence and the number of AAs in the sequence, as proposed by Coux et al. ( 1996 1996 ), a length of 0.26 nm of the cleavage product is close to 1 AA. Therefore, the different average fragment lengths generated by cCP and iCP production may relate to the gap between the active sites of cCP and iCP. 2 2 cCP PDB id 3UNE 3 Distance between active sites of mouse 20S cCP (nm) (Thr1) β 1c (Thr1) β 2c (Thr1) β 5c β ring (Thr1) β 1c 2.75 6.11 (Thr1) β 2c 2.75 6.17 (Thr1) β 5c 6.11 6.17 β * ring (Thr1) β * 1c 2.77 4.87 5.66 (Thr1) β * 2c 4.87 6.37 3.93 (Thr1) β * 5c 5.66 3.93 4.76 5 5 2 2 iCP PDB id 3UNH 3 Distance between active sites of mouse iCP (nm) (Thr1) β 1i (Thr1) β 2i (Thr1) β 5i 5 β ring (Thr1) β 1i 2.79 6.26 (Thr1) β 2i 2.79 6.33 (Thr1) β 5i 6.26 6.33 β * ring (Thr1) β * 1i 3.03 5.05 5.86 (Thr1) β * 2i 5.05 6.54 4.09 (Thr1) β * 5i 5.86 4.09 4.99 The KSMM+pair coefficient method achieved accuracies of 75.0%, 72.3%, and 83.1% using cCP, iCP, and MHC I ligand data, respectively (Table 2 2 ). These results are comparable to those of SVM. This demonstrates that the KSMM+pair coefficient method could serve as a protein-protein interaction predictive algorithm. Using Saxová’s test set as a benchmark, we found that the predictive performance of our optimal iCP and cCP and MHC C-terminus cleavage models was superior to that of the reference models (Holzhütter and Kloetzel, 2000 2000 ; Nussbaum et al., 2001 2001 ; Keşmir et al., 2002 2002 ) (Table 3 3 ). Our cleavage models also helped improve HLA-A * 0201 peptide identification when integrated with MHC I binding predictions (Fig. 1 1 ), i.e., the AUC values increased from 0.82 to 0.91. The improved AUC values indicate that the 8-residue, 4 residues on each side of the cleavage/non-cleavage site of immuno-, constitutive, or MHC I restricted C-terminus samples, could precisely reflect the information of cleavage fragments generated by 20S cCP and iCP. They also demonstrate that both MHC I ligands and proteasomal in vitro degradation bear a close resemblance to naturally processed digestion. KSMM-i performed better than KSMM-c in cCP cleavage site identification (Table 2 2 ) and in single or combined HLA-A * 0201 peptide identification (Fig. 1 1 ). This suggests that, compared with cCP, iCP is more specific in cleavage site selection and generates MHC I ligands more efficiently. As for KSMM-ld, it can be regarded as another type of cCP cleavage model. Most of the MHC I epitopes are processed and eluted in an environment without inducing the expression of iCPs. Differences in the performance of KSMM-c, KSMM-i, and KSMM-ld in combined HLA-A * 0201 peptide identification were small (Fig. 1 1 ). This may result from the fact that the selectivity of the MHC I molecule may reduce the gap in MHC I ligand generation efficiency between iCP and cCP. iCP and cCP in vitro digestion data could more accurately reflect the pure information of proteasomal cleavage fragments with respect to the C-terminus of MHC I restricted samples. This may be because MHC I restricted samples are likely to contain not only the information of proteasomal cleavage fragments, but also the information of peptides binding to MHC I and TAP transport (Liu et al., 2009 2009 ). KSMM-ld is markedly better than KSMM-c/i in MHC I epitope identification (0.78 vs. 0.66 and 0.72, respectively; Fig. 1 1 ), but KSMM-ld is no better than KSMM-c or KSMM-i when combined with MHC-binding prediction. The MHC I and TAP binding information in the Antigen-human is responsible for the excellent performance of KSMM-ld in single MHC I epitope identification. In Fig. 2 2 , the height of each AA symbol is proportional to its absolute w * value, which is the contribution of the AA in a sequence position to the usage of potential cleavage sites. The height of all the AA symbols stacked at each position along P4–P4′ is proportional to the sum of the corresponding absolute scores for the 20 possible AAs at the positions which are the contribution to possible cleavage sites. The AAs on adjacent positions of the cleavage site, i.e., P1, P3′ positions, have a distinct effect on the potential cleavage site, demonstrating that both cCP and iCP species are selective in the hydrolysis of peptide bonds. For the purpose of interpreting the AA characteristics in cCP and iCP cleavage-site flanking positions (Fig. 2 2 ), the crystal structures of mouse 20S iCP and cCP (PDB id: 3UNE and 3UNH) were used to find AAs which make nonbonding contact