Inken Wohlers

Supratentorial ependymomas of childhood carry C11orf95-RELA fusions leading to pathological activation of the NF-κB signaling pathway.

Methods). This fusion was not detectable in a large series of infratentorial and spinal ependymomas including myxopapillary ependymomas. It was also not detectable in ependymoblastomas, medulloblastomas or pineoblastomas. 14/19 samples from supratentorial ependymomas expressed this novel fusion transcript resulting in an N-terminal part of C11orf95 encoding 212aa of the hypothetical 678aa protein fused to relA that is thereby uncoupled from its normal upstream regulators. The c-terminal relA part of the putative fusion protein contains almost the full relA sequence; only the first three amino acids (encoded by exon 2) are deleted in-frame. The occurrence of the fusion did not seem to be correlated to a specific histology, although several but not all cases showed clear cell morphology, and the fusionpositive cases showed no predominant location within the supratentorium (supplementary Table 1). Interestingly, all five reLA fusion-negative samples were from female patients (two-tailed Fisher’s exact test, p = 0.03). The physiological function of C11orf95 is unclear. It has been described as a fusion partner of the gene coding the transcription factor Mkl2 in benign chondroid lipomas representing the molecular correlate of the t(11;16) (q13;p13) translocation found in these tumors [3, 5]. The breakpoint in chondroid lipomas is at exon 5, whereas it is at exon 2 in supratentorial ependymomas. RELA encodes relA (NF-κB3), a 65-kDa protein which interacts with IκB and p50 in the central signaling complex in the NF-κB pathway. After activation of cell surface receptors the signal is transmitted by the IKK complex that phosphorylates IκB and thereby controls the translocation of relA/p50 into the nucleus and transcription of specific target genes (reviewed in [4]). The key physiological function of NF-κB signaling is the orchestration of the inflammatory responses to both Clinical observations and studies on genetic alterations and gene expression indicated that supratentorial ependymomas differ from ependymomas of infratentorial or spinal location [6, 8]. To further elucidate the pathogenesis of supratentorial ependymomas, we performed paired-end rNA sequencing in 19 tumor samples from 18 patients (for clinical data, see supplementary Table 1). Mapping of the reads predicted a novel recurrent fusion mrNA between C11orf95, a gene with unknown function, and v-rel avian reticuloendotheliosis viral oncogene homolog A (RELA) encoding the relA p65 subunit of the central NF-κB complex. RELA is located approximately 1.9 Mbp telomeric from C11orf95 on the same chromosomal band 11q13. We confirmed the fusion by sanger sequencing of the cDNA (Fig. 1a; primer sequences, see supplementary

Towards optimal alignment of protein structure distance matrices

MOTIVATION Structural alignments of proteins are important for identification of structural similarities, homology detection and functional annotation. The structural alignment problem is well studied and computationally difficult. Many different scoring schemes for structural similarity as well as many algorithms for finding high-scoring alignments have been proposed. Algorithms using contact map overlap (CMO) as scoring function are currently the only practical algorithms able to compute provably optimal alignments. RESULTS We propose a new mathematical model for the alignment of inter-residue distance matrices, building upon previous work on maximum CMO. Our model includes all elements needed to emulate various scoring schemes for the alignment of protein distance matrices. The algorithm that we use to compute alignments is practical only for sparse distance matrices. Therefore, we propose a more effective scoring function, which uses a distance threshold and only positive structural scores. We show that even under these restrictions our approach is in terms of alignment accuracy competitive with state-of-the-art structural alignment algorithms, whereas it additionally either proves the optimality of an alignment or returns bounds on the optimal score. Our novel method is freely available and constitutes an important promising step towards truly provably optimal structural alignments of proteins. AVAILABILITY An executable of our program PAUL is available at http://planet-lisa.net/.

CSA: comprehensive comparison of pairwise protein structure alignments

CSA is a web server for the computation, evaluation and comprehensive comparison of pairwise protein structure alignments. Its exact alignment engine computes either optimal, top-scoring alignments or heuristic alignments with quality guarantee for the inter-residue distance-based scorings of contact map overlap, PAUL, DALI and MATRAS. These and additional, uploaded alignments are compared using a number of quality measures and intuitive visualizations. CSA brings new insight into the structural relationship of the protein pairs under investigation and is a valuable tool for studying structural similarities. It is available at http://csa.project.cwi.nl.

