Entropy and complexity of finite sequences as fluctuating quantities

Jiménez-Montaño, Miguel A.; Ebeling, W.; Pohl, Thomas; Rapp, Paul E.

doi:10.1016/s0303-2647(01)00171-x

Cited by 30 publications

(25 citation statements)

References 22 publications

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“…N-gram models are frequently applied to speech recognition and natural language tagging [38], but they have also been applied to sequence analysis and motif identification [32,39-41]. We built two types of cleavage models.…”

Section: Resultsmentioning

confidence: 99%

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

2010

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BackgroundProteasomes 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.ResultsWe 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.ConclusionsWe 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/.

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Section: Resultsmentioning

confidence: 99%

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

2010

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“…Language models including n-gram models are most frequently applied in speech recognition and natural language tagging (Rosenfeld 2000), but have also been applied to the sequence analysis and motif identification (Jimenez-Montano et al 2002;Wu and Shivakumar 1994;Wu et al 1996). Cleavage by the proteasome occurs at preferential sites within the protein, and sequence signals from antigenic peptides processed by the proteasome are especially conserved at position P1 of the cleavage site (the Cterminus of antigenic peptide) and its immediate flanking P1' residue .…”

Section: Prediction Of Mhci-restricted and Mhcii-restricted T-cell Epmentioning

confidence: 99%

Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

et al. 2004

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We introduced previously an on-line resource, RANKPEP that uses position specific scoring matrices (PSSMs) or profiles for the prediction of peptide-MHC class I (MHCI) binding as a basis for CD8 T-cell epitope identification. Here, using PSSMs that are structurally consistent with the binding mode of MHC class II (MHCII) ligands, we have extended RANKPEP to prediction of peptide-MHCII binding and anticipation of CD4 T-cell epitopes. Currently, 88 and 50 different MHCI and MHCII molecules, respectively, can be targeted for peptide binding predictions in RANKPEP. Because appropriate processing of antigenic peptides must occur prior to major histocompatibility complex (MHC) binding, cleavage site prediction methods are important adjuncts for T-cell epitope discovery. Given that the C-terminus of most MHCI-restricted epitopes results from proteasomal cleavage, we have modeled the cleavage site from known MHCI-restricted epitopes using statistical language models. The RANKPEP server now determines whether the Cterminus of any predicted MHCI ligand may result from such proteasomal cleavage. Also implemented is a variability masking function. This feature focuses prediction on conserved rather than highly variable protein segments encoded by infectious genomes, thereby offering identification of invariant T-cell epitopes to thwart mutation as an immune evasion mechanism.

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“…S-measure: For the purpose of comparing an observable M computed for symbolic sequences of finite length to their surrogate counterparts, Ref. [36] (1) ] (note that this corresponds to the construction procedure 1 for surrogate sequences in Ref. [36]), using 10 2 surrogate sequences for averaging obtained by standard random shuffling.…”

Section: Resultsmentioning

confidence: 99%

Analysis of the phase transition in the two-dimensional Ising ferromagnet using a Lempel-Ziv string-parsing scheme and black-box data-compression utilities

Melchert

Hartmann

2015

Phys. Rev. E

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In this work we consider information-theoretical observables to analyze short symbolic sequences, comprising time-series that represent the orientation of a single spin in a 2D Ising ferromagnet on a square lattice of size L 2 = 128 2 , for different system temperatures T . The latter were chosen from an interval enclosing the critical point Tc of the model. At small temperatures the sequences are thus very regular, at high temperatures they are maximally random. In the vicinity of the critical point, nontrivial, long-range correlations appear. Here, we implement estimators for the entropy rate, excess entropy (i.e. "complexity") and multi-information. First, we implement a Lempel-Ziv string parsing scheme, providing seemingly elaborate entropy rate and multi-information estimates and an approximate estimator for the excess entropy. Furthermore, we apply easy-to-use black-box data compression utilities, providing approximate estimators only. For comparison and to yield results for benchmarking purposes we implement the information-theoretic observables also based on the well-established M -block Shannon entropy, which is more tedious to apply compared to the the first two "algorithmic" entropy estimation procedures. To test how well one can exploit the potential of such data compression techniques, we aim at detecting the critical point the 2D Ising ferromagnet. Among the above observables, the multi-information, which is known to exhibit an isolated peak at the critical point, is very easy to replicate by means of both efficient algorithmic entropy estimation procedures. Finally, we assess how good the various algorithmic entropy estimates compare to the more conventional block entropy estimates and illustrate a simple modification that yields enhanced results.

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Entropy and complexity of finite sequences as fluctuating quantities

Cited by 30 publications

References 22 publications

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

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

Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

Analysis of the phase transition in the two-dimensional Ising ferromagnet using a Lempel-Ziv string-parsing scheme and black-box data-compression utilities

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