with residue Thr1 of active subunits (i.e., where there is at least one AA atom at ≤0.5 nm distance from any other atom of Thr1) (Holzhütter and Kloetzel, 2000 2000 ). Table 5 5 lists all the AAs making nonbonding contact with the Thr1 of active subunits. The AAs surrounding the active sites of 20S iCP and cCP differ in β 1 and β 5 : the electropositive and basic AA Arg45 in β 1c is substituted by the neutral AA Leu45 in β 1i , and the neutral AA Ala46 in β 5c is substituted by the neutral AA Ser46 in β 5i . It may simply be that, compared to β 1c , the AAs surrounding the active site of the β 1i subunit are less electropositive and tend to be more neutral on the whole, preferring to cleave after the neutral AAs. The substitution of neutral Ala46 in β 5c by neutral Ser46 in β 5i makes little difference to the physicochemical characteristics surrounding the β 5 active sites and does not alter the hydrolysis preference of β 5i . Relative to cCP, iCP preferentially cleaves peptides after nonpolar AAs (Seifert et al., 2010 2010 ). So the cleavage difference between cCP and iCP may relate to the change in the physicochemical characteristics surrounding β 1 -subunit active sites. PDB id: 3UNE for cCP and 3UNH for iCP. Neutral AAs are colored black, electropositive and basic AAs are colored blue, and electronegative and acidic AAs are colored red (Note: for interpretation of the references to color in this figure legend, the reader is referred to the web version of this article) 2 AA No. 6 AAs making nonbonding contact with their own Thr1 β 1c β 1i β 2c β 2i β 5c β 5i 1 Thr2 Thr2 Thr2 Thr2 Thr2 Thr2 2 Ile3 Ile3 Ile3 Ile3 Thr3 Thr3 3 Asp17 Asp17 Asp17 Asp17 Asp17 Asp17 4 Arg19 Arg19 Arg19 Arg19 Arg19 Arg19 5 Lys33 Lys33 Lys33 Lys33 Lys33 Lys33 6 Arg45 Leu45 Gly45 Gly45 Met45 Met45 7 Ser46 Ser46 Ala46 Ala46 Ala46 Ser46 8 Gly47 Gly47 Gly47 Gly47 Gly47 Gly47 The AA specificities of the P1 position correspond to the integral binding characteristics of the S1 pockets of three active subunits (Fig. 2 2 ). A binding mechanism is common to all active sites of cCP and iCP (Huber et al., 2012 2012 ). Its selectivity depends, apart from the reactive warhead, solely on the interactions with S1 pockets. According to this mechanism, besides the P1 position, the proteasomal selective specificities in the P3′ position also relate to the S1 pockets of active subunits. The length of the unfolded peptide between the cleavage site and the P3′ position is 3 AAs, which is about 1.2–1.6 nm. Distances between any pair of pocket centers of the six active subunits of two β rings have been calculated (Table 6 6 ). The Ser130 (Gly130) C α atoms of active subunits, which are close to the S1 pocket centers, are used as the S1 pocket centers. Of all 12 pairs of distances for cCP and iCP, the distances between the S1 pockets centers of β 1 and β 1 * are 1.48 and 1.56 nm, resepctively, and fall within the range 1.2–1.6 nm; the remaining 11 distances both for cCP and iCP are much longer than 1.6 nm. The AA specificities of the P3′ position are associated with the preference of the S1 pocket of β 1 * , and the AA specificities of the P1 position most probably reflect the characteristics of the S1 pocket of the β 1 subunit. Together, this demonstrates the selectivity of the cCP and iCP for certain cleavage sites, and that this selectivity may be related to the hydrolysis characteristics of their β 1 subunits. Ser130 (Gly130) Cα atoms of active subunits are used as the S1 pocket centers 2 2 cCP PDB id 3UNE 3 Distance between S1 pocket centers of active subunits of mouse 20S cCP (nm) (Ser130) β 1c (Gly130) β 2c (Ser130) β 5c β ring (Ser130) β 1c 2.83 5.79 (Gly130) β 2c 2.83 6.29 (Ser130) β 5c 5.79 6.29 β * ring (Ser130) β * 1c 1.56 3.79 5.66 (Gly130) β * 2c 3.79 6.04 4.43 (Ser130) β * 5c 5.66 4.43 3.71 5 5 2 2 iCP PDB id 3UNH 3 Distance between S1 pocket centers of active subunits of mouse iCP (nm) (Ser130) β 1i (Gly130) β 2i (Ser130) β 5i 5 β ring (Ser130) β 1i 2.94 10.7 (Gly130) β 2i 2.94 11.0 (Ser130) β 5i 10.7 11.0 β * ring (Ser130) β * 1i 1.48 4.11 9.67 (Gly130) β * 2i 4.11 11.5 10.9 (Ser130) β * 5i 9.67 10.9 3.71 Nevertheless, this leads to a contradiction. The in vitro cleavage samples are a collection of peptide fragments generated by three types of active subunits (‘caspase-like’ β 1 , ‘trypsin-like’ β 2 , and ‘chymotrypsin-like’ β 5 ); and the AA specificity information reflected by Fig. 2 2 is also a comprehensive reflection of different