Algorithm engineering for optimal alignment of protein structure distance matrices

Protein structural alignment is an important problem in computational biology. In this paper, we present first successes on provably optimal pairwise alignment of protein inter-residue distance matrices, using the popular dali scoring function. We introduce the structural alignment problem formally, which enables us to express a variety of scoring functions used in previous work as special cases in a unified framework. Further, we propose the first mathematical model for computing optimal structural alignments based on dense inter-residue distance matrices. We therefore reformulate the problem as a special graph problem and give a tight integer linear programming model. We then present algorithm engineering techniques to handle the huge integer linear programs of real-life distance matrix alignment problems. Applying these techniques, we can compute provably optimal dali alignments for the very first time.

PAUL: protein structural alignment using integer linear programming and Lagrangian relaxation

Computational Biology and Applied Algorithmics Group,Max-Planck-Institut fur Informatik, 66123 Saarbrucken, GermanyE-mail: Inken Wohlers* - I.Wohlers@cwi.nl*Corresponding authorfrom Fifth International Society for Computational Biology (ISCB) Student Council SymposiumStockholm, Sweden 27 June 2009Published: 19 October 2009BMC Bioinformatics 2009, 10(Suppl 13):P2 doi: 10.1186/1471-2105-10-S13-P2This article is available from: http://www.biomedcentral.com/1471-2105/10/S13/P2© 2009 Wohlers et al; licensee BioMed Central Ltd.This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DALIX: Optimal DALI Protein Structure Alignment

We present a mathematical model and exact algorithm for optimally aligning protein structures using the DALI scoring model. This scoring model is based on comparing the interresidue distance matrices of proteins and is used in the popular DALI software tool, a heuristic method for protein structure alignment. Our model and algorithm extend an integer linear programming approach that has been previously applied for the related, but simpler, contact map overlap problem. To this end, we introduce a novel type of constraint that handles negative score values and relax it in a Lagrangian fashion. The new algorithm, which we call DALIX, is applicable to any distance matrix-based scoring scheme. We also review options that allow to consider fewer pairs of interresidue distances explicitly because their large number hinders the optimization process. Using four known data sets of varying structural similarity, we compute many provably score-optimal DALI alignments. This allowed, for the first time, to evaluate the DALI heuristic in sound mathematical terms. The results indicate that DALI usually computes optimal or close to optimal alignments. However, we detect a subset of small proteins for which DALI fails to generate any significant alignment, although such alignments do exist.

Recurrent alterations of TNFAIP3 (A20) in T-cell large granular lymphocytic leukemia

The pathogenesis of T‐cell large granular lymphocytic leukemia (T‐LGL) is poorly understood, as STAT3 mutations are the only known frequent genetic lesions. Here, we identified non‐synonymous alterations in the TNFAIP3 tumor suppressor gene in 3 of 39 T‐LGL. In two cases these were somatic mutations, in one case the somatic origin was likely. A further case harbored a SNP that is a known risk allele for autoimmune diseases and B cell lymphomas. Thus, TNFAIP3 mutations represent recurrent genetic lesions in T‐LGL that affect about 8% of cases, likely contributing to deregulated NF‐κB activity in this leukemia.

Mapping proteins in the presence of paralogs using units of coevolution

BackgroundWe study the problem of mapping proteins between two protein families in the presence of paralogs. This problem occurs as a difficult subproblem in coevolution-based computational approaches for protein-protein interaction prediction.ResultsSimilar to prior approaches, our method is based on the idea that coevolution implies equal rates of sequence evolution among the interacting proteins, and we provide a first attempt to quantify this notion in a formal statistical manner. We call the units that are central to this quantification scheme the units of coevolution. A unit consists of two mapped protein pairs and its score quantifies the coevolution of the pairs. This quantification allows us to provide a maximum likelihood formulation of the paralog mapping problem and to cast it into a binary quadratic programming formulation.ConclusionCUPID, our software tool based on a Lagrangian relaxation of this formulation, makes it, for the first time, possible to compute state-of-the-art quality pairings in a few minutes of runtime. In summary, we suggest a novel alternative to the earlier available approaches, which is statistically sound and computationally feasible.

Exact Protein Structure Classification Using the Maximum Contact Map Overlap Metric

In this work we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO) metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows to avoid pairwise comparisons on the entire database and thus to significantly accelerate exploring the protein space compared to non-metric spaces. We show on a gold-standard classification benchmark set of 6,759 and 67,609 proteins, resp., that our exact k-nearest neighbor scheme classifies up to 95% and 99% of queries correctly. Our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on contact map overlap.

Automatic Classification of Protein Structure Using the Maximum Contact Map Overlap Metric

In this work, we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO) metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN) scheme classifies up to 224 out of 236 queries correctly and on a larger, extended version of the benchmark with 60; 850 additional structures, up to 1361 out of 1369 queries. Our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.