hydrolysis characteristics of active subunits. This resulted in low predictive accuracies of the cCP and iCP models (Table 2 2 ). If more digestion data for the β 1i / β 1c -, β 2i / β 2c -, and β 5i / β 5c -subunits were available, a predictive model specific for a single active subunit could be established; this could improve the proteasomal predictive accuracy. The following results may explain the apparent contradiction. Muchamuel et al. ( 2009 2009 ) showed that β 1i / β 1c - and β 2i / β 2c -subunits have much higher activities than β 5i / β 5c -subunits; β 2i / β 2c -subunits harbor very spacious S1 pockets and therefore do not show marked specificities (Huber et al., 2012 2012 ); however, functional data indicate that incorporation of i-subunits enhances the activity of β 1i (Nussbaum et al., 1998 1998 ), thereby resulting in the stronger preference of iCPs for hydrophobic AAs at P1. All these results suggest that the β 1 subunit is both specific and active, relative to the β 2 and β 5 subunits. These results are consistent with our conclusion. Using the absolute amounts of AAs found in positions close to the cleavage sites generated by 20S proteasomes (6.135×10 −9 mol cCP and 6.370×10 −9 mol iCP cleavage sites in Toes et al. ( 2001 2001 ), and 15.964×10 −9 mol cCP and 9.164×10 −9 mol iCP cleavage sites in Tenzer et al. ( 2004 2004 )), we calculated the log-odds ratios of all 20 AAs at P1 and P3′ (log-odds ratio, lg( f i / p i ), where f i is the frequency of occurrence of AA i at a certain position, and p i is the background frequency of AA i in the Swiss-Prot database (Bairoch and Apweiler, 2000 2000 )). The sign of the log-odds ratio could reflect the preference of the position for the AA i , i.e., a positive sign ‘+’ implies that the AA i at this position is beneficial to the usage of the cleavage site, whereas a negative sign ‘−’ implies that the AA at this position is not. The analysis of yeast enolase degradation data (Toes et al., 2001 2001 ) is shown in Table 7 7 . The comparison of all 20 signs at P1 and P3′ shows that there are 16 identical signs at P1 and P3′ for cCP and 14 for iCP. This demonstrates that the preference for AAs at P1 and P3′ is the same for AAs at cCP and iCP ( P < 0.001 and P < 0.05). This may be related to the selectivities of the two β 1 subunits. The analysis of prion degradation data shows that there are 13 identical signs at P1 and P3′ for cCP and 10 for iCP (Tenzer et al., 2004 2004 ). This result, to some extent, reflects a similar preference of cCP and iCP for AAs at P1 and P3′ ( P > 0.1 and P ≥0.5). This may be due to the highly biased composition of AAs in the prion sequence, i.e., about 20% Gly and many repeat regions (Tenzer et al., 2004 2004 ). Signs of log-odds ratios reflect the preference of the position for the amino acid (AA) i ; i.e., ‘+’ implies that AA i at this position is beneficial to the usage of the cleavage site, whereas ‘−’ implies it is not 2 AA 2 cCP 2 iCP P1 P3′ P1 P3′ Ala + + + + Cys − − − − Asp + + − − Glu + + − − Phe − − + − Gly − + − + His − − − − Ile + − + − Lys − + − − Leu + + + + Met − − − − Asn − − − + Arg − − − − Pro − − − − Arg − − − − Ser − − − − Thr − − − − Val + + − + Trp − − − − Tyr + − + − Conclusions 1 The KSMM+pair coefficient method could serve as a competitive bioinformatic tool. The three proteasomal cleavage predictive models in this paper are reasonable when the window size is 8 AAs. The cCP and iCP in vitro cleavage data show additional specificities in the MHC I-peptide processing and presentation pathway. The cleavage specificities of cCP and iCP towards substrate proteins may be related to the physicochemical characteristics of their β 1 -subunit active sites. References I Alvarez , L Sesma , M Marcilla , M Ramos , M Marti , E Camafeita , JA de Castro . Identification of novel HLA-B27 ligands derived from polymorphic regions of its own or other class I molecules based on direct generation by 20S proteasome . J Biol Chem , 2001 . 276 ( 35 ): 32729 - 32737 . 11435436 DOI: 10.1074/jbc.M104663200 http://doi.org/10.1074/jbc.M104663200 . Identification of novel HLA-B27 ligands derived from polymorphic regions of its own or other class I molecules based on direct generation by 20S proteasome A Bairoch , R Apweiler . The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000 . Nucleic Acids Res , 2000 . 28 ( 1 ): 45 - 48 . 10592178 DOI: 10.1093/bib/3.3.275 http://doi.org/10.1093/bib/3.3.275 . The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000 NJ Beekman , PA van Veelen , T van Hall , A Neisig , A Sijts , M Camps , PM Kloetzel , JJ Neefjes , CJ Melief , F Ossendorp . Abrogation of CTL epitope processing by single amino acid substitution flanking the C-terminal proteasome cleavage site . J Immunol , 2000 . 164 ( 4 ): 1898 - 1905 . 10657639 . Abrogation of CTL epitope processing by single amino acid substitution flanking the C-terminal proteasome cleavage site M Bhasin , GPS Raghava . Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences . Nucleic Acids , 2005 . 33 ( s2 ): W202 - W207 . DOI: 10.1093/nar/gki587 http://doi.org/10.1093/nar/gki587 . Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences M Bhasin , H Singh , GPS Raghava . MHCBN: a comprehensive database of MHC binding and non-binding peptides . Bioinformatics , 2003 . 19 ( 5 ): 665 - 666 . 12651731 DOI: 10.1093/bioinformatics/btg055 http://doi.org/10.1093/bioinformatics/btg055 . MHCBN: a comprehensive database of MHC binding and non-binding peptides MJ Blythe , IA Doytchinova , DR Flower . JenPep: a database of quantitative functional peptide data for immunology . Bioinformatics , 2002 . 18 ( 3 ): 434 - 439 . 11934742 DOI: 10.1093/bioinformatics/18.3.434 http://doi.org/10.1093/bioinformatics/18.3.434 . JenPep: a database of quantitative functional peptide data for immunology P Cascio , C Hilton , AF Kisselev , KL Rock , AL Goldberg . 26S proteasomes and immunoproteasomes produce mainly N-extended versions of an antigenic peptide . EMBO J , 2001 . 20 ( 10 ): 2357 - 2366 . 11350924 DOI: 10.1093/emboj/20.10.2357 http://doi.org/10.1093/emboj/20.10.2357 . 26S proteasomes and immunoproteasomes produce mainly N-extended versions of an antigenic peptide CC Chang , CJ Lin . LIBSVM: a library for support vector machines . ACM Trans Intell Syst Technol , 2011 . 2 ( 3 ): 27 DOI: 10.1145/1961189.1961199 http://doi.org/10.1145/1961189.1961199 . LIBSVM: a library for support vector machines J Chapiro , S Claverol , F Piette , W Ma , V Stroobant , B Guillaume , JE Gairin , S Morel , O Burlet-Schiltz , B Monsarrat , 等 . Destructive cleavage of antigenic peptides either by the immunoproteasome or by the standard proteasome results in differential antigen presentation . J Immunol , 2006 . 176 ( 2 ): 1053 - 1061 . 16393993 DOI: 10.1002/ijc.25911 http://doi.org/10.1002/ijc.25911 . Destructive cleavage of antigenic peptides either by the immunoproteasome or by the standard proteasome results in differential antigen presentation O Coux , K Tanaka , AL Goldberg . Structure and functions of the 20S and 26S proteasomes . Ann Rev Biochem , 1996 . 65 801 - 847 . 8811196 DOI: 10.1146/annurev.bi.65.070196.004101 http://doi.org/10.1146/annurev.bi.65.070196.004101 . Structure and functions of the 20S and 26S proteasomes LR Dick , C Aldrich , SC Jameson , CR Moomaw , BC Pramanik , CK Doyle , GN DeMartino , MJ Bevan , JM Forman , CA Slaughter . Proteolytic processing of ovalbumin and beta-galactosidase by the proteasome to a yield antigenic peptides . J Immunol , 1994 . 152 ( 8 ): 3884 - 3894 . 8144958 . Proteolytic processing of ovalbumin and beta-galactosidase by the proteasome to a yield antigenic peptides TP Dick , AK Nussbaum , M Deeg , W Heinemeyer , M Groll , M Schirle , W Keilholz , S Stevanovic , DH Wolf , R Huber , 等 . Contribution of proteasomal beta-subunits to the cleavage of peptide substrates analyzed with yeast mutants . J Biol Chem , 1998 . 273 ( 40 ): 25637 - 25646 . 9748229 DOI: 10.1074/jbc.273.40.25637 http://doi.org/10.1074/jbc.273.40.25637 . Contribution of proteasomal beta-subunits to the cleavage of peptide substrates analyzed with yeast mutants CM Diez-Rivero , EM Lafuente , PA Reche . Computational analysis and modeling of cleavage by the immunoproteasome and the constitutive proteasome . BMC Bioinf , 2010 . 11 ( 1 ): 479 DOI: 10.1186/1471-2105-11-479 http://doi.org/10.1186/1471-2105-11-479 . Computational analysis and modeling of cleavage by the immunoproteasome and the constitutive proteasome B Ehring , TH Meyer , C Eckerskorn , F Lottspeich , R Tampé . Effects of major-histocompatibility-complex-encoded subunits on the peptidase and proteolytic activities of human 20S proteasomes. Cleavage of proteins and antigenic peptides . Eur J Biochem , 1996 . 235 ( 1-2 ): 404 - 415 . 8631360 DOI: 10.1111/j.1432-1033.1996.00404.x http://doi.org/10.1111/j.1432-1033.1996.00404.x . Effects of major-histocompatibility-complex-encoded subunits on the peptidase and proteolytic activities of human 20S proteasomes. Cleavage of proteins and antigenic peptides NPN Emmerich , AK Nussbaum , S Stevanovic , M Priemer , REM Toes , HG Rammensee . The human 26S and 20S proteasomes generate overlapping but different sets of peptide fragments from a model protein substrate . J Biol Chem , 2000 . 275 ( 28 ): 21140 - 21148 . 10801794 DOI: 10.1074/jbc.M000740200 http://doi.org/10.1074/jbc.M000740200 . The human 26S and 20S proteasomes generate overlapping but different sets of peptide fragments from a model protein substrate I Ginodi , T Vider-Shalit , L Tsaban , Y Louzoun . Precise score for the prediction of peptides cleaved by the proteasome . Bioinformatics , 2008 . 24 ( 4 ): 477 - 483 . 18216070 DOI: 10.1093/bioinformatics/btm616 http://doi.org/10.1093/bioinformatics/btm616 . Precise score for the prediction of peptides cleaved by the proteasome AL Goldberg , P Cascio , T Saric , KL Rock . The importance of the proteasome and subsequent proteolytic steps in the generation of antigenic peptides . Mol Immunol , 2002 . 39 147 - 164 . 12200047 DOI: 10.1371/journal.pbio.0040267 http://doi.org/10.1371/journal.pbio.0040267 . The importance of the proteasome and subsequent proteolytic steps in the generation of antigenic peptides DS Goldobin , A Zaikin . Towards quantitative prediction of proteasomal digestion patterns of proteins . J Stat Mech , 2009 . 2009 P01009 DOI: 10.1088/1742-5468/2009/01/P01009 http://doi.org/10.1088/1742-5468/2009/01/P01009 . Towards quantitative prediction of proteasomal digestion patterns of proteins W Heinemeyer , PC Ramos , RJ Dohmen . The ultimate nanoscale mincer: assembly, structure and active sites of the 20S proteasome core . Cell Mol Life Sci , 2004 . 61 ( 13 ): 1562 - 1578 . 15224181 DOI: 10.1007/s00018-004-4130-z http://doi.org/10.1007/s00018-004-4130-z . The ultimate nanoscale mincer: assembly, structure and active sites of the 20S proteasome core HG Holzhütter , PM Kloetzel . A kinetic model of vertebrate 20S proteasome accounting for the generation of major proteolytic fragments from oligomeric peptide substrates . Biophys J , 2000 . 79 ( 3 ): 1196 - 1205 . 10968984 DOI: 10.1016/S0006-3495(00)76374-0 http://doi.org/10.1016/S0006-3495(00)76374-0 . A kinetic model of vertebrate 20S proteasome accounting for the generation of major proteolytic fragments from oligomeric peptide substrates EM Huber , M Basler , R Schwab , W Heinemeyer , CJ Kirk , M Groettrup , M Groll . Immuno- and constitutive proteasome crystal structures reveal differences in substrate and inhibitor specificity . Cell , 2012 . 148 ( 4 ): 727 - 738 . 22341445 DOI: 10.1016/j.cell.2011.12.030 http://doi.org/10.1016/j.cell.2011.12.030 . Immuno- and constitutive proteasome crystal structures reveal differences in substrate and inhibitor specificity L Jacob , JP Vert . Efficient peptide-MHC-I binding prediction for alleles with few known binders . Bioinformatics , 2008 . 24 ( 3 ): 358 - 366 . 18083718 DOI: 10.1186/1471-2105-9-363 http://doi.org/10.1186/1471-2105-9-363 . Efficient peptide-MHC-I binding prediction for alleles with few known binders C Keşmir , AK Nussbaum , H Schild , V Detours , S Brunak . Prediction of proteasome cleavage motifs by neural networks . Prot Eng , 2002 . 15 ( 4 ): 287 - 296 . DOI: 10.1093/protein/15.4.287 http://doi.org/10.1093/protein/15.4.287 . Prediction of proteasome cleavage motifs by neural networks C Keşmir , V van Noort , RJ de Boer , P Hogeweg . Bioinformatic analysis of functional differences between the immunoproteasome and the constitutive proteasome . Immunogenetics , 2003 . 55 ( 7 ): 437 - 449 . 12955356 DOI: 10.1007/s00251-003-0585-6 http://doi.org/10.1007/s00251-003-0585-6 . Bioinformatic analysis of functional differences between the immunoproteasome and the constitutive proteasome A Kosmrlj , EL Read , Y Qi , TM Allen , M Altfeld , SG Deeks , F Pereyra , M Carrington , BD Walker , AK Chakraborty . Effects of thymic selection of the T-cell repertoire on HLA class I-associated control of HIV infection . Nature , 2010 . 465 ( 7296 ): 350 - 354 . 20445539 DOI: 10.1038/nature08997 http://doi.org/10.1038/nature08997 . Effects of thymic selection of the T-cell repertoire on HLA class I-associated control of HIV infection D Leibovitz , Y Koch , F Pitzer , M Fridkin , A Dantes , W Baumeister , A Amsterdam . Sequential degradation of the neuropeptide gonadotropin-releasing hormone by the 20 S granulosa cell proteasomes . FEBS Lett , 1994 . 346 ( 2-3 ): 203 - 206 . 8013634 DOI: 10.1016/0014-5793(94)00472-2 http://doi.org/10.1016/0014-5793(94)00472-2 . Sequential degradation of the neuropeptide gonadotropin-releasing hormone by the 20 S granulosa cell proteasomes T Liu , W Liu , Z Song , CB Jiao , MH Zhu , XG Wang . Computational prediction of the specificities of proteasome interaction with antigen protein . Cell Mol Immunol , 2009 . 6 ( 2 ): 135 - 142 . 19403064 DOI: 10.1038/cmi.2009.19 http://doi.org/10.1038/cmi.2009.19 . Computational prediction of the specificities of proteasome interaction with antigen protein M Lucchiari-Hartz , V Lindo , N Hitziger , S Gaedicke , L Saveanu , PM Endert . Differential proteasomal processing of hydrophobic and hydrophilic protein regions: contribution to cytotoxic T lymphocyte epitope clustering in HIV-1-Nef . PNAS , 2003 . 100 ( 13 ): 7755 - 7760 . 12810958 DOI: 10.1073/pnas.1232228100 http://doi.org/10.1073/pnas.1232228100 . Differential proteasomal processing of hydrophobic and hydrophilic protein regions: contribution to cytotoxic T lymphocyte epitope clustering in HIV-1-Nef WB Ma , N Vigneron , J Chapiro , V Stroobant , C Germeau , T Boon , PG Coulie , BJ van den Eynde . A MAGE-C2 antigenic peptide processed by the immunoproteasome is recognized by cytolytic T cells isolated from a melanoma patient after successful immunotherapy . Int J Cancer , 2011 . 129 ( 10 ): 2427 - 2434 . 21207413 DOI: 10.1002/ijc.25911 http://doi.org/10.1002/ijc.25911 . A MAGE-C2 antigenic peptide processed by the immunoproteasome is recognized by cytolytic T cells isolated from a melanoma patient after successful immunotherapy N Metropolis , S Ulam . The Monte Carlo method . J Am Stat Assoc , 1949 . 44 ( 247 ): 335 - 341 . 18139350 DOI: 10.2307/2280232 http://doi.org/10.2307/2280232 . The Monte Carlo method B Mommaas , J Kamp , JW Drijfhout , N Beekman , F Ossendorp , P van Veelen , J den Haan , E Goulmy , T Mutis . Identification of a novel HLA-B60-restricted T cell epitope of the minor histocompatibility antigen HA-1 locus . J Immunol , 2002 . 169 ( 6 ): 3131 - 3136 . 12218130 . Identification of a novel HLA-B60-restricted T cell epitope of the minor histocompatibility antigen HA-1 locus S Morel , F Lévy , O Burlet-Schiltz , F Brasseur , M Probst-Kepper , AL Peitrequin , B Monsarrat , R van Velthoven , JC Cerottini , T Boon , 等 . Processing of some antigens by the standard proteasome but not by the immunoproteasome results in poor presentation by dendritic cells . Immunity , 2000 . 12 ( 1 ): 107 - 117 . 10661410 DOI: 10.1016/S1074-7613(00)80163-6 http://doi.org/10.1016/S1074-7613(00)80163-6 . Processing of some antigens by the standard proteasome but not by the immunoproteasome results in poor presentation by dendritic cells T Muchamuel , M Basler , MA Aujay , E Suzuki , KW Kalim , C Lauer , C Sylvain , ER Ring , J Shields , J Jiang , 等 . A selective inhibitor of the immunoproteasome subunit LMP7 blocks cytokine production and attenuates progression of experimental arthritis . Nat Med , 2009 . 15 ( 7 ): 781 - 787 . 19525961 DOI: 10.1038/nm.1978 http://doi.org/10.1038/nm.1978 . A selective inhibitor of the immunoproteasome subunit LMP7 blocks cytokine production and attenuates progression of experimental arthritis G Niedermann , S Butz , HG Ihlenfeldt , R Grimm , M Lucchiari , H Hoschützky , G Jung , B Maier , K Eichmann . Contribution of proteasome-mediated proteolysis to the hierarchy of epitopes presented by major histocompatibility complex class I molecules . Immunity , 1995 . 2 ( 3 ): 289 - 299 . 7535183 DOI: 10.1016/1074-7613(95)90053-5 http://doi.org/10.1016/1074-7613(95)90053-5 . Contribution of proteasome-mediated proteolysis to the hierarchy of epitopes presented by major histocompatibility complex class I molecules G Niedermann , G King , S Butz , U Birsner , R Grimm , J Shabanowitz , DF Hunt , K Eichmann . The proteolytic fragments generated by vertebrate proteasomes: structural relationships to major histocompatibility complex class I binding peptides . PNAS , 1996 . 93 ( 16 ): 8572 - 8577 . 8710912 DOI: 10.1073/pnas.93.16.8572 http://doi.org/10.1073/pnas.93.16.8572 . The proteolytic fragments generated by vertebrate proteasomes: structural relationships to major histocompatibility complex class I binding peptides G Niedermann , R Grimm , E Geier , M Maurer , C Realini , C Gartmann , J Soll , S Omura , MC Rechsteiner , W Baumeister , 等 . Potential immunocompetence of proteolytic fragments produced by proteasomes before evolution of the vertebrate immune system . J Exp Med , 1997 . 186 ( 2 ): 209 - 220 . 9221750 DOI: 10.1084/jem.186.2.209 http://doi.org/10.1084/jem.186.2.209 . Potential immunocompetence of proteolytic fragments produced by proteasomes before evolution of the vertebrate immune system M Nielsen , C Lundegaard , O Lund . Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method . BMC Bioinf , 2007 . 8 ( 1 ): 238 DOI: 10.1186/1471-2105-8-238 http://doi.org/10.1186/1471-2105-8-238 . Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method AK Nussbaum , TP Dick , W Keilholz . Cleavage motifs of the yeast 20S proteasome b subunits deduced from digests of enolase 1 . PNAS , 1998 . 95 ( 21 ): 12504 - 12509 . 9770515 DOI: 10.1073/pnas.95.21.12504 http://doi.org/10.1073/pnas.95.21.12504 . Cleavage motifs of the yeast 20S proteasome b subunits deduced from digests of enolase 1 AK Nussbaum , C Kuttler , KP Hadeler , HG Rammensee , H Schild . PAProC: a prediction algorithm for proteasomal cleavages available on the WWW . Immunogenetics , 2001 . 53 ( 2 ): 87 - 94 . 11345595 DOI: 10.1007/s002510100300 http://doi.org/10.1007/s002510100300 . PAProC: a prediction algorithm for proteasomal cleavages available on the WWW F Ossendorp , A Neisig , T Ruppert , M Groettrup , A Sijts , E Mengedë , PM Kloetzel , J Neefjes , U Koszinowski , C Melief . A single residue exchange within a viral ctl epitope alters proteasome-mediated degradation resulting in lack of antigen presentation . Immunity , 1996 . 5 ( 2 ): 115 - 124 . 8769475 DOI: 10.1016/S1074-7613(00)80488-4 http://doi.org/10.1016/S1074-7613(00)80488-4 . A single residue exchange within a viral ctl epitope alters proteasome-mediated degradation resulting in lack of antigen presentation B Peters , A Sette . Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method . BMC Bioinf , 2005 . 6 ( 1 ): 132 DOI: 10.1186/1471-2105-6-132 http://doi.org/10.1186/1471-2105-6-132 . Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method B Peters , K Janek , U Kuckelkorn , HG Holzhütter . Assessment of proteasomal cleavage probabilities from kinetic analysis of time-dependent product formation . J Mol Biol , 2002 . 318 ( 3 ): 847 - 862 . 12054828 DOI: 10.1016/S0022-2836(02)00167-5 http://doi.org/10.1016/S0022-2836(02)00167-5 . Assessment of proteasomal cleavage probabilities from kinetic analysis of time-dependent product formation B Peters , W Tong , J Sidney , A Sette , Z Weng . Examining the independent binding assumption for binding of peptide epitopes to MHC-I molecules . Bioinformatics , 2003 . 19 ( 14 ): 1765 - 1772 . 14512347 DOI: 10.1093/bioinformatics/btg247 http://doi.org/10.1093/bioinformatics/btg247 . Examining the independent binding assumption for binding of peptide epitopes to MHC-I molecules AJ Rivett . Purification of a liver alkaline protease which degrades oxidatively modified glutamine synthetase. Characterization as a high molecular weight cysteine proteinase . J Biol Chem , 1985 . 260 ( 23 ): 12600 - 12606 . 2864341 . Purification of a liver alkaline protease which degrades oxidatively modified glutamine synthetase. Characterization as a high molecular weight cysteine proteinase M Sandberg , L Eriksson , J Jonsson , M Sjöström , S Wold . New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids . J Med Chem , 1998 . 41 ( 14 ): 2481 - 2491 . 9651153 DOI: 10.1021/jm9700575 http://doi.org/10.1021/jm9700575 . New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids P Saxová , S Buus , S Brunak , C Keşmir . Predicting proteasomal cleavage sites: a comparison of available methods . Int Immunol , 2003 . 15 ( 7 ): 781 - 787 . 12807816 DOI: 10.1093/intimm/dxg084 http://doi.org/10.1093/intimm/dxg084 . Predicting proteasomal cleavage sites: a comparison of available methods ES Schultz , J Chapiro , C Lurquin , S Claverol , O Burlet-Schiltz , G Warnier , V Russo , S Morel , F Levy , T Boon , 等 . The production of a new MAGE-3 peptide presented to cytolytic T lymphocytes by HLA-B40 requires the immunoproteasome . J Exp Med , 2002 . 195 ( 4 ): 391 - 399 . 11854353 DOI: 10.1084/jem.20011974 http://doi.org/10.1084/jem.20011974 . The production of a new MAGE-3 peptide presented to cytolytic T lymphocytes by HLA-B40 requires the immunoproteasome U Seifert , LP Bialy , F Ebstein , D Bech-Otschir , A Voigt , F Schröter , T Prozorovski , N Lange , J Steffen , M Rieger , 等 . Immunoproteasomes preserve protein homeostasis upon interferon-induced oxidative stress . Cell , 2010 . 142 ( 4 ): 613 - 624 . 20723761 DOI: 10.1016/j.cell.2010.07.036 http://doi.org/10.1016/j.cell.2010.07.036 . Immunoproteasomes preserve protein homeostasis upon interferon-induced oxidative stress N Shimbara , H Nakajima , N Tanahashi , K Ogawa , S Niwa , A Uenaka , E Nakayama , K Tanaka . Double-cleavage production of the CTL epitope by proteasomes and PA28: role of the flanking region . Genes Cells , 1997 . 2 ( 12 ): 785 - 800 . 9544705 DOI: 10.1046/j.1365-2443.1997.1610359.x http://doi.org/10.1046/j.1365-2443.1997.1610359.x . Double-cleavage production of the CTL epitope by proteasomes and PA28: role of the flanking region AV Sorokin , ER Kim , LP Ovchinnikov . Proteasome system of protein degradation and processing . Biochemistry , 2009 . 74 ( 13 ): 1411 - 1442 . 20210701 DOI: 10.1134/S000629790913001X http://doi.org/10.1134/S000629790913001X . Proteasome system of protein degradation and processing Y Sun , AJ Sijts , M Song , K Janek , AK Nussbaum , S Kral , M Schirle , S Stevanovic , A Paschen , H Schild , 等 . Expression of the proteasome activator PA28 rescues the presentation of a cytotoxic T lymphocyte epitope on melanoma cells . Cancer Res , 2002 . 62 ( 10 ): 2875 - 2882 . 12019167 . Expression of the proteasome activator PA28 rescues the presentation of a cytotoxic T lymphocyte epitope on melanoma cells JA Swets . Measuring the accuracy of diagnostic systems . Science , 1988 . 240 ( 4857 ): 1285 - 1293 . 3287615 DOI: 10.1126/science.3287615 http://doi.org/10.1126/science.3287615 . Measuring the accuracy of diagnostic systems S Tenzer , L Stoltze , B Schonfisch , J Dengjel , M Muller , S Stevanovic . Quantitative analysis of prion protein degradation by constitutive and immuno-20S proteasomes indicates differences correlated with disease susceptibility . J Immunol , 2004 . 172 ( 2 ): 1083 - 1091 . 14707082 . Quantitative analysis of prion protein degradation by constitutive and immuno-20S proteasomes indicates differences correlated with disease susceptibility S Tenzer , B Peters , S Bulik , O Schoor , C Lemmel , MM Schatz , PM Kloetzel , HG Rammensee , H Schild , HG Holzhütter . Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding . Cell Mol Life Sci , 2005 . 62 ( 9 ): 1025 - 1037 . 15868101 DOI: 10.1007/s00018-005-4528-2 http://doi.org/10.1007/s00018-005-4528-2 . Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding RE Toes , AK Nussbaum , S Degermann , M Schirle , NP Emmerich . Discrete cleavage motifs of constitutive and immunoproteasomes revealed by quantitative analysis of cleavage products . J Exp Med , 2001 . 194 ( 1 ): 1 - 12 . 11435468 DOI: 10.1084/jem.194.1.1 http://doi.org/10.1084/jem.194.1.1 . Discrete cleavage motifs of constitutive and immunoproteasomes revealed by quantitative analysis of cleavage products N Vigneron , V Stroobant , J Chapiro , A Ooms , G Degiovanni , S Morel , P van der Bruggen , T Boon , BJ van den Eynde . An antigenic peptide produced by peptide splicing in the proteasome . Science , 2004 . 304 ( 5670 ): 587 - 590 . 15001714 DOI: 10.1126/science.1095522 http://doi.org/10.1126/science.1095522 . An antigenic peptide produced by peptide splicing in the proteasome EH Warren , NJ Vigneron , MA Gavin , PG Coulie , V Stroobant , A Dalet , SS Tykodi , SM Xuereb , JK Mito , SR Riddell , 等 . An antigen produced by splicing of noncontiguous peptides in the reverse order . Science , 2006 . 313 ( 5792 ): 1444 - 1447 . 16960008 DOI: 10.1126/science.1130660 http://doi.org/10.1126/science.1130660 . An antigen produced by splicing of noncontiguous peptides in the reverse order T Wenzel , C Eckerskorn , F Lottspeich , W Baumeister . Existence of a molecular ruler in proteasomes suggested by analysis of degradation products . FEBS Lett , 1994 . 349 ( 2 ): 205 - 209 . 8050567 DOI: 10.1016/0014-5793(94)00665-2 http://doi.org/10.1016/0014-5793(94)00665-2 . Existence of a molecular ruler in proteasomes suggested by analysis of degradation products Compliance with ethics guidelines Yu-feng LU, Hao SHENG, Yi ZHANG, and Zhi-yang LI declare that they have no conflict of interest. This article does not contain any studies with human or animal subjects performed by any of the authors. 10.1631/jzus.B1200299 Project (No.11271059) supported by the National Natural Science Foundation of China 2012-11-04 2013-08-13 2013-04-15 2022-01-05 Copyright © Zhejiang University and Springer-Verlag Berlin Heidelberg 2013 2013 Journal of Zhejiang University-SCIENCE B (Biomedicine and Biotechnology) 